From c6724a39f461169375d71a60d9b6968eea88623c Mon Sep 17 00:00:00 2001 From: Jacob Lee Date: Mon, 29 Jan 2024 23:25:25 -0800 Subject: [PATCH 001/157] Fix rephrase step in chatbot use case (#16763) --- docs/docs/use_cases/chatbots/quickstart.ipynb | 45 +++++++++---------- docs/docs/use_cases/chatbots/retrieval.ipynb | 21 +++++---- 2 files changed, 32 insertions(+), 34 deletions(-) diff --git a/docs/docs/use_cases/chatbots/quickstart.ipynb b/docs/docs/use_cases/chatbots/quickstart.ipynb index 916193952ebc9e9..ea2748b75e425b6 100644 --- a/docs/docs/use_cases/chatbots/quickstart.ipynb +++ b/docs/docs/use_cases/chatbots/quickstart.ipynb @@ -105,7 +105,7 @@ { "data": { "text/plain": [ - "AIMessage(content=\"J'adore la programmation.\")" + "AIMessage(content=\"J'adore programmer.\")" ] }, "execution_count": 3, @@ -169,7 +169,7 @@ { "data": { "text/plain": [ - "AIMessage(content='I said \"J\\'adore la programmation\" which means \"I love programming\" in French.')" + "AIMessage(content='I said \"J\\'adore la programmation,\" which means \"I love programming\" in French.')" ] }, "execution_count": 5, @@ -342,7 +342,7 @@ { "data": { "text/plain": [ - "AIMessage(content='I said \"J\\'adore la programmation,\" which means \"I love programming\" in French.')" + "AIMessage(content='I said \"J\\'adore la programmation,\" which is the French translation for \"I love programming.\"')" ] }, "execution_count": 10, @@ -535,7 +535,7 @@ { "data": { "text/plain": [ - "'LangSmith can help with testing in several ways:\\n\\n1. Dataset Expansion: LangSmith allows you to quickly edit examples and add them to datasets, which expands the surface area of your evaluation sets. This helps in testing a wider range of scenarios and inputs.\\n\\n2. Model Fine-Tuning: You can use LangSmith to fine-tune a model for improved quality or reduced costs, which is essential for testing changes and ensuring optimized performance.\\n\\n3. Monitoring: LangSmith can be used to monitor your application by logging all traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise. This monitoring capability is crucial for testing and evaluating the performance of your application in production.\\n\\n4. Construction of Datasets: LangSmith simplifies the process of constructing datasets, either by using existing datasets or by hand-crafting small datasets for rigorous testing of changes.\\n\\nOverall, LangSmith provides tools and capabilities that support thorough testing of applications and models, ultimately contributing to the reliability and performance of your systems.'" + "'LangSmith can assist with testing by providing the capability to quickly edit examples and add them to datasets. This allows for the expansion of evaluation sets or fine-tuning of a model for improved quality or reduced costs. Additionally, LangSmith simplifies the construction of small datasets by hand, providing a convenient way to rigorously test changes in the application.'" ] }, "execution_count": 17, @@ -606,7 +606,7 @@ " Document(page_content='inputs, and see what happens. At some point though, our application is performing\\nwell and we want to be more rigorous about testing changes. We can use a dataset\\nthat we’ve constructed along the way (see above). Alternatively, we could spend some\\ntime constructing a small dataset by hand. For these situations, LangSmith simplifies', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='have been building and using LangSmith with the goal of bridging this gap. This is our tactical user guide to outline effective ways to use LangSmith and maximize its benefits.On by default\\u200bAt LangChain, all of us have LangSmith’s tracing running in the background by default. On the Python side, this is achieved by setting environment variables, which we establish whenever we launch a virtual environment or open our bash shell and leave them set. The same principle applies to most JavaScript', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'LangSmith can help with testing by simplifying the process of constructing and using datasets for testing and evaluation. It allows you to quickly edit examples and add them to datasets, thereby expanding the surface area of your evaluation sets. This can be useful for fine-tuning a model for improved quality or reduced costs. Additionally, LangSmith enables monitoring of your application by allowing you to log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. This helps in rigorously testing changes and ensuring that your application performs well in production.'}" + " 'answer': 'LangSmith can help with testing in several ways:\\n\\n1. Dataset Expansion: LangSmith enables quick editing of examples and adding them to datasets, which expands the surface area of evaluation sets. This allows for more comprehensive testing of models and applications.\\n\\n2. Fine-Tuning Models: LangSmith facilitates the fine-tuning of models for improved quality or reduced costs. This is beneficial for optimizing the performance of models during testing.\\n\\n3. Monitoring: LangSmith can be used to monitor applications, log traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise during testing. This monitoring helps in ensuring the reliability and performance of the application during testing phases.\\n\\nOverall, LangSmith helps in making testing more rigorous and comprehensive, whether by expanding datasets, fine-tuning models, or monitoring application performance.'}" ] }, "execution_count": 19, @@ -633,13 +633,13 @@ "data": { "text/plain": [ "{'messages': [HumanMessage(content='how can langsmith help with testing?'),\n", - " AIMessage(content='LangSmith can help with testing by simplifying the process of constructing and using datasets for testing and evaluation. It allows you to quickly edit examples and add them to datasets, thereby expanding the surface area of your evaluation sets. This can be useful for fine-tuning a model for improved quality or reduced costs. Additionally, LangSmith enables monitoring of your application by allowing you to log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. This helps in rigorously testing changes and ensuring that your application performs well in production.'),\n", + " AIMessage(content='LangSmith can help with testing in several ways:\\n\\n1. Dataset Expansion: LangSmith enables quick editing of examples and adding them to datasets, which expands the surface area of evaluation sets. This allows for more comprehensive testing of models and applications.\\n\\n2. Fine-Tuning Models: LangSmith facilitates the fine-tuning of models for improved quality or reduced costs. This is beneficial for optimizing the performance of models during testing.\\n\\n3. Monitoring: LangSmith can be used to monitor applications, log traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise during testing. This monitoring helps in ensuring the reliability and performance of the application during testing phases.\\n\\nOverall, LangSmith helps in making testing more rigorous and comprehensive, whether by expanding datasets, fine-tuning models, or monitoring application performance.'),\n", " HumanMessage(content='tell me more about that!')],\n", " 'context': [Document(page_content='however, there is still no complete substitute for human review to get the utmost quality and reliability from your application.', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content=\"against these known issues.Why is this so impactful? When building LLM applications, it’s often common to start without a dataset of any kind. This is part of the power of LLMs! They are amazing zero-shot learners, making it possible to get started as easily as possible. But this can also be a curse -- as you adjust the prompt, you're wandering blind. You don’t have any examples to benchmark your changes against.LangSmith addresses this problem by including an “Add to Dataset” button for each\", metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='playground. Here, you can modify the prompt and re-run it to observe the resulting changes to the output - as many times as needed!Currently, this feature supports only OpenAI and Anthropic models and works for LLM and Chat Model calls. We plan to extend its functionality to more LLM types, chains, agents, and retrievers in the future.What is the exact sequence of events?\\u200bIn complicated chains and agents, it can often be hard to understand what is going on under the hood. What calls are being', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'LangSmith facilitates testing by providing the capability to edit examples and add them to datasets, which in turn expands the range of scenarios for evaluating your application. This feature enables you to fine-tune your model for better quality and cost-effectiveness. Additionally, LangSmith allows for monitoring applications by logging traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise. This comprehensive testing and monitoring functionality ensure that your application is robust and performs optimally in a production environment.'}" + " 'answer': 'Certainly! LangSmith offers the following capabilities to aid in testing:\\n\\n1. Dataset Expansion: By allowing quick editing of examples and adding them to datasets, LangSmith enables the expansion of evaluation sets. This is crucial for thorough testing of models and applications, as it broadens the range of scenarios and inputs that can be used to assess performance.\\n\\n2. Fine-Tuning Models: LangSmith supports the fine-tuning of models to enhance their quality and reduce operational costs. This capability is valuable during testing as it enables the optimization of model performance based on specific testing requirements and objectives.\\n\\n3. Monitoring: LangSmith provides monitoring features that allow for the logging of traces, visualization of latency and token usage statistics, and troubleshooting of issues as they occur during testing. This real-time monitoring helps in identifying and addressing any issues that may impact the reliability and performance of the application during testing.\\n\\nBy leveraging these features, LangSmith enhances the testing process by enabling comprehensive dataset expansion, model fine-tuning, and real-time monitoring to ensure the quality and reliability of applications and models.'}" ] }, "execution_count": 20, @@ -676,7 +676,7 @@ { "data": { "text/plain": [ - "'LangSmith provides a convenient way to modify prompts and re-run them to observe the resulting changes to the output. This \"Add to Dataset\" feature allows you to iteratively adjust the prompt and observe the impact on the output, enabling you to test and refine your prompts as many times as needed. This is particularly valuable when working with language model applications, as it helps address the challenge of starting without a dataset and allows for benchmarking changes against existing examples. Currently, this feature supports OpenAI and Anthropic models for LLM and Chat Model calls, with plans to extend its functionality to more LLM types, chains, agents, and retrievers in the future. Overall, LangSmith\\'s testing capabilities provide a valuable tool for refining and optimizing language model applications.'" + "\"LangSmith offers the capability to quickly edit examples and add them to datasets, thereby enhancing the scope of evaluation sets. This feature is particularly valuable for testing as it allows for a more thorough assessment of model performance and application behavior.\\n\\nFurthermore, LangSmith enables the fine-tuning of models to enhance quality and reduce costs, which can significantly impact testing outcomes. By adjusting and refining models, developers can ensure that they are thoroughly tested and optimized for various scenarios and use cases.\\n\\nAdditionally, LangSmith provides monitoring functionality, allowing users to log traces, visualize latency and token usage statistics, and troubleshoot specific issues as they encounter them during testing. This real-time monitoring and troubleshooting capability contribute to the overall effectiveness and reliability of the testing process.\\n\\nIn essence, LangSmith's features are designed to improve the quality and reliability of testing by expanding evaluation sets, fine-tuning models, and providing comprehensive monitoring capabilities. These aspects collectively contribute to a more robust and thorough testing process for applications and models.\"" ] }, "execution_count": 21, @@ -705,7 +705,7 @@ "source": [ "## Query transformation\n", "\n", - "There's more optimization we'll cover here - in the above example, when we asked a followup question, `tell me more about that!`, you might notice that the retrieved docs don't directly include information about testing. This is because we're passing `tell me more about that!` verbatim as a query to the retriever. The output in the retrieval chain is still okay because the document chain retrieval chain can generate an answer based on the chat history, but we could be retrieving more rich and informative documents:" + "There's one more optimization we'll cover here - in the above example, when we asked a followup question, `tell me more about that!`, you might notice that the retrieved docs don't directly include information about testing. This is because we're passing `tell me more about that!` verbatim as a query to the retriever. The output in the retrieval chain is still okay because the document chain retrieval chain can generate an answer based on the chat history, but we could be retrieving more rich and informative documents:" ] }, { @@ -786,13 +786,12 @@ "\n", "query_transforming_retriever_chain = RunnableBranch(\n", " (\n", - " # Both empty string and empty list evaluate to False\n", - " lambda x: not x.get(\"messages\", False),\n", - " # If no messages, then we just pass the last message's content to retriever\n", + " lambda x: len(x.get(\"messages\", [])) == 1,\n", + " # If only one message, then we just pass that message's content to retriever\n", " (lambda x: x[\"messages\"][-1].content) | retriever,\n", " ),\n", " # If messages, then we pass inputs to LLM chain to transform the query, then pass to retriever\n", - " prompt | chat | StrOutputParser() | retriever,\n", + " query_transform_prompt | chat | StrOutputParser() | retriever,\n", ").with_config(run_name=\"chat_retriever_chain\")" ] }, @@ -836,12 +835,12 @@ "data": { "text/plain": [ "{'messages': [HumanMessage(content='how can langsmith help with testing?'),\n", - " AIMessage(content='LangSmith can help with testing by providing tracing capabilities that allow you to monitor and log all traces, visualize latency, and track token usage statistics. This can be invaluable for testing the performance and reliability of your prompts, chains, and agents. Additionally, LangSmith enables you to troubleshoot specific issues as they arise during testing, allowing for more effective debugging and optimization of your applications.')],\n", + " AIMessage(content='LangSmith can assist with testing in several ways. It allows you to quickly edit examples and add them to datasets, expanding the range of evaluation sets. This can help in fine-tuning a model for improved quality or reduced costs. Additionally, LangSmith simplifies the construction of small datasets by hand, providing a convenient way to rigorously test changes in your application. Furthermore, it enables monitoring of your application by logging all traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise.')],\n", " 'context': [Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", + " Document(page_content='inputs, and see what happens. At some point though, our application is performing\\nwell and we want to be more rigorous about testing changes. We can use a dataset\\nthat we’ve constructed along the way (see above). Alternatively, we could spend some\\ntime constructing a small dataset by hand. For these situations, LangSmith simplifies', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", - " Document(page_content='LangSmith Overview and User Guide | 🦜️🛠️ LangSmith', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='have been building and using LangSmith with the goal of bridging this gap. This is our tactical user guide to outline effective ways to use LangSmith and maximize its benefits.On by default\\u200bAt LangChain, all of us have LangSmith’s tracing running in the background by default. On the Python side, this is achieved by setting environment variables, which we establish whenever we launch a virtual environment or open our bash shell and leave them set. The same principle applies to most JavaScript', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'LangSmith can help with testing by providing tracing capabilities that allow you to monitor and log all traces, visualize latency, and track token usage statistics. This can be invaluable for testing the performance and reliability of your prompts, chains, and agents. Additionally, LangSmith enables you to troubleshoot specific issues as they arise during testing, allowing for more effective debugging and optimization of your applications.'}" + " 'answer': 'LangSmith can assist with testing in several ways. It allows you to quickly edit examples and add them to datasets, expanding the range of evaluation sets. This can help in fine-tuning a model for improved quality or reduced costs. Additionally, LangSmith simplifies the construction of small datasets by hand, providing a convenient way to rigorously test changes in your application. Furthermore, it enables monitoring of your application by logging all traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise.'}" ] }, "execution_count": 26, @@ -870,13 +869,13 @@ "data": { "text/plain": [ "{'messages': [HumanMessage(content='how can langsmith help with testing?'),\n", - " AIMessage(content='LangSmith can help with testing by providing tracing capabilities that allow you to monitor and log all traces, visualize latency, and track token usage statistics. This can be invaluable for testing the performance and reliability of your prompts, chains, and agents. Additionally, LangSmith enables you to troubleshoot specific issues as they arise during testing, allowing for more effective debugging and optimization of your applications.'),\n", + " AIMessage(content='LangSmith can assist with testing in several ways. It allows you to quickly edit examples and add them to datasets, expanding the range of evaluation sets. This can help in fine-tuning a model for improved quality or reduced costs. Additionally, LangSmith simplifies the construction of small datasets by hand, providing a convenient way to rigorously test changes in your application. Furthermore, it enables monitoring of your application by logging all traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise.'),\n", " HumanMessage(content='tell me more about that!')],\n", - " 'context': [Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", - " Document(page_content='environments through process.env1.The benefit here is that all calls to LLMs, chains, agents, tools, and retrievers are logged to LangSmith. Around 90% of the time we don’t even look at the traces, but the 10% of the time that we do… it’s so helpful. We can use LangSmith to debug:An unexpected end resultWhy an agent is loopingWhy a chain was slower than expectedHow many tokens an agent usedDebugging\\u200bDebugging LLMs, chains, and agents can be tough. LangSmith helps solve the following pain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", + " 'context': [Document(page_content='LangSmith Overview and User Guide | 🦜️🛠️ LangSmith', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", + " Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", - " Document(page_content='have been building and using LangSmith with the goal of bridging this gap. This is our tactical user guide to outline effective ways to use LangSmith and maximize its benefits.On by default\\u200bAt LangChain, all of us have LangSmith’s tracing running in the background by default. On the Python side, this is achieved by setting environment variables, which we establish whenever we launch a virtual environment or open our bash shell and leave them set. The same principle applies to most JavaScript', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': \"Certainly! LangSmith's tracing capabilities allow you to monitor and log all traces of your application, providing visibility into the behavior and performance of your prompts, chains, and agents during testing. This can help you identify any unexpected end results, performance bottlenecks, or excessive token usage. By visualizing latency and token usage statistics, you can gain insights into the efficiency and resource consumption of your application, enabling you to make informed decisions about optimization and fine-tuning. Additionally, LangSmith's troubleshooting features empower you to address specific issues that may arise during testing, ultimately contributing to the reliability and quality of your applications.\"}" + " Document(page_content='inputs, and see what happens. At some point though, our application is performing\\nwell and we want to be more rigorous about testing changes. We can use a dataset\\nthat we’ve constructed along the way (see above). Alternatively, we could spend some\\ntime constructing a small dataset by hand. For these situations, LangSmith simplifies', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", + " 'answer': 'Certainly! LangSmith simplifies the process of constructing and editing datasets, which is essential for testing and fine-tuning models. By quickly editing examples and adding them to datasets, you can expand the surface area of your evaluation sets, leading to improved model quality and potentially reduced costs. Additionally, LangSmith provides monitoring capabilities for your application, allowing you to log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. This comprehensive monitoring functionality helps ensure the reliability and performance of your application in production.'}" ] }, "execution_count": 27, @@ -896,9 +895,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To help you understand what's happening internally, [this LangSmith trace](https://smith.langchain.com/public/e8b88115-d8d8-4332-b987-7c3003234746/r) shows the first invocation. You can see that the user's initial query is passed directly to the retriever, which return suitable docs.\n", + "To help you understand what's happening internally, [this LangSmith trace](https://smith.langchain.com/public/42f8993b-7d19-42d3-990a-6608a73c5824/r) shows the first invocation. You can see that the user's initial query is passed directly to the retriever, which return suitable docs.\n", "\n", - "The invocation for followup question, [illustrated by this LangSmith trace](https://smith.langchain.com/public/ebb566ad-b69d-496e-b307-66bf70224c31/r) rephrases the user's initial question to something more relevant to testing with LangSmith, resulting in higher quality docs.\n", + "The invocation for followup question, [illustrated by this LangSmith trace](https://smith.langchain.com/public/7b463791-868b-42bd-8035-17b471e9c7cd/r) rephrases the user's initial question to something more relevant to testing with LangSmith, resulting in higher quality docs.\n", "\n", "And we now have a chatbot capable of conversational retrieval!\n", "\n", diff --git a/docs/docs/use_cases/chatbots/retrieval.ipynb b/docs/docs/use_cases/chatbots/retrieval.ipynb index 0a97c23f2a8ee57..5f69fb0a5da2241 100644 --- a/docs/docs/use_cases/chatbots/retrieval.ipynb +++ b/docs/docs/use_cases/chatbots/retrieval.ipynb @@ -265,7 +265,7 @@ { "data": { "text/plain": [ - "\"I don't know about LangSmith's specific capabilities for testing LLM applications. It's best to directly reach out to LangSmith or check their website for information on their services related to LLM application testing.\"" + "\"I don't know about LangSmith's specific capabilities for testing LLM applications. It's best to reach out to LangSmith directly to inquire about their services and how they can assist with testing your LLM applications.\"" ] }, "execution_count": 9, @@ -339,7 +339,7 @@ " Document(page_content='LangSmith Overview and User Guide | 🦜️🛠️ LangSmith', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content=\"does that affect the output?\\u200bSo you notice a bad output, and you go into LangSmith to see what's going on. You find the faulty LLM call and are now looking at the exact input. You want to try changing a word or a phrase to see what happens -- what do you do?We constantly ran into this issue. Initially, we copied the prompt to a playground of sorts. But this got annoying, so we built a playground of our own! When examining an LLM call, you can click the Open in Playground button to access this\", metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'Yes, LangSmith can help test and evaluate your LLM applications. It simplifies the initial setup and provides features for monitoring your application, logging all traces, visualizing latency and token usage statistics, and troubleshooting specific issues. It can also be used to edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.'}" + " 'answer': 'Yes, LangSmith can help test and evaluate your LLM applications. It simplifies the initial setup, and you can use it to monitor your application, log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise.'}" ] }, "execution_count": 11, @@ -466,9 +466,8 @@ "\n", "query_transforming_retriever_chain = RunnableBranch(\n", " (\n", - " # Both empty string and empty list evaluate to False\n", - " lambda x: not x.get(\"messages\", False),\n", - " # If no messages, then we just pass the last message's content to retriever\n", + " lambda x: len(x.get(\"messages\", [])) == 1,\n", + " # If only one message, then we just pass that message's content to retriever\n", " (lambda x: x[\"messages\"][-1].content) | retriever,\n", " ),\n", " # If messages, then we pass inputs to LLM chain to transform the query, then pass to retriever\n", @@ -533,11 +532,11 @@ "data": { "text/plain": [ "{'messages': [HumanMessage(content='Can LangSmith help test my LLM applications?')],\n", - " 'context': [Document(page_content='LangSmith Overview and User Guide | 🦜️🛠️ LangSmith', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", - " Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", + " 'context': [Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", + " Document(page_content='LangSmith Overview and User Guide | 🦜️🛠️ LangSmith', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", - " Document(page_content='datasets\\u200bLangSmith makes it easy to curate datasets. However, these aren’t just useful inside LangSmith; they can be exported for use in other contexts. Notable applications include exporting for use in OpenAI Evals or fine-tuning, such as with FireworksAI.To set up tracing in Deno, web browsers, or other runtime environments without access to the environment, check out the FAQs.↩PreviousLangSmithNextTracingOn by defaultDebuggingWhat was the exact input to the LLM?If I edit the prompt, how does', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'Yes, LangSmith can help test and evaluate your LLM applications. It provides tools for monitoring your application, logging traces, visualizing latency and token usage statistics, and troubleshooting specific issues as they arise. Additionally, you can quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.'}" + " Document(page_content=\"does that affect the output?\\u200bSo you notice a bad output, and you go into LangSmith to see what's going on. You find the faulty LLM call and are now looking at the exact input. You want to try changing a word or a phrase to see what happens -- what do you do?We constantly ran into this issue. Initially, we copied the prompt to a playground of sorts. But this got annoying, so we built a playground of our own! When examining an LLM call, you can click the Open in Playground button to access this\", metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", + " 'answer': 'Yes, LangSmith can help test and evaluate LLM (Language Model) applications. It simplifies the initial setup, and you can use it to monitor your application, log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise.'}" ] }, "execution_count": 16, @@ -570,7 +569,7 @@ " Document(page_content='You can also quickly edit examples and add them to datasets to expand the surface area of your evaluation sets or to fine-tune a model for improved quality or reduced costs.Monitoring\\u200bAfter all this, your app might finally ready to go in production. LangSmith can also be used to monitor your application in much the same way that you used for debugging. You can log all traces, visualize latency and token usage statistics, and troubleshoot specific issues as they arise. Each run can also be', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='Skip to main content🦜️🛠️ LangSmith DocsPython DocsJS/TS DocsSearchGo to AppLangSmithOverviewTracingTesting & EvaluationOrganizationsHubLangSmith CookbookOverviewOn this pageLangSmith Overview and User GuideBuilding reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.Over the past two months, we at LangChain', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'}),\n", " Document(page_content='LangSmith makes it easy to manually review and annotate runs through annotation queues.These queues allow you to select any runs based on criteria like model type or automatic evaluation scores, and queue them up for human review. As a reviewer, you can then quickly step through the runs, viewing the input, output, and any existing tags before adding your own feedback.We often use this for a couple of reasons:To assess subjective qualities that automatic evaluators struggle with, like', metadata={'description': 'Building reliable LLM applications can be challenging. LangChain simplifies the initial setup, but there is still work needed to bring the performance of prompts, chains and agents up the level where they are reliable enough to be used in production.', 'language': 'en', 'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith Overview and User Guide | 🦜️🛠️ LangSmith'})],\n", - " 'answer': 'LangSmith simplifies the initial setup for building reliable LLM applications. It provides features for manually reviewing and annotating runs through annotation queues, allowing you to select runs based on criteria like model type or automatic evaluation scores, and queue them up for human review. As a reviewer, you can quickly step through the runs, view the input, output, and any existing tags before adding your own feedback. This can be especially useful for assessing subjective qualities that automatic evaluators struggle with.'}" + " 'answer': 'LangSmith simplifies the initial setup for building reliable LLM applications, but it acknowledges that there is still work needed to bring the performance of prompts, chains, and agents up to the level where they are reliable enough to be used in production. It also provides the capability to manually review and annotate runs through annotation queues, allowing you to select runs based on criteria like model type or automatic evaluation scores for human review. This feature is particularly useful for assessing subjective qualities that automatic evaluators struggle with.'}" ] }, "execution_count": 17, @@ -596,7 +595,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can check out [this LangSmith trace](https://smith.langchain.com/public/8a4b8ab0-f2dc-4b50-9473-6c6c0c9c289d/r) to see the internal query transformation step for yourself.\n", + "You can check out [this LangSmith trace](https://smith.langchain.com/public/bb329a3b-e92a-4063-ad78-43f720fbb5a2/r) to see the internal query transformation step for yourself.\n", "\n", "## Streaming\n", "\n", From 442fa52b304dbf79b771508ad952b60b8f28dd3e Mon Sep 17 00:00:00 2001 From: Rihards Gravis Date: Tue, 30 Jan 2024 17:13:54 +0200 Subject: [PATCH 002/157] [partners]: langchain-robocorp ease dependency version (#16765) --- libs/partners/robocorp/poetry.lock | 274 ++++++++++++-------------- libs/partners/robocorp/pyproject.toml | 2 +- 2 files changed, 130 insertions(+), 146 deletions(-) diff --git a/libs/partners/robocorp/poetry.lock b/libs/partners/robocorp/poetry.lock index 468bfa069324cdb..5e35940466351b0 100644 --- a/libs/partners/robocorp/poetry.lock +++ b/libs/partners/robocorp/poetry.lock @@ -251,7 +251,7 @@ files = [ [[package]] name = "langchain-core" -version = "0.1.14" +version = "0.1.17" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -277,13 +277,13 @@ url = "../../core" [[package]] name = "langsmith" -version = "0.0.83" +version = "0.0.84" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = ">=3.8.1,<4.0" files = [ - {file = "langsmith-0.0.83-py3-none-any.whl", hash = "sha256:a5bb7ac58c19a415a9d5f51db56dd32ee2cd7343a00825bbc2018312eb3d122a"}, - {file = "langsmith-0.0.83.tar.gz", hash = "sha256:94427846b334ad9bdbec3266fee12903fe9f5448f628667689d0412012aaf392"}, + {file = "langsmith-0.0.84-py3-none-any.whl", hash = "sha256:9ae1ab777018e2174f68e8f53c88e7a7feb8dbf1c458b473644a3d5e22dc1eb7"}, + {file = "langsmith-0.0.84.tar.gz", hash = "sha256:dd163f89bca14c86759c651a72917c6d45f7dd18435d7bc65dc205a23dd9ec8d"}, ] [package.dependencies] @@ -364,13 +364,13 @@ files = [ [[package]] name = "pluggy" -version = "1.3.0" +version = "1.4.0" description = "plugin and hook calling mechanisms for python" optional = false python-versions = ">=3.8" files = [ - {file = "pluggy-1.3.0-py3-none-any.whl", hash = "sha256:d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7"}, - {file = "pluggy-1.3.0.tar.gz", hash = "sha256:cf61ae8f126ac6f7c451172cf30e3e43d3ca77615509771b3a984a0730651e12"}, + {file = "pluggy-1.4.0-py3-none-any.whl", hash = "sha256:7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981"}, + {file = "pluggy-1.4.0.tar.gz", hash = "sha256:8c85c2876142a764e5b7548e7d9a0e0ddb46f5185161049a79b7e974454223be"}, ] [package.extras] @@ -379,18 +379,18 @@ testing = ["pytest", "pytest-benchmark"] [[package]] name = "pydantic" -version = "2.5.3" +version = "2.6.0" description = "Data validation using Python type hints" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "pydantic-2.5.3-py3-none-any.whl", hash = "sha256:d0caf5954bee831b6bfe7e338c32b9e30c85dfe080c843680783ac2b631673b4"}, - {file = "pydantic-2.5.3.tar.gz", hash = "sha256:b3ef57c62535b0941697cce638c08900d87fcb67e29cfa99e8a68f747f393f7a"}, + {file = "pydantic-2.6.0-py3-none-any.whl", hash = "sha256:1440966574e1b5b99cf75a13bec7b20e3512e8a61b894ae252f56275e2c465ae"}, + {file = "pydantic-2.6.0.tar.gz", hash = "sha256:ae887bd94eb404b09d86e4d12f93893bdca79d766e738528c6fa1c849f3c6bcf"}, ] [package.dependencies] annotated-types = ">=0.4.0" -pydantic-core = "2.14.6" +pydantic-core = "2.16.1" typing-extensions = ">=4.6.1" [package.extras] @@ -398,116 +398,90 @@ email = ["email-validator (>=2.0.0)"] [[package]] name = "pydantic-core" -version = 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b/libs/partners/robocorp/pyproject.toml @@ -14,7 +14,7 @@ license = "MIT" python = ">=3.8.1,<4.0" langchain-core = ">=0.0.12" requests = "^2.31.0" -types-requests = "^2.31.0.20231231" +types-requests = "^2.31.0.6" langsmith = ">=0.0.83,<0.1" [tool.poetry.group.test] From a372b236759be2c5903ec9c9705dc010534512f3 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 30 Jan 2024 07:15:25 -0800 Subject: [PATCH 003/157] robocorp: release 0.0.3 (#16789) --- libs/partners/robocorp/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/partners/robocorp/pyproject.toml b/libs/partners/robocorp/pyproject.toml index cf68de08e99fa1f..00175bc2c213905 100644 --- a/libs/partners/robocorp/pyproject.toml +++ b/libs/partners/robocorp/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-robocorp" -version = "0.0.2" +version = "0.0.3" description = "An integration package connecting Robocorp and LangChain" authors = [] readme = "README.md" From 4acd2654a312144431950efe3b490f9025f5e341 Mon Sep 17 00:00:00 2001 From: Alexander Conway Date: Tue, 30 Jan 2024 12:14:58 -0500 Subject: [PATCH 004/157] Report which file was errored on in DirectoryLoader (#16790) The current implementation leaves it up to the particular file loader implementation to report the file on which an error was encountered - in my case pdfminer was simply saying it could not parse a file as a PDF, but I didn't know which of my hundreds of files it was failing on. No reason not to log the particular item on which an error was encountered, and it should be an immense debugging assistant. --- libs/community/langchain_community/document_loaders/directory.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/community/langchain_community/document_loaders/directory.py b/libs/community/langchain_community/document_loaders/directory.py index 66f22301194e467..3837133a693325c 100644 --- a/libs/community/langchain_community/document_loaders/directory.py +++ b/libs/community/langchain_community/document_loaders/directory.py @@ -103,6 +103,7 @@ def load_file( if self.silent_errors: logger.warning(f"Error loading file {str(item)}: {e}") else: + logger.error(f"Error loading file {str(item)}") raise e finally: if pbar: From b0347f3e2b4771454eb1479fa18b3f5b88f5e201 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Tue, 30 Jan 2024 09:39:46 -0800 Subject: [PATCH 005/157] docs: add csv use case (#16756) --- docs/docs/use_cases/csv.ipynb | 781 ++++++++++++++++++ docs/docs/use_cases/data_generation.ipynb | 1 - docs/docs/use_cases/extraction.ipynb | 3 +- docs/docs/use_cases/sql/index.ipynb | 2 +- docs/docs/use_cases/summarization.ipynb | 1 - docs/docs/use_cases/tagging.ipynb | 3 +- docs/docs/use_cases/tool_use/index.ipynb | 2 +- docs/docs/use_cases/web_scraping.ipynb | 3 +- .../agent_toolkits/sql/base.py | 11 +- .../tools/sql_database/tool.py | 27 +- libs/core/langchain_core/prompts/chat.py | 8 + libs/core/langchain_core/prompts/image.py | 3 + .../agents/agent_toolkits/csv/base.py | 43 +- .../agents/agent_toolkits/pandas/base.py | 409 ++++----- .../agents/openai_functions_agent/base.py | 2 +- 15 files changed, 1038 insertions(+), 261 deletions(-) create mode 100644 docs/docs/use_cases/csv.ipynb diff --git a/docs/docs/use_cases/csv.ipynb b/docs/docs/use_cases/csv.ipynb new file mode 100644 index 000000000000000..bd60ae799f55db7 --- /dev/null +++ b/docs/docs/use_cases/csv.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "d00a802f-a27e-43a5-af1e-500d4bb70859", + "metadata": {}, + "source": [ + "---\n", + "sidebar_position: 0.3\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "674a0d41-e3e3-4423-a995-25d40128c518", + "metadata": {}, + "source": [ + "# CSV\n", + "\n", + "LLMs are great for building question-answering systems over various types of data sources. In this section we'll go over how to build Q&A systems over data stored in a CSV file(s). Like working with SQL databases, the key to working with CSV files is to give an LLM access to tools for querying and interacting with the data. The two main ways to do this are to either:\n", + "\n", + "* **RECOMMENDED**: Load the CSV(s) into a SQL database, and use the approaches outlined in the [SQL use case docs](/docs/use_cases/sql/).\n", + "* Give the LLM access to a Python environment where it can use libraries like Pandas to interact with the data.\n", + "\n", + "## ⚠️ Security note ⚠️\n", + "\n", + "Both approaches mentioned above carry significant risks. Using SQL requires executing model-generated SQL queries. Using a library like Pandas requires letting the model execute Python code. Since it is easier to tightly scope SQL connection permissions and sanitize SQL queries than it is to sandbox Python environments, **we HIGHLY recommend interacting with CSV data via SQL.** For more on general security best practices, [see here](/docs/security)." + ] + }, + { + "cell_type": "markdown", + "id": "d20c20d7-71e1-4808-9012-48278f3a9b94", + "metadata": {}, + "source": [ + "## Setup\n", + "Dependencies for this guide:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fcf245-b0aa-4aee-8f0a-9c9cf94b065e", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain langchain-openai langchain-community langchain-experimental pandas" + ] + }, + { + "cell_type": "markdown", + "id": "7f2e34a3-0978-4856-8844-d8dfc6d5ac51", + "metadata": {}, + "source": [ + "Set required environment variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "53913d79-4a11-4bc6-bb49-dea2cc8c453b", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n", + "\n", + "# Using LangSmith is recommended but not required. Uncomment below lines to use.\n", + "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "c23b4232-2f6a-4eb5-b0cb-1d48a9e02fcc", + "metadata": {}, + "source": [ + "Download the [Titanic dataset](https://www.kaggle.com/datasets/yasserh/titanic-dataset) if you don't already have it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2c5e524-781c-4b8e-83ec-d302023f8767", + "metadata": {}, + "outputs": [], + "source": [ + "!wget https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv -O titanic.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8431551e-e0d7-4702-90e3-12c53161a479", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(887, 8)\n", + "['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Siblings/Spouses Aboard', 'Parents/Children Aboard', 'Fare']\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"titanic.csv\")\n", + "print(df.shape)\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "1779ab07-b715-49e5-ab2a-2e6be7d02927", + "metadata": {}, + "source": [ + "## SQL\n", + "\n", + "Using SQL to interact with CSV data is the recommended approach because it is easier to limit permissions and sanitize queries than with arbitrary Python.\n", + "\n", + "Most SQL databases make it easy to load a CSV file in as a table ([DuckDB](https://duckdb.org/docs/data/csv/overview.html), [SQLite](https://www.sqlite.org/csv.html), etc.). Once you've done this you can use all of the chain and agent-creating techniques outlined in the [SQL use case guide](/docs/use_cases/sql/). Here's a quick example of how we might do this with SQLite:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f61e9886-4713-4c88-87d4-dab439687f43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "887" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_community.utilities import SQLDatabase\n", + "from sqlalchemy import create_engine\n", + "\n", + "engine = create_engine(\"sqlite:///titanic.db\")\n", + "df.to_sql(\"titanic\", engine, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fa314f1f-d764-41a2-8f27-163cd071c562", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sqlite\n", + "['titanic']\n" + ] + }, + { + "data": { + "text/plain": [ + "\"[(1, 2, 'Master. Alden Gates Caldwell', 'male', 0.83, 0, 2, 29.0), (0, 3, 'Master. Eino Viljami Panula', 'male', 1.0, 4, 1, 39.6875), (1, 3, 'Miss. Eleanor Ileen Johnson', 'female', 1.0, 1, 1, 11.1333), (1, 2, 'Master. Richard F Becker', 'male', 1.0, 2, 1, 39.0), (1, 1, 'Master. Hudson Trevor Allison', 'male', 0.92, 1, 2, 151.55), (1, 3, 'Miss. Maria Nakid', 'female', 1.0, 0, 2, 15.7417), (0, 3, 'Master. Sidney Leonard Goodwin', 'male', 1.0, 5, 2, 46.9), (1, 3, 'Miss. Helene Barbara Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 3, 'Miss. Eugenie Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 2, 'Master. Viljo Hamalainen', 'male', 0.67, 1, 1, 14.5), (1, 3, 'Master. Bertram Vere Dean', 'male', 1.0, 1, 2, 20.575), (1, 3, 'Master. Assad Alexander Thomas', 'male', 0.42, 0, 1, 8.5167), (1, 2, 'Master. Andre Mallet', 'male', 1.0, 0, 2, 37.0042), (1, 2, 'Master. George Sibley Richards', 'male', 0.83, 1, 1, 18.75)]\"" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db = SQLDatabase(engine=engine)\n", + "print(db.dialect)\n", + "print(db.get_usable_table_names())\n", + "db.run(\"SELECT * FROM titanic WHERE Age < 2;\")" + ] + }, + { + "cell_type": "markdown", + "id": "42f5a3c3-707c-4331-9f5f-0cb4919763dd", + "metadata": {}, + "source": [ + "And create a [SQL agent](/docs/use_cases/sql/agents) to interact with it:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "edd92649-b178-47bd-b2b7-d5d4e14b3512", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.agent_toolkits import create_sql_agent\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n", + "agent_executor = create_sql_agent(llm, db=db, agent_type=\"openai-tools\", verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9680e2c0-7957-4dba-9183-9782865176a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_list_tables` with `{}`\n", + "\n", + "\n", + "\u001b[0m\u001b[38;5;200m\u001b[1;3mtitanic\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_schema` with `{'table_names': 'titanic'}`\n", + "\n", + "\n", + "\u001b[0m\u001b[33;1m\u001b[1;3m\n", + "CREATE TABLE titanic (\n", + "\t\"Survived\" BIGINT, \n", + "\t\"Pclass\" BIGINT, \n", + "\t\"Name\" TEXT, \n", + "\t\"Sex\" TEXT, \n", + "\t\"Age\" FLOAT, \n", + "\t\"Siblings/Spouses Aboard\" BIGINT, \n", + "\t\"Parents/Children Aboard\" BIGINT, \n", + "\t\"Fare\" FLOAT\n", + ")\n", + "\n", + "/*\n", + "3 rows from titanic table:\n", + "Survived\tPclass\tName\tSex\tAge\tSiblings/Spouses Aboard\tParents/Children Aboard\tFare\n", + "0\t3\tMr. Owen Harris Braund\tmale\t22.0\t1\t0\t7.25\n", + "1\t1\tMrs. John Bradley (Florence Briggs Thayer) Cumings\tfemale\t38.0\t1\t0\t71.2833\n", + "1\t3\tMiss. Laina Heikkinen\tfemale\t26.0\t0\t0\t7.925\n", + "*/\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_query` with `{'query': 'SELECT AVG(Age) AS AverageAge FROM titanic WHERE Survived = 1'}`\n", + "responded: To find the average age of survivors, I will query the \"titanic\" table and calculate the average of the \"Age\" column for the rows where \"Survived\" is equal to 1.\n", + "\n", + "Here is the SQL query:\n", + "\n", + "```sql\n", + "SELECT AVG(Age) AS AverageAge\n", + "FROM titanic\n", + "WHERE Survived = 1\n", + "```\n", + "\n", + "Executing this query will give us the average age of the survivors.\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m[(28.408391812865496,)]\u001b[0m\u001b[32;1m\u001b[1;3mThe average age of the survivors is approximately 28.41 years.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input': \"what's the average age of survivors\",\n", + " 'output': 'The average age of the survivors is approximately 28.41 years.'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_executor.invoke({\"input\": \"what's the average age of survivors\"})" + ] + }, + { + "cell_type": "markdown", + "id": "4d1eb128-842b-4018-87ab-bb269147f6ec", + "metadata": {}, + "source": [ + "This approach easily generalizes to multiple CSVs, since we can just load each of them into our database as it's own table. Head to the [SQL guide](/docs/use_cases/sql/) for more." + ] + }, + { + "cell_type": "markdown", + "id": "fe7f2d91-2377-49dd-97a3-19d48a750715", + "metadata": {}, + "source": [ + "## Pandas\n", + "\n", + "Instead of SQL we can also use data analysis libraries like pandas and the code generating abilities of LLMs to interact with CSV data. Again, **this approach is not fit for production use cases unless you have extensive safeguards in place**. For this reason, our code-execution utilities and constructors live in the `langchain-experimental` package.\n", + "\n", + "### Chain\n", + "\n", + "Most LLMs have been trained on enough pandas Python code that they can generate it just by being asked to:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cd02e72d-31bf-4ed3-b4fd-643011dab236", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "```python\n", + "correlation = df['Age'].corr(df['Fare'])\n", + "correlation\n", + "```\n" + ] + } + ], + "source": [ + "ai_msg = llm.invoke(\n", + " \"I have a pandas DataFrame 'df' with columns 'Age' and 'Fare'. Write code to compute the correlation between the two columns. Return Markdown for a Python code snippet and nothing else.\"\n", + ")\n", + "print(ai_msg.content)" + ] + }, + { + "cell_type": "markdown", + "id": "f5e84003-5c39-496b-afa7-eaa50a01b7bb", + "metadata": {}, + "source": [ + "We can combine this ability with a Python-executing tool to create a simple data analysis chain. We'll first want to load our CSV table as a dataframe, and give the tool access to this dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d8132f75-12d4-4294-b446-2d114e603f4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32.30542018038331" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_experimental.tools import PythonAstREPLTool\n", + "\n", + "df = pd.read_csv(\"titanic.csv\")\n", + "tool = PythonAstREPLTool(locals={\"df\": df})\n", + "tool.invoke(\"df['Fare'].mean()\")" + ] + }, + { + "cell_type": "markdown", + "id": "ab1b2e7c-6ea8-4674-98eb-a43c69f5c19d", + "metadata": {}, + "source": [ + "To help enforce proper use of our Python tool, we'll using [function calling](/docs/modules/model_io/chat/function_calling):" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2d30dbca-2d19-4574-bc78-43753f648eb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6TZsNaCqOcbP7lqWudosQTd6', 'function': {'arguments': '{\\n \"query\": \"df[[\\'Age\\', \\'Fare\\']].corr()\"\\n}', 'name': 'python_repl_ast'}, 'type': 'function'}]})" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n", + "llm_with_tools.invoke(\n", + " \"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bdec46fb-7296-443c-9e97-cfa9045ff21d", + "metadata": {}, + "source": [ + "We'll add a [OpenAI tools output parser](/docs/modules/model_io/output_parsers/types/openai_tools) to extract the function call as a dict:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f0b658cb-722b-43e8-84ad-62ba8929169a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'query': \"df[['Age', 'Fare']].corr()\"}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.output_parsers.openai_tools import JsonOutputKeyToolsParser\n", + "\n", + "parser = JsonOutputKeyToolsParser(tool.name, return_single=True)\n", + "(llm_with_tools | parser).invoke(\n", + " \"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "59362ea0-cc5a-4841-b87c-51d6a87d5810", + "metadata": {}, + "source": [ + "And combine with a prompt so that we can just specify a question without needing to specify the dataframe info every invocation:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0bd2ecba-90c6-4301-8cc1-bd021a7f74fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'query': \"df[['Age', 'Fare']].corr()\"}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = f\"\"\"You have access to a pandas dataframe `df`. \\\n", + "Here is the output of `df.head().to_markdown()`:\n", + "\n", + "```\n", + "{df.head().to_markdown()}\n", + "```\n", + "\n", + "Given a user question, write the Python code to answer it. \\\n", + "Return ONLY the valid Python code and nothing else. \\\n", + "Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"system\", system), (\"human\", \"{question}\")]\n", + ")\n", + "code_chain = prompt | llm_with_tools | parser\n", + "code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})" + ] + }, + { + "cell_type": "markdown", + "id": "63989e47-c0af-409e-9766-83c3fe6d69bb", + "metadata": {}, + "source": [ + "And lastly we'll add our Python tool so that the generated code is actually executed:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "745b5b2c-2eda-441e-8459-275dc1d4d9aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.11232863699941621" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain = prompt | llm_with_tools | parser | tool # noqa\n", + "chain.invoke({\"question\": \"What's the correlation between age and fare\"})" + ] + }, + { + "cell_type": "markdown", + "id": "fbb12764-4a90-4e84-88b4-a25949084ea2", + "metadata": {}, + "source": [ + "And just like that we have a simple data analysis chain. We can take a peak at the intermediate steps by looking at the LangSmith trace: https://smith.langchain.com/public/b1309290-7212-49b7-bde2-75b39a32b49a/r\n", + "\n", + "We could add an additional LLM call at the end to generate a conversational response, so that we're not just responding with the tool output. For this we'll want to add a chat history `MessagesPlaceholder` to our prompt:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3fe3818d-0657-4729-ac46-ab5d4860d8f6", + "metadata": {}, + "outputs": [], + "source": [ + "from operator import itemgetter\n", + "\n", + "from langchain_core.messages import ToolMessage\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import MessagesPlaceholder\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "\n", + "system = f\"\"\"You have access to a pandas dataframe `df`. \\\n", + "Here is the output of `df.head().to_markdown()`:\n", + "\n", + "```\n", + "{df.head().to_markdown()}\n", + "```\n", + "\n", + "Given a user question, write the Python code to answer it. \\\n", + "Don't assume you have access to any libraries other than built-in Python ones and pandas.\n", + "Respond directly to the question once you have enough information to answer it.\"\"\"\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " system,\n", + " ),\n", + " (\"human\", \"{question}\"),\n", + " # This MessagesPlaceholder allows us to optionally append an arbitrary number of messages\n", + " # at the end of the prompt using the 'chat_history' arg.\n", + " MessagesPlaceholder(\"chat_history\", optional=True),\n", + " ]\n", + ")\n", + "\n", + "\n", + "def _get_chat_history(x: dict) -> list:\n", + " \"\"\"Parse the chain output up to this point into a list of chat history messages to insert in the prompt.\"\"\"\n", + " ai_msg = x[\"ai_msg\"]\n", + " tool_call_id = x[\"ai_msg\"].additional_kwargs[\"tool_calls\"][0][\"id\"]\n", + " tool_msg = ToolMessage(tool_call_id=tool_call_id, content=str(x[\"tool_output\"]))\n", + " return [ai_msg, tool_msg]\n", + "\n", + "\n", + "chain = (\n", + " RunnablePassthrough.assign(ai_msg=prompt | llm_with_tools)\n", + " .assign(tool_output=itemgetter(\"ai_msg\") | parser | tool)\n", + " .assign(chat_history=_get_chat_history)\n", + " .assign(response=prompt | llm | StrOutputParser())\n", + " .pick([\"tool_output\", \"response\"])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "03e14712-9959-4f2d-94d5-4ac2bd9f3f08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tool_output': 0.11232863699941621,\n", + " 'response': 'The correlation between age and fare is approximately 0.112.'}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain.invoke({\"question\": \"What's the correlation between age and fare\"})" + ] + }, + { + "cell_type": "markdown", + "id": "245a5a91-c6d2-4a40-9b9f-eb38f78c9d22", + "metadata": {}, + "source": [ + "Here's the LangSmith trace for this run: https://smith.langchain.com/public/ca689f8a-5655-4224-8bcf-982080744462/r" + ] + }, + { + "cell_type": "markdown", + "id": "6c24b4f4-abbf-4891-b200-814eb9c35bec", + "metadata": {}, + "source": [ + "### Agent\n", + "\n", + "For complex questions it can be helpful for an LLM to be able to iteratively execute code while maintaining the inputs and outputs of its previous executions. This is where Agents come into play. They allow an LLM to decide how many times a tool needs to be invoked and keep track of the executions it's made so far. The [create_pandas_dataframe_agent](https://api.python.langchain.com/en/latest/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html) is a built-in agent that makes it easy to work with dataframes:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b8b3a781-189f-48ff-b541-f5ed2f65e3e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"df[['Age', 'Fare']].corr()\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m Age Fare\n", + "Age 1.000000 0.112329\n", + "Fare 0.112329 1.000000\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"df[['Fare', 'Survived']].corr()\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m Fare Survived\n", + "Fare 1.000000 0.256179\n", + "Survived 0.256179 1.000000\u001b[0m\u001b[32;1m\u001b[1;3mThe correlation between age and fare is 0.112329, while the correlation between fare and survival is 0.256179. Therefore, the correlation between fare and survival is greater than the correlation between age and fare.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input': \"What's the correlation between age and fare? is that greater than the correlation between fare and survival?\",\n", + " 'output': 'The correlation between age and fare is 0.112329, while the correlation between fare and survival is 0.256179. Therefore, the correlation between fare and survival is greater than the correlation between age and fare.'}" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_experimental.agents import create_pandas_dataframe_agent\n", + "\n", + "agent = create_pandas_dataframe_agent(llm, df, agent_type=\"openai-tools\", verbose=True)\n", + "agent.invoke(\n", + " {\n", + " \"input\": \"What's the correlation between age and fare? is that greater than the correlation between fare and survival?\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a65322f3-b13c-4949-82b2-4517b9a0859d", + "metadata": {}, + "source": [ + "Here's the LangSmith trace for this run: https://smith.langchain.com/public/8e6c23cc-782c-4203-bac6-2a28c770c9f0/r" + ] + }, + { + "cell_type": "markdown", + "id": "68492261-faef-47e7-8009-e20ef1420d5a", + "metadata": {}, + "source": [ + "### Multiple CSVs\n", + "\n", + "To handle multiple CSVs (or dataframes) we just need to pass multiple dataframes to our Python tool. Our `create_pandas_dataframe_agent` constructor can do this out of the box, we can pass in a list of dataframes instead of just one. If we're constructing a chain ourselves, we can do something like:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "bb528ab0-4aed-43fd-8a15-a1fe02a33d9e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.14384991262954416" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_1 = df[[\"Age\", \"Fare\"]]\n", + "df_2 = df[[\"Fare\", \"Survived\"]]\n", + "\n", + "tool = PythonAstREPLTool(locals={\"df_1\": df_1, \"df_2\": df_2})\n", + "llm_with_tool = llm.bind_tools(tools=[tool], tool_choice=tool.name)\n", + "df_template = \"\"\"```python\n", + "{df_name}.head().to_markdown()\n", + ">>> {df_head}\n", + "```\"\"\"\n", + "df_context = \"\\n\\n\".join(\n", + " df_template.format(df_head=_df.head().to_markdown(), df_name=df_name)\n", + " for _df, df_name in [(df_1, \"df_1\"), (df_2, \"df_2\")]\n", + ")\n", + "\n", + "system = f\"\"\"You have access to a number of pandas dataframes. \\\n", + "Here is a sample of rows from each dataframe and the python code that was used to generate the sample:\n", + "\n", + "{df_context}\n", + "\n", + "Given a user question about the dataframes, write the Python code to answer it. \\\n", + "Don't assume you have access to any libraries other than built-in Python ones and pandas. \\\n", + "Make sure to refer only to the variables mentioned above.\"\"\"\n", + "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n", + "\n", + "chain = prompt | llm_with_tool | parser | tool\n", + "chain.invoke(\n", + " {\n", + " \"question\": \"return the difference in the correlation between age and fare and the correlation between fare and survival\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7043363f-4ab1-41de-9318-c556e4ae66bc", + "metadata": {}, + "source": [ + "Here's the LangSmith trace for this run: https://smith.langchain.com/public/653e499f-179c-4757-8041-f5e2a5f11fcc/r" + ] + }, + { + "cell_type": "markdown", + "id": "a2256d09-23c2-4e52-bfc6-c84eba538586", + "metadata": {}, + "source": [ + "### Sandboxed code execution\n", + "\n", + "There are a number of tools like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly) that provide sandboxed environments for Python code execution, to allow for safer code-executing chains and agents." + ] + }, + { + "cell_type": "markdown", + "id": "1728e791-f114-41e6-aa12-0436fdeeedae", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "For more advanced data analysis applications we recommend checking out:\n", + "\n", + "* [SQL use case](/docs/use_cases/sql/): Many of the challenges of working with SQL db's and CSV's are generic to any structured data type, so it's useful to read the SQL techniques even if you're using Pandas for CSV data analysis.\n", + "* [Tool use](/docs/use_cases/tool_use/): Guides on general best practices when working with chains and agents that invoke tools\n", + "* [Agents](/docs/use_cases/agents/): Understand the fundamentals of building LLM agents.\n", + "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/toolkits/spark)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "poetry-venv", + "language": "python", + "name": "poetry-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/use_cases/data_generation.ipynb b/docs/docs/use_cases/data_generation.ipynb index 7445c2b60356f77..3c394bce6d839ff 100644 --- a/docs/docs/use_cases/data_generation.ipynb +++ b/docs/docs/use_cases/data_generation.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "source": [ "---\n", - "sidebar-position: 1\n", "title: Synthetic data generation\n", "---" ] diff --git a/docs/docs/use_cases/extraction.ipynb b/docs/docs/use_cases/extraction.ipynb index 167d8c47022a294..0b374f92080f5db 100644 --- a/docs/docs/use_cases/extraction.ipynb +++ b/docs/docs/use_cases/extraction.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 1\n", "title: Extraction\n", "---" ] @@ -611,7 +610,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/use_cases/sql/index.ipynb b/docs/docs/use_cases/sql/index.ipynb index 1b706c831b16898..9b415ce954c8742 100644 --- a/docs/docs/use_cases/sql/index.ipynb +++ b/docs/docs/use_cases/sql/index.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 0.5\n", + "sidebar_position: 0.1\n", "---" ] }, diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index d1c097a93e5b653..a23634abab594af 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 1\n", "title: Summarization\n", "---" ] diff --git a/docs/docs/use_cases/tagging.ipynb b/docs/docs/use_cases/tagging.ipynb index be8837a16710839..0d9d2988b141bac 100644 --- a/docs/docs/use_cases/tagging.ipynb +++ b/docs/docs/use_cases/tagging.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 1\n", "title: Tagging\n", "---" ] @@ -415,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/use_cases/tool_use/index.ipynb b/docs/docs/use_cases/tool_use/index.ipynb index b31e5bb397b6625..0f18e5f9d375935 100644 --- a/docs/docs/use_cases/tool_use/index.ipynb +++ b/docs/docs/use_cases/tool_use/index.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 0.9\n", + "sidebar_position: 0.2\n", "---" ] }, diff --git a/docs/docs/use_cases/web_scraping.ipynb b/docs/docs/use_cases/web_scraping.ipynb index 19848f19d6aa626..40a28caf564c9d4 100644 --- a/docs/docs/use_cases/web_scraping.ipynb +++ b/docs/docs/use_cases/web_scraping.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "source": [ "---\n", - "sidebar_position: 1\n", "title: Web scraping\n", "---" ] @@ -670,7 +669,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/libs/community/langchain_community/agent_toolkits/sql/base.py b/libs/community/langchain_community/agent_toolkits/sql/base.py index 66c3c57dc6e4ca4..42953fb0a2ea2ec 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/base.py +++ b/libs/community/langchain_community/agent_toolkits/sql/base.py @@ -2,7 +2,7 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union +from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence, Union from langchain_core.messages import AIMessage, SystemMessage from langchain_core.prompts import BasePromptTemplate, PromptTemplate @@ -40,7 +40,6 @@ def create_sql_agent( prefix: Optional[str] = None, suffix: Optional[str] = None, format_instructions: Optional[str] = None, - input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, @@ -70,9 +69,6 @@ def create_sql_agent( format_instructions: Formatting instructions to pass to ZeroShotAgent.create_prompt() when 'agent_type' is "zero-shot-react-description". Otherwise ignored. - input_variables: DEPRECATED. Input variables to explicitly specify as part of - ZeroShotAgent.create_prompt() when 'agent_type' is - "zero-shot-react-description". Otherwise ignored. top_k: Number of rows to query for by default. max_iterations: Passed to AgentExecutor init. max_execution_time: Passed to AgentExecutor init. @@ -203,7 +199,10 @@ def create_sql_agent( ) else: - raise ValueError(f"Agent type {agent_type} not supported at the moment.") + raise ValueError( + f"Agent type {agent_type} not supported at the moment. Must be one of " + "'openai-tools', 'openai-functions', or 'zero-shot-react-description'." + ) return AgentExecutor( name="SQL Agent Executor", diff --git a/libs/community/langchain_community/tools/sql_database/tool.py b/libs/community/langchain_community/tools/sql_database/tool.py index 91dcffa04b0be9f..850cecb45107b63 100644 --- a/libs/community/langchain_community/tools/sql_database/tool.py +++ b/libs/community/langchain_community/tools/sql_database/tool.py @@ -1,8 +1,8 @@ # flake8: noqa """Tools for interacting with a SQL database.""" -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Type -from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator +from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.language_models import BaseLanguageModel from langchain_core.callbacks import ( @@ -24,12 +24,16 @@ class Config(BaseTool.Config): pass +class _QuerySQLDataBaseToolInput(BaseModel): + query: str = Field(..., description="A detailed and correct SQL query.") + + class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool): """Tool for querying a SQL database.""" name: str = "sql_db_query" description: str = """ - Input to this tool is a detailed and correct SQL query, output is a result from the database. + Execute a SQL query against the database and get back the result.. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. """ @@ -43,15 +47,22 @@ def _run( return self.db.run_no_throw(query) +class _InfoSQLDatabaseToolInput(BaseModel): + table_names: str = Field( + ..., + description=( + "A comma-separated list of the table names for which to return the schema. " + "Example input: 'table1, table2, table3'" + ), + ) + + class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool): """Tool for getting metadata about a SQL database.""" name: str = "sql_db_schema" - description: str = """ - Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. - - Example Input: "table1, table2, table3" - """ + description: str = "Get the schema and sample rows for the specified SQL tables." + args_schema: Type[BaseModel] = _InfoSQLDatabaseToolInput def _run( self, diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py index 6553614d102d81f..cfb3192cfadf147 100644 --- a/libs/core/langchain_core/prompts/chat.py +++ b/libs/core/langchain_core/prompts/chat.py @@ -466,6 +466,14 @@ def format(self, **kwargs: Any) -> BaseMessage: content=content, additional_kwargs=self.additional_kwargs ) + def pretty_repr(self, html: bool = False) -> str: + # TODO: Handle partials + title = self.__class__.__name__.replace("MessagePromptTemplate", " Message") + title = get_msg_title_repr(title, bold=html) + prompts = self.prompt if isinstance(self.prompt, list) else [self.prompt] + prompt_reprs = "\n\n".join(prompt.pretty_repr(html=html) for prompt in prompts) + return f"{title}\n\n{prompt_reprs}" + class HumanMessagePromptTemplate(_StringImageMessagePromptTemplate): """Human message prompt template. This is a message sent from the user.""" diff --git a/libs/core/langchain_core/prompts/image.py b/libs/core/langchain_core/prompts/image.py index d3d2d94da13dc05..3a3b16117e438e9 100644 --- a/libs/core/langchain_core/prompts/image.py +++ b/libs/core/langchain_core/prompts/image.py @@ -74,3 +74,6 @@ def format( # Don't check literal values here: let the API check them output["detail"] = detail # type: ignore[typeddict-item] return output + + def pretty_repr(self, html: bool = False) -> str: + raise NotImplementedError() diff --git a/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py b/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py index 8691aed0fea02f9..a7fa928c23c406b 100644 --- a/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py +++ b/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py @@ -1,26 +1,55 @@ -from io import IOBase -from typing import Any, List, Optional, Union +from __future__ import annotations -from langchain.agents.agent import AgentExecutor -from langchain_core.language_models import BaseLanguageModel +from io import IOBase +from typing import TYPE_CHECKING, Any, List, Optional, Union from langchain_experimental.agents.agent_toolkits.pandas.base import ( create_pandas_dataframe_agent, ) +if TYPE_CHECKING: + from langchain.agents.agent import AgentExecutor + from langchain_core.language_models import LanguageModelLike + def create_csv_agent( - llm: BaseLanguageModel, + llm: LanguageModelLike, path: Union[str, IOBase, List[Union[str, IOBase]]], pandas_kwargs: Optional[dict] = None, **kwargs: Any, ) -> AgentExecutor: - """Create csv agent by loading to a dataframe and using pandas agent.""" + """Create pandas dataframe agent by loading csv to a dataframe. + + Args: + llm: Language model to use for the agent. + path: A string path, file-like object or a list of string paths/file-like + objects that can be read in as pandas DataFrames with pd.read_csv(). + pandas_kwargs: Named arguments to pass to pd.read_csv(). + **kwargs: Additional kwargs to pass to langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent(). + + Returns: + An AgentExecutor with the specified agent_type agent and access to + a PythonAstREPLTool with the loaded DataFrame(s) and any user-provided extra_tools. + + Example: + .. code-block:: python + + from langchain_openai import ChatOpenAI + from langchain_experimental.agents import create_csv_agent + + llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) + agent_executor = create_pandas_dataframe_agent( + llm, + "titanic.csv", + agent_type="openai-tools", + verbose=True + ) + """ # noqa: E501 try: import pandas as pd except ImportError: raise ImportError( - "pandas package not found, please install with `pip install pandas`" + "pandas package not found, please install with `pip install pandas`." ) _kwargs = pandas_kwargs or {} diff --git a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py index 1ab58c20c0dd1a9..741bfc7a48feca7 100644 --- a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py +++ b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py @@ -1,16 +1,26 @@ """Agent for working with pandas objects.""" -from typing import Any, Dict, List, Optional, Sequence, Tuple - -from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent +import warnings +from typing import Any, Dict, List, Literal, Optional, Sequence, Union + +from langchain.agents import AgentType, create_openai_tools_agent, create_react_agent +from langchain.agents.agent import ( + AgentExecutor, + BaseMultiActionAgent, + BaseSingleActionAgent, + RunnableAgent, + RunnableMultiActionAgent, +) from langchain.agents.mrkl.base import ZeroShotAgent -from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent -from langchain.agents.types import AgentType -from langchain.callbacks.base import BaseCallbackManager -from langchain.chains.llm import LLMChain -from langchain.schema import BasePromptTemplate -from langchain.tools import BaseTool -from langchain_core.language_models import BaseLanguageModel +from langchain.agents.openai_functions_agent.base import ( + OpenAIFunctionsAgent, + create_openai_functions_agent, +) +from langchain_core.callbacks import BaseCallbackManager +from langchain_core.language_models import LanguageModelLike from langchain_core.messages import SystemMessage +from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate +from langchain_core.tools import BaseTool +from langchain_core.utils.interactive_env import is_interactive_env from langchain_experimental.agents.agent_toolkits.pandas.prompt import ( FUNCTIONS_WITH_DF, @@ -28,257 +38,121 @@ def _get_multi_prompt( dfs: List[Any], + *, prefix: Optional[str] = None, suffix: Optional[str] = None, - input_variables: Optional[List[str]] = None, include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, - extra_tools: Sequence[BaseTool] = (), -) -> Tuple[BasePromptTemplate, List[BaseTool]]: - num_dfs = len(dfs) + tools: Sequence[BaseTool] = (), +) -> BasePromptTemplate: if suffix is not None: suffix_to_use = suffix - include_dfs_head = True elif include_df_in_prompt: suffix_to_use = SUFFIX_WITH_MULTI_DF - include_dfs_head = True else: suffix_to_use = SUFFIX_NO_DF - include_dfs_head = False - if input_variables is None: - input_variables = ["input", "agent_scratchpad", "num_dfs"] - if include_dfs_head: - input_variables += ["dfs_head"] - - if prefix is None: - prefix = MULTI_DF_PREFIX + prefix = prefix if prefix is not None else MULTI_DF_PREFIX - df_locals = {} - for i, dataframe in enumerate(dfs): - df_locals[f"df{i + 1}"] = dataframe - tools = [PythonAstREPLTool(locals=df_locals)] + list(extra_tools) prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix_to_use, - input_variables=input_variables, ) partial_prompt = prompt.partial() - if "dfs_head" in input_variables: + if "dfs_head" in partial_prompt.input_variables: dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs]) - partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs), dfs_head=dfs_head) - if "num_dfs" in input_variables: - partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs)) - return partial_prompt, tools + partial_prompt = partial_prompt.partial(dfs_head=dfs_head) + if "num_dfs" in partial_prompt.input_variables: + partial_prompt = partial_prompt.partial(num_dfs=str(len(dfs))) + return partial_prompt def _get_single_prompt( df: Any, + *, prefix: Optional[str] = None, suffix: Optional[str] = None, - input_variables: Optional[List[str]] = None, include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, - extra_tools: Sequence[BaseTool] = (), -) -> Tuple[BasePromptTemplate, List[BaseTool]]: + tools: Sequence[BaseTool] = (), +) -> BasePromptTemplate: if suffix is not None: suffix_to_use = suffix - include_df_head = True elif include_df_in_prompt: suffix_to_use = SUFFIX_WITH_DF - include_df_head = True else: suffix_to_use = SUFFIX_NO_DF - include_df_head = False - - if input_variables is None: - input_variables = ["input", "agent_scratchpad"] - if include_df_head: - input_variables += ["df_head"] - - if prefix is None: - prefix = PREFIX - - tools = [PythonAstREPLTool(locals={"df": df})] + list(extra_tools) + prefix = prefix if prefix is not None else PREFIX prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix_to_use, - input_variables=input_variables, ) partial_prompt = prompt.partial() - if "df_head" in input_variables: - partial_prompt = partial_prompt.partial( - df_head=str(df.head(number_of_head_rows).to_markdown()) - ) - return partial_prompt, tools + if "df_head" in partial_prompt.input_variables: + df_head = str(df.head(number_of_head_rows).to_markdown()) + partial_prompt = partial_prompt.partial(df_head=df_head) + return partial_prompt -def _get_prompt_and_tools( - df: Any, - prefix: Optional[str] = None, - suffix: Optional[str] = None, - input_variables: Optional[List[str]] = None, - include_df_in_prompt: Optional[bool] = True, - number_of_head_rows: int = 5, - extra_tools: Sequence[BaseTool] = (), -) -> Tuple[BasePromptTemplate, List[BaseTool]]: - try: - import pandas as pd - - pd.set_option("display.max_columns", None) - except ImportError: - raise ImportError( - "pandas package not found, please install with `pip install pandas`" - ) - - if include_df_in_prompt is not None and suffix is not None: - raise ValueError("If suffix is specified, include_df_in_prompt should not be.") - - if isinstance(df, list): - for item in df: - if not isinstance(item, pd.DataFrame): - raise ValueError(f"Expected pandas object, got {type(df)}") - return _get_multi_prompt( - df, - prefix=prefix, - suffix=suffix, - input_variables=input_variables, - include_df_in_prompt=include_df_in_prompt, - number_of_head_rows=number_of_head_rows, - extra_tools=extra_tools, - ) - else: - if not isinstance(df, pd.DataFrame): - raise ValueError(f"Expected pandas object, got {type(df)}") - return _get_single_prompt( - df, - prefix=prefix, - suffix=suffix, - input_variables=input_variables, - include_df_in_prompt=include_df_in_prompt, - number_of_head_rows=number_of_head_rows, - extra_tools=extra_tools, - ) +def _get_prompt(df: Any, **kwargs: Any) -> BasePromptTemplate: + return ( + _get_multi_prompt(df, **kwargs) + if isinstance(df, list) + else _get_single_prompt(df, **kwargs) + ) def _get_functions_single_prompt( df: Any, + *, prefix: Optional[str] = None, - suffix: Optional[str] = None, + suffix: str = "", include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, -) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]: - if suffix is not None: - suffix_to_use = suffix - if include_df_in_prompt: - suffix_to_use = suffix_to_use.format( - df_head=str(df.head(number_of_head_rows).to_markdown()) - ) - elif include_df_in_prompt: - suffix_to_use = FUNCTIONS_WITH_DF.format( - df_head=str(df.head(number_of_head_rows).to_markdown()) - ) - else: - suffix_to_use = "" - - if prefix is None: - prefix = PREFIX_FUNCTIONS - - tools = [PythonAstREPLTool(locals={"df": df})] - system_message = SystemMessage(content=prefix + suffix_to_use) +) -> ChatPromptTemplate: + if include_df_in_prompt: + df_head = str(df.head(number_of_head_rows).to_markdown()) + suffix = (suffix or FUNCTIONS_WITH_DF).format(df_head=df_head) + prefix = prefix if prefix is not None else PREFIX_FUNCTIONS + system_message = SystemMessage(content=prefix + suffix) prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message) - return prompt, tools + return prompt def _get_functions_multi_prompt( dfs: Any, - prefix: Optional[str] = None, - suffix: Optional[str] = None, + *, + prefix: str = "", + suffix: str = "", include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, -) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]: - if suffix is not None: - suffix_to_use = suffix - if include_df_in_prompt: - dfs_head = "\n\n".join( - [d.head(number_of_head_rows).to_markdown() for d in dfs] - ) - suffix_to_use = suffix_to_use.format( - dfs_head=dfs_head, - ) - elif include_df_in_prompt: +) -> ChatPromptTemplate: + if include_df_in_prompt: dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs]) - suffix_to_use = FUNCTIONS_WITH_MULTI_DF.format( - dfs_head=dfs_head, - ) - else: - suffix_to_use = "" - - if prefix is None: - prefix = MULTI_DF_PREFIX_FUNCTIONS - prefix = prefix.format(num_dfs=str(len(dfs))) - - df_locals = {} - for i, dataframe in enumerate(dfs): - df_locals[f"df{i + 1}"] = dataframe - tools = [PythonAstREPLTool(locals=df_locals)] - system_message = SystemMessage(content=prefix + suffix_to_use) + suffix = (suffix or FUNCTIONS_WITH_MULTI_DF).format(dfs_head=dfs_head) + prefix = (prefix or MULTI_DF_PREFIX_FUNCTIONS).format(num_dfs=str(len(dfs))) + system_message = SystemMessage(content=prefix + suffix) prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message) - return prompt, tools + return prompt -def _get_functions_prompt_and_tools( - df: Any, - prefix: Optional[str] = None, - suffix: Optional[str] = None, - input_variables: Optional[List[str]] = None, - include_df_in_prompt: Optional[bool] = True, - number_of_head_rows: int = 5, -) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]: - try: - import pandas as pd - - pd.set_option("display.max_columns", None) - except ImportError: - raise ImportError( - "pandas package not found, please install with `pip install pandas`" - ) - if input_variables is not None: - raise ValueError("`input_variables` is not supported at the moment.") - - if include_df_in_prompt is not None and suffix is not None: - raise ValueError("If suffix is specified, include_df_in_prompt should not be.") - - if isinstance(df, list): - for item in df: - if not isinstance(item, pd.DataFrame): - raise ValueError(f"Expected pandas object, got {type(df)}") - return _get_functions_multi_prompt( - df, - prefix=prefix, - suffix=suffix, - include_df_in_prompt=include_df_in_prompt, - number_of_head_rows=number_of_head_rows, - ) - else: - if not isinstance(df, pd.DataFrame): - raise ValueError(f"Expected pandas object, got {type(df)}") - return _get_functions_single_prompt( - df, - prefix=prefix, - suffix=suffix, - include_df_in_prompt=include_df_in_prompt, - number_of_head_rows=number_of_head_rows, - ) +def _get_functions_prompt(df: Any, **kwargs: Any) -> ChatPromptTemplate: + return ( + _get_functions_multi_prompt(df, **kwargs) + if isinstance(df, list) + else _get_functions_single_prompt(df, **kwargs) + ) def create_pandas_dataframe_agent( - llm: BaseLanguageModel, + llm: LanguageModelLike, df: Any, - agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION, + agent_type: Union[ + AgentType, Literal["openai-tools"] + ] = AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[BaseCallbackManager] = None, prefix: Optional[str] = None, suffix: Optional[str] = None, @@ -292,54 +166,131 @@ def create_pandas_dataframe_agent( include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, extra_tools: Sequence[BaseTool] = (), - **kwargs: Dict[str, Any], + **kwargs: Any, ) -> AgentExecutor: - """Construct a pandas agent from an LLM and dataframe.""" - agent: BaseSingleActionAgent - base_tools: Sequence[BaseTool] + """Construct a Pandas agent from an LLM and dataframe(s). + + Args: + llm: Language model to use for the agent. + df: Pandas dataframe or list of Pandas dataframes. + agent_type: One of "openai-tools", "openai-functions", or + "zero-shot-react-description". Defaults to "zero-shot-react-description". + "openai-tools" is recommended over "openai-functions". + callback_manager: DEPRECATED. Pass "callbacks" key into 'agent_executor_kwargs' + instead to pass constructor callbacks to AgentExecutor. + prefix: Prompt prefix string. + suffix: Prompt suffix string. + input_variables: DEPRECATED. Input variables automatically inferred from + constructed prompt. + verbose: AgentExecutor verbosity. + return_intermediate_steps: Passed to AgentExecutor init. + max_iterations: Passed to AgentExecutor init. + max_execution_time: Passed to AgentExecutor init. + early_stopping_method: Passed to AgentExecutor init. + agent_executor_kwargs: Arbitrary additional AgentExecutor args. + include_df_in_prompt: Whether to include the first number_of_head_rows in the + prompt. Must be None if suffix is not None. + number_of_head_rows: Number of initial rows to include in prompt if + include_df_in_prompt is True. + extra_tools: Additional tools to give to agent on top of a PythonAstREPLTool. + **kwargs: DEPRECATED. Not used, kept for backwards compatibility. + + Returns: + An AgentExecutor with the specified agent_type agent and access to + a PythonAstREPLTool with the DataFrame(s) and any user-provided extra_tools. + + Example: + + .. code-block:: python + + from langchain_openai import ChatOpenAI + from langchain_experimental.agents import create_pandas_dataframe_agent + import pandas as pd + + df = pd.read_csv("titanic.csv") + llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) + agent_executor = create_pandas_dataframe_agent( + llm, + df, + agent_type="openai-tools", + verbose=True + ) + + """ # noqa: E501 + try: + import pandas as pd + except ImportError as e: + raise ImportError( + "pandas package not found, please install with `pip install pandas`" + ) from e + + if is_interactive_env(): + pd.set_option("display.max_columns", None) + + for _df in df if isinstance(df, list) else [df]: + if not isinstance(_df, pd.DataFrame): + raise ValueError(f"Expected pandas DataFrame, got {type(_df)}") + + if input_variables: + kwargs = kwargs or {} + kwargs["input_variables"] = input_variables + if kwargs: + warnings.warn( + f"Received additional kwargs {kwargs} which are no longer supported." + ) + + df_locals = {} + if isinstance(df, list): + for i, dataframe in enumerate(df): + df_locals[f"df{i + 1}"] = dataframe + else: + df_locals["df"] = df + tools = [PythonAstREPLTool(locals=df_locals)] + list(extra_tools) + if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION: - prompt, base_tools = _get_prompt_and_tools( + if include_df_in_prompt is not None and suffix is not None: + raise ValueError( + "If suffix is specified, include_df_in_prompt should not be." + ) + prompt = _get_prompt( df, prefix=prefix, suffix=suffix, - input_variables=input_variables, include_df_in_prompt=include_df_in_prompt, number_of_head_rows=number_of_head_rows, - extra_tools=extra_tools, - ) - tools = base_tools - llm_chain = LLMChain( - llm=llm, - prompt=prompt, - callback_manager=callback_manager, + tools=tools, ) - tool_names = [tool.name for tool in tools] - agent = ZeroShotAgent( - llm_chain=llm_chain, - allowed_tools=tool_names, - callback_manager=callback_manager, - **kwargs, + agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] = RunnableAgent( + runnable=create_react_agent(llm, tools, prompt), # type: ignore + input_keys_arg=["input"], + return_keys_arg=["output"], ) - elif agent_type == AgentType.OPENAI_FUNCTIONS: - _prompt, base_tools = _get_functions_prompt_and_tools( + elif agent_type in (AgentType.OPENAI_FUNCTIONS, "openai-tools"): + prompt = _get_functions_prompt( df, prefix=prefix, suffix=suffix, - input_variables=input_variables, include_df_in_prompt=include_df_in_prompt, number_of_head_rows=number_of_head_rows, ) - tools = list(base_tools) + list(extra_tools) - agent = OpenAIFunctionsAgent( - llm=llm, - prompt=_prompt, - tools=tools, - callback_manager=callback_manager, - **kwargs, - ) + if agent_type == AgentType.OPENAI_FUNCTIONS: + agent = RunnableAgent( + runnable=create_openai_functions_agent(llm, tools, prompt), # type: ignore + input_keys_arg=["input"], + return_keys_arg=["output"], + ) + else: + agent = RunnableMultiActionAgent( + runnable=create_openai_tools_agent(llm, tools, prompt), # type: ignore + input_keys_arg=["input"], + return_keys_arg=["output"], + ) else: - raise ValueError(f"Agent type {agent_type} not supported at the moment.") - return AgentExecutor.from_agent_and_tools( + raise ValueError( + f"Agent type {agent_type} not supported at the moment. Must be one of " + "'openai-tools', 'openai-functions', or 'zero-shot-react-description'." + ) + return AgentExecutor( agent=agent, tools=tools, callback_manager=callback_manager, diff --git a/libs/langchain/langchain/agents/openai_functions_agent/base.py b/libs/langchain/langchain/agents/openai_functions_agent/base.py index 7d776d72fb721e7..47e9b8d99fc542b 100644 --- a/libs/langchain/langchain/agents/openai_functions_agent/base.py +++ b/libs/langchain/langchain/agents/openai_functions_agent/base.py @@ -175,7 +175,7 @@ def create_prompt( content="You are a helpful AI assistant." ), extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None, - ) -> BasePromptTemplate: + ) -> ChatPromptTemplate: """Create prompt for this agent. Args: From 881dc28d2ca853a8f111f0b2297892f4a896b523 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Tue, 30 Jan 2024 09:40:28 -0800 Subject: [PATCH 006/157] Fix Dep Recommendation (#16793) Tools are different than functions --- libs/core/langchain_core/utils/function_calling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py index a852fe517ed5a1a..a4449b655b84525 100644 --- a/libs/core/langchain_core/utils/function_calling.py +++ b/libs/core/langchain_core/utils/function_calling.py @@ -89,7 +89,7 @@ def convert_pydantic_to_openai_function( @deprecated( "0.1.16", - alternative="langchain_core.utils.function_calling.convert_to_openai_function()", + alternative="langchain_core.utils.function_calling.convert_to_openai_tool()", removal="0.2.0", ) def convert_pydantic_to_openai_tool( @@ -253,7 +253,7 @@ def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription: @deprecated( "0.1.16", - alternative="langchain_core.utils.function_calling.convert_to_openai_function()", + alternative="langchain_core.utils.function_calling.convert_to_openai_tool()", removal="0.2.0", ) def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription: From ef2bd745cbe328cc1e6e40cf7177bd7eff01e8be Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Tue, 30 Jan 2024 12:51:45 -0500 Subject: [PATCH 007/157] docs: Update doc-string in base callback managers (#15885) Update doc-strings with a comment about on_llm_start vs. on_chat_model_start. --- libs/core/langchain_core/callbacks/base.py | 33 ++++++++++++++++++---- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/libs/core/langchain_core/callbacks/base.py b/libs/core/langchain_core/callbacks/base.py index ff5e7770c5aa5b4..eb4d1de70603098 100644 --- a/libs/core/langchain_core/callbacks/base.py +++ b/libs/core/langchain_core/callbacks/base.py @@ -166,7 +166,12 @@ def on_llm_start( metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Any: - """Run when LLM starts running.""" + """Run when LLM starts running. + + **ATTENTION**: This method is called for non-chat models (regular LLMs). If + you're implementing a handler for a chat model, + you should use on_chat_model_start instead. + """ def on_chat_model_start( self, @@ -179,7 +184,13 @@ def on_chat_model_start( metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Any: - """Run when a chat model starts running.""" + """Run when a chat model starts running. + + **ATTENTION**: This method is called for chat models. If you're implementing + a handler for a non-chat model, you should use on_llm_start instead. + """ + # NotImplementedError is thrown intentionally + # Callback handler will fall back to on_llm_start if this is exception is thrown raise NotImplementedError( f"{self.__class__.__name__} does not implement `on_chat_model_start`" ) @@ -308,7 +319,12 @@ async def on_llm_start( metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> None: - """Run when LLM starts running.""" + """Run when LLM starts running. + + **ATTENTION**: This method is called for non-chat models (regular LLMs). If + you're implementing a handler for a chat model, + you should use on_chat_model_start instead. + """ async def on_chat_model_start( self, @@ -321,7 +337,13 @@ async def on_chat_model_start( metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Any: - """Run when a chat model starts running.""" + """Run when a chat model starts running. + + **ATTENTION**: This method is called for chat models. If you're implementing + a handler for a non-chat model, you should use on_llm_start instead. + """ + # NotImplementedError is thrown intentionally + # Callback handler will fall back to on_llm_start if this is exception is thrown raise NotImplementedError( f"{self.__class__.__name__} does not implement `on_chat_model_start`" ) @@ -359,8 +381,9 @@ async def on_llm_error( **kwargs: Any, ) -> None: """Run when LLM errors. + Args: - error (BaseException): The error that occurred. + error: The error that occurred. kwargs (Any): Additional keyword arguments. - response (LLMResult): The response which was generated before the error occurred. From daf820c77bf6b06849a9b6738df4b8a6da4e70ce Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Tue, 30 Jan 2024 10:00:52 -0800 Subject: [PATCH 008/157] community[patch]: undo create_sql_agent breaking (#16797) --- .../langchain_community/agent_toolkits/sql/base.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/libs/community/langchain_community/agent_toolkits/sql/base.py b/libs/community/langchain_community/agent_toolkits/sql/base.py index 42953fb0a2ea2ec..73915732cf18c53 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/base.py +++ b/libs/community/langchain_community/agent_toolkits/sql/base.py @@ -2,7 +2,7 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence, Union +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union from langchain_core.messages import AIMessage, SystemMessage from langchain_core.prompts import BasePromptTemplate, PromptTemplate @@ -40,6 +40,7 @@ def create_sql_agent( prefix: Optional[str] = None, suffix: Optional[str] = None, format_instructions: Optional[str] = None, + input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, @@ -69,6 +70,7 @@ def create_sql_agent( format_instructions: Formatting instructions to pass to ZeroShotAgent.create_prompt() when 'agent_type' is "zero-shot-react-description". Otherwise ignored. + input_variables: DEPRECATED. top_k: Number of rows to query for by default. max_iterations: Passed to AgentExecutor init. max_execution_time: Passed to AgentExecutor init. @@ -119,6 +121,9 @@ def create_sql_agent( raise ValueError( "Must provide exactly one of 'toolkit' or 'db'. Received both." ) + if input_variables: + kwargs = kwargs or {} + kwargs["input_variables"] = input_variables if kwargs: warnings.warn( f"Received additional kwargs {kwargs} which are no longer supported." From bf9068516ea302025ad585e22625fabd87841503 Mon Sep 17 00:00:00 2001 From: Raphael Date: Tue, 30 Jan 2024 22:47:45 +0100 Subject: [PATCH 009/157] community[minor]: add the ability to load existing transcripts from AssemblyAI by their id. (#16051) - **Description:** the existing AssemblyAI API allows to pass a path or an url to transcribe an audio file and turn in into Langchain Documents, this PR allows to get existing transcript by their transcript id and turn them into Documents. - **Issue:** not related to an existing issue - **Dependencies:** requests --------- Co-authored-by: Harrison Chase --- .../document_loaders/assemblyai.py | 109 ++++++++++++++++++ 1 file changed, 109 insertions(+) diff --git a/libs/community/langchain_community/document_loaders/assemblyai.py b/libs/community/langchain_community/document_loaders/assemblyai.py index 0dd64256ab27a19..d3947d9f71b2f45 100644 --- a/libs/community/langchain_community/document_loaders/assemblyai.py +++ b/libs/community/langchain_community/document_loaders/assemblyai.py @@ -3,6 +3,7 @@ from enum import Enum from typing import TYPE_CHECKING, List, Optional +import requests from langchain_core.documents import Document from langchain_community.document_loaders.base import BaseLoader @@ -110,3 +111,111 @@ def load(self) -> List[Document]: return [Document(page_content=transcript.export_subtitles_vtt())] else: raise ValueError("Unknown transcript format.") + + +class AssemblyAIAudioLoaderById(BaseLoader): + """ + Loader for AssemblyAI audio transcripts. + + It uses the AssemblyAI API to get an existing transcription + and loads the transcribed text into one or more Documents, + depending on the specified format. + + """ + + def __init__(self, transcript_id, api_key, transcript_format): + """ + Initializes the AssemblyAI AssemblyAIAudioLoaderById. + + Args: + transcript_id: Id of an existing transcription. + transcript_format: Transcript format to use. + See class ``TranscriptFormat`` for more info. + api_key: AssemblyAI API key. + """ + + self.api_key = api_key + self.transcript_id = transcript_id + self.transcript_format = transcript_format + + def load(self) -> List[Document]: + """Load data into Document objects.""" + HEADERS = {"authorization": self.api_key} + + if self.transcript_format == TranscriptFormat.TEXT: + try: + transcript_response = requests.get( + f"https://api.assemblyai.com/v2/transcript/{self.transcript_id}", + headers=HEADERS, + ) + transcript_response.raise_for_status() + except Exception as e: + print(f"An error occurred: {e}") + raise + + transcript = transcript_response.json()["text"] + + return [ + Document(page_content=transcript, metadata=transcript_response.json()) + ] + elif self.transcript_format == TranscriptFormat.PARAGRAPHS: + try: + paragraphs_response = requests.get( + f"https://api.assemblyai.com/v2/transcript/{self.transcript_id}/paragraphs", + headers=HEADERS, + ) + paragraphs_response.raise_for_status() + except Exception as e: + print(f"An error occurred: {e}") + raise + + paragraphs = paragraphs_response.json()["paragraphs"] + + return [Document(page_content=p["text"], metadata=p) for p in paragraphs] + + elif self.transcript_format == TranscriptFormat.SENTENCES: + try: + sentences_response = requests.get( + f"https://api.assemblyai.com/v2/transcript/{self.transcript_id}/sentences", + headers=HEADERS, + ) + sentences_response.raise_for_status() + except Exception as e: + print(f"An error occurred: {e}") + raise + + sentences = sentences_response.json()["sentences"] + + return [Document(page_content=s["text"], metadata=s) for s in sentences] + + elif self.transcript_format == TranscriptFormat.SUBTITLES_SRT: + try: + srt_response = requests.get( + f"https://api.assemblyai.com/v2/transcript/{self.transcript_id}/srt", + headers=HEADERS, + ) + srt_response.raise_for_status() + except Exception as e: + print(f"An error occurred: {e}") + raise + + srt = srt_response.text + + return [Document(page_content=srt)] + + elif self.transcript_format == TranscriptFormat.SUBTITLES_VTT: + try: + vtt_response = requests.get( + f"https://api.assemblyai.com/v2/transcript/{self.transcript_id}/vtt", + headers=HEADERS, + ) + vtt_response.raise_for_status() + except Exception as e: + print(f"An error occurred: {e}") + raise + + vtt = vtt_response.text + + return [Document(page_content=vtt)] + else: + raise ValueError("Unknown transcript format.") From bb3b6bde33dbee0eec935068bc0164e170250cd9 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 30 Jan 2024 15:49:56 -0800 Subject: [PATCH 010/157] openai[minor]: change to secretstr (#16803) --- libs/partners/openai/Makefile | 5 +- .../langchain_openai/chat_models/azure.py | 28 ++++++--- .../langchain_openai/chat_models/base.py | 16 ++--- .../langchain_openai/embeddings/azure.py | 26 +++++--- .../langchain_openai/embeddings/base.py | 25 ++++++-- .../openai/langchain_openai/llms/azure.py | 35 ++++++----- .../openai/langchain_openai/llms/base.py | 22 ++++--- .../embeddings/test_azure.py | 4 +- .../integration_tests/llms/test_azure.py | 2 +- .../openai/tests/unit_tests/test_secrets.py | 62 +++++++++++++++++++ 10 files changed, 162 insertions(+), 63 deletions(-) create mode 100644 libs/partners/openai/tests/unit_tests/test_secrets.py diff --git a/libs/partners/openai/Makefile b/libs/partners/openai/Makefile index e318d45f8b359f5..31aae646a558c3d 100644 --- a/libs/partners/openai/Makefile +++ b/libs/partners/openai/Makefile @@ -6,10 +6,9 @@ all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ -test: - poetry run pytest $(TEST_FILE) +integration_tests: TEST_FILE=tests/integration_tests/ -tests: +test tests integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/openai/langchain_openai/chat_models/azure.py b/libs/partners/openai/langchain_openai/chat_models/azure.py index 1584165128ff556..64b0ebc6ca3b45e 100644 --- a/libs/partners/openai/langchain_openai/chat_models/azure.py +++ b/libs/partners/openai/langchain_openai/chat_models/azure.py @@ -3,12 +3,12 @@ import logging import os -from typing import Any, Callable, Dict, List, Union +from typing import Any, Callable, Dict, List, Optional, Union import openai from langchain_core.outputs import ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_openai.chat_models.base import ChatOpenAI @@ -71,9 +71,9 @@ class AzureChatOpenAI(ChatOpenAI): """ openai_api_version: str = Field(default="", alias="api_version") """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.""" - openai_api_key: Union[str, None] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" - azure_ad_token: Union[str, None] = None + azure_ad_token: Optional[SecretStr] = None """Your Azure Active Directory token. Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided. @@ -111,11 +111,14 @@ def validate_environment(cls, values: Dict) -> Dict: # Check OPENAI_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. - values["openai_api_key"] = ( + openai_api_key = ( values["openai_api_key"] or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") ) + values["openai_api_key"] = ( + convert_to_secret_str(openai_api_key) if openai_api_key else None + ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) @@ -131,8 +134,9 @@ def validate_environment(cls, values: Dict) -> Dict: values["azure_endpoint"] = values["azure_endpoint"] or os.getenv( "AZURE_OPENAI_ENDPOINT" ) - values["azure_ad_token"] = values["azure_ad_token"] or os.getenv( - "AZURE_OPENAI_AD_TOKEN" + azure_ad_token = values["azure_ad_token"] or os.getenv("AZURE_OPENAI_AD_TOKEN") + values["azure_ad_token"] = ( + convert_to_secret_str(azure_ad_token) if azure_ad_token else None ) values["openai_api_type"] = get_from_dict_or_env( @@ -168,8 +172,12 @@ def validate_environment(cls, values: Dict) -> Dict: "api_version": values["openai_api_version"], "azure_endpoint": values["azure_endpoint"], "azure_deployment": values["deployment_name"], - "api_key": values["openai_api_key"], - "azure_ad_token": values["azure_ad_token"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, + "azure_ad_token": values["azure_ad_token"].get_secret_value() + if values["azure_ad_token"] + else None, "azure_ad_token_provider": values["azure_ad_token_provider"], "organization": values["openai_organization"], "base_url": values["openai_api_base"], diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index ab517e75aa7c447..d0bed6f92106be7 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -52,10 +52,11 @@ ToolMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils import ( + convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, ) @@ -240,10 +241,7 @@ def is_lc_serializable(cls) -> bool: """What sampling temperature to use.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" - # When updating this to use a SecretStr - # Check for classes that derive from this class (as some of them - # may assume openai_api_key is a str) - openai_api_key: Optional[str] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" openai_api_base: Optional[str] = Field(default=None, alias="base_url") """Base URL path for API requests, leave blank if not using a proxy or service @@ -321,8 +319,8 @@ def validate_environment(cls, values: Dict) -> Dict: if values["n"] > 1 and values["streaming"]: raise ValueError("n must be 1 when streaming.") - values["openai_api_key"] = get_from_dict_or_env( - values, "openai_api_key", "OPENAI_API_KEY" + values["openai_api_key"] = convert_to_secret_str( + get_from_dict_or_env(values, "openai_api_key", "OPENAI_API_KEY") ) # Check OPENAI_ORGANIZATION for backwards compatibility. values["openai_organization"] = ( @@ -341,7 +339,9 @@ def validate_environment(cls, values: Dict) -> Dict: ) client_params = { - "api_key": values["openai_api_key"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], diff --git a/libs/partners/openai/langchain_openai/embeddings/azure.py b/libs/partners/openai/langchain_openai/embeddings/azure.py index ca0221252be8bc9..69288364c05b827 100644 --- a/libs/partners/openai/langchain_openai/embeddings/azure.py +++ b/libs/partners/openai/langchain_openai/embeddings/azure.py @@ -5,8 +5,8 @@ from typing import Callable, Dict, Optional, Union import openai -from langchain_core.pydantic_v1 import Field, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_openai.embeddings.base import OpenAIEmbeddings @@ -39,9 +39,9 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): If given sets the base client URL to include `/deployments/{azure_deployment}`. Note: this means you won't be able to use non-deployment endpoints. """ - openai_api_key: Union[str, None] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" - azure_ad_token: Union[str, None] = None + azure_ad_token: Optional[SecretStr] = None """Your Azure Active Directory token. Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided. @@ -64,11 +64,14 @@ def validate_environment(cls, values: Dict) -> Dict: # Check OPENAI_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. - values["openai_api_key"] = ( + openai_api_key = ( values["openai_api_key"] or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") ) + values["openai_api_key"] = ( + convert_to_secret_str(openai_api_key) if openai_api_key else None + ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) @@ -92,8 +95,9 @@ def validate_environment(cls, values: Dict) -> Dict: values["azure_endpoint"] = values["azure_endpoint"] or os.getenv( "AZURE_OPENAI_ENDPOINT" ) - values["azure_ad_token"] = values["azure_ad_token"] or os.getenv( - "AZURE_OPENAI_AD_TOKEN" + azure_ad_token = values["azure_ad_token"] or os.getenv("AZURE_OPENAI_AD_TOKEN") + values["azure_ad_token"] = ( + convert_to_secret_str(azure_ad_token) if azure_ad_token else None ) # Azure OpenAI embedding models allow a maximum of 16 texts # at a time in each batch @@ -122,8 +126,12 @@ def validate_environment(cls, values: Dict) -> Dict: "api_version": values["openai_api_version"], "azure_endpoint": values["azure_endpoint"], "azure_deployment": values["deployment"], - "api_key": values["openai_api_key"], - "azure_ad_token": values["azure_ad_token"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, + "azure_ad_token": values["azure_ad_token"].get_secret_value() + if values["azure_ad_token"] + else None, "azure_ad_token_provider": values["azure_ad_token_provider"], "organization": values["openai_organization"], "base_url": values["openai_api_base"], diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py index 0e3c8e3eac51093..cd0987ea4317770 100644 --- a/libs/partners/openai/langchain_openai/embeddings/base.py +++ b/libs/partners/openai/langchain_openai/embeddings/base.py @@ -22,8 +22,18 @@ import openai import tiktoken from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator -from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names +from langchain_core.pydantic_v1 import ( + BaseModel, + Extra, + Field, + SecretStr, + root_validator, +) +from langchain_core.utils import ( + convert_to_secret_str, + get_from_dict_or_env, + get_pydantic_field_names, +) logger = logging.getLogger(__name__) @@ -70,7 +80,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): openai_proxy: Optional[str] = None embedding_ctx_length: int = 8191 """The maximum number of tokens to embed at once.""" - openai_api_key: Optional[str] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" openai_organization: Optional[str] = Field(default=None, alias="organization") """Automatically inferred from env var `OPENAI_ORG_ID` if not provided.""" @@ -152,9 +162,12 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env( + openai_api_key = get_from_dict_or_env( values, "openai_api_key", "OPENAI_API_KEY" ) + values["openai_api_key"] = ( + convert_to_secret_str(openai_api_key) if openai_api_key else None + ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) @@ -196,7 +209,9 @@ def validate_environment(cls, values: Dict) -> Dict: "please use the `AzureOpenAIEmbeddings` class." ) client_params = { - "api_key": values["openai_api_key"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], diff --git a/libs/partners/openai/langchain_openai/llms/azure.py b/libs/partners/openai/langchain_openai/llms/azure.py index d719c609015f86e..f307d37d54b422a 100644 --- a/libs/partners/openai/langchain_openai/llms/azure.py +++ b/libs/partners/openai/langchain_openai/llms/azure.py @@ -2,18 +2,11 @@ import logging import os -from typing import ( - Any, - Callable, - Dict, - List, - Mapping, - Union, -) +from typing import Any, Callable, Dict, List, Mapping, Optional, Union import openai -from langchain_core.pydantic_v1 import Field, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_openai.llms.base import BaseOpenAI @@ -52,9 +45,9 @@ class AzureOpenAI(BaseOpenAI): """ openai_api_version: str = Field(default="", alias="api_version") """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.""" - openai_api_key: Union[str, None] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" - azure_ad_token: Union[str, None] = None + azure_ad_token: Optional[SecretStr] = None """Your Azure Active Directory token. Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided. @@ -92,17 +85,21 @@ def validate_environment(cls, values: Dict) -> Dict: # Check OPENAI_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. - values["openai_api_key"] = ( + openai_api_key = ( values["openai_api_key"] or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") ) + values["openai_api_key"] = ( + convert_to_secret_str(openai_api_key) if openai_api_key else None + ) values["azure_endpoint"] = values["azure_endpoint"] or os.getenv( "AZURE_OPENAI_ENDPOINT" ) - values["azure_ad_token"] = values["azure_ad_token"] or os.getenv( - "AZURE_OPENAI_AD_TOKEN" + azure_ad_token = values["azure_ad_token"] or os.getenv("AZURE_OPENAI_AD_TOKEN") + values["azure_ad_token"] = ( + convert_to_secret_str(azure_ad_token) if azure_ad_token else None ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" @@ -150,8 +147,12 @@ def validate_environment(cls, values: Dict) -> Dict: "api_version": values["openai_api_version"], "azure_endpoint": values["azure_endpoint"], "azure_deployment": values["deployment_name"], - "api_key": values["openai_api_key"], - "azure_ad_token": values["azure_ad_token"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, + "azure_ad_token": values["azure_ad_token"].get_secret_value() + if values["azure_ad_token"] + else None, "azure_ad_token_provider": values["azure_ad_token_provider"], "organization": values["openai_organization"], "base_url": values["openai_api_base"], diff --git a/libs/partners/openai/langchain_openai/llms/base.py b/libs/partners/openai/langchain_openai/llms/base.py index ef9af629ed023d2..27c2afd5eef405f 100644 --- a/libs/partners/openai/langchain_openai/llms/base.py +++ b/libs/partners/openai/langchain_openai/llms/base.py @@ -27,8 +27,12 @@ ) from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator -from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator +from langchain_core.utils import ( + convert_to_secret_str, + get_from_dict_or_env, + get_pydantic_field_names, +) from langchain_core.utils.utils import build_extra_kwargs logger = logging.getLogger(__name__) @@ -104,10 +108,7 @@ def lc_attributes(self) -> Dict[str, Any]: """Generates best_of completions server-side and returns the "best".""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" - # When updating this to use a SecretStr - # Check for classes that derive from this class (as some of them - # may assume openai_api_key is a str) - openai_api_key: Optional[str] = Field(default=None, alias="api_key") + openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" openai_api_base: Optional[str] = Field(default=None, alias="base_url") """Base URL path for API requests, leave blank if not using a proxy or service @@ -175,9 +176,12 @@ def validate_environment(cls, values: Dict) -> Dict: if values["streaming"] and values["best_of"] > 1: raise ValueError("Cannot stream results when best_of > 1.") - values["openai_api_key"] = get_from_dict_or_env( + openai_api_key = get_from_dict_or_env( values, "openai_api_key", "OPENAI_API_KEY" ) + values["openai_api_key"] = ( + convert_to_secret_str(openai_api_key) if openai_api_key else None + ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) @@ -194,7 +198,9 @@ def validate_environment(cls, values: Dict) -> Dict: ) client_params = { - "api_key": values["openai_api_key"], + "api_key": values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None, "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], diff --git a/libs/partners/openai/tests/integration_tests/embeddings/test_azure.py b/libs/partners/openai/tests/integration_tests/embeddings/test_azure.py index e270faf0923586e..e7ca52283832a0d 100644 --- a/libs/partners/openai/tests/integration_tests/embeddings/test_azure.py +++ b/libs/partners/openai/tests/integration_tests/embeddings/test_azure.py @@ -22,7 +22,7 @@ def _get_embeddings(**kwargs: Any) -> AzureOpenAIEmbeddings: return AzureOpenAIEmbeddings( azure_deployment=DEPLOYMENT_NAME, api_version=OPENAI_API_VERSION, - openai_api_base=OPENAI_API_BASE, + azure_endpoint=OPENAI_API_BASE, openai_api_key=OPENAI_API_KEY, **kwargs, ) @@ -109,7 +109,7 @@ def test_azure_openai_embedding_with_empty_string() -> None: openai.AzureOpenAI( api_version=OPENAI_API_VERSION, api_key=OPENAI_API_KEY, - base_url=embedding.openai_api_base, + azure_endpoint=OPENAI_API_BASE, azure_deployment=DEPLOYMENT_NAME, ) # type: ignore .embeddings.create(input="", model="text-embedding-ada-002") diff --git a/libs/partners/openai/tests/integration_tests/llms/test_azure.py b/libs/partners/openai/tests/integration_tests/llms/test_azure.py index 66486bb2b74d278..c00e5afbd12ab6f 100644 --- a/libs/partners/openai/tests/integration_tests/llms/test_azure.py +++ b/libs/partners/openai/tests/integration_tests/llms/test_azure.py @@ -22,7 +22,7 @@ def _get_llm(**kwargs: Any) -> AzureOpenAI: return AzureOpenAI( deployment_name=DEPLOYMENT_NAME, openai_api_version=OPENAI_API_VERSION, - openai_api_base=OPENAI_API_BASE, + azure_endpoint=OPENAI_API_BASE, openai_api_key=OPENAI_API_KEY, **kwargs, ) diff --git a/libs/partners/openai/tests/unit_tests/test_secrets.py b/libs/partners/openai/tests/unit_tests/test_secrets.py new file mode 100644 index 000000000000000..a90fbb4b7dbab71 --- /dev/null +++ b/libs/partners/openai/tests/unit_tests/test_secrets.py @@ -0,0 +1,62 @@ +from langchain_openai import ( + AzureChatOpenAI, + AzureOpenAI, + AzureOpenAIEmbeddings, + ChatOpenAI, + OpenAI, + OpenAIEmbeddings, +) + + +def test_chat_openai_secrets() -> None: + o = ChatOpenAI(openai_api_key="foo") + s = str(o) + assert "foo" not in s + + +def test_openai_secrets() -> None: + o = OpenAI(openai_api_key="foo") + s = str(o) + assert "foo" not in s + + +def test_openai_embeddings_secrets() -> None: + o = OpenAIEmbeddings(openai_api_key="foo") + s = str(o) + assert "foo" not in s + + +def test_azure_chat_openai_secrets() -> None: + o = AzureChatOpenAI( + openai_api_key="foo1", + azure_endpoint="endpoint", + azure_ad_token="foo2", + api_version="version", + ) + s = str(o) + assert "foo1" not in s + assert "foo2" not in s + + +def test_azure_openai_secrets() -> None: + o = AzureOpenAI( + openai_api_key="foo1", + azure_endpoint="endpoint", + azure_ad_token="foo2", + api_version="version", + ) + s = str(o) + assert "foo1" not in s + assert "foo2" not in s + + +def test_azure_openai_embeddings_secrets() -> None: + o = AzureOpenAIEmbeddings( + openai_api_key="foo1", + azure_endpoint="endpoint", + azure_ad_token="foo2", + api_version="version", + ) + s = str(o) + assert "foo1" not in s + assert "foo2" not in s From 36c0392dbe8b776abd63bcb8234151c6e151e1c1 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 30 Jan 2024 16:01:47 -0800 Subject: [PATCH 011/157] infra: remove unnecessary tests on partner packages (#16808) --- .github/scripts/check_diff.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index 15192fe5daf1dab..ee20daa547ac49f 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -36,13 +36,7 @@ elif "libs/partners" in file: partner_dir = file.split("/")[2] if os.path.isdir(f"libs/partners/{partner_dir}"): - dirs_to_run.update( - ( - f"libs/partners/{partner_dir}", - "libs/langchain", - "libs/experimental", - ) - ) + dirs_to_run.add(f"libs/partners/{partner_dir}") # Skip if the directory was deleted elif "libs/langchain" in file: dirs_to_run.update(("libs/langchain", "libs/experimental")) From c37ca45825b37eed90d773691772a6be4da4322b Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 30 Jan 2024 16:06:19 -0800 Subject: [PATCH 012/157] nvidia-trt: remove tritonclient all extra dep (#16749) --- libs/partners/nvidia-trt/poetry.lock | 940 +----------------------- libs/partners/nvidia-trt/pyproject.toml | 2 +- 2 files changed, 19 insertions(+), 923 deletions(-) diff --git a/libs/partners/nvidia-trt/poetry.lock b/libs/partners/nvidia-trt/poetry.lock index e42a149dcdaa990..975cafd40344d77 100644 --- a/libs/partners/nvidia-trt/poetry.lock +++ b/libs/partners/nvidia-trt/poetry.lock @@ -1,114 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. - -[[package]] -name = "aiohttp" -version = "3.9.1" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - 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def lazy_load( - self, - ) -> Iterator[Document]: + def lazy_load(self) -> Iterator[Document]: """A lazy loader for Documents.""" raise NotImplementedError( f"{self.__class__.__name__} does not implement lazy_load()" ) + async def alazy_load(self) -> AsyncIterator[Document]: + """A lazy loader for Documents.""" + iterator = await run_in_executor(None, self.lazy_load) + done = object() + while True: + doc = await run_in_executor(None, next, iterator, done) + if doc is done: + break + yield doc + class BaseBlobParser(ABC): """Abstract interface for blob parsers. diff --git a/libs/community/langchain_community/document_loaders/merge.py b/libs/community/langchain_community/document_loaders/merge.py index c93963e70cacef5..9ef1a0fd3c1027d 100644 --- a/libs/community/langchain_community/document_loaders/merge.py +++ b/libs/community/langchain_community/document_loaders/merge.py @@ -1,4 +1,4 @@ -from typing import Iterator, List +from typing import AsyncIterator, Iterator, List from langchain_core.documents import Document @@ -26,3 +26,9 @@ def lazy_load(self) -> Iterator[Document]: def load(self) -> List[Document]: """Load docs.""" return list(self.lazy_load()) + + async def alazy_load(self) -> AsyncIterator[Document]: + """Lazy load docs from each individual loader.""" + for loader in self.loaders: + async for document in loader.alazy_load(): + yield document diff --git a/libs/community/tests/unit_tests/document_loaders/test_base.py b/libs/community/tests/unit_tests/document_loaders/test_base.py index 18a3646aa9acda8..e966cf193b6bfa2 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_base.py +++ b/libs/community/tests/unit_tests/document_loaders/test_base.py @@ -1,9 +1,9 @@ """Test Base Schema of documents.""" -from typing import Iterator +from typing import Iterator, List from langchain_core.documents import Document -from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader from langchain_community.document_loaders.blob_loaders import Blob @@ -27,3 +27,20 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]: docs = parser.parse(Blob(data="who?")) assert len(docs) == 1 assert docs[0].page_content == "foo" + + +async def test_default_aload() -> None: + class FakeLoader(BaseLoader): + def load(self) -> List[Document]: + return list(self.lazy_load()) + + def lazy_load(self) -> Iterator[Document]: + yield from [ + Document(page_content="foo"), + Document(page_content="bar"), + ] + + loader = FakeLoader() + docs = loader.load() + assert docs == [Document(page_content="foo"), Document(page_content="bar")] + assert docs == [doc async for doc in loader.alazy_load()] diff --git a/libs/langchain/langchain/indexes/_api.py b/libs/langchain/langchain/indexes/_api.py index 2f91c2ae45c6ce4..8dcf6e0c93a4705 100644 --- a/libs/langchain/langchain/indexes/_api.py +++ b/libs/langchain/langchain/indexes/_api.py @@ -391,7 +391,7 @@ async def _to_async_iterator(iterator: Iterable[T]) -> AsyncIterator[T]: async def aindex( - docs_source: Union[Iterable[Document], AsyncIterator[Document]], + docs_source: Union[BaseLoader, Iterable[Document], AsyncIterator[Document]], record_manager: RecordManager, vector_store: VectorStore, *, @@ -469,16 +469,22 @@ async def aindex( # implementation which just raises a NotImplementedError raise ValueError("Vectorstore has not implemented the delete method") - if isinstance(docs_source, BaseLoader): - raise NotImplementedError( - "Not supported yet. Please pass an async iterator of documents." - ) async_doc_iterator: AsyncIterator[Document] - - if hasattr(docs_source, "__aiter__"): - async_doc_iterator = docs_source # type: ignore[assignment] + if isinstance(docs_source, BaseLoader): + try: + async_doc_iterator = docs_source.alazy_load() + except NotImplementedError: + # Exception triggered when neither lazy_load nor alazy_load are implemented. + # * The default implementation of alazy_load uses lazy_load. + # * The default implementation of lazy_load raises NotImplementedError. + # In such a case, we use the load method and convert it to an async + # iterator. + async_doc_iterator = _to_async_iterator(docs_source.load()) else: - async_doc_iterator = _to_async_iterator(docs_source) + if hasattr(docs_source, "__aiter__"): + async_doc_iterator = docs_source # type: ignore[assignment] + else: + async_doc_iterator = _to_async_iterator(docs_source) source_id_assigner = _get_source_id_assigner(source_id_key) diff --git a/libs/langchain/tests/unit_tests/indexes/test_indexing.py b/libs/langchain/tests/unit_tests/indexes/test_indexing.py index 5febe24ffeeb845..59ab527543bae50 100644 --- a/libs/langchain/tests/unit_tests/indexes/test_indexing.py +++ b/libs/langchain/tests/unit_tests/indexes/test_indexing.py @@ -43,15 +43,8 @@ def load(self) -> List[Document]: async def alazy_load( self, ) -> AsyncIterator[Document]: - async def async_generator() -> AsyncIterator[Document]: - for document in self.documents: - yield document - - return async_generator() - - async def aload(self) -> List[Document]: - """Load the documents from the source.""" - return [doc async for doc in await self.alazy_load()] + for document in self.documents: + yield document class InMemoryVectorStore(VectorStore): @@ -232,7 +225,7 @@ async def test_aindexing_same_content( ] ) - assert await aindex(await loader.alazy_load(), arecord_manager, vector_store) == { + assert await aindex(loader, arecord_manager, vector_store) == { "num_added": 2, "num_deleted": 0, "num_skipped": 0, @@ -243,9 +236,7 @@ async def test_aindexing_same_content( for _ in range(2): # Run the indexing again - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store - ) == { + assert await aindex(loader, arecord_manager, vector_store) == { "num_added": 0, "num_deleted": 0, "num_skipped": 2, @@ -347,9 +338,7 @@ async def test_aindex_simple_delete_full( with patch.object( arecord_manager, "aget_time", return_value=datetime(2021, 1, 1).timestamp() ): - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store, cleanup="full" - ) == { + assert await aindex(loader, arecord_manager, vector_store, cleanup="full") == { "num_added": 2, "num_deleted": 0, "num_skipped": 0, @@ -359,9 +348,7 @@ async def test_aindex_simple_delete_full( with patch.object( arecord_manager, "aget_time", return_value=datetime(2021, 1, 1).timestamp() ): - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store, cleanup="full" - ) == { + assert await aindex(loader, arecord_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 2, @@ -382,9 +369,7 @@ async def test_aindex_simple_delete_full( with patch.object( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store, cleanup="full" - ) == { + assert await aindex(loader, arecord_manager, vector_store, cleanup="full") == { "num_added": 1, "num_deleted": 1, "num_skipped": 1, @@ -402,9 +387,7 @@ async def test_aindex_simple_delete_full( with patch.object( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store, cleanup="full" - ) == { + assert await aindex(loader, arecord_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 2, @@ -473,7 +456,7 @@ async def test_aincremental_fails_with_bad_source_ids( with pytest.raises(ValueError): # Should raise an error because no source id function was specified await aindex( - await loader.alazy_load(), + loader, arecord_manager, vector_store, cleanup="incremental", @@ -482,7 +465,7 @@ async def test_aincremental_fails_with_bad_source_ids( with pytest.raises(ValueError): # Should raise an error because no source id function was specified await aindex( - await loader.alazy_load(), + loader, arecord_manager, vector_store, cleanup="incremental", @@ -593,7 +576,7 @@ async def test_ano_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader, arecord_manager, vector_store, cleanup=None, @@ -610,7 +593,7 @@ async def test_ano_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader, arecord_manager, vector_store, cleanup=None, @@ -640,7 +623,7 @@ async def test_ano_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader, arecord_manager, vector_store, cleanup=None, @@ -779,7 +762,7 @@ async def test_aincremental_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader.lazy_load(), arecord_manager, vector_store, cleanup="incremental", @@ -803,7 +786,7 @@ async def test_aincremental_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 2).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader.lazy_load(), arecord_manager, vector_store, cleanup="incremental", @@ -838,7 +821,7 @@ async def test_aincremental_delete( arecord_manager, "aget_time", return_value=datetime(2021, 1, 3).timestamp() ): assert await aindex( - await loader.alazy_load(), + loader.lazy_load(), arecord_manager, vector_store, cleanup="incremental", @@ -883,9 +866,7 @@ async def test_aindexing_with_no_docs( """Check edge case when loader returns no new docs.""" loader = ToyLoader(documents=[]) - assert await aindex( - await loader.alazy_load(), arecord_manager, vector_store, cleanup="full" - ) == { + assert await aindex(loader, arecord_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 0, From 2e5949b6f8bc340a992b9f9f9fb4751f87979e15 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 31 Jan 2024 11:59:39 -0800 Subject: [PATCH 014/157] core(minor): Add bulk add messages to BaseChatMessageHistory interface (#15709) * Add bulk add_messages method to the interface. * Update documentation for add_ai_message and add_human_message to denote them as being marked for deprecation. We should stop using them as they create more incorrect (inefficient) ways of doing things --- libs/core/langchain_core/chat_history.py | 60 ++++++++++++++-- .../tests/unit_tests/chat_history/__init__.py | 0 .../chat_history/test_chat_history.py | 68 +++++++++++++++++++ 3 files changed, 121 insertions(+), 7 deletions(-) create mode 100644 libs/core/tests/unit_tests/chat_history/__init__.py create mode 100644 libs/core/tests/unit_tests/chat_history/test_chat_history.py diff --git a/libs/core/langchain_core/chat_history.py b/libs/core/langchain_core/chat_history.py index f8d9a4dabf89206..e558c1479ed0ccb 100644 --- a/libs/core/langchain_core/chat_history.py +++ b/libs/core/langchain_core/chat_history.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import List, Union +from typing import List, Sequence, Union from langchain_core.messages import ( AIMessage, @@ -14,9 +14,18 @@ class BaseChatMessageHistory(ABC): """Abstract base class for storing chat message history. - See `ChatMessageHistory` for default implementation. + Implementations should over-ride the add_messages method to handle bulk addition + of messages. + + The default implementation of add_message will correctly call add_messages, so + it is not necessary to implement both methods. + + When used for updating history, users should favor usage of `add_messages` + over `add_message` or other variants like `add_user_message` and `add_ai_message` + to avoid unnecessary round-trips to the underlying persistence layer. + + Example: Shows a default implementation. - Example: .. code-block:: python class FileChatMessageHistory(BaseChatMessageHistory): @@ -29,8 +38,13 @@ def messages(self): messages = json.loads(f.read()) return messages_from_dict(messages) - def add_message(self, message: BaseMessage) -> None: - messages = self.messages.append(_message_to_dict(message)) + def add_messages(self, messages: Sequence[BaseMessage]) -> None: + all_messages = list(self.messages) # Existing messages + all_messages.extend(messages) # Add new messages + + serialized = [message_to_dict(message) for message in all_messages] + # Can be further optimized by only writing new messages + # using append mode. with open(os.path.join(storage_path, session_id), 'w') as f: json.dump(f, messages) @@ -45,6 +59,12 @@ def clear(self): def add_user_message(self, message: Union[HumanMessage, str]) -> None: """Convenience method for adding a human message string to the store. + Please note that this is a convenience method. Code should favor the + bulk add_messages interface instead to save on round-trips to the underlying + persistence layer. + + This method may be deprecated in a future release. + Args: message: The human message to add """ @@ -56,6 +76,12 @@ def add_user_message(self, message: Union[HumanMessage, str]) -> None: def add_ai_message(self, message: Union[AIMessage, str]) -> None: """Convenience method for adding an AI message string to the store. + Please note that this is a convenience method. Code should favor the bulk + add_messages interface instead to save on round-trips to the underlying + persistence layer. + + This method may be deprecated in a future release. + Args: message: The AI message to add. """ @@ -64,18 +90,38 @@ def add_ai_message(self, message: Union[AIMessage, str]) -> None: else: self.add_message(AIMessage(content=message)) - @abstractmethod def add_message(self, message: BaseMessage) -> None: """Add a Message object to the store. Args: message: A BaseMessage object to store. """ - raise NotImplementedError() + if type(self).add_messages != BaseChatMessageHistory.add_messages: + # This means that the sub-class has implemented an efficient add_messages + # method, so we should usage of add_message to that. + self.add_messages([message]) + else: + raise NotImplementedError( + "add_message is not implemented for this class. " + "Please implement add_message or add_messages." + ) + + def add_messages(self, messages: Sequence[BaseMessage]) -> None: + """Add a list of messages. + + Implementations should over-ride this method to handle bulk addition of messages + in an efficient manner to avoid unnecessary round-trips to the underlying store. + + Args: + messages: A list of BaseMessage objects to store. + """ + for message in messages: + self.add_message(message) @abstractmethod def clear(self) -> None: """Remove all messages from the store""" def __str__(self) -> str: + """Return a string representation of the chat history.""" return get_buffer_string(self.messages) diff --git a/libs/core/tests/unit_tests/chat_history/__init__.py b/libs/core/tests/unit_tests/chat_history/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/core/tests/unit_tests/chat_history/test_chat_history.py b/libs/core/tests/unit_tests/chat_history/test_chat_history.py new file mode 100644 index 000000000000000..0f6b696e8fbcbdd --- /dev/null +++ b/libs/core/tests/unit_tests/chat_history/test_chat_history.py @@ -0,0 +1,68 @@ +from typing import List, Sequence + +from langchain_core.chat_history import BaseChatMessageHistory +from langchain_core.messages import BaseMessage, HumanMessage + + +def test_add_message_implementation_only() -> None: + """Test implementation of add_message only.""" + + class SampleChatHistory(BaseChatMessageHistory): + def __init__(self, *, store: List[BaseMessage]) -> None: + self.store = store + + def add_message(self, message: BaseMessage) -> None: + """Add a message to the store.""" + self.store.append(message) + + def clear(self) -> None: + """Clear the store.""" + raise NotImplementedError() + + store: List[BaseMessage] = [] + chat_history = SampleChatHistory(store=store) + chat_history.add_message(HumanMessage(content="Hello")) + assert len(store) == 1 + assert store[0] == HumanMessage(content="Hello") + chat_history.add_message(HumanMessage(content="World")) + assert len(store) == 2 + assert store[1] == HumanMessage(content="World") + + chat_history.add_messages( + [HumanMessage(content="Hello"), HumanMessage(content="World")] + ) + assert len(store) == 4 + assert store[2] == HumanMessage(content="Hello") + assert store[3] == HumanMessage(content="World") + + +def test_bulk_message_implementation_only() -> None: + """Test that SampleChatHistory works as expected.""" + store: List[BaseMessage] = [] + + class BulkAddHistory(BaseChatMessageHistory): + def __init__(self, *, store: List[BaseMessage]) -> None: + self.store = store + + def add_messages(self, message: Sequence[BaseMessage]) -> None: + """Add a message to the store.""" + self.store.extend(message) + + def clear(self) -> None: + """Clear the store.""" + raise NotImplementedError() + + chat_history = BulkAddHistory(store=store) + chat_history.add_message(HumanMessage(content="Hello")) + assert len(store) == 1 + assert store[0] == HumanMessage(content="Hello") + chat_history.add_message(HumanMessage(content="World")) + assert len(store) == 2 + assert store[1] == HumanMessage(content="World") + + chat_history.add_messages( + [HumanMessage(content="Hello"), HumanMessage(content="World")] + ) + assert len(store) == 4 + assert store[2] == HumanMessage(content="Hello") + assert store[3] == HumanMessage(content="World") From 17e886388bde3cbc8132ac0c4467dfe90e4027b3 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 31 Jan 2024 16:46:35 -0800 Subject: [PATCH 015/157] nomic: init pkg (#16853) Co-authored-by: Lance Martin --- .github/workflows/_integration_test.yml | 1 + .github/workflows/_release.yml | 1 + cookbook/nomic_embeddings.ipynb | 301 ++++ docs/docs/integrations/providers/nomic.ipynb | 53 + .../integrations/text_embedding/nomic.ipynb | 132 ++ libs/partners/nomic/.gitignore | 1 + libs/partners/nomic/LICENSE | 21 + libs/partners/nomic/Makefile | 61 + libs/partners/nomic/README.md | 23 + .../nomic/langchain_nomic/__init__.py | 5 + .../nomic/langchain_nomic/embeddings.py | 71 + libs/partners/nomic/langchain_nomic/py.typed | 0 libs/partners/nomic/poetry.lock | 1310 +++++++++++++++++ libs/partners/nomic/pyproject.toml | 94 ++ libs/partners/nomic/scripts/check_imports.py | 17 + libs/partners/nomic/scripts/check_pydantic.sh | 27 + libs/partners/nomic/scripts/lint_imports.sh | 17 + libs/partners/nomic/tests/__init__.py | 0 .../nomic/tests/integration_tests/__init__.py | 0 .../tests/integration_tests/test_compile.py | 7 + .../integration_tests/test_embeddings.py | 19 + .../nomic/tests/unit_tests/__init__.py | 0 .../nomic/tests/unit_tests/test_embeddings.py | 9 + .../nomic/tests/unit_tests/test_imports.py | 9 + 24 files changed, 2179 insertions(+) create mode 100644 cookbook/nomic_embeddings.ipynb create mode 100644 docs/docs/integrations/providers/nomic.ipynb create mode 100644 docs/docs/integrations/text_embedding/nomic.ipynb create mode 100644 libs/partners/nomic/.gitignore create mode 100644 libs/partners/nomic/LICENSE create mode 100644 libs/partners/nomic/Makefile create mode 100644 libs/partners/nomic/README.md create mode 100644 libs/partners/nomic/langchain_nomic/__init__.py create mode 100644 libs/partners/nomic/langchain_nomic/embeddings.py create mode 100644 libs/partners/nomic/langchain_nomic/py.typed create mode 100644 libs/partners/nomic/poetry.lock create mode 100644 libs/partners/nomic/pyproject.toml create mode 100644 libs/partners/nomic/scripts/check_imports.py create mode 100755 libs/partners/nomic/scripts/check_pydantic.sh create mode 100755 libs/partners/nomic/scripts/lint_imports.sh create mode 100644 libs/partners/nomic/tests/__init__.py create mode 100644 libs/partners/nomic/tests/integration_tests/__init__.py create mode 100644 libs/partners/nomic/tests/integration_tests/test_compile.py create mode 100644 libs/partners/nomic/tests/integration_tests/test_embeddings.py create mode 100644 libs/partners/nomic/tests/unit_tests/__init__.py create mode 100644 libs/partners/nomic/tests/unit_tests/test_embeddings.py create mode 100644 libs/partners/nomic/tests/unit_tests/test_imports.py diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index daab2fdc9ed8cf2..e0dcf25c1356975 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -56,6 +56,7 @@ jobs: GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }} GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} + NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} run: | make integration_tests diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index fc6b2bd24e90bd3..c37d87e0ad12447 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -175,6 +175,7 @@ jobs: GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }} GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} + NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} run: make integration_tests working-directory: ${{ inputs.working-directory }} diff --git a/cookbook/nomic_embeddings.ipynb b/cookbook/nomic_embeddings.ipynb new file mode 100644 index 000000000000000..32fdfbba36fff89 --- /dev/null +++ b/cookbook/nomic_embeddings.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8da6094-30c7-43f3-a608-c91717b673db", + "metadata": {}, + "source": [ + "# Nomic Embeddings\n", + "\n", + "Nomic has released a new embedding model with strong performance for long context retrieval (8k context window).\n", + "\n", + "## Signup\n", + "\n", + "Get your API token, then run:\n", + "```\n", + "! nomic login\n", + "```\n", + "\n", + "Then run with your generated API token \n", + "```\n", + "! nomic login < token > \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f737ec15-e9ab-4629-b54c-24be69e8b60b", + "metadata": {}, + "outputs": [], + "source": [ + "! nomic login" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8ab7434a-2930-42b5-9164-dc2c03abe232", + "metadata": {}, + "outputs": [], + "source": [ + "! nomic login token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3501e2a-4686-4b95-8a1c-f19e035ea354", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U langchain-nomic" + ] + }, + { + "cell_type": "markdown", + "id": "134475f2-f256-4c13-9712-c55783e6a4e2", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Let's test 3 interesting blog posts." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "01c4d270-171e-45c2-a1b6-e350faa74117", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import WebBaseLoader\n", + "\n", + "urls = [\n", + " \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n", + " \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n", + " \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n", + "]\n", + "\n", + "docs = [WebBaseLoader(url).load() for url in urls]\n", + "docs_list = [item for sublist in docs for item in sublist]" + ] + }, + { + "cell_type": "markdown", + "id": "75ab7f74-873c-4d84-af5a-5cf19c61239d", + "metadata": {}, + "source": [ + "## Splitting \n", + "\n", + "Long context retrieval " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f512e128-629e-4304-926f-94fe5c999527", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.text_splitter import CharacterTextSplitter\n", + "\n", + "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n", + " chunk_size=7500, chunk_overlap=100\n", + ")\n", + "doc_splits = text_splitter.split_documents(docs_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d2a69cf0-e3ab-4c92-a1d0-10da45c08b3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The document is 6562 tokens\n", + "The document is 3037 tokens\n", + "The document is 6092 tokens\n", + "The document is 1050 tokens\n", + "The document is 6933 tokens\n", + "The document is 5560 tokens\n" + ] + } + ], + "source": [ + "import tiktoken\n", + "\n", + "encoding = tiktoken.get_encoding(\"cl100k_base\")\n", + "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n", + "for d in doc_splits:\n", + " print(\"The document is %s tokens\" % len(encoding.encode(d.page_content)))" + ] + }, + { + "cell_type": "markdown", + "id": "c58d1e9b-e98e-4bd9-b52f-4dfc2a4e69f4", + "metadata": {}, + "source": [ + "## Index \n", + "\n", + "Nomic embeddings [here](https://docs.nomic.ai/reference/endpoints/nomic-embed-text). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76447866-bf8b-412b-93bc-d6ea8ec35952", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", + "from langchain_nomic import NomicEmbeddings\n", + "from langchain_nomic.embeddings import NomicEmbeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "15b3eab2-2689-49d4-8cb0-67ef2adcbc49", + "metadata": {}, + "outputs": [], + "source": [ + "# Add to vectorDB\n", + "vectorstore = Chroma.from_documents(\n", + " documents=doc_splits,\n", + " collection_name=\"rag-chroma\",\n", + " embedding=NomicEmbeddings(model=\"nomic-embed-text-v1\"),\n", + ")\n", + "retriever = vectorstore.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "41131122-3591-4566-aac1-ed19d496820a", + "metadata": {}, + "source": [ + "## RAG Chain\n", + "\n", + "We can use the Mistral `v0.2`, which is [fine-tuned for 32k context](https://x.com/dchaplot/status/1734198245067243629?s=20).\n", + "\n", + "We can [use Ollama](https://ollama.ai/library/mistral) -\n", + "```\n", + "ollama pull mistral:instruct\n", + "```\n", + "\n", + "We can also run [GPT-4 128k](https://openai.com/blog/new-models-and-developer-products-announced-at-devday). " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1397de64-5b4a-4001-adc5-570ff8d31ff6", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models import ChatOllama\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Prompt\n", + "template = \"\"\"Answer the question based only on the following context:\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "# LLM API\n", + "model = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n", + "\n", + "# Local LLM\n", + "ollama_llm = \"mistral:instruct\"\n", + "model_local = ChatOllama(model=ollama_llm)\n", + "\n", + "# Chain\n", + "chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | model_local\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1548e00c-1ff6-4e88-aa13-69badf2088fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' Agents, especially those used in artificial intelligence and natural language processing, can have different types of memory. Here are some common types:\\n\\n1. **Short-term memory** or working memory: This is a small capacity, high-turnover memory that holds information temporarily while the agent processes it. Short-term memory is essential for tasks requiring attention and quick response, such as parsing sentences or following instructions.\\n\\n2. **Long-term memory**: This is a large capacity, low-turnover memory where agents store information for extended periods. Long-term memory enables learning from experiences, accessing past knowledge, and improving performance over time.\\n\\n3. **Explicit memory** or declarative memory: Agents use explicit memory to store and recall facts, concepts, and rules that can be expressed in natural language. This type of memory is crucial for problem solving and reasoning.\\n\\n4. **Implicit memory** or procedural memory: Implicit memory refers to the acquisition and retention of skills and habits. The agent learns through repeated experiences without necessarily being aware of it.\\n\\n5. **Connectionist memory**: Connectionist memory, also known as neural networks, is inspired by the structure and function of biological brains. Connectionist models learn and store information in interconnected nodes or artificial neurons. This type of memory enables the model to recognize patterns and generalize knowledge.\\n\\n6. **Hybrid memory systems**: Many advanced agents employ a combination of different memory types to maximize their learning potential and performance. These hybrid systems can integrate short-term, long-term, explicit, implicit, and connectionist memories.'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Question\n", + "chain.invoke(\"What are the types of agent memory?\")" + ] + }, + { + "cell_type": "markdown", + "id": "5ec5b4c3-757d-44df-92ea-dd5f08017dd6", + "metadata": {}, + "source": [ + "**Mistral**\n", + "\n", + "Trace: 24k prompt tokens.\n", + "\n", + "* https://smith.langchain.com/public/3e04d475-ea08-4ee3-ae66-6416a93d8b08/r\n", + "\n", + "--- \n", + "\n", + "Some considerations are noted in the [needle in a haystack analysis](https://twitter.com/GregKamradt/status/1722386725635580292?lang=en):\n", + "\n", + "* LLMs may suffer with retrieval from large context depending on where the information is placed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ffb6b63-17ee-42d8-b1fb-d6a866e98458", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/providers/nomic.ipynb b/docs/docs/integrations/providers/nomic.ipynb new file mode 100644 index 000000000000000..a7475ef36eabda8 --- /dev/null +++ b/docs/docs/integrations/providers/nomic.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nomic\n", + "\n", + "Nomic currently offers two products:\n", + "\n", + "- Atlas: their Visual Data Engine\n", + "- GPT4All: their Open Source Edge Language Model Ecosystem\n", + "\n", + "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from langchain_nomic import NomicEmbeddings" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/text_embedding/nomic.ipynb b/docs/docs/integrations/text_embedding/nomic.ipynb new file mode 100644 index 000000000000000..6f6b11d4aa51f33 --- /dev/null +++ b/docs/docs/integrations/text_embedding/nomic.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "afaf8039", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: Nomic\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e49f1e0d", + "metadata": {}, + "source": [ + "# NomicEmbeddings\n", + "\n", + "This notebook covers how to get started with Nomic embedding models.\n", + "\n", + "## Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c3bef91", + "metadata": {}, + "outputs": [], + "source": [ + "# install package\n", + "!pip install -U langchain-nomic" + ] + }, + { + "cell_type": "markdown", + "id": "2b4f3e15", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "Make sure to set the following environment variables:\n", + "\n", + "- `NOMIC_API_KEY`\n", + "\n", + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62e0dbc3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain_nomic.embeddings import NomicEmbeddings\n", + "\n", + "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12fcfb4b", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings.embed_query(\"My query to look up\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f2e6104", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings.embed_documents(\n", + " [\"This is a content of the document\", \"This is another document\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46739f68", + "metadata": {}, + "outputs": [], + "source": [ + "# async embed query\n", + "await embeddings.aembed_query(\"My query to look up\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e48632ea", + "metadata": {}, + "outputs": [], + "source": [ + "# async embed documents\n", + "await embeddings.aembed_documents(\n", + " [\"This is a content of the document\", \"This is another document\"]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/partners/nomic/.gitignore b/libs/partners/nomic/.gitignore new file mode 100644 index 000000000000000..bee8a64b79a9959 --- /dev/null +++ b/libs/partners/nomic/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/nomic/LICENSE b/libs/partners/nomic/LICENSE new file mode 100644 index 000000000000000..426b65090341f37 --- /dev/null +++ b/libs/partners/nomic/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/nomic/Makefile b/libs/partners/nomic/Makefile new file mode 100644 index 000000000000000..db9c3cfaeef212f --- /dev/null +++ b/libs/partners/nomic/Makefile @@ -0,0 +1,61 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +integration_tests: TEST_FILE = tests/integration_tests/ + +test integration_tests: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/nomic --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_nomic +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_nomic -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/nomic/README.md b/libs/partners/nomic/README.md new file mode 100644 index 000000000000000..c57ac50e13f3bf7 --- /dev/null +++ b/libs/partners/nomic/README.md @@ -0,0 +1,23 @@ +# langchain-nomic + +This package contains the LangChain integration with Nomic + +## Installation + +```bash +pip install -U langchain-nomic +``` + +And you should configure credentials by setting the following environment variables: + +* `NOMIC_API_KEY`: your nomic API key + +## Embeddings + +`NomicEmbeddings` class exposes embeddings from Nomic. + +```python +from langchain_nomic import NomicEmbeddings + +embeddings = NomicEmbeddings() +embeddings.embed_query("What is the meaning of life?") \ No newline at end of file diff --git a/libs/partners/nomic/langchain_nomic/__init__.py b/libs/partners/nomic/langchain_nomic/__init__.py new file mode 100644 index 000000000000000..75f3facd5071577 --- /dev/null +++ b/libs/partners/nomic/langchain_nomic/__init__.py @@ -0,0 +1,5 @@ +from langchain_nomic.embeddings import NomicEmbeddings + +__all__ = [ + "NomicEmbeddings", +] diff --git a/libs/partners/nomic/langchain_nomic/embeddings.py b/libs/partners/nomic/langchain_nomic/embeddings.py new file mode 100644 index 000000000000000..36c476df0d71722 --- /dev/null +++ b/libs/partners/nomic/langchain_nomic/embeddings.py @@ -0,0 +1,71 @@ +import os +from typing import List, Optional + +import nomic # type: ignore +from langchain_core.embeddings import Embeddings + + +class NomicEmbeddings(Embeddings): + """NomicEmbeddings embedding model. + + Example: + .. code-block:: python + + from langchain_nomic import NomicEmbeddings + + model = NomicEmbeddings() + """ + + def __init__( + self, + *, + model: str, + nomic_api_key: Optional[str] = None, + ): + """Initialize NomicEmbeddings model. + + Args: + model: model name + nomic_api_key: optionally, set the Nomic API key. Uses the NOMIC_API_KEY + environment variable by default. + """ + _api_key = nomic_api_key or os.environ.get("NOMIC_API_KEY") + if _api_key: + nomic.login(_api_key) + self.model = model + + def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]: + """Embed texts. + + Args: + texts: list of texts to embed + task_type: the task type to use when embedding. One of `search_query`, + `search_document`, `classification`, `clustering` + """ + # TODO: do this via nomic.embed when fixed in nomic sdk + from nomic import embed + + output = embed.text(texts=texts, model=self.model, task_type=task_type) + return output["embeddings"] + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Embed search docs. + + Args: + texts: list of texts to embed as documents + """ + return self.embed( + texts=texts, + task_type="search_document", + ) + + def embed_query(self, text: str) -> List[float]: + """Embed query text. + + Args: + text: query text + """ + return self.embed( + texts=[text], + task_type="search_query", + )[0] diff --git a/libs/partners/nomic/langchain_nomic/py.typed b/libs/partners/nomic/langchain_nomic/py.typed new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/nomic/poetry.lock b/libs/partners/nomic/poetry.lock new file mode 100644 index 000000000000000..77c5f064198c68b --- /dev/null +++ b/libs/partners/nomic/poetry.lock @@ -0,0 +1,1310 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} + +[[package]] +name = "anyio" +version = "4.2.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "attrs" +version = "23.2.0" +description = "Classes Without Boilerplate" +optional = false +python-versions = ">=3.7" +files = [ + {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, + {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"}, +] + +[package.extras] +cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] +dev = ["attrs[tests]", "pre-commit"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] +tests = ["attrs[tests-no-zope]", "zope-interface"] +tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] +tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] + +[[package]] +name = "certifi" +version = "2023.11.17" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, + {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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+nomic = "^3.0.7" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = {path = "../../core", develop = true} + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/nomic/scripts/check_imports.py b/libs/partners/nomic/scripts/check_imports.py new file mode 100644 index 000000000000000..fd21a4975b7f0ba --- /dev/null +++ b/libs/partners/nomic/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/nomic/scripts/check_pydantic.sh b/libs/partners/nomic/scripts/check_pydantic.sh new file mode 100755 index 000000000000000..06b5bb81ae23615 --- /dev/null +++ b/libs/partners/nomic/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/nomic/scripts/lint_imports.sh b/libs/partners/nomic/scripts/lint_imports.sh new file mode 100755 index 000000000000000..695613c7ba8fd6d --- /dev/null +++ b/libs/partners/nomic/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/nomic/tests/__init__.py b/libs/partners/nomic/tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/nomic/tests/integration_tests/__init__.py b/libs/partners/nomic/tests/integration_tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/nomic/tests/integration_tests/test_compile.py b/libs/partners/nomic/tests/integration_tests/test_compile.py new file mode 100644 index 000000000000000..33ecccdfa0fbda2 --- /dev/null +++ b/libs/partners/nomic/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/nomic/tests/integration_tests/test_embeddings.py b/libs/partners/nomic/tests/integration_tests/test_embeddings.py new file mode 100644 index 000000000000000..6800deb839f38f0 --- /dev/null +++ b/libs/partners/nomic/tests/integration_tests/test_embeddings.py @@ -0,0 +1,19 @@ +"""Test Nomic embeddings.""" +from langchain_nomic.embeddings import NomicEmbeddings + + +def test_langchain_nomic_embedding_documents() -> None: + """Test cohere embeddings.""" + documents = ["foo bar"] + embedding = NomicEmbeddings(model="nomic-embed-text-v1") + output = embedding.embed_documents(documents) + assert len(output) == 1 + assert len(output[0]) > 0 + + +def test_langchain_nomic_embedding_query() -> None: + """Test cohere embeddings.""" + document = "foo bar" + embedding = NomicEmbeddings(model="nomic-embed-text-v1") + output = embedding.embed_query(document) + assert len(output) > 0 diff --git a/libs/partners/nomic/tests/unit_tests/__init__.py b/libs/partners/nomic/tests/unit_tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/nomic/tests/unit_tests/test_embeddings.py b/libs/partners/nomic/tests/unit_tests/test_embeddings.py new file mode 100644 index 000000000000000..b1d67e93f21626b --- /dev/null +++ b/libs/partners/nomic/tests/unit_tests/test_embeddings.py @@ -0,0 +1,9 @@ +"""Test embedding model integration.""" + + +from langchain_nomic.embeddings import NomicEmbeddings + + +def test_initialization() -> None: + """Test embedding model initialization.""" + NomicEmbeddings(model="nomic-embed-text-v1") diff --git a/libs/partners/nomic/tests/unit_tests/test_imports.py b/libs/partners/nomic/tests/unit_tests/test_imports.py new file mode 100644 index 000000000000000..342069a8160c007 --- /dev/null +++ b/libs/partners/nomic/tests/unit_tests/test_imports.py @@ -0,0 +1,9 @@ +from langchain_nomic import __all__ + +EXPECTED_ALL = [ + "NomicEmbeddings", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) From a0ec04549575de5547faa5381fa69fef61405ac8 Mon Sep 17 00:00:00 2001 From: Christophe Bornet Date: Thu, 1 Feb 2024 02:10:47 +0100 Subject: [PATCH 016/157] Add async methods to BaseStore (#16669) - **Description:** The BaseStore methods are currently blocking. Some implementations (AstraDBStore, RedisStore) would benefit from having async methods. Also once we have async methods for BaseStore, we can implement the async `aembed_documents` in CacheBackedEmbeddings to cache the embeddings asynchronously. * adds async methods amget, amset, amedelete and ayield_keys to BaseStore * implements the async methods for InMemoryStore * adds tests for InMemoryStore async methods - **Twitter handle:** cbornet_ --- libs/core/langchain_core/stores.py | 64 ++++++++++++++++++- libs/langchain/langchain/storage/in_memory.py | 49 ++++++++++++++ .../unit_tests/storage/test_in_memory.py | 47 ++++++++++++++ 3 files changed, 159 insertions(+), 1 deletion(-) diff --git a/libs/core/langchain_core/stores.py b/libs/core/langchain_core/stores.py index 8363fca3891f98e..bb2f09f929ac678 100644 --- a/libs/core/langchain_core/stores.py +++ b/libs/core/langchain_core/stores.py @@ -1,5 +1,17 @@ from abc import ABC, abstractmethod -from typing import Generic, Iterator, List, Optional, Sequence, Tuple, TypeVar, Union +from typing import ( + AsyncIterator, + Generic, + Iterator, + List, + Optional, + Sequence, + Tuple, + TypeVar, + Union, +) + +from langchain_core.runnables import run_in_executor K = TypeVar("K") V = TypeVar("V") @@ -20,6 +32,18 @@ def mget(self, keys: Sequence[K]) -> List[Optional[V]]: If a key is not found, the corresponding value will be None. """ + async def amget(self, keys: Sequence[K]) -> List[Optional[V]]: + """Get the values associated with the given keys. + + Args: + keys (Sequence[K]): A sequence of keys. + + Returns: + A sequence of optional values associated with the keys. + If a key is not found, the corresponding value will be None. + """ + return await run_in_executor(None, self.mget, keys) + @abstractmethod def mset(self, key_value_pairs: Sequence[Tuple[K, V]]) -> None: """Set the values for the given keys. @@ -28,6 +52,14 @@ def mset(self, key_value_pairs: Sequence[Tuple[K, V]]) -> None: key_value_pairs (Sequence[Tuple[K, V]]): A sequence of key-value pairs. """ + async def amset(self, key_value_pairs: Sequence[Tuple[K, V]]) -> None: + """Set the values for the given keys. + + Args: + key_value_pairs (Sequence[Tuple[K, V]]): A sequence of key-value pairs. + """ + return await run_in_executor(None, self.mset, key_value_pairs) + @abstractmethod def mdelete(self, keys: Sequence[K]) -> None: """Delete the given keys and their associated values. @@ -36,6 +68,14 @@ def mdelete(self, keys: Sequence[K]) -> None: keys (Sequence[K]): A sequence of keys to delete. """ + async def amdelete(self, keys: Sequence[K]) -> None: + """Delete the given keys and their associated values. + + Args: + keys (Sequence[K]): A sequence of keys to delete. + """ + return await run_in_executor(None, self.mdelete, keys) + @abstractmethod def yield_keys( self, *, prefix: Optional[str] = None @@ -52,5 +92,27 @@ def yield_keys( depending on what makes more sense for the given store. """ + async def ayield_keys( + self, *, prefix: Optional[str] = None + ) -> Union[AsyncIterator[K], AsyncIterator[str]]: + """Get an iterator over keys that match the given prefix. + + Args: + prefix (str): The prefix to match. + + Returns: + Iterator[K | str]: An iterator over keys that match the given prefix. + + This method is allowed to return an iterator over either K or str + depending on what makes more sense for the given store. + """ + iterator = await run_in_executor(None, self.yield_keys, prefix=prefix) + done = object() + while True: + item = await run_in_executor(None, lambda it: next(it, done), iterator) + if item is done: + break + yield item + ByteStore = BaseStore[str, bytes] diff --git a/libs/langchain/langchain/storage/in_memory.py b/libs/langchain/langchain/storage/in_memory.py index 03679a34909d93f..310f81ce28f7c20 100644 --- a/libs/langchain/langchain/storage/in_memory.py +++ b/libs/langchain/langchain/storage/in_memory.py @@ -5,6 +5,7 @@ """ from typing import ( Any, + AsyncIterator, Dict, Generic, Iterator, @@ -60,6 +61,18 @@ def mget(self, keys: Sequence[str]) -> List[Optional[V]]: """ return [self.store.get(key) for key in keys] + async def amget(self, keys: Sequence[str]) -> List[Optional[V]]: + """Get the values associated with the given keys. + + Args: + keys (Sequence[str]): A sequence of keys. + + Returns: + A sequence of optional values associated with the keys. + If a key is not found, the corresponding value will be None. + """ + return self.mget(keys) + def mset(self, key_value_pairs: Sequence[Tuple[str, V]]) -> None: """Set the values for the given keys. @@ -72,6 +85,17 @@ def mset(self, key_value_pairs: Sequence[Tuple[str, V]]) -> None: for key, value in key_value_pairs: self.store[key] = value + async def amset(self, key_value_pairs: Sequence[Tuple[str, V]]) -> None: + """Set the values for the given keys. + + Args: + key_value_pairs (Sequence[Tuple[str, V]]): A sequence of key-value pairs. + + Returns: + None + """ + return self.mset(key_value_pairs) + def mdelete(self, keys: Sequence[str]) -> None: """Delete the given keys and their associated values. @@ -82,6 +106,14 @@ def mdelete(self, keys: Sequence[str]) -> None: if key in self.store: del self.store[key] + async def amdelete(self, keys: Sequence[str]) -> None: + """Delete the given keys and their associated values. + + Args: + keys (Sequence[str]): A sequence of keys to delete. + """ + self.mdelete(keys) + def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]: """Get an iterator over keys that match the given prefix. @@ -98,6 +130,23 @@ def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]: if key.startswith(prefix): yield key + async def ayield_keys(self, prefix: Optional[str] = None) -> AsyncIterator[str]: + """Get an async iterator over keys that match the given prefix. + + Args: + prefix (str, optional): The prefix to match. Defaults to None. + + Returns: + AsyncIterator[str]: An async iterator over keys that match the given prefix. + """ + if prefix is None: + for key in self.store.keys(): + yield key + else: + for key in self.store.keys(): + if key.startswith(prefix): + yield key + InMemoryStore = InMemoryBaseStore[Any] InMemoryByteStore = InMemoryBaseStore[bytes] diff --git a/libs/langchain/tests/unit_tests/storage/test_in_memory.py b/libs/langchain/tests/unit_tests/storage/test_in_memory.py index 4e969a01dd8f15e..a12233b4f67b251 100644 --- a/libs/langchain/tests/unit_tests/storage/test_in_memory.py +++ b/libs/langchain/tests/unit_tests/storage/test_in_memory.py @@ -13,6 +13,18 @@ def test_mget() -> None: assert non_existent_value == [None] +async def test_amget() -> None: + store = InMemoryStore() + await store.amset([("key1", "value1"), ("key2", "value2")]) + + values = await store.amget(["key1", "key2"]) + assert values == ["value1", "value2"] + + # Test non-existent key + non_existent_value = await store.amget(["key3"]) + assert non_existent_value == [None] + + def test_mset() -> None: store = InMemoryStore() store.mset([("key1", "value1"), ("key2", "value2")]) @@ -21,6 +33,14 @@ def test_mset() -> None: assert values == ["value1", "value2"] +async def test_amset() -> None: + store = InMemoryStore() + await store.amset([("key1", "value1"), ("key2", "value2")]) + + values = await store.amget(["key1", "key2"]) + assert values == ["value1", "value2"] + + def test_mdelete() -> None: store = InMemoryStore() store.mset([("key1", "value1"), ("key2", "value2")]) @@ -34,6 +54,19 @@ def test_mdelete() -> None: store.mdelete(["key3"]) # No error should be raised +async def test_amdelete() -> None: + store = InMemoryStore() + await store.amset([("key1", "value1"), ("key2", "value2")]) + + await store.amdelete(["key1"]) + + values = await store.amget(["key1", "key2"]) + assert values == [None, "value2"] + + # Test deleting non-existent key + await store.amdelete(["key3"]) # No error should be raised + + def test_yield_keys() -> None: store = InMemoryStore() store.mset([("key1", "value1"), ("key2", "value2"), ("key3", "value3")]) @@ -46,3 +79,17 @@ def test_yield_keys() -> None: keys_with_invalid_prefix = list(store.yield_keys(prefix="x")) assert keys_with_invalid_prefix == [] + + +async def test_ayield_keys() -> None: + store = InMemoryStore() + await store.amset([("key1", "value1"), ("key2", "value2"), ("key3", "value3")]) + + keys = [key async for key in store.ayield_keys()] + assert set(keys) == {"key1", "key2", "key3"} + + keys_with_prefix = [key async for key in store.ayield_keys(prefix="key")] + assert set(keys_with_prefix) == {"key1", "key2", "key3"} + + keys_with_invalid_prefix = [key async for key in store.ayield_keys(prefix="x")] + assert keys_with_invalid_prefix == [] From bb7325114629604b00167f00d49e704a7d477d8d Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 07:33:15 -0800 Subject: [PATCH 017/157] core[patch]: Release 0.1.18 (#16870) --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 809f54153a96ffb..37b06c857f0e96e 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-core" -version = "0.1.17" +version = "0.1.18" description = "Building applications with LLMs through composability" authors = [] license = "MIT" From 2b4abed25c03c82569968b9a40e23a9fa5c9c2fa Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 07:33:34 -0800 Subject: [PATCH 018/157] commmunity[patch]: Release 0.0.17 (#16871) --- libs/community/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index e048b7b304bce47..4080d8c7ee72706 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-community" -version = "0.0.16" +version = "0.0.17" description = "Community contributed LangChain integrations." authors = [] license = "MIT" From db442c635b98eb3a056a8592755a6ce8a96c3c45 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 08:10:29 -0800 Subject: [PATCH 019/157] langchain[patch]: Release 0.1.5 (#16881) --- libs/langchain/poetry.lock | 10 +++++----- libs/langchain/pyproject.toml | 4 ++-- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 987a173f836f365..51244301a2f17ec 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -3049,6 +3049,7 @@ files = [ {file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:227b178b22a7f91ae88525810441791b1ca1fc71c86f03190911793be15cec3d"}, {file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = 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contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -3466,7 +3467,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"] +extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"] [package.source] type = "directory" @@ -3474,7 +3475,7 @@ url = "../community" [[package]] name = "langchain-core" -version = "0.1.16" +version = "0.1.18" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -5806,7 +5807,6 @@ files = [ {file = "pymongo-4.5.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6422b6763b016f2ef2beedded0e546d6aa6ba87910f9244d86e0ac7690f75c96"}, {file = "pymongo-4.5.0-cp312-cp312-win32.whl", hash = "sha256:77cfff95c1fafd09e940b3fdcb7b65f11442662fad611d0e69b4dd5d17a81c60"}, {file = "pymongo-4.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:e57d859b972c75ee44ea2ef4758f12821243e99de814030f69a3decb2aa86807"}, - {file = "pymongo-4.5.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8443f3a8ab2d929efa761c6ebce39a6c1dca1c9ac186ebf11b62c8fe1aef53f4"}, {file = "pymongo-4.5.0-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:2b0176f9233a5927084c79ff80b51bd70bfd57e4f3d564f50f80238e797f0c8a"}, {file = "pymongo-4.5.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:89b3f2da57a27913d15d2a07d58482f33d0a5b28abd20b8e643ab4d625e36257"}, {file = "pymongo-4.5.0-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:5caee7bd08c3d36ec54617832b44985bd70c4cbd77c5b313de6f7fce0bb34f93"}, @@ -9149,4 +9149,4 @@ text-helpers = ["chardet"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "1f0d8707a249814b4b96af0d775e5f1484f332af232a8b3e855c263efbb19fc2" +content-hash = "8756afa5050a3ce1c3a504ad819fb85240fa56575a32f1590cbdfbbc9102b576" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index bf6f30f3c287ea2..539a29d4d82fbc8 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain" -version = "0.1.4" +version = "0.1.5" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -13,7 +13,7 @@ langchain-server = "langchain.server:main" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" langchain-core = ">=0.1.16,<0.2" -langchain-community = ">=0.0.14,<0.1" +langchain-community = ">=0.0.17,<0.1" langsmith = ">=0.0.83,<0.1" pydantic = ">=1,<3" SQLAlchemy = ">=1.4,<3" From 9e7d9f93907ed4f28c1a696f3dbdddca65b94da8 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 08:16:30 -0800 Subject: [PATCH 020/157] infra: bump langchain min test reqs (#16882) --- libs/langchain/_test_minimum_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain/_test_minimum_requirements.txt b/libs/langchain/_test_minimum_requirements.txt index 94464e1678dacb7..84c789c84d7eef5 100644 --- a/libs/langchain/_test_minimum_requirements.txt +++ b/libs/langchain/_test_minimum_requirements.txt @@ -1,2 +1,2 @@ langchain-core==0.1.16 -langchain-community==0.0.14 +langchain-community==0.0.17 From 65ba5c220bddb2abdffc031fc83f5ce8761ab5e5 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 08:27:39 -0800 Subject: [PATCH 021/157] experimental[patch]: Release 0.0.50 (#16883) --- .../_test_minimum_requirements.txt | 2 +- libs/experimental/poetry.lock | 235 +++--------------- libs/experimental/pyproject.toml | 4 +- 3 files changed, 38 insertions(+), 203 deletions(-) diff --git a/libs/experimental/_test_minimum_requirements.txt b/libs/experimental/_test_minimum_requirements.txt index 2842cbb7c64313e..a44a2a0d1ef14a5 100644 --- a/libs/experimental/_test_minimum_requirements.txt +++ b/libs/experimental/_test_minimum_requirements.txt @@ -1,2 +1,2 @@ langchain-core==0.1.8 -langchain==0.1.0 \ No newline at end of file +langchain==0.1.5 diff --git a/libs/experimental/poetry.lock b/libs/experimental/poetry.lock index f5fd6116fbbcd70..2508cde290e6439 100644 --- a/libs/experimental/poetry.lock +++ b/libs/experimental/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "aiohttp" version = "3.8.6" description = "Async http client/server framework (asyncio)" -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -113,7 +112,6 @@ speedups = ["Brotli", "aiodns", "cchardet"] name = "aiosignal" version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -128,7 +126,6 @@ frozenlist = ">=1.1.0" name = "annotated-types" version = "0.6.0" description = "Reusable constraint types to use with typing.Annotated" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -143,7 +140,6 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} name = "anyio" version = "3.7.1" description = "High level compatibility layer for multiple asynchronous event loop implementations" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -165,7 +161,6 @@ trio = ["trio (<0.22)"] name = "appnope" version = "0.1.3" description = "Disable App Nap on macOS >= 10.9" -category = "dev" optional = false python-versions = "*" files = [ @@ -177,7 +172,6 @@ files = [ name = "argon2-cffi" version = "23.1.0" description = "Argon2 for Python" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -198,7 +192,6 @@ typing = ["mypy"] name = "argon2-cffi-bindings" version = "21.2.0" description = "Low-level CFFI bindings for Argon2" -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -236,7 +229,6 @@ tests = ["pytest"] name = "arrow" version = "1.3.0" description = "Better dates & times for Python" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -250,13 +242,12 @@ types-python-dateutil = ">=2.8.10" [package.extras] doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"] -test = ["dateparser (>=1.0.0,<2.0.0)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (>=3.0.0,<4.0.0)"] +test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"] [[package]] name = "asttokens" version = "2.4.1" description = "Annotate AST trees with source code positions" -category = "dev" optional = false python-versions = "*" files = [ @@ -275,7 +266,6 @@ test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] name = "async-lru" version = "2.0.4" description = "Simple LRU cache for asyncio" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -290,7 +280,6 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""} name = "async-timeout" version = "4.0.3" description = "Timeout context manager for asyncio programs" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -302,7 +291,6 @@ files = [ name = "attrs" version = "23.1.0" description = "Classes Without Boilerplate" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -321,7 +309,6 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte name = "babel" version = "2.13.1" description = "Internationalization utilities" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -340,7 +327,6 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] name = "backcall" version = "0.2.0" description = "Specifications for callback functions passed in to an API" -category = "dev" optional = false python-versions = "*" files = [ @@ -352,7 +338,6 @@ files = [ name = "beautifulsoup4" version = "4.12.2" description = "Screen-scraping library" -category = "dev" optional = false python-versions = ">=3.6.0" files = [ @@ -371,7 +356,6 @@ lxml = ["lxml"] name = "bleach" version = "6.1.0" description = "An easy safelist-based HTML-sanitizing tool." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -390,7 +374,6 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] name = "blis" version = "0.7.11" description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension." -category = "main" optional = true python-versions = "*" files = [ @@ -440,7 +423,6 @@ numpy = [ name = "catalogue" version = "2.0.10" description = "Super lightweight function registries for your library" -category = "main" optional = true python-versions = ">=3.6" files = [ @@ -452,7 +434,6 @@ files = [ name = "certifi" version = "2023.7.22" description = "Python package for providing Mozilla's CA Bundle." -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -464,7 +445,6 @@ files = [ name = "cffi" version = "1.16.0" description = "Foreign Function Interface for Python calling C code." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -529,7 +509,6 @@ pycparser = "*" name = "charset-normalizer" version = "3.3.2" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." -category = "main" optional = false python-versions = ">=3.7.0" files = [ @@ -629,7 +608,6 @@ files = [ name = "click" version = "8.1.7" description = "Composable command line interface toolkit" -category = "main" optional = true python-versions = ">=3.7" files = [ @@ -644,7 +622,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} name = "cloudpathlib" version = "0.16.0" description = "pathlib-style classes for cloud storage services." -category = "main" optional = true python-versions = ">=3.7" files = [ @@ -665,7 +642,6 @@ s3 = ["boto3"] name = "colorama" version = "0.4.6" description = "Cross-platform colored terminal text." -category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" files = [ @@ -677,7 +653,6 @@ files = [ name = "comm" version = "0.2.0" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -695,7 +670,6 @@ test = ["pytest"] name = "confection" version = "0.1.3" description = "The sweetest config system for Python" -category = "main" optional = true python-versions = ">=3.6" files = [ @@ -711,7 +685,6 @@ srsly = ">=2.4.0,<3.0.0" name = "cymem" version = "2.0.8" description = "Manage calls to calloc/free through Cython" -category = "main" optional = true python-versions = "*" files = [ @@ -754,7 +727,6 @@ files = [ name = "dataclasses-json" version = "0.6.1" description = "Easily serialize dataclasses to and from JSON." -category = "main" optional = false python-versions = ">=3.7,<4.0" files = [ @@ -770,7 +742,6 @@ typing-inspect = ">=0.4.0,<1" name = "debugpy" version = "1.8.0" description = "An implementation of the Debug Adapter Protocol for Python" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -798,7 +769,6 @@ files = [ name = "decorator" version = "5.1.1" description = "Decorators for Humans" -category = "dev" optional = false python-versions = ">=3.5" files = [ @@ -810,7 +780,6 @@ files = [ name = "defusedxml" version = "0.7.1" description = "XML bomb protection for Python stdlib modules" -category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -822,7 +791,6 @@ files = [ name = "exceptiongroup" version = "1.1.3" description = "Backport of PEP 654 (exception groups)" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -837,7 +805,6 @@ test = ["pytest (>=6)"] name = "executing" version = "2.0.1" description = "Get the currently executing AST node of a frame, and other information" -category = "dev" optional = false python-versions = ">=3.5" files = [ @@ -852,7 +819,6 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth name = "faker" version = "19.13.0" description = "Faker is a Python package that generates fake data for you." -category = "main" optional = true python-versions = ">=3.8" files = [ @@ -868,7 +834,6 @@ typing-extensions = {version = ">=3.10.0.1", markers = "python_version <= \"3.8\ name = "fastjsonschema" version = "2.18.1" description = "Fastest Python implementation of JSON schema" -category = "dev" optional = false python-versions = "*" files = [ @@ -883,7 +848,6 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc name = "filelock" version = "3.13.1" description = "A platform independent file lock." -category = "main" optional = true python-versions = ">=3.8" files = [ @@ -900,7 +864,6 @@ typing = ["typing-extensions (>=4.8)"] name = "fqdn" version = "1.5.1" description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" -category = "dev" optional = false python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" files = [ @@ -912,7 +875,6 @@ files = [ name = "frozenlist" version = "1.4.0" description = "A list-like structure which implements collections.abc.MutableSequence" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -983,7 +945,6 @@ files = [ name = "fsspec" version = "2023.10.0" description = "File-system specification" -category = "main" optional = true python-versions = ">=3.8" files = [ @@ -1019,7 +980,6 @@ tqdm = ["tqdm"] name = "greenlet" version = "3.0.1" description = "Lightweight in-process concurrent programming" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1090,7 +1050,6 @@ test = ["objgraph", "psutil"] name = "huggingface-hub" version = "0.17.3" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" -category = "main" optional = true python-versions = ">=3.8.0" files = [ @@ -1124,7 +1083,6 @@ typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" -category = "main" optional = false python-versions = ">=3.5" files = [ @@ -1136,7 +1094,6 @@ files = [ name = "importlib-metadata" version = "6.8.0" description = "Read metadata from Python packages" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1156,7 +1113,6 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs name = "importlib-resources" version = "6.1.1" description = "Read resources from Python packages" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1175,7 +1131,6 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", name = "iniconfig" version = "2.0.0" description = "brain-dead simple config-ini parsing" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1187,7 +1142,6 @@ files = [ name = "ipykernel" version = "6.26.0" description = "IPython Kernel for Jupyter" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1201,7 +1155,7 @@ comm = ">=0.1.1" debugpy = ">=1.6.5" ipython = ">=7.23.1" jupyter-client = ">=6.1.12" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" matplotlib-inline = ">=0.1" nest-asyncio = "*" packaging = "*" @@ -1221,7 +1175,6 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio" name = "ipython" version = "8.12.3" description = "IPython: Productive Interactive Computing" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1261,7 +1214,6 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa name = "ipywidgets" version = "8.1.1" description = "Jupyter interactive widgets" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1283,7 +1235,6 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] name = "isoduration" version = "20.11.0" description = "Operations with ISO 8601 durations" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1298,7 +1249,6 @@ arrow = ">=0.15.0" name = "jedi" version = "0.19.1" description = "An autocompletion tool for Python that can be used for text editors." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -1318,7 +1268,6 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] name = "jinja2" version = "3.1.2" description = "A very fast and expressive template engine." -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1336,7 +1285,6 @@ i18n = ["Babel (>=2.7)"] name = "joblib" version = "1.3.2" description = "Lightweight pipelining with Python functions" -category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1348,7 +1296,6 @@ files = [ name = "json5" version = "0.9.14" description = "A Python implementation of the JSON5 data format." -category = "dev" optional = false python-versions = "*" files = [ @@ -1363,7 +1310,6 @@ dev = ["hypothesis"] name = "jsonpatch" version = "1.33" description = "Apply JSON-Patches (RFC 6902)" -category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ @@ -1378,7 +1324,6 @@ jsonpointer = ">=1.9" name = "jsonpointer" version = "2.4" description = "Identify specific nodes in a JSON document (RFC 6901)" -category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ @@ -1390,7 +1335,6 @@ files = [ name = "jsonschema" version = "4.19.2" description = "An implementation of JSON Schema validation for Python" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1422,7 +1366,6 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339- name = "jsonschema-specifications" version = "2023.7.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1438,7 +1381,6 @@ referencing = ">=0.28.0" name = "jupyter" version = "1.0.0" description = "Jupyter metapackage. Install all the Jupyter components in one go." -category = "dev" optional = false python-versions = "*" files = [ @@ -1459,7 +1401,6 @@ qtconsole = "*" name = "jupyter-client" version = "8.6.0" description = "Jupyter protocol implementation and client libraries" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1469,7 +1410,7 @@ files = [ [package.dependencies] importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" python-dateutil = ">=2.8.2" pyzmq = ">=23.0" tornado = ">=6.2" @@ -1483,7 +1424,6 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt name = "jupyter-console" version = "6.6.3" description = "Jupyter terminal console" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1495,7 +1435,7 @@ files = [ ipykernel = ">=6.14" ipython = "*" jupyter-client = ">=7.0.0" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" prompt-toolkit = ">=3.0.30" pygments = "*" pyzmq = ">=17" @@ -1508,7 +1448,6 @@ test = ["flaky", "pexpect", "pytest"] name = "jupyter-core" version = "5.5.0" description = "Jupyter core package. A base package on which Jupyter projects rely." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1529,7 +1468,6 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] name = "jupyter-events" version = "0.9.0" description = "Jupyter Event System library" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1555,7 +1493,6 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p name = "jupyter-lsp" version = "2.2.0" description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1571,7 +1508,6 @@ jupyter-server = ">=1.1.2" name = "jupyter-server" version = "2.10.0" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1584,7 +1520,7 @@ anyio = ">=3.1.0" argon2-cffi = "*" jinja2 = "*" jupyter-client = ">=7.4.4" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" jupyter-events = ">=0.6.0" jupyter-server-terminals = "*" nbconvert = ">=6.4.4" @@ -1608,7 +1544,6 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc name = "jupyter-server-terminals" version = "0.4.4" description = "A Jupyter Server Extension Providing Terminals." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1628,7 +1563,6 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", name = "jupyterlab" version = "4.0.8" description = "JupyterLab computational environment" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1662,7 +1596,6 @@ test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-cons name = "jupyterlab-pygments" version = "0.2.2" description = "Pygments theme using JupyterLab CSS variables" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1674,7 +1607,6 @@ files = [ name = "jupyterlab-server" version = "2.25.0" description = "A set of server components for JupyterLab and JupyterLab like applications." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1701,7 +1633,6 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v name = "jupyterlab-widgets" version = "3.0.9" description = "Jupyter interactive widgets for JupyterLab" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1711,9 +1642,8 @@ files = [ [[package]] name = "langchain" -version = "0.1.4" +version = "0.1.5" description = "Building applications with LLMs through composability" -category = "main" optional = false python-versions = ">=3.8.1,<4.0" files = [] @@ -1724,7 +1654,7 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" -langchain-community = ">=0.0.14,<0.1" +langchain-community = ">=0.0.17,<0.1" langchain-core = ">=0.1.16,<0.2" langsmith = ">=0.0.83,<0.1" numpy = "^1" @@ -1742,7 +1672,7 @@ cli = ["typer (>=0.9.0,<0.10.0)"] cohere = ["cohere (>=4,<5)"] docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] +extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] javascript = ["esprima (>=4.0.1,<5.0.0)"] llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"] openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"] @@ -1755,9 +1685,8 @@ url = "../langchain" [[package]] name = "langchain-community" -version = "0.0.16" +version = "0.0.17" description = "Community contributed LangChain integrations." -category = "main" optional = false python-versions = ">=3.8.1,<4.0" files = [] @@ -1776,7 +1705,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai 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Easy to code. Based on Python type hints." -category = "main" optional = true python-versions = ">=3.6" files = [ @@ -4791,7 +4643,6 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6. name = "types-python-dateutil" version = "2.8.19.14" description = "Typing stubs for python-dateutil" -category = "dev" optional = false python-versions = "*" files = [ @@ -4803,7 +4654,6 @@ files = [ name = "types-pyyaml" version = "6.0.12.12" description = "Typing stubs for PyYAML" -category = "dev" optional = false python-versions = "*" files = [ @@ -4815,7 +4665,6 @@ files = [ name = "types-requests" version = "2.31.0.10" description = "Typing stubs for requests" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4830,7 +4679,6 @@ urllib3 = ">=2" name = "typing-extensions" version = "4.8.0" description = "Backported and Experimental Type Hints for Python 3.8+" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -4842,7 +4690,6 @@ files = [ name = "typing-inspect" version = "0.9.0" description = "Runtime inspection utilities for typing module." -category = "main" optional = false python-versions = "*" files = [ @@ -4858,7 +4705,6 @@ typing-extensions = ">=3.7.4" name = "uri-template" version = "1.3.0" description = "RFC 6570 URI Template Processor" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4873,7 +4719,6 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake name = "urllib3" version = "2.0.7" description = "HTTP library with thread-safe connection pooling, file post, and more." -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4891,7 +4736,6 @@ zstd = ["zstandard (>=0.18.0)"] name = "vowpal-wabbit-next" version = "0.6.0" description = "Experimental python bindings for VowpalWabbit" -category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4919,7 +4763,6 @@ numpy = "*" name = "wasabi" version = "1.1.2" description = "A lightweight console printing and formatting toolkit" -category = "main" optional = true python-versions = ">=3.6" files = [ @@ -4934,7 +4777,6 @@ colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python name = "wcwidth" version = "0.2.9" description = "Measures the displayed width of unicode strings in a terminal" -category = "dev" optional = false python-versions = "*" files = [ @@ -4946,7 +4788,6 @@ files = [ name = "weasel" version = "0.3.4" description = "Weasel: A small and easy workflow system" -category = "main" optional = true python-versions = ">=3.6" files = [ @@ -4969,7 +4810,6 @@ wasabi = ">=0.9.1,<1.2.0" name = "webcolors" version = "1.13" description = "A library for working with the color formats defined by HTML and CSS." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4985,7 +4825,6 @@ tests = ["pytest", "pytest-cov"] name = "webencodings" version = "0.5.1" description = "Character encoding aliases for legacy web content" -category = "dev" optional = false python-versions = "*" files = [ @@ -4997,7 +4836,6 @@ files = [ name = "websocket-client" version = "1.6.4" description = "WebSocket client for Python with low level API options" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -5014,7 +4852,6 @@ test = ["websockets"] name = "widgetsnbextension" version = "4.0.9" description = "Jupyter interactive widgets for Jupyter Notebook" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -5026,7 +4863,6 @@ files = [ name = "yarl" version = "1.9.2" description = "Yet another URL library" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -5114,7 +4950,6 @@ multidict = ">=4.0" name = "zipp" version = "3.17.0" description = "Backport of pathlib-compatible object wrapper for zip files" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -5132,4 +4967,4 @@ extended-testing = ["faker", "jinja2", "presidio-analyzer", "presidio-anonymizer [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "c86278b6b1b53825281b2f5ffe74659a334cfbcbaa15e00a22a440fe44db7d88" +content-hash = "a0aff712899a3039507fe3a4166fbd766b00ab4c00a7b56092762848a54917e2" diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml index 1a7d1f1256c6419..5c63c0732ec3559 100644 --- a/libs/experimental/pyproject.toml +++ b/libs/experimental/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-experimental" -version = "0.0.49" +version = "0.0.50" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -11,7 +11,7 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" langchain-core = "^0.1.7" -langchain = "^0.1" +langchain = "^0.1.5" presidio-anonymizer = {version = "^2.2.352", optional = true} presidio-analyzer = {version = "^2.2.352", optional = true} faker = {version = "^19.3.1", optional = true} From c2d09fb151e5c3232b63eec6a6a8d6264f388ea5 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 08:35:21 -0800 Subject: [PATCH 022/157] infra: bump exp min test reqs (#16884) --- libs/experimental/_test_minimum_requirements.txt | 2 +- libs/experimental/poetry.lock | 2 +- libs/experimental/pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/experimental/_test_minimum_requirements.txt b/libs/experimental/_test_minimum_requirements.txt index a44a2a0d1ef14a5..078f131ef8dcdf4 100644 --- a/libs/experimental/_test_minimum_requirements.txt +++ b/libs/experimental/_test_minimum_requirements.txt @@ -1,2 +1,2 @@ -langchain-core==0.1.8 +langchain-core==0.1.16 langchain==0.1.5 diff --git a/libs/experimental/poetry.lock b/libs/experimental/poetry.lock index 2508cde290e6439..4e75e3d6904b163 100644 --- a/libs/experimental/poetry.lock +++ b/libs/experimental/poetry.lock @@ -4967,4 +4967,4 @@ extended-testing = ["faker", "jinja2", "presidio-analyzer", "presidio-anonymizer [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "a0aff712899a3039507fe3a4166fbd766b00ab4c00a7b56092762848a54917e2" +content-hash = "11321a2551a49e9cb432348f03e991d1f13368624b1e4df5a721b794114b4f02" diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml index 5c63c0732ec3559..38537c956e18e9a 100644 --- a/libs/experimental/pyproject.toml +++ b/libs/experimental/pyproject.toml @@ -10,7 +10,7 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = "^0.1.7" +langchain-core = "^0.1.16" langchain = "^0.1.5" presidio-anonymizer = {version = "^2.2.352", optional = true} presidio-analyzer = {version = "^2.2.352", optional = true} From 7d03d8f586f123e5059cbd0f45cb4c701bf0976f Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Thu, 1 Feb 2024 10:17:26 -0800 Subject: [PATCH 023/157] docs: fix docstring examples (#16889) --- .../agents/agent_toolkits/csv/base.py | 18 +++++++-------- .../agents/agent_toolkits/pandas/base.py | 23 +++++++++---------- 2 files changed, 20 insertions(+), 21 deletions(-) diff --git a/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py b/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py index a7fa928c23c406b..e757714d6d8d377 100644 --- a/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py +++ b/libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py @@ -34,16 +34,16 @@ def create_csv_agent( Example: .. code-block:: python - from langchain_openai import ChatOpenAI - from langchain_experimental.agents import create_csv_agent + from langchain_openai import ChatOpenAI + from langchain_experimental.agents import create_csv_agent - llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) - agent_executor = create_pandas_dataframe_agent( - llm, - "titanic.csv", - agent_type="openai-tools", - verbose=True - ) + llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) + agent_executor = create_pandas_dataframe_agent( + llm, + "titanic.csv", + agent_type="openai-tools", + verbose=True + ) """ # noqa: E501 try: import pandas as pd diff --git a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py index 741bfc7a48feca7..0970c39595b7035 100644 --- a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py +++ b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py @@ -200,21 +200,20 @@ def create_pandas_dataframe_agent( a PythonAstREPLTool with the DataFrame(s) and any user-provided extra_tools. Example: - .. code-block:: python - from langchain_openai import ChatOpenAI - from langchain_experimental.agents import create_pandas_dataframe_agent - import pandas as pd + from langchain_openai import ChatOpenAI + from langchain_experimental.agents import create_pandas_dataframe_agent + import pandas as pd - df = pd.read_csv("titanic.csv") - llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) - agent_executor = create_pandas_dataframe_agent( - llm, - df, - agent_type="openai-tools", - verbose=True - ) + df = pd.read_csv("titanic.csv") + llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) + agent_executor = create_pandas_dataframe_agent( + llm, + df, + agent_type="openai-tools", + verbose=True + ) """ # noqa: E501 try: From 78a1af48488ecab2a1a9bb6264d3aefb6c8a6bdc Mon Sep 17 00:00:00 2001 From: Christophe Bornet Date: Thu, 1 Feb 2024 19:33:06 +0100 Subject: [PATCH 024/157] langchain[patch]: Add async methods to MultiVectorRetriever (#16878) Adds async support to multi vector retriever --- .../langchain/retrievers/multi_vector.py | 32 ++++++++++++++++++- .../retrievers/test_multi_vector.py | 13 ++++++++ 2 files changed, 44 insertions(+), 1 deletion(-) diff --git a/libs/langchain/langchain/retrievers/multi_vector.py b/libs/langchain/langchain/retrievers/multi_vector.py index 167fc5d4cb81cec..2ac989914aeb555 100644 --- a/libs/langchain/langchain/retrievers/multi_vector.py +++ b/libs/langchain/langchain/retrievers/multi_vector.py @@ -1,7 +1,10 @@ from enum import Enum from typing import Dict, List, Optional -from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.callbacks import ( + AsyncCallbackManagerForRetrieverRun, + CallbackManagerForRetrieverRun, +) from langchain_core.documents import Document from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.retrievers import BaseRetriever @@ -71,3 +74,30 @@ def _get_relevant_documents( ids.append(d.metadata[self.id_key]) docs = self.docstore.mget(ids) return [d for d in docs if d is not None] + + async def _aget_relevant_documents( + self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun + ) -> List[Document]: + """Asynchronously get documents relevant to a query. + Args: + query: String to find relevant documents for + run_manager: The callbacks handler to use + Returns: + List of relevant documents + """ + if self.search_type == SearchType.mmr: + sub_docs = await self.vectorstore.amax_marginal_relevance_search( + query, **self.search_kwargs + ) + else: + sub_docs = await self.vectorstore.asimilarity_search( + query, **self.search_kwargs + ) + + # We do this to maintain the order of the ids that are returned + ids = [] + for d in sub_docs: + if self.id_key in d.metadata and d.metadata[self.id_key] not in ids: + ids.append(d.metadata[self.id_key]) + docs = await self.docstore.amget(ids) + return [d for d in docs if d is not None] diff --git a/libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py b/libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py index 0d5f9a1836807f8..648d825214137be 100644 --- a/libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py +++ b/libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py @@ -28,3 +28,16 @@ def test_multi_vector_retriever_initialization() -> None: results = retriever.invoke("1") assert len(results) > 0 assert results[0].page_content == "test document" + + +async def test_multi_vector_retriever_initialization_async() -> None: + vectorstore = InMemoryVectorstoreWithSearch() + retriever = MultiVectorRetriever( + vectorstore=vectorstore, docstore=InMemoryStore(), doc_id="doc_id" + ) + documents = [Document(page_content="test document", metadata={"doc_id": "1"})] + await retriever.vectorstore.aadd_documents(documents, ids=["1"]) + await retriever.docstore.amset(list(zip(["1"], documents))) + results = await retriever.ainvoke("1") + assert len(results) > 0 + assert results[0].page_content == "test document" From 93366861c720d74cc9c150f3d9c43d564df3a5dc Mon Sep 17 00:00:00 2001 From: Harel Gal Date: Thu, 1 Feb 2024 20:41:48 +0200 Subject: [PATCH 025/157] docs: Indicated Guardrails for Amazon Bedrock preview status (#16769) Added notification about limited preview status of Guardrails for Amazon Bedrock feature to code example. --------- Co-authored-by: Piyush Jain --- docs/docs/integrations/llms/bedrock.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/docs/integrations/llms/bedrock.ipynb b/docs/docs/integrations/llms/bedrock.ipynb index 0c1748cc47fd7b6..717fbe3480f4493 100644 --- a/docs/docs/integrations/llms/bedrock.ipynb +++ b/docs/docs/integrations/llms/bedrock.ipynb @@ -111,8 +111,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Guardrails for Amazon Bedrock example \n", + "\n", + "## Guardrails for Amazon Bedrock (Preview) \n", + "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n", + "**Note**: Guardrails for Amazon Bedrock is currently in preview and not generally available. Reach out through your usual AWS Support contacts if you’d like access to this feature.\n", "In this section, we are going to set up a Bedrock language model with specific guardrails that include tracing capabilities. " ] }, From 9d458d089a7c908949413f1fc73392af9f169d7f Mon Sep 17 00:00:00 2001 From: Christophe Bornet Date: Thu, 1 Feb 2024 19:51:07 +0100 Subject: [PATCH 026/157] community: Factorize AstraDB components constructors (#16779) * Adds `AstraDBEnvironment` class and use it in `AstraDBLoader`, `AstraDBCache`, `AstraDBSemanticCache`, `AstraDBBaseStore` and `AstraDBChatMessageHistory` * Create an `AsyncAstraDB` if we only have an `AstraDB` and vice-versa so: * we always have an instance of `AstraDB` * we always have an instance of `AsyncAstraDB` for recent versions of astrapy * Create collection if not exists in `AstraDBBaseStore` * Some typing improvements Note: `AstraDB` `VectorStore` not using `AstraDBEnvironment` at the moment. This will be done after the `langchain-astradb` package is out. --- libs/community/langchain_community/cache.py | 82 +++++------------ .../chat_message_histories/astradb.py | 45 +++------- .../document_loaders/astradb.py | 90 +++++++------------ .../langchain_community/storage/astradb.py | 34 ++++--- .../langchain_community/utilities/astradb.py | 84 +++++++++++++++++ 5 files changed, 168 insertions(+), 167 deletions(-) create mode 100644 libs/community/langchain_community/utilities/astradb.py diff --git a/libs/community/langchain_community/cache.py b/libs/community/langchain_community/cache.py index 0ad635092e8ad21..b20016829427979 100644 --- a/libs/community/langchain_community/cache.py +++ b/libs/community/langchain_community/cache.py @@ -60,12 +60,14 @@ from langchain_core.outputs import ChatGeneration, Generation from langchain_core.utils import get_from_env +from langchain_community.utilities.astradb import AstraDBEnvironment from langchain_community.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento + from astrapy.db import AstraDB from cassandra.cluster import Session as CassandraSession @@ -1262,7 +1264,7 @@ def __init__( collection_name: str = ASTRA_DB_CACHE_DEFAULT_COLLECTION_NAME, token: Optional[str] = None, api_endpoint: Optional[str] = None, - astra_db_client: Optional[Any] = None, # 'astrapy.db.AstraDB' if passed + astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, ): """ @@ -1278,39 +1280,17 @@ def __init__( namespace (Optional[str]): namespace (aka keyspace) where the collection is created. Defaults to the database's "default namespace". """ - try: - from astrapy.db import ( - AstraDB as LibAstraDB, - ) - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) - # Conflicting-arg checks: - if astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' to AstraDB if passing " - "'token' and 'api_endpoint'." - ) - - self.collection_name = collection_name - self.token = token - self.api_endpoint = api_endpoint - self.namespace = namespace - - if astra_db_client is not None: - self.astra_db = astra_db_client - else: - self.astra_db = LibAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) + astra_env = AstraDBEnvironment( + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + namespace=namespace, + ) + self.astra_db = astra_env.astra_db self.collection = self.astra_db.create_collection( - collection_name=self.collection_name, + collection_name=collection_name, ) + self.collection_name = collection_name @staticmethod def _make_id(prompt: str, llm_string: str) -> str: @@ -1364,7 +1344,7 @@ def delete_through_llm( def delete(self, prompt: str, llm_string: str) -> None: """Evict from cache if there's an entry.""" doc_id = self._make_id(prompt, llm_string) - return self.collection.delete_one(doc_id) + self.collection.delete_one(doc_id) def clear(self, **kwargs: Any) -> None: """Clear cache. This is for all LLMs at once.""" @@ -1395,7 +1375,7 @@ def __init__( collection_name: str = ASTRA_DB_CACHE_DEFAULT_COLLECTION_NAME, token: Optional[str] = None, api_endpoint: Optional[str] = None, - astra_db_client: Optional[Any] = None, # 'astrapy.db.AstraDB' if passed + astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, embedding: Embeddings, metric: Optional[str] = None, @@ -1423,22 +1403,13 @@ def __init__( The default score threshold is tuned to the default metric. Tune it carefully yourself if switching to another distance metric. """ - try: - from astrapy.db import ( - AstraDB as LibAstraDB, - ) - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) - # Conflicting-arg checks: - if astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' to AstraDB if passing " - "'token' and 'api_endpoint'." - ) + astra_env = AstraDBEnvironment( + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + namespace=namespace, + ) + self.astra_db = astra_env.astra_db self.embedding = embedding self.metric = metric @@ -1457,18 +1428,7 @@ def _cache_embedding(text: str) -> List[float]: self.embedding_dimension = self._get_embedding_dimension() self.collection_name = collection_name - self.token = token - self.api_endpoint = api_endpoint - self.namespace = namespace - if astra_db_client is not None: - self.astra_db = astra_db_client - else: - self.astra_db = LibAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) self.collection = self.astra_db.create_collection( collection_name=self.collection_name, dimension=self.embedding_dimension, diff --git a/libs/community/langchain_community/chat_message_histories/astradb.py b/libs/community/langchain_community/chat_message_histories/astradb.py index 27e4dc5c936eb75..7257476101a38b4 100644 --- a/libs/community/langchain_community/chat_message_histories/astradb.py +++ b/libs/community/langchain_community/chat_message_histories/astradb.py @@ -3,11 +3,12 @@ import json import time -import typing -from typing import List, Optional +from typing import TYPE_CHECKING, List, Optional -if typing.TYPE_CHECKING: - from astrapy.db import AstraDB as LibAstraDB +from langchain_community.utilities.astradb import AstraDBEnvironment + +if TYPE_CHECKING: + from astrapy.db import AstraDB from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.messages import ( @@ -42,40 +43,22 @@ def __init__( collection_name: str = DEFAULT_COLLECTION_NAME, token: Optional[str] = None, api_endpoint: Optional[str] = None, - astra_db_client: Optional[LibAstraDB] = None, # type 'astrapy.db.AstraDB' + astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, ) -> None: """Create an Astra DB chat message history.""" - try: - from astrapy.db import AstraDB as LibAstraDB - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) + astra_env = AstraDBEnvironment( + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + namespace=namespace, + ) + self.astra_db = astra_env.astra_db - # Conflicting-arg checks: - if astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' to AstraDB if passing " - "'token' and 'api_endpoint'." - ) + self.collection = self.astra_db.create_collection(collection_name) self.session_id = session_id self.collection_name = collection_name - self.token = token - self.api_endpoint = api_endpoint - self.namespace = namespace - if astra_db_client is not None: - self.astra_db = astra_db_client - else: - self.astra_db = LibAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) - self.collection = self.astra_db.create_collection(self.collection_name) @property def messages(self) -> List[BaseMessage]: # type: ignore diff --git a/libs/community/langchain_community/document_loaders/astradb.py b/libs/community/langchain_community/document_loaders/astradb.py index 3562d424892c969..2af1e77fde189a5 100644 --- a/libs/community/langchain_community/document_loaders/astradb.py +++ b/libs/community/langchain_community/document_loaders/astradb.py @@ -16,8 +16,10 @@ ) from langchain_core.documents import Document +from langchain_core.runnables import run_in_executor from langchain_community.document_loaders.base import BaseLoader +from langchain_community.utilities.astradb import AstraDBEnvironment if TYPE_CHECKING: from astrapy.db import AstraDB, AsyncAstraDB @@ -42,21 +44,15 @@ def __init__( nb_prefetched: int = 1000, extraction_function: Callable[[Dict], str] = json.dumps, ) -> None: - try: - from astrapy.db import AstraDB - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) - - # Conflicting-arg checks: - if astra_db_client is not None or async_astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' or 'async_astra_db_client' to " - "AstraDB if passing 'token' and 'api_endpoint'." - ) + astra_env = AstraDBEnvironment( + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + async_astra_db_client=async_astra_db_client, + namespace=namespace, + ) + self.astra_env = astra_env + self.collection = astra_env.astra_db.collection(collection_name) self.collection_name = collection_name self.filter = filter_criteria self.projection = projection @@ -64,47 +60,11 @@ def __init__( self.nb_prefetched = nb_prefetched self.extraction_function = extraction_function - astra_db = astra_db_client - async_astra_db = async_astra_db_client - - if token and api_endpoint: - astra_db = AstraDB( - token=token, - api_endpoint=api_endpoint, - namespace=namespace, - ) - try: - from astrapy.db import AsyncAstraDB - - async_astra_db = AsyncAstraDB( - token=token, - api_endpoint=api_endpoint, - namespace=namespace, - ) - except (ImportError, ModuleNotFoundError): - pass - if not astra_db and not async_astra_db: - raise ValueError( - "Must provide 'astra_db_client' or 'async_astra_db_client' or 'token' " - "and 'api_endpoint'" - ) - self.collection = astra_db.collection(collection_name) if astra_db else None - if async_astra_db: - from astrapy.db import AsyncAstraDBCollection - - self.async_collection = AsyncAstraDBCollection( - astra_db=async_astra_db, collection_name=collection_name - ) - else: - self.async_collection = None - def load(self) -> List[Document]: """Eagerly load the content.""" return list(self.lazy_load()) def lazy_load(self) -> Iterator[Document]: - if not self.collection: - raise ValueError("Missing AstraDB client") queue = Queue(self.nb_prefetched) t = threading.Thread(target=self.fetch_results, args=(queue,)) t.start() @@ -120,9 +80,27 @@ async def aload(self) -> List[Document]: return [doc async for doc in self.alazy_load()] async def alazy_load(self) -> AsyncIterator[Document]: - if not self.async_collection: - raise ValueError("Missing AsyncAstraDB client") - async for doc in self.async_collection.paginated_find( + if not self.astra_env.async_astra_db: + iterator = run_in_executor( + None, + self.collection.paginated_find, + filter=self.filter, + options=self.find_options, + projection=self.projection, + sort=None, + prefetched=True, + ) + done = object() + while True: + item = await run_in_executor(None, lambda it: next(it, done), iterator) + if item is done: + break + yield item + return + async_collection = await self.astra_env.async_astra_db.collection( + self.collection_name + ) + async for doc in async_collection.paginated_find( filter=self.filter, options=self.find_options, projection=self.projection, @@ -132,8 +110,8 @@ async def alazy_load(self) -> AsyncIterator[Document]: yield Document( page_content=self.extraction_function(doc), metadata={ - "namespace": self.async_collection.astra_db.namespace, - "api_endpoint": self.async_collection.astra_db.base_url, + "namespace": async_collection.astra_db.namespace, + "api_endpoint": async_collection.astra_db.base_url, "collection": self.collection_name, }, ) diff --git a/libs/community/langchain_community/storage/astradb.py b/libs/community/langchain_community/storage/astradb.py index f38874fb8356d4f..79d5bebcd37ff61 100644 --- a/libs/community/langchain_community/storage/astradb.py +++ b/libs/community/langchain_community/storage/astradb.py @@ -1,6 +1,9 @@ +from __future__ import annotations + import base64 from abc import ABC, abstractmethod from typing import ( + TYPE_CHECKING, Any, Generic, Iterator, @@ -13,6 +16,11 @@ from langchain_core.stores import BaseStore, ByteStore +from langchain_community.utilities.astradb import AstraDBEnvironment + +if TYPE_CHECKING: + from astrapy.db import AstraDB + V = TypeVar("V") @@ -22,31 +30,19 @@ def __init__( collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, - astra_db_client: Optional[Any] = None, # 'astrapy.db.AstraDB' if passed + astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, ) -> None: - try: - from astrapy.db import AstraDB, AstraDBCollection - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) - - # Conflicting-arg checks: - if astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' to AstraDB if passing " - "'token' and 'api_endpoint'." - ) - - astra_db = astra_db_client or AstraDB( + astra_env = AstraDBEnvironment( token=token, api_endpoint=api_endpoint, + astra_db_client=astra_db_client, namespace=namespace, ) - self.collection = AstraDBCollection(collection_name, astra_db=astra_db) + self.astra_db = astra_env.astra_db + self.collection = self.astra_db.create_collection( + collection_name=collection_name, + ) @abstractmethod def decode_value(self, value: Any) -> Optional[V]: diff --git a/libs/community/langchain_community/utilities/astradb.py b/libs/community/langchain_community/utilities/astradb.py new file mode 100644 index 000000000000000..3ad3d3274974d25 --- /dev/null +++ b/libs/community/langchain_community/utilities/astradb.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + from astrapy.db import ( + AstraDB, + AsyncAstraDB, + ) + + +class AstraDBEnvironment: + def __init__( + self, + token: Optional[str] = None, + api_endpoint: Optional[str] = None, + astra_db_client: Optional[AstraDB] = None, + async_astra_db_client: Optional[AsyncAstraDB] = None, + namespace: Optional[str] = None, + ) -> None: + self.token = token + self.api_endpoint = api_endpoint + astra_db = astra_db_client + self.async_astra_db = async_astra_db_client + self.namespace = namespace + + from astrapy import db + + try: + from astrapy.db import AstraDB + except (ImportError, ModuleNotFoundError): + raise ImportError( + "Could not import a recent astrapy python package. " + "Please install it with `pip install --upgrade astrapy`." + ) + + supports_async = hasattr(db, "AsyncAstraDB") + + # Conflicting-arg checks: + if astra_db_client is not None or async_astra_db_client is not None: + if token is not None or api_endpoint is not None: + raise ValueError( + "You cannot pass 'astra_db_client' or 'async_astra_db_client' to " + "AstraDBEnvironment if passing 'token' and 'api_endpoint'." + ) + + if token and api_endpoint: + astra_db = AstraDB( + token=self.token, + api_endpoint=self.api_endpoint, + namespace=self.namespace, + ) + if supports_async: + self.async_astra_db = db.AsyncAstraDB( + token=self.token, + api_endpoint=self.api_endpoint, + namespace=self.namespace, + ) + + if astra_db: + self.astra_db = astra_db + else: + if self.async_astra_db: + self.astra_db = AstraDB( + token=self.async_astra_db.token, + api_endpoint=self.async_astra_db.base_url, + api_path=self.async_astra_db.api_path, + api_version=self.async_astra_db.api_version, + namespace=self.async_astra_db.namespace, + ) + else: + raise ValueError( + "Must provide 'astra_db_client' or 'async_astra_db_client' or " + "'token' and 'api_endpoint'" + ) + + if not self.async_astra_db and self.astra_db and supports_async: + self.async_astra_db = db.AsyncAstraDB( + token=self.astra_db.token, + api_endpoint=self.astra_db.base_url, + api_path=self.astra_db.api_path, + api_version=self.astra_db.api_version, + namespace=self.astra_db.namespace, + ) From c5b01ac621e7c615acdc3cbbde21c68092975d03 Mon Sep 17 00:00:00 2001 From: Qihui Xie Date: Fri, 2 Feb 2024 03:03:25 +0800 Subject: [PATCH 027/157] community[patch]: support LIKE comparator (full text match) in Qdrant (#12769) **Description:** Support [Qdrant full text match filtering](https://qdrant.tech/documentation/concepts/filtering/#full-text-match) by adding Comparator.LIKE to QdrantTranslator. --- libs/langchain/langchain/retrievers/self_query/qdrant.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/libs/langchain/langchain/retrievers/self_query/qdrant.py b/libs/langchain/langchain/retrievers/self_query/qdrant.py index e76c716db26a7af..c99287751a15838 100644 --- a/libs/langchain/langchain/retrievers/self_query/qdrant.py +++ b/libs/langchain/langchain/retrievers/self_query/qdrant.py @@ -31,6 +31,7 @@ class QdrantTranslator(Visitor): Comparator.LTE, Comparator.GT, Comparator.GTE, + Comparator.LIKE, ) """Subset of allowed logical comparators.""" @@ -69,6 +70,10 @@ def visit_comparison(self, comparison: Comparison) -> rest.FieldCondition: return rest.FieldCondition( key=attribute, match=rest.MatchValue(value=comparison.value) ) + if comparison.comparator == Comparator.LIKE: + return rest.FieldCondition( + key=attribute, match=rest.MatchText(text=comparison.value) + ) kwargs = {comparison.comparator.value: comparison.value} return rest.FieldCondition(key=attribute, range=rest.Range(**kwargs)) From 5b3fc86cfd67b02a32151f8898d76fd98e7fa142 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 1 Feb 2024 11:09:37 -0800 Subject: [PATCH 028/157] infra: ci naming (#16890) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Make it clearer how to run equivalent commands locally Not a perfect 1:1, but will help people get started ![Screenshot 2024-02-01 at 10 53 34 AM](https://github.com/langchain-ai/langchain/assets/9557659/da271aaf-d5db-41e3-9379-cb1d8a0232c5) --- .github/workflows/_all_ci.yml | 6 +++- .../workflows/_compile_integration_test.yml | 2 +- .github/workflows/_dependencies.yml | 2 +- .github/workflows/_lint.yml | 1 + .github/workflows/_test.yml | 2 +- .github/workflows/check_diffs.yml | 3 +- .github/workflows/codespell.yml | 4 +-- .github/workflows/doc_lint.yml | 4 ++- .github/workflows/templates_ci.yml | 36 ------------------- 9 files changed, 16 insertions(+), 44 deletions(-) delete mode 100644 .github/workflows/templates_ci.yml diff --git a/.github/workflows/_all_ci.yml b/.github/workflows/_all_ci.yml index a2c4e06d6c15d18..61caad6ebd23660 100644 --- a/.github/workflows/_all_ci.yml +++ b/.github/workflows/_all_ci.yml @@ -36,30 +36,35 @@ env: jobs: lint: + name: make lint uses: ./.github/workflows/_lint.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit test: + name: make test uses: ./.github/workflows/_test.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit compile-integration-tests: + name: poetry run pytest -m compile tests/integration_tests uses: ./.github/workflows/_compile_integration_test.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit dependencies: + name: check dependencies uses: ./.github/workflows/_dependencies.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit extended-tests: + name: make extended_tests / python${{ matrix.python-version }} runs-on: ubuntu-latest strategy: matrix: @@ -68,7 +73,6 @@ jobs: - "3.9" - "3.10" - "3.11" - name: Python ${{ matrix.python-version }} extended tests defaults: run: working-directory: ${{ inputs.working-directory }} diff --git a/.github/workflows/_compile_integration_test.yml b/.github/workflows/_compile_integration_test.yml index 12cd3d737b2d740..750ce00a77f27e0 100644 --- a/.github/workflows/_compile_integration_test.yml +++ b/.github/workflows/_compile_integration_test.yml @@ -24,7 +24,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: Python ${{ matrix.python-version }} + name: python${{ matrix.python-version }} steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_dependencies.yml b/.github/workflows/_dependencies.yml index 1414f63472fa057..4c4604796936812 100644 --- a/.github/workflows/_dependencies.yml +++ b/.github/workflows/_dependencies.yml @@ -28,7 +28,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: dependencies - Python ${{ matrix.python-version }} + name: dependency checks / python${{ matrix.python-version }} steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index 07978a25cf25889..a6e12761d978631 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -21,6 +21,7 @@ env: jobs: build: + name: python${{ matrix.python-version }} runs-on: ubuntu-latest strategy: matrix: diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml index 55c5f79fcbcf173..d2552d3ecc0b988 100644 --- a/.github/workflows/_test.yml +++ b/.github/workflows/_test.yml @@ -28,7 +28,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: Python ${{ matrix.python-version }} + name: python${{ matrix.python-version }} steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/check_diffs.yml b/.github/workflows/check_diffs.yml index 83dd2d58638fa87..9a2e11dd3a0e4ae 100644 --- a/.github/workflows/check_diffs.yml +++ b/.github/workflows/check_diffs.yml @@ -1,5 +1,5 @@ --- -name: Check library diffs +name: CI on: push: @@ -32,6 +32,7 @@ jobs: outputs: dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }} ci: + name: cd ${{ matrix.working-directory }} needs: [ build ] strategy: matrix: diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml index 82474e92890c2c4..a5a63b996de86ee 100644 --- a/.github/workflows/codespell.yml +++ b/.github/workflows/codespell.yml @@ -1,5 +1,5 @@ --- -name: Codespell +name: CI / cd . / make spell_check on: push: @@ -12,7 +12,7 @@ permissions: jobs: codespell: - name: Check for spelling errors + name: (Check for spelling errors) runs-on: ubuntu-latest steps: diff --git a/.github/workflows/doc_lint.yml b/.github/workflows/doc_lint.yml index 7a236dc8bb0cdd7..b1d1932672f253b 100644 --- a/.github/workflows/doc_lint.yml +++ b/.github/workflows/doc_lint.yml @@ -1,5 +1,5 @@ --- -name: Docs, templates, cookbook lint +name: CI / cd . on: push: @@ -15,6 +15,7 @@ on: jobs: check: + name: Check for "from langchain import x" imports runs-on: ubuntu-latest steps: @@ -28,6 +29,7 @@ jobs: git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0 lint: + name: make lint uses: ./.github/workflows/_lint.yml with: diff --git a/.github/workflows/templates_ci.yml b/.github/workflows/templates_ci.yml deleted file mode 100644 index b886fc7aaff8134..000000000000000 --- a/.github/workflows/templates_ci.yml +++ /dev/null @@ -1,36 +0,0 @@ ---- -name: templates CI - -on: - push: - branches: [ master ] - pull_request: - paths: - - '.github/actions/poetry_setup/action.yml' - - '.github/tools/**' - - '.github/workflows/_lint.yml' - - '.github/workflows/templates_ci.yml' - - 'templates/**' - workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI - -# If another push to the same PR or branch happens while this workflow is still running, -# cancel the earlier run in favor of the next run. -# -# There's no point in testing an outdated version of the code. GitHub only allows -# a limited number of job runners to be active at the same time, so it's better to cancel -# pointless jobs early so that more useful jobs can run sooner. -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -env: - POETRY_VERSION: "1.7.1" - WORKDIR: "templates" - -jobs: - lint: - uses: - ./.github/workflows/_lint.yml - with: - working-directory: templates - secrets: inherit From 0bca0f4c24c0446b0ce6b29af6dc6afc99abaf70 Mon Sep 17 00:00:00 2001 From: Shorthills AI <141953346+ShorthillsAI@users.noreply.github.com> Date: Fri, 2 Feb 2024 00:58:15 +0530 Subject: [PATCH 029/157] Docs: Fixed grammatical mistake (#16858) Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com> Co-authored-by: Sanskar Tanwar <142409040+SanskarTanwarShorthillsAI@users.noreply.github.com> Co-authored-by: UpneetShorthillsAI <144228282+UpneetShorthillsAI@users.noreply.github.com> Co-authored-by: HarshGuptaShorthillsAI <144897987+HarshGuptaShorthillsAI@users.noreply.github.com> Co-authored-by: AdityaKalraShorthillsAI <143726711+AdityaKalraShorthillsAI@users.noreply.github.com> Co-authored-by: SakshiShorthillsAI <144228183+SakshiShorthillsAI@users.noreply.github.com> Co-authored-by: AashiGuptaShorthillsAI <144897730+AashiGuptaShorthillsAI@users.noreply.github.com> Co-authored-by: ShamshadAhmedShorthillsAI <144897733+ShamshadAhmedShorthillsAI@users.noreply.github.com> Co-authored-by: ManpreetShorthillsAI <142380984+ManpreetShorthillsAI@users.noreply.github.com> Co-authored-by: Aayush <142384656+AayushShorthillsAI@users.noreply.github.com> Co-authored-by: BajrangBishnoiShorthillsAi <148060486+BajrangBishnoiShorthillsAi@users.noreply.github.com> --- docs/docs/_templates/integration.mdx | 4 ++-- docs/docs/guides/deployments/index.mdx | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/docs/_templates/integration.mdx b/docs/docs/_templates/integration.mdx index 234c8cc09eefa64..5e686ad3fc1224b 100644 --- a/docs/docs/_templates/integration.mdx +++ b/docs/docs/_templates/integration.mdx @@ -37,7 +37,7 @@ from langchain_community.llms import integration_class_REPLACE_ME ## Text Embedding Models -See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME) +See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME). ```python from langchain_community.embeddings import integration_class_REPLACE_ME @@ -45,7 +45,7 @@ from langchain_community.embeddings import integration_class_REPLACE_ME ## Chat models -See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME) +See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME). ```python from langchain_community.chat_models import integration_class_REPLACE_ME diff --git a/docs/docs/guides/deployments/index.mdx b/docs/docs/guides/deployments/index.mdx index c075c3b92ee925e..cdebe6c311c9d96 100644 --- a/docs/docs/guides/deployments/index.mdx +++ b/docs/docs/guides/deployments/index.mdx @@ -98,7 +98,7 @@ The LLM landscape is evolving at an unprecedented pace, with new libraries and m ### Model composition -Deploying systems like LangChain demands the ability to piece together different models and connect them via logic. Take the example of building a natural language input SQL query engine. Querying an LLM and obtaining the SQL command is only part of the system. You need to extract metadata from the connected database, construct a prompt for the LLM, run the SQL query on an engine, collect and feed back the response to the LLM as the query runs, and present the results to the user. This demonstrates the need to seamlessly integrate various complex components built in Python into a dynamic chain of logical blocks that can be served together. +Deploying systems like LangChain demands the ability to piece together different models and connect them via logic. Take the example of building a natural language input SQL query engine. Querying an LLM and obtaining the SQL command is only part of the system. You need to extract metadata from the connected database, construct a prompt for the LLM, run the SQL query on an engine, collect and feedback the response to the LLM as the query runs, and present the results to the user. This demonstrates the need to seamlessly integrate various complex components built in Python into a dynamic chain of logical blocks that can be served together. ## Cloud providers From b1e7130d8a91d9f7a5d4ff6c9466216b7112c3fc Mon Sep 17 00:00:00 2001 From: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Date: Thu, 1 Feb 2024 11:28:58 -0800 Subject: [PATCH 030/157] Minor update to Nomic cookbook (#16886) --- cookbook/nomic_embedding_rag.ipynb | 350 +++++++++++++++++++++++++++++ cookbook/nomic_embeddings.ipynb | 301 ------------------------- 2 files changed, 350 insertions(+), 301 deletions(-) create mode 100644 cookbook/nomic_embedding_rag.ipynb delete mode 100644 cookbook/nomic_embeddings.ipynb diff --git a/cookbook/nomic_embedding_rag.ipynb b/cookbook/nomic_embedding_rag.ipynb new file mode 100644 index 000000000000000..dd1e06e8157122b --- /dev/null +++ b/cookbook/nomic_embedding_rag.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "attachments": { + "4015a2e2-3400-4539-bd93-0d987ec5a44e.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "d8da6094-30c7-43f3-a608-c91717b673db", + "metadata": {}, + "source": [ + "# Nomic Embeddings\n", + "\n", + "Nomic has released a new embedding model with strong performance for long context retrieval (8k context window).\n", + "\n", + "The cookbook walks through the process of building and deploying (via LangServe) a RAG app using Nomic embeddings.\n", + "\n", + "![Screenshot 2024-02-01 at 9.14.15 AM.png](attachment:4015a2e2-3400-4539-bd93-0d987ec5a44e.png)\n", + "\n", + "## Signup\n", + "\n", + "Get your API token, then run:\n", + "```\n", + "! nomic login\n", + "```\n", + "\n", + "Then run with your generated API token \n", + "```\n", + "! nomic login < token > \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f737ec15-e9ab-4629-b54c-24be69e8b60b", + "metadata": {}, + "outputs": [], + "source": [ + "! nomic login" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ab7434a-2930-42b5-9164-dc2c03abe232", + "metadata": {}, + "outputs": [], + "source": [ + "! nomic login token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3501e2a-4686-4b95-8a1c-f19e035ea354", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85cecf42-0144-425b-86c8-219ff17c0195", + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: LangSmith API keys\n", + "import os\n", + "\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n", + "os.environ[\"LANGCHAIN_API_KEY\"] = \"api_key\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "134475f2-f256-4c13-9712-c55783e6a4e2", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Let's test 3 interesting blog posts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01c4d270-171e-45c2-a1b6-e350faa74117", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import WebBaseLoader\n", + "\n", + "urls = [\n", + " \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n", + " \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n", + " \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n", + "]\n", + "\n", + "docs = [WebBaseLoader(url).load() for url in urls]\n", + "docs_list = [item for sublist in docs for item in sublist]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "75ab7f74-873c-4d84-af5a-5cf19c61239d", + "metadata": {}, + "source": [ + "## Splitting \n", + "\n", + "Long context retrieval " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f512e128-629e-4304-926f-94fe5c999527", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.text_splitter import CharacterTextSplitter\n", + "\n", + "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n", + " chunk_size=7500, chunk_overlap=100\n", + ")\n", + "doc_splits = text_splitter.split_documents(docs_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2a69cf0-e3ab-4c92-a1d0-10da45c08b3b", + "metadata": {}, + "outputs": [], + "source": [ + "import tiktoken\n", + "\n", + "encoding = tiktoken.get_encoding(\"cl100k_base\")\n", + "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n", + "for d in doc_splits:\n", + " print(\"The document is %s tokens\" % len(encoding.encode(d.page_content)))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c58d1e9b-e98e-4bd9-b52f-4dfc2a4e69f4", + "metadata": {}, + "source": [ + "## Index \n", + "\n", + "Nomic embeddings [here](https://docs.nomic.ai/reference/endpoints/nomic-embed-text). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76447866-bf8b-412b-93bc-d6ea8ec35952", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", + "from langchain_nomic import NomicEmbeddings\n", + "from langchain_nomic.embeddings import NomicEmbeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15b3eab2-2689-49d4-8cb0-67ef2adcbc49", + "metadata": {}, + "outputs": [], + "source": [ + "# Add to vectorDB\n", + "vectorstore = Chroma.from_documents(\n", + " documents=doc_splits,\n", + " collection_name=\"rag-chroma\",\n", + " embedding=NomicEmbeddings(model=\"nomic-embed-text-v1\"),\n", + ")\n", + "retriever = vectorstore.as_retriever()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "41131122-3591-4566-aac1-ed19d496820a", + "metadata": {}, + "source": [ + "## RAG Chain\n", + "\n", + "We can use the Mistral `v0.2`, which is [fine-tuned for 32k context](https://x.com/dchaplot/status/1734198245067243629?s=20).\n", + "\n", + "We can [use Ollama](https://ollama.ai/library/mistral) -\n", + "```\n", + "ollama pull mistral:instruct\n", + "```\n", + "\n", + "We can also run [GPT-4 128k](https://openai.com/blog/new-models-and-developer-products-announced-at-devday). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1397de64-5b4a-4001-adc5-570ff8d31ff6", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models import ChatOllama\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Prompt\n", + "template = \"\"\"Answer the question based only on the following context:\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "# LLM API\n", + "model = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n", + "\n", + "# Local LLM\n", + "ollama_llm = \"mistral:instruct\"\n", + "model_local = ChatOllama(model=ollama_llm)\n", + "\n", + "# Chain\n", + "chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | model_local\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1548e00c-1ff6-4e88-aa13-69badf2088fb", + "metadata": {}, + "outputs": [], + "source": [ + "# Question\n", + "chain.invoke(\"What are the types of agent memory?\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5ec5b4c3-757d-44df-92ea-dd5f08017dd6", + "metadata": {}, + "source": [ + "**Mistral**\n", + "\n", + "Trace: 24k prompt tokens.\n", + "\n", + "* https://smith.langchain.com/public/3e04d475-ea08-4ee3-ae66-6416a93d8b08/r\n", + "\n", + "--- \n", + "\n", + "Some considerations are noted in the [needle in a haystack analysis](https://twitter.com/GregKamradt/status/1722386725635580292?lang=en):\n", + "\n", + "* LLMs may suffer with retrieval from large context depending on where the information is placed." + ] + }, + { + "attachments": { + "0afd4ea4-7ba2-4bfb-8e6d-57300e7a651f.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "de7e6f9e-0c69-47a7-be8a-0ae9233e036c", + "metadata": {}, + "source": [ + "## LangServe\n", + "\n", + "Create a LangServe app. \n", + "\n", + "![Screenshot 2024-02-01 at 10.36.05 AM.png](attachment:0afd4ea4-7ba2-4bfb-8e6d-57300e7a651f.png)\n", + "\n", + "```\n", + "$ conda create -n template-testing-env python=3.11\n", + "$ conda activate template-testing-env\n", + "$ pip install -U \"langchain-cli[serve]\" \"langserve[all]\"\n", + "$ langchain app new .\n", + "$ poetry add langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain\n", + "$ poetry install\n", + "```\n", + "\n", + "---\n", + "\n", + "Add above logic to new file `chain.py`.\n", + "\n", + "---\n", + "\n", + "Add to `server.py` -\n", + "\n", + "```\n", + "from app.chain import chain as nomic_chain\n", + "add_routes(app, nomic_chain, path=\"/nomic-rag\")\n", + "```\n", + "\n", + "Run - \n", + "```\n", + "$ poetry run langchain serve\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b4f8022-8aa2-4df4-be7c-635568ef8e24", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cookbook/nomic_embeddings.ipynb b/cookbook/nomic_embeddings.ipynb deleted file mode 100644 index 32fdfbba36fff89..000000000000000 --- a/cookbook/nomic_embeddings.ipynb +++ /dev/null @@ -1,301 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d8da6094-30c7-43f3-a608-c91717b673db", - "metadata": {}, - "source": [ - "# Nomic Embeddings\n", - "\n", - "Nomic has released a new embedding model with strong performance for long context retrieval (8k context window).\n", - "\n", - "## Signup\n", - "\n", - "Get your API token, then run:\n", - "```\n", - "! nomic login\n", - "```\n", - "\n", - "Then run with your generated API token \n", - "```\n", - "! nomic login < token > \n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f737ec15-e9ab-4629-b54c-24be69e8b60b", - "metadata": {}, - "outputs": [], - "source": [ - "! nomic login" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8ab7434a-2930-42b5-9164-dc2c03abe232", - "metadata": {}, - "outputs": [], - "source": [ - "! nomic login token" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3501e2a-4686-4b95-8a1c-f19e035ea354", - "metadata": {}, - "outputs": [], - "source": [ - "! pip install -U langchain-nomic" - ] - }, - { - "cell_type": "markdown", - "id": "134475f2-f256-4c13-9712-c55783e6a4e2", - "metadata": {}, - "source": [ - "## Document Loading\n", - "\n", - "Let's test 3 interesting blog posts." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "01c4d270-171e-45c2-a1b6-e350faa74117", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.document_loaders import WebBaseLoader\n", - "\n", - "urls = [\n", - " \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n", - " \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n", - " \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n", - "]\n", - "\n", - "docs = [WebBaseLoader(url).load() for url in urls]\n", - "docs_list = [item for sublist in docs for item in sublist]" - ] - }, - { - "cell_type": "markdown", - "id": "75ab7f74-873c-4d84-af5a-5cf19c61239d", - "metadata": {}, - "source": [ - "## Splitting \n", - "\n", - "Long context retrieval " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f512e128-629e-4304-926f-94fe5c999527", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", - "\n", - "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n", - " chunk_size=7500, chunk_overlap=100\n", - ")\n", - "doc_splits = text_splitter.split_documents(docs_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d2a69cf0-e3ab-4c92-a1d0-10da45c08b3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The document is 6562 tokens\n", - "The document is 3037 tokens\n", - "The document is 6092 tokens\n", - "The document is 1050 tokens\n", - "The document is 6933 tokens\n", - "The document is 5560 tokens\n" - ] - } - ], - "source": [ - "import tiktoken\n", - "\n", - "encoding = tiktoken.get_encoding(\"cl100k_base\")\n", - "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n", - "for d in doc_splits:\n", - " print(\"The document is %s tokens\" % len(encoding.encode(d.page_content)))" - ] - }, - { - "cell_type": "markdown", - "id": "c58d1e9b-e98e-4bd9-b52f-4dfc2a4e69f4", - "metadata": {}, - "source": [ - "## Index \n", - "\n", - "Nomic embeddings [here](https://docs.nomic.ai/reference/endpoints/nomic-embed-text). " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "76447866-bf8b-412b-93bc-d6ea8ec35952", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from langchain_community.vectorstores import Chroma\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", - "from langchain_nomic import NomicEmbeddings\n", - "from langchain_nomic.embeddings import NomicEmbeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "15b3eab2-2689-49d4-8cb0-67ef2adcbc49", - "metadata": {}, - "outputs": [], - "source": [ - "# Add to vectorDB\n", - "vectorstore = Chroma.from_documents(\n", - " documents=doc_splits,\n", - " collection_name=\"rag-chroma\",\n", - " embedding=NomicEmbeddings(model=\"nomic-embed-text-v1\"),\n", - ")\n", - "retriever = vectorstore.as_retriever()" - ] - }, - { - "cell_type": "markdown", - "id": "41131122-3591-4566-aac1-ed19d496820a", - "metadata": {}, - "source": [ - "## RAG Chain\n", - "\n", - "We can use the Mistral `v0.2`, which is [fine-tuned for 32k context](https://x.com/dchaplot/status/1734198245067243629?s=20).\n", - "\n", - "We can [use Ollama](https://ollama.ai/library/mistral) -\n", - "```\n", - "ollama pull mistral:instruct\n", - "```\n", - "\n", - "We can also run [GPT-4 128k](https://openai.com/blog/new-models-and-developer-products-announced-at-devday). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1397de64-5b4a-4001-adc5-570ff8d31ff6", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.chat_models import ChatOllama\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "# Prompt\n", - "template = \"\"\"Answer the question based only on the following context:\n", - "{context}\n", - "\n", - "Question: {question}\n", - "\"\"\"\n", - "prompt = ChatPromptTemplate.from_template(template)\n", - "\n", - "# LLM API\n", - "model = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n", - "\n", - "# Local LLM\n", - "ollama_llm = \"mistral:instruct\"\n", - "model_local = ChatOllama(model=ollama_llm)\n", - "\n", - "# Chain\n", - "chain = (\n", - " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", - " | prompt\n", - " | model_local\n", - " | StrOutputParser()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1548e00c-1ff6-4e88-aa13-69badf2088fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' Agents, especially those used in artificial intelligence and natural language processing, can have different types of memory. Here are some common types:\\n\\n1. **Short-term memory** or working memory: This is a small capacity, high-turnover memory that holds information temporarily while the agent processes it. Short-term memory is essential for tasks requiring attention and quick response, such as parsing sentences or following instructions.\\n\\n2. **Long-term memory**: This is a large capacity, low-turnover memory where agents store information for extended periods. Long-term memory enables learning from experiences, accessing past knowledge, and improving performance over time.\\n\\n3. **Explicit memory** or declarative memory: Agents use explicit memory to store and recall facts, concepts, and rules that can be expressed in natural language. This type of memory is crucial for problem solving and reasoning.\\n\\n4. **Implicit memory** or procedural memory: Implicit memory refers to the acquisition and retention of skills and habits. The agent learns through repeated experiences without necessarily being aware of it.\\n\\n5. **Connectionist memory**: Connectionist memory, also known as neural networks, is inspired by the structure and function of biological brains. Connectionist models learn and store information in interconnected nodes or artificial neurons. This type of memory enables the model to recognize patterns and generalize knowledge.\\n\\n6. **Hybrid memory systems**: Many advanced agents employ a combination of different memory types to maximize their learning potential and performance. These hybrid systems can integrate short-term, long-term, explicit, implicit, and connectionist memories.'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Question\n", - "chain.invoke(\"What are the types of agent memory?\")" - ] - }, - { - "cell_type": "markdown", - "id": "5ec5b4c3-757d-44df-92ea-dd5f08017dd6", - "metadata": {}, - "source": [ - "**Mistral**\n", - "\n", - "Trace: 24k prompt tokens.\n", - "\n", - "* https://smith.langchain.com/public/3e04d475-ea08-4ee3-ae66-6416a93d8b08/r\n", - "\n", - "--- \n", - "\n", - "Some considerations are noted in the [needle in a haystack analysis](https://twitter.com/GregKamradt/status/1722386725635580292?lang=en):\n", - "\n", - "* LLMs may suffer with retrieval from large context depending on where the information is placed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ffb6b63-17ee-42d8-b1fb-d6a866e98458", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 13a6756067a2e47ad4fd2972259a174d91e03a05 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 1 Feb 2024 11:39:00 -0800 Subject: [PATCH 031/157] infra: ci naming 2 (#16893) --- .github/workflows/_all_ci.yml | 10 +++++----- .github/workflows/_compile_integration_test.yml | 2 +- .github/workflows/_dependencies.yml | 2 +- .github/workflows/_lint.yml | 2 +- .github/workflows/_test.yml | 2 +- .github/workflows/doc_lint.yml | 2 +- 6 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/_all_ci.yml b/.github/workflows/_all_ci.yml index 61caad6ebd23660..f6b27f029d1a10d 100644 --- a/.github/workflows/_all_ci.yml +++ b/.github/workflows/_all_ci.yml @@ -36,35 +36,35 @@ env: jobs: lint: - name: make lint + name: "-" uses: ./.github/workflows/_lint.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit test: - name: make test + name: "-" uses: ./.github/workflows/_test.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit compile-integration-tests: - name: poetry run pytest -m compile tests/integration_tests + name: "-" uses: ./.github/workflows/_compile_integration_test.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit dependencies: - name: check dependencies + name: "-" uses: ./.github/workflows/_dependencies.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit extended-tests: - name: make extended_tests / python${{ matrix.python-version }} + name: "make extended_tests #${{ matrix.python-version }}" runs-on: ubuntu-latest strategy: matrix: diff --git a/.github/workflows/_compile_integration_test.yml b/.github/workflows/_compile_integration_test.yml index 750ce00a77f27e0..d8b5b9b0aa48636 100644 --- a/.github/workflows/_compile_integration_test.yml +++ b/.github/workflows/_compile_integration_test.yml @@ -24,7 +24,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: python${{ matrix.python-version }} + name: "poetry run pytest -m compile tests/integration_tests #${{ matrix.python-version }}" steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_dependencies.yml b/.github/workflows/_dependencies.yml index 4c4604796936812..dd12a25c05c0e71 100644 --- a/.github/workflows/_dependencies.yml +++ b/.github/workflows/_dependencies.yml @@ -28,7 +28,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: dependency checks / python${{ matrix.python-version }} + name: dependency checks ${{ matrix.python-version }} steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index a6e12761d978631..a0c1944a325d2b0 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -21,7 +21,7 @@ env: jobs: build: - name: python${{ matrix.python-version }} + name: "make lint #${{ matrix.python-version }}" runs-on: ubuntu-latest strategy: matrix: diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml index d2552d3ecc0b988..928cab763ee6a64 100644 --- a/.github/workflows/_test.yml +++ b/.github/workflows/_test.yml @@ -28,7 +28,7 @@ jobs: - "3.9" - "3.10" - "3.11" - name: python${{ matrix.python-version }} + name: "make test #${{ matrix.python-version }}" steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/doc_lint.yml b/.github/workflows/doc_lint.yml index b1d1932672f253b..298e1509b010180 100644 --- a/.github/workflows/doc_lint.yml +++ b/.github/workflows/doc_lint.yml @@ -29,7 +29,7 @@ jobs: git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0 lint: - name: make lint + name: "-" uses: ./.github/workflows/_lint.yml with: From c2ca6612fe97476a55a0a785b01fe7e2366e01d8 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Thu, 1 Feb 2024 12:05:57 -0800 Subject: [PATCH 032/157] refactor `langchain.prompts.example_selector` (#15369) The `langchain.prompts.example_selector` [still holds several artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.prompts) that belongs to `community`. If they moved to `langchain_community.example_selectors`, the `langchain.prompts` namespace would be effectively removed which is great. - moved a class and afunction to `langchain_community` Note: - Previously, the `langchain.prompts.example_selector` artifacts were moved into the `langchain_core.exampe_selectors`. See the flattened namespace (`.prompts` was removed)! Similar flattening was implemented for the `langchain_core` as the `langchain_core.exampe_selectors`. --------- Co-authored-by: Erick Friis --- .../example_selectors/__init__.py | 10 ++ .../example_selectors/ngram_overlap.py | 114 +++++++++++++++++ .../prompts/example_selector/ngram_overlap.py | 121 ++---------------- 3 files changed, 133 insertions(+), 112 deletions(-) create mode 100644 libs/community/langchain_community/example_selectors/__init__.py create mode 100644 libs/community/langchain_community/example_selectors/ngram_overlap.py diff --git a/libs/community/langchain_community/example_selectors/__init__.py b/libs/community/langchain_community/example_selectors/__init__.py new file mode 100644 index 000000000000000..70654d689b4eb86 --- /dev/null +++ b/libs/community/langchain_community/example_selectors/__init__.py @@ -0,0 +1,10 @@ +"""Logic for selecting examples to include in prompts.""" +from langchain_community.example_selectors.ngram_overlap import ( + NGramOverlapExampleSelector, + ngram_overlap_score, +) + +__all__ = [ + "NGramOverlapExampleSelector", + "ngram_overlap_score", +] diff --git a/libs/community/langchain_community/example_selectors/ngram_overlap.py b/libs/community/langchain_community/example_selectors/ngram_overlap.py new file mode 100644 index 000000000000000..c8d662f8e1fc283 --- /dev/null +++ b/libs/community/langchain_community/example_selectors/ngram_overlap.py @@ -0,0 +1,114 @@ +"""Select and order examples based on ngram overlap score (sentence_bleu score). + +https://www.nltk.org/_modules/nltk/translate/bleu_score.html +https://aclanthology.org/P02-1040.pdf +""" +from typing import Dict, List + +import numpy as np +from langchain_core.example_selectors import BaseExampleSelector +from langchain_core.prompts import PromptTemplate +from langchain_core.pydantic_v1 import BaseModel, root_validator + + +def ngram_overlap_score(source: List[str], example: List[str]) -> float: + """Compute ngram overlap score of source and example as sentence_bleu score + from NLTK package. + + Use sentence_bleu with method1 smoothing function and auto reweighting. + Return float value between 0.0 and 1.0 inclusive. + https://www.nltk.org/_modules/nltk/translate/bleu_score.html + https://aclanthology.org/P02-1040.pdf + """ + from nltk.translate.bleu_score import ( + SmoothingFunction, # type: ignore + sentence_bleu, + ) + + hypotheses = source[0].split() + references = [s.split() for s in example] + + return float( + sentence_bleu( + references, + hypotheses, + smoothing_function=SmoothingFunction().method1, + auto_reweigh=True, + ) + ) + + +class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel): + """Select and order examples based on ngram overlap score (sentence_bleu score + from NLTK package). + + https://www.nltk.org/_modules/nltk/translate/bleu_score.html + https://aclanthology.org/P02-1040.pdf + """ + + examples: List[dict] + """A list of the examples that the prompt template expects.""" + + example_prompt: PromptTemplate + """Prompt template used to format the examples.""" + + threshold: float = -1.0 + """Threshold at which algorithm stops. Set to -1.0 by default. + + For negative threshold: + select_examples sorts examples by ngram_overlap_score, but excludes none. + For threshold greater than 1.0: + select_examples excludes all examples, and returns an empty list. + For threshold equal to 0.0: + select_examples sorts examples by ngram_overlap_score, + and excludes examples with no ngram overlap with input. + """ + + @root_validator(pre=True) + def check_dependencies(cls, values: Dict) -> Dict: + """Check that valid dependencies exist.""" + try: + from nltk.translate.bleu_score import ( # noqa: F401 + SmoothingFunction, + sentence_bleu, + ) + except ImportError as e: + raise ImportError( + "Not all the correct dependencies for this ExampleSelect exist." + "Please install nltk with `pip install nltk`." + ) from e + + return values + + def add_example(self, example: Dict[str, str]) -> None: + """Add new example to list.""" + self.examples.append(example) + + def select_examples(self, input_variables: Dict[str, str]) -> List[dict]: + """Return list of examples sorted by ngram_overlap_score with input. + + Descending order. + Excludes any examples with ngram_overlap_score less than or equal to threshold. + """ + inputs = list(input_variables.values()) + examples = [] + k = len(self.examples) + score = [0.0] * k + first_prompt_template_key = self.example_prompt.input_variables[0] + + for i in range(k): + score[i] = ngram_overlap_score( + inputs, [self.examples[i][first_prompt_template_key]] + ) + + while True: + arg_max = np.argmax(score) + if (score[arg_max] < self.threshold) or abs( + score[arg_max] - self.threshold + ) < 1e-9: + break + + examples.append(self.examples[arg_max]) + score[arg_max] = self.threshold - 1.0 + + return examples diff --git a/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py b/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py index c439c946ed30dcc..db1be277e46d504 100644 --- a/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py +++ b/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py @@ -1,112 +1,9 @@ -"""Select and order examples based on ngram overlap score (sentence_bleu score). - -https://www.nltk.org/_modules/nltk/translate/bleu_score.html -https://aclanthology.org/P02-1040.pdf -""" -from typing import Dict, List - -import numpy as np -from langchain_core.example_selectors.base import BaseExampleSelector -from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, root_validator - - -def ngram_overlap_score(source: List[str], example: List[str]) -> float: - """Compute ngram overlap score of source and example as sentence_bleu score. - - Use sentence_bleu with method1 smoothing function and auto reweighting. - Return float value between 0.0 and 1.0 inclusive. - https://www.nltk.org/_modules/nltk/translate/bleu_score.html - https://aclanthology.org/P02-1040.pdf - """ - from nltk.translate.bleu_score import ( - SmoothingFunction, # type: ignore - sentence_bleu, - ) - - hypotheses = source[0].split() - references = [s.split() for s in example] - - return float( - sentence_bleu( - references, - hypotheses, - smoothing_function=SmoothingFunction().method1, - auto_reweigh=True, - ) - ) - - -class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel): - """Select and order examples based on ngram overlap score (sentence_bleu score). - - https://www.nltk.org/_modules/nltk/translate/bleu_score.html - https://aclanthology.org/P02-1040.pdf - """ - - examples: List[dict] - """A list of the examples that the prompt template expects.""" - - example_prompt: PromptTemplate - """Prompt template used to format the examples.""" - - threshold: float = -1.0 - """Threshold at which algorithm stops. Set to -1.0 by default. - - For negative threshold: - select_examples sorts examples by ngram_overlap_score, but excludes none. - For threshold greater than 1.0: - select_examples excludes all examples, and returns an empty list. - For threshold equal to 0.0: - select_examples sorts examples by ngram_overlap_score, - and excludes examples with no ngram overlap with input. - """ - - @root_validator(pre=True) - def check_dependencies(cls, values: Dict) -> Dict: - """Check that valid dependencies exist.""" - try: - from nltk.translate.bleu_score import ( # noqa: F401 - SmoothingFunction, - sentence_bleu, - ) - except ImportError as e: - raise ImportError( - "Not all the correct dependencies for this ExampleSelect exist." - "Please install nltk with `pip install nltk`." - ) from e - - return values - - def add_example(self, example: Dict[str, str]) -> None: - """Add new example to list.""" - self.examples.append(example) - - def select_examples(self, input_variables: Dict[str, str]) -> List[dict]: - """Return list of examples sorted by ngram_overlap_score with input. - - Descending order. - Excludes any examples with ngram_overlap_score less than or equal to threshold. - """ - inputs = list(input_variables.values()) - examples = [] - k = len(self.examples) - score = [0.0] * k - first_prompt_template_key = self.example_prompt.input_variables[0] - - for i in range(k): - score[i] = ngram_overlap_score( - inputs, [self.examples[i][first_prompt_template_key]] - ) - - while True: - arg_max = np.argmax(score) - if (score[arg_max] < self.threshold) or abs( - score[arg_max] - self.threshold - ) < 1e-9: - break - - examples.append(self.examples[arg_max]) - score[arg_max] = self.threshold - 1.0 - - return examples +from langchain_community.example_selectors.ngram_overlap import ( + NGramOverlapExampleSelector, + ngram_overlap_score, +) + +__all__ = [ + "NGramOverlapExampleSelector", + "ngram_overlap_score", +] From f7c709b40e9bc4bb7895aae4809b12fd2444f7ab Mon Sep 17 00:00:00 2001 From: akira wu <36568720+akirawuc@users.noreply.github.com> Date: Fri, 2 Feb 2024 05:30:29 +0800 Subject: [PATCH 033/157] doc: fix typo in message_history.ipynb (#16877) - **Description:** just fixed a small typo in the documentation in the `expression_language/how_to/message_history` session [here](https://python.langchain.com/docs/expression_language/how_to/message_history) --- docs/docs/expression_language/how_to/message_history.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/expression_language/how_to/message_history.ipynb b/docs/docs/expression_language/how_to/message_history.ipynb index 774c2b92d94d0d3..6d93423a96455c9 100644 --- a/docs/docs/expression_language/how_to/message_history.ipynb +++ b/docs/docs/expression_language/how_to/message_history.ipynb @@ -7,7 +7,7 @@ "source": [ "# Add message history (memory)\n", "\n", - "The `RunnableWithMessageHistory` let's us add message history to certain types of chains.\n", + "The `RunnableWithMessageHistory` let us add message history to certain types of chains.\n", "\n", "Specifically, it can be used for any Runnable that takes as input one of\n", "\n", From b1a847366ca795cad58ead38e3b11608856c1584 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 1 Feb 2024 16:37:40 -0800 Subject: [PATCH 034/157] community: revert SQL Stores (#16912) This reverts commit cfc225ecb3f442b06f552d7eff2ec73b39f9beb5. https://github.com/langchain-ai/langchain/pull/15909#issuecomment-1922418097 These will have existed in langchain-community 0.0.16 and 0.0.17. --- docs/docs/integrations/stores/sql.ipynb | 186 ---------- .../langchain_community/storage/__init__.py | 6 - .../langchain_community/storage/sql.py | 345 ------------------ .../integration_tests/storage/test_sql.py | 228 ------------ .../tests/unit_tests/storage/test_imports.py | 2 - .../tests/unit_tests/storage/test_sql.py | 7 - 6 files changed, 774 deletions(-) delete mode 100644 docs/docs/integrations/stores/sql.ipynb delete mode 100644 libs/community/langchain_community/storage/sql.py delete mode 100644 libs/community/tests/integration_tests/storage/test_sql.py delete mode 100644 libs/community/tests/unit_tests/storage/test_sql.py diff --git a/docs/docs/integrations/stores/sql.ipynb b/docs/docs/integrations/stores/sql.ipynb deleted file mode 100644 index ecb2f472a8fc14d..000000000000000 --- a/docs/docs/integrations/stores/sql.ipynb +++ /dev/null @@ -1,186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "sidebar_label: SQL\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SQLStore\n", - "\n", - "The `SQLStrStore` and `SQLDocStore` implement remote data access and persistence to store strings or LangChain documents in your SQL instance." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['value1', 'value2']\n", - "['key2']\n", - "['key2']\n" - ] - } - ], - "source": [ - "from langchain_community.storage import SQLStrStore\n", - "\n", - "# simple example using an SQLStrStore to store strings\n", - "# same as you would use in \"InMemoryStore\" but using SQL persistence\n", - "CONNECTION_STRING = \"postgresql+psycopg2://user:pass@localhost:5432/db\"\n", - "COLLECTION_NAME = \"test_collection\"\n", - "\n", - "store = SQLStrStore(\n", - " collection_name=COLLECTION_NAME,\n", - " connection_string=CONNECTION_STRING,\n", - ")\n", - "store.mset([(\"key1\", \"value1\"), (\"key2\", \"value2\")])\n", - "print(store.mget([\"key1\", \"key2\"]))\n", - "# ['value1', 'value2']\n", - "store.mdelete([\"key1\"])\n", - "print(list(store.yield_keys()))\n", - "# ['key2']\n", - "print(list(store.yield_keys(prefix=\"k\")))\n", - "# ['key2']\n", - "# delete the COLLECTION_NAME collection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Integration with ParentRetriever and PGVector\n", - "\n", - "When using PGVector, you already have a SQL instance running. Here is a convenient way of using this instance to store documents associated to vectors. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prepare the PGVector vectorestore with something like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.vectorstores import PGVector\n", - "from langchain_openai import OpenAIEmbeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "embeddings = OpenAIEmbeddings()\n", - "vector_db = PGVector.from_existing_index(\n", - " embedding=embeddings,\n", - " collection_name=COLLECTION_NAME,\n", - " connection_string=CONNECTION_STRING,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then create the parent retiever using `SQLDocStore` to persist the documents" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.document_loaders import TextLoader\n", - "from langchain.retrievers import ParentDocumentRetriever\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", - "from langchain_community.storage import SQLDocStore\n", - "\n", - "CONNECTION_STRING = \"postgresql+psycopg2://user:pass@localhost:5432/db\"\n", - "COLLECTION_NAME = \"state_of_the_union_test\"\n", - "docstore = SQLDocStore(\n", - " collection_name=COLLECTION_NAME,\n", - " connection_string=CONNECTION_STRING,\n", - ")\n", - "\n", - "loader = TextLoader(\"./state_of_the_union.txt\")\n", - "documents = loader.load()\n", - "\n", - "parent_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n", - "child_splitter = RecursiveCharacterTextSplitter(chunk_size=50)\n", - "\n", - "retriever = ParentDocumentRetriever(\n", - " vectorstore=vector_db,\n", - " docstore=docstore,\n", - " child_splitter=child_splitter,\n", - " parent_splitter=parent_splitter,\n", - ")\n", - "retriever.add_documents(documents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete a collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.storage import SQLStrStore\n", - "\n", - "# delete the COLLECTION_NAME collection\n", - "CONNECTION_STRING = \"postgresql+psycopg2://user:pass@localhost:5432/db\"\n", - "COLLECTION_NAME = \"test_collection\"\n", - "store = SQLStrStore(\n", - " collection_name=COLLECTION_NAME,\n", - " connection_string=CONNECTION_STRING,\n", - ")\n", - "store.delete_collection()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/libs/community/langchain_community/storage/__init__.py b/libs/community/langchain_community/storage/__init__.py index 5c28015e57437a3..494591b03c7139c 100644 --- a/libs/community/langchain_community/storage/__init__.py +++ b/libs/community/langchain_community/storage/__init__.py @@ -11,10 +11,6 @@ AstraDBStore, ) from langchain_community.storage.redis import RedisStore -from langchain_community.storage.sql import ( - SQLDocStore, - SQLStrStore, -) from langchain_community.storage.upstash_redis import ( UpstashRedisByteStore, UpstashRedisStore, @@ -26,6 +22,4 @@ "RedisStore", "UpstashRedisByteStore", "UpstashRedisStore", - "SQLDocStore", - "SQLStrStore", ] diff --git a/libs/community/langchain_community/storage/sql.py b/libs/community/langchain_community/storage/sql.py deleted file mode 100644 index 7baaf642859361d..000000000000000 --- a/libs/community/langchain_community/storage/sql.py +++ /dev/null @@ -1,345 +0,0 @@ -"""SQL storage that persists data in a SQL database -and supports data isolation using collections.""" -from __future__ import annotations - -import uuid -from typing import Any, Generic, Iterator, List, Optional, Sequence, Tuple, TypeVar - -import sqlalchemy -from sqlalchemy import JSON, UUID -from sqlalchemy.orm import Session, relationship - -try: - from sqlalchemy.orm import declarative_base -except ImportError: - from sqlalchemy.ext.declarative import declarative_base - -from langchain_core.documents import Document -from langchain_core.load import Serializable, dumps, loads -from langchain_core.stores import BaseStore - -V = TypeVar("V") - -ITERATOR_WINDOW_SIZE = 1000 - -Base = declarative_base() # type: Any - - -_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain" - - -class BaseModel(Base): - """Base model for the SQL stores.""" - - __abstract__ = True - uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - - -_classes: Any = None - - -def _get_storage_stores() -> Any: - global _classes - if _classes is not None: - return _classes - - class CollectionStore(BaseModel): - """Collection store.""" - - __tablename__ = "langchain_storage_collection" - - name = sqlalchemy.Column(sqlalchemy.String) - cmetadata = sqlalchemy.Column(JSON) - - items = relationship( - "ItemStore", - back_populates="collection", - passive_deletes=True, - ) - - @classmethod - def get_by_name( - cls, session: Session, name: str - ) -> Optional["CollectionStore"]: - # type: ignore - return session.query(cls).filter(cls.name == name).first() - - @classmethod - def get_or_create( - cls, - session: Session, - name: str, - cmetadata: Optional[dict] = None, - ) -> Tuple["CollectionStore", bool]: - """ - Get or create a collection. - Returns [Collection, bool] where the bool is True if the collection was created. - """ # noqa: E501 - created = False - collection = cls.get_by_name(session, name) - if collection: - return collection, created - - collection = cls(name=name, cmetadata=cmetadata) - session.add(collection) - session.commit() - created = True - return collection, created - - class ItemStore(BaseModel): - """Item store.""" - - __tablename__ = "langchain_storage_items" - - collection_id = sqlalchemy.Column( - UUID(as_uuid=True), - sqlalchemy.ForeignKey( - f"{CollectionStore.__tablename__}.uuid", - ondelete="CASCADE", - ), - ) - collection = relationship(CollectionStore, back_populates="items") - - content = sqlalchemy.Column(sqlalchemy.String, nullable=True) - - # custom_id : any user defined id - custom_id = sqlalchemy.Column(sqlalchemy.String, nullable=True) - - _classes = (ItemStore, CollectionStore) - - return _classes - - -class SQLBaseStore(BaseStore[str, V], Generic[V]): - """SQL storage - - Args: - connection_string: SQL connection string that will be passed to SQLAlchemy. - collection_name: The name of the collection to use. (default: langchain) - NOTE: Collections are useful to isolate your data in a given a database. - This is not the name of the table, but the name of the collection. - The tables will be created when initializing the store (if not exists) - So, make sure the user has the right permissions to create tables. - pre_delete_collection: If True, will delete the collection if it exists. - (default: False). Useful for testing. - engine_args: SQLAlchemy's create engine arguments. - - Example: - .. code-block:: python - - from langchain_community.storage import SQLDocStore - from langchain_community.embeddings.openai import OpenAIEmbeddings - - # example using an SQLDocStore to store Document objects for - # a ParentDocumentRetriever - CONNECTION_STRING = "postgresql+psycopg2://user:pass@localhost:5432/db" - COLLECTION_NAME = "state_of_the_union_test" - docstore = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - ) - child_splitter = RecursiveCharacterTextSplitter(chunk_size=400) - vectorstore = ... - - retriever = ParentDocumentRetriever( - vectorstore=vectorstore, - docstore=docstore, - child_splitter=child_splitter, - ) - - # example using an SQLStrStore to store strings - # same example as in "InMemoryStore" but using SQL persistence - store = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - ) - store.mset([('key1', 'value1'), ('key2', 'value2')]) - store.mget(['key1', 'key2']) - # ['value1', 'value2'] - store.mdelete(['key1']) - list(store.yield_keys()) - # ['key2'] - list(store.yield_keys(prefix='k')) - # ['key2'] - - # delete the COLLECTION_NAME collection - docstore.delete_collection() - """ - - def __init__( - self, - connection_string: str, - collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, - collection_metadata: Optional[dict] = None, - pre_delete_collection: bool = False, - connection: Optional[sqlalchemy.engine.Connection] = None, - engine_args: Optional[dict[str, Any]] = None, - ) -> None: - self.connection_string = connection_string - self.collection_name = collection_name - self.collection_metadata = collection_metadata - self.pre_delete_collection = pre_delete_collection - self.engine_args = engine_args or {} - # Create a connection if not provided, otherwise use the provided connection - self._conn = connection if connection else self.__connect() - self.__post_init__() - - def __post_init__( - self, - ) -> None: - """Initialize the store.""" - ItemStore, CollectionStore = _get_storage_stores() - self.CollectionStore = CollectionStore - self.ItemStore = ItemStore - self.__create_tables_if_not_exists() - self.__create_collection() - - def __connect(self) -> sqlalchemy.engine.Connection: - engine = sqlalchemy.create_engine(self.connection_string, **self.engine_args) - conn = engine.connect() - return conn - - def __create_tables_if_not_exists(self) -> None: - with self._conn.begin(): - Base.metadata.create_all(self._conn) - - def __create_collection(self) -> None: - if self.pre_delete_collection: - self.delete_collection() - with Session(self._conn) as session: - self.CollectionStore.get_or_create( - session, self.collection_name, cmetadata=self.collection_metadata - ) - - def delete_collection(self) -> None: - with Session(self._conn) as session: - collection = self.__get_collection(session) - if not collection: - return - session.delete(collection) - session.commit() - - def __get_collection(self, session: Session) -> Any: - return self.CollectionStore.get_by_name(session, self.collection_name) - - def __del__(self) -> None: - if self._conn: - self._conn.close() - - def __serialize_value(self, obj: V) -> str: - if isinstance(obj, Serializable): - return dumps(obj) - return obj - - def __deserialize_value(self, obj: V) -> str: - try: - return loads(obj) - except Exception: - return obj - - def mget(self, keys: Sequence[str]) -> List[Optional[V]]: - """Get the values associated with the given keys. - - Args: - keys (Sequence[str]): A sequence of keys. - - Returns: - A sequence of optional values associated with the keys. - If a key is not found, the corresponding value will be None. - """ - with Session(self._conn) as session: - collection = self.__get_collection(session) - - items = ( - session.query(self.ItemStore.content, self.ItemStore.custom_id) - .where( - sqlalchemy.and_( - self.ItemStore.custom_id.in_(keys), - self.ItemStore.collection_id == (collection.uuid), - ) - ) - .all() - ) - - ordered_values = {key: None for key in keys} - for item in items: - v = item[0] - val = self.__deserialize_value(v) if v is not None else v - k = item[1] - ordered_values[k] = val - - return [ordered_values[key] for key in keys] - - def mset(self, key_value_pairs: Sequence[Tuple[str, V]]) -> None: - """Set the values for the given keys. - - Args: - key_value_pairs (Sequence[Tuple[str, V]]): A sequence of key-value pairs. - - Returns: - None - """ - with Session(self._conn) as session: - collection = self.__get_collection(session) - if not collection: - raise ValueError("Collection not found") - for id, item in key_value_pairs: - content = self.__serialize_value(item) - item_store = self.ItemStore( - content=content, - custom_id=id, - collection_id=collection.uuid, - ) - session.add(item_store) - session.commit() - - def mdelete(self, keys: Sequence[str]) -> None: - """Delete the given keys and their associated values. - - Args: - keys (Sequence[str]): A sequence of keys to delete. - """ - with Session(self._conn) as session: - collection = self.__get_collection(session) - if not collection: - raise ValueError("Collection not found") - if keys is not None: - stmt = sqlalchemy.delete(self.ItemStore).where( - sqlalchemy.and_( - self.ItemStore.custom_id.in_(keys), - self.ItemStore.collection_id == (collection.uuid), - ) - ) - session.execute(stmt) - session.commit() - - def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]: - """Get an iterator over keys that match the given prefix. - - Args: - prefix (str, optional): The prefix to match. Defaults to None. - - Returns: - Iterator[str]: An iterator over keys that match the given prefix. - """ - with Session(self._conn) as session: - collection = self.__get_collection(session) - start = 0 - while True: - stop = start + ITERATOR_WINDOW_SIZE - query = session.query(self.ItemStore.custom_id).where( - self.ItemStore.collection_id == (collection.uuid) - ) - if prefix is not None: - query = query.filter(self.ItemStore.custom_id.startswith(prefix)) - items = query.slice(start, stop).all() - - if len(items) == 0: - break - for item in items: - yield item[0] - start += ITERATOR_WINDOW_SIZE - - -SQLDocStore = SQLBaseStore[Document] -SQLStrStore = SQLBaseStore[str] diff --git a/libs/community/tests/integration_tests/storage/test_sql.py b/libs/community/tests/integration_tests/storage/test_sql.py deleted file mode 100644 index c09b9745c65cd6a..000000000000000 --- a/libs/community/tests/integration_tests/storage/test_sql.py +++ /dev/null @@ -1,228 +0,0 @@ -"""Implement integration tests for SQL storage.""" -import os - -from langchain_core.documents import Document - -from langchain_community.storage.sql import SQLDocStore, SQLStrStore - - -def connection_string_from_db_params() -> str: - """Return connection string from database parameters.""" - dbdriver = os.environ.get("TEST_SQL_DBDRIVER", "postgresql+psycopg2") - host = os.environ.get("TEST_SQL_HOST", "localhost") - port = int(os.environ.get("TEST_SQL_PORT", "5432")) - database = os.environ.get("TEST_SQL_DATABASE", "postgres") - user = os.environ.get("TEST_SQL_USER", "postgres") - password = os.environ.get("TEST_SQL_PASSWORD", "postgres") - return f"{dbdriver}://{user}:{password}@{host}:{port}/{database}" - - -CONNECTION_STRING = connection_string_from_db_params() -COLLECTION_NAME = "test_collection" -COLLECTION_NAME_2 = "test_collection_2" - - -def test_str_store_mget() -> None: - store = SQLStrStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store.mset([("key1", "value1"), ("key2", "value2")]) - - values = store.mget(["key1", "key2"]) - assert values == ["value1", "value2"] - - # Test non-existent key - non_existent_value = store.mget(["key3"]) - assert non_existent_value == [None] - store.delete_collection() - - -def test_str_store_mset() -> None: - store = SQLStrStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store.mset([("key1", "value1"), ("key2", "value2")]) - - values = store.mget(["key1", "key2"]) - assert values == ["value1", "value2"] - store.delete_collection() - - -def test_str_store_mdelete() -> None: - store = SQLStrStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store.mset([("key1", "value1"), ("key2", "value2")]) - - store.mdelete(["key1"]) - - values = store.mget(["key1", "key2"]) - assert values == [None, "value2"] - - # Test deleting non-existent key - store.mdelete(["key3"]) # No error should be raised - store.delete_collection() - - -def test_str_store_yield_keys() -> None: - store = SQLStrStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store.mset([("key1", "value1"), ("key2", "value2"), ("key3", "value3")]) - - keys = list(store.yield_keys()) - assert set(keys) == {"key1", "key2", "key3"} - - keys_with_prefix = list(store.yield_keys(prefix="key")) - assert set(keys_with_prefix) == {"key1", "key2", "key3"} - - keys_with_invalid_prefix = list(store.yield_keys(prefix="x")) - assert keys_with_invalid_prefix == [] - store.delete_collection() - - -def test_str_store_collection() -> None: - """Test that collections are isolated within a db.""" - store_1 = SQLStrStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store_2 = SQLStrStore( - collection_name=COLLECTION_NAME_2, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - - store_1.mset([("key1", "value1"), ("key2", "value2")]) - store_2.mset([("key3", "value3"), ("key4", "value4")]) - - values = store_1.mget(["key1", "key2"]) - assert values == ["value1", "value2"] - values = store_1.mget(["key3", "key4"]) - assert values == [None, None] - - values = store_2.mget(["key1", "key2"]) - assert values == [None, None] - values = store_2.mget(["key3", "key4"]) - assert values == ["value3", "value4"] - - store_1.delete_collection() - store_2.delete_collection() - - -def test_doc_store_mget() -> None: - store = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - doc_1 = Document(page_content="value1") - doc_2 = Document(page_content="value2") - store.mset([("key1", doc_1), ("key2", doc_2)]) - - values = store.mget(["key1", "key2"]) - assert values == [doc_1, doc_2] - - # Test non-existent key - non_existent_value = store.mget(["key3"]) - assert non_existent_value == [None] - store.delete_collection() - - -def test_doc_store_mset() -> None: - store = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - doc_1 = Document(page_content="value1") - doc_2 = Document(page_content="value2") - store.mset([("key1", doc_1), ("key2", doc_2)]) - - values = store.mget(["key1", "key2"]) - assert values == [doc_1, doc_2] - store.delete_collection() - - -def test_doc_store_mdelete() -> None: - store = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - doc_1 = Document(page_content="value1") - doc_2 = Document(page_content="value2") - store.mset([("key1", doc_1), ("key2", doc_2)]) - - store.mdelete(["key1"]) - - values = store.mget(["key1", "key2"]) - assert values == [None, doc_2] - - # Test deleting non-existent key - store.mdelete(["key3"]) # No error should be raised - store.delete_collection() - - -def test_doc_store_yield_keys() -> None: - store = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - doc_1 = Document(page_content="value1") - doc_2 = Document(page_content="value2") - doc_3 = Document(page_content="value3") - store.mset([("key1", doc_1), ("key2", doc_2), ("key3", doc_3)]) - - keys = list(store.yield_keys()) - assert set(keys) == {"key1", "key2", "key3"} - - keys_with_prefix = list(store.yield_keys(prefix="key")) - assert set(keys_with_prefix) == {"key1", "key2", "key3"} - - keys_with_invalid_prefix = list(store.yield_keys(prefix="x")) - assert keys_with_invalid_prefix == [] - store.delete_collection() - - -def test_doc_store_collection() -> None: - """Test that collections are isolated within a db.""" - store_1 = SQLDocStore( - collection_name=COLLECTION_NAME, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - store_2 = SQLDocStore( - collection_name=COLLECTION_NAME_2, - connection_string=CONNECTION_STRING, - pre_delete_collection=True, - ) - doc_1 = Document(page_content="value1") - doc_2 = Document(page_content="value2") - doc_3 = Document(page_content="value3") - doc_4 = Document(page_content="value4") - store_1.mset([("key1", doc_1), ("key2", doc_2)]) - store_2.mset([("key3", doc_3), ("key4", doc_4)]) - - values = store_1.mget(["key1", "key2"]) - assert values == [doc_1, doc_2] - values = store_1.mget(["key3", "key4"]) - assert values == [None, None] - - values = store_2.mget(["key1", "key2"]) - assert values == [None, None] - values = store_2.mget(["key3", "key4"]) - assert values == [doc_3, doc_4] - - store_1.delete_collection() - store_2.delete_collection() diff --git a/libs/community/tests/unit_tests/storage/test_imports.py b/libs/community/tests/unit_tests/storage/test_imports.py index 0cc4914a7406dae..27bcec56d4b267f 100644 --- a/libs/community/tests/unit_tests/storage/test_imports.py +++ b/libs/community/tests/unit_tests/storage/test_imports.py @@ -6,8 +6,6 @@ "RedisStore", "UpstashRedisByteStore", "UpstashRedisStore", - "SQLDocStore", - "SQLStrStore", ] diff --git a/libs/community/tests/unit_tests/storage/test_sql.py b/libs/community/tests/unit_tests/storage/test_sql.py deleted file mode 100644 index 1d988414637a870..000000000000000 --- a/libs/community/tests/unit_tests/storage/test_sql.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Light weight unit test that attempts to import SQLDocStore/SQLStrStore. -""" - - -def test_import_storage() -> None: - """Attempt to import storage modules.""" - from langchain_community.storage.sql import SQLDocStore, SQLStrStore # noqa From a265878d711b63836d25e2442362bacbd3cdabe1 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 1 Feb 2024 16:43:10 -0800 Subject: [PATCH 035/157] langchain_openai[patch]: Invoke callback prior to yielding token (#16909) All models should be calling the callback for new token prior to yielding the token. Not doing this can cause callbacks for downstream steps to be called prior to the callback for the new token; causing issues in astream_events APIs and other things that depend in callback ordering being correct. We need to make this change for all chat models. --- libs/partners/openai/langchain_openai/chat_models/base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index d0bed6f92106be7..b5b7199edba3766 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -424,9 +424,9 @@ def _stream( chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info or None ) - yield chunk if run_manager: run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs) + yield chunk def _generate( self, @@ -516,11 +516,11 @@ async def _astream( chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info or None ) - yield chunk if run_manager: await run_manager.on_llm_new_token( token=chunk.text, chunk=chunk, logprobs=logprobs ) + yield chunk async def _agenerate( self, From 6fc28352552e23575b117294922e6d2bae234263 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 1 Feb 2024 17:29:38 -0800 Subject: [PATCH 036/157] docs: fix broken links (#16855) --- docs/docs/get_started/introduction.mdx | 3 --- .../model_io/prompts/quick_start.ipynb | 2 +- docs/docs/use_cases/csv.ipynb | 2 +- .../question_answering/quickstart.ipynb | 3 ++- docs/docs/use_cases/summarization.ipynb | 24 ++++--------------- templates/chat-bot-feedback/README.md | 2 +- templates/sql-pgvector/README.md | 2 +- 7 files changed, 11 insertions(+), 27 deletions(-) diff --git a/docs/docs/get_started/introduction.mdx b/docs/docs/get_started/introduction.mdx index 4bf8b1fa9b59057..35411f51ad9a6f9 100644 --- a/docs/docs/get_started/introduction.mdx +++ b/docs/docs/get_started/introduction.mdx @@ -93,6 +93,3 @@ Head to the reference section for full documentation of all classes and methods ### [Developer's guide](/docs/contributing) Check out the developer's guide for guidelines on contributing and help getting your dev environment set up. -### [Community](/docs/community) -Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s. - diff --git a/docs/docs/modules/model_io/prompts/quick_start.ipynb b/docs/docs/modules/model_io/prompts/quick_start.ipynb index cb3887db32c27ec..7b393c3687fcd48 100644 --- a/docs/docs/modules/model_io/prompts/quick_start.ipynb +++ b/docs/docs/modules/model_io/prompts/quick_start.ipynb @@ -101,7 +101,7 @@ "metadata": {}, "source": [ "You can create custom prompt templates that format the prompt in any way you want.\n", - "For more information, see [Custom Prompt Templates](./custom_prompt_template).\n", + "For more information, see [Prompt Template Composition](./composition).\n", "\n", "## `ChatPromptTemplate`\n", "\n", diff --git a/docs/docs/use_cases/csv.ipynb b/docs/docs/use_cases/csv.ipynb index bd60ae799f55db7..7ef34f8412f49ef 100644 --- a/docs/docs/use_cases/csv.ipynb +++ b/docs/docs/use_cases/csv.ipynb @@ -752,7 +752,7 @@ "\n", "* [SQL use case](/docs/use_cases/sql/): Many of the challenges of working with SQL db's and CSV's are generic to any structured data type, so it's useful to read the SQL techniques even if you're using Pandas for CSV data analysis.\n", "* [Tool use](/docs/use_cases/tool_use/): Guides on general best practices when working with chains and agents that invoke tools\n", - "* [Agents](/docs/use_cases/agents/): Understand the fundamentals of building LLM agents.\n", + "* [Agents](/docs/modules/agents/): Understand the fundamentals of building LLM agents.\n", "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/toolkits/spark)." ] } diff --git a/docs/docs/use_cases/question_answering/quickstart.ipynb b/docs/docs/use_cases/question_answering/quickstart.ipynb index 178dd71a4f58362..1f464a9615d1083 100644 --- a/docs/docs/use_cases/question_answering/quickstart.ipynb +++ b/docs/docs/use_cases/question_answering/quickstart.ipynb @@ -586,11 +586,12 @@ "Vector stores are commonly used for retrieval, but there are other ways to do retrieval, too.\n", "\n", "`Retriever`: An object that returns `Document`s given a text query\n", + "\n", "- [Docs](/docs/modules/data_connection/retrievers/): Further documentation on the interface and built-in retrieval techniques. Some of which include:\n", " - `MultiQueryRetriever` [generates variants of the input question](/docs/modules/data_connection/retrievers/MultiQueryRetriever) to improve retrieval hit rate.\n", " - `MultiVectorRetriever` (diagram below) instead generates [variants of the embeddings](/docs/modules/data_connection/retrievers/multi_vector), also in order to improve retrieval hit rate.\n", " - `Max marginal relevance` selects for [relevance and diversity](https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf) among the retrieved documents to avoid passing in duplicate context.\n", - " - Documents can be filtered during vector store retrieval using [`metadata` filters](/docs/use_cases/question_answering/document-context-aware-QA).\n", + " - Documents can be filtered during vector store retrieval using metadata filters, such as with a [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query).\n", "- [Integrations](/docs/integrations/retrievers/): Integrations with retrieval services.\n", "- [Interface](https://api.python.langchain.com/en/latest/retrievers/langchain_core.retrievers.BaseRetriever.html): API reference for the base interface." ] diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index a23634abab594af..21dc63dde8ef44c 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -45,9 +45,9 @@ "\n", "A central question for building a summarizer is how to pass your documents into the LLM's context window. Two common approaches for this are:\n", "\n", - "1. `Stuff`: Simply \"stuff\" all your documents into a single prompt. This is the simplest approach (see [here](/docs/modules/chains/document/stuff) for more on the `StuffDocumentsChains`, which is used for this method).\n", + "1. `Stuff`: Simply \"stuff\" all your documents into a single prompt. This is the simplest approach (see [here](/docs/modules/chains#lcel-chains) for more on the `create_stuff_documents_chain` constructor, which is used for this method).\n", "\n", - "2. `Map-reduce`: Summarize each document on it's own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](/docs/modules/chains/document/map_reduce) for more on the `MapReduceDocumentsChain`, which is used for this method)." + "2. `Map-reduce`: Summarize each document on it's own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](/docs/modules/chains#legacy-chains) for more on the `MapReduceDocumentsChain`, which is used for this method)." ] }, { @@ -523,7 +523,7 @@ "source": [ "## Option 3. Refine\n", " \n", - "[Refine](/docs/modules/chains/document/refine) is similar to map-reduce:\n", + "[RefineDocumentsChain](/docs/modules/chains#legacy-chains) is similar to map-reduce:\n", "\n", "> The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.\n", "\n", @@ -647,24 +647,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "0ddd522e-30dc-4f6a-b993-c4f97e656c4f", "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "`run` not supported when there is not exactly one output key. Got ['output_text', 'intermediate_steps'].", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchains\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AnalyzeDocumentChain\n\u001b[1;32m 3\u001b[0m summarize_document_chain \u001b[38;5;241m=\u001b[39m AnalyzeDocumentChain(combine_docs_chain\u001b[38;5;241m=\u001b[39mchain, text_splitter\u001b[38;5;241m=\u001b[39mtext_splitter)\n\u001b[0;32m----> 4\u001b[0m \u001b[43msummarize_document_chain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/langchain/libs/langchain/langchain/chains/base.py:496\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convenience method for executing chain.\u001b[39;00m\n\u001b[1;32m 460\u001b[0m \n\u001b[1;32m 461\u001b[0m \u001b[38;5;124;03mThe main difference between this method and `Chain.__call__` is that this\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[38;5;124;03m # -> \"The temperature in Boise is...\"\u001b[39;00m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;66;03m# Run at start to make sure this is possible/defined\u001b[39;00m\n\u001b[0;32m--> 496\u001b[0m _output_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_output_key\u001b[49m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m args \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwargs:\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n", - "File \u001b[0;32m~/langchain/libs/langchain/langchain/chains/base.py:445\u001b[0m, in \u001b[0;36mChain._run_output_key\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_output_key\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 445\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 446\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` not supported when there is not exactly \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone output key. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 448\u001b[0m )\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[0;31mValueError\u001b[0m: `run` not supported when there is not exactly one output key. Got ['output_text', 'intermediate_steps']." - ] - } - ], + "outputs": [], "source": [ "from langchain.chains import AnalyzeDocumentChain\n", "\n", diff --git a/templates/chat-bot-feedback/README.md b/templates/chat-bot-feedback/README.md index da9936b915b943c..f12aef309b17008 100644 --- a/templates/chat-bot-feedback/README.md +++ b/templates/chat-bot-feedback/README.md @@ -1,6 +1,6 @@ # Chat Bot Feedback Template -This template shows how to evaluate your chat bot without explicit user feedback. It defines a simple chat bot in [chain.py](./chat_bot_feedback/chain.py) and custom evaluator that scores bot response effectiveness based on the subsequent user response. You can apply this run evaluator to your own chat bot by calling `with_config` on the chat bot before serving. You can also directly deploy your chat app using this template. +This template shows how to evaluate your chat bot without explicit user feedback. It defines a simple chat bot in [chain.py](https://github.com/langchain-ai/langchain/blob/master/templates/chat-bot-feedback/chat_bot_feedback/chain.py) and custom evaluator that scores bot response effectiveness based on the subsequent user response. You can apply this run evaluator to your own chat bot by calling `with_config` on the chat bot before serving. You can also directly deploy your chat app using this template. [Chat bots](https://python.langchain.com/docs/use_cases/chatbots) are one of the most common interfaces for deploying LLMs. The quality of chat bots varies, making continuous development important. But users are wont to leave explicit feedback through mechanisms like thumbs-up or thumbs-down buttons. Furthermore, traditional analytics such as "session length" or "conversation length" often lack clarity. However, multi-turn conversations with a chat bot can provide a wealth of information, which we can transform into metrics for fine-tuning, evaluation, and product analytics. diff --git a/templates/sql-pgvector/README.md b/templates/sql-pgvector/README.md index 128769bdde1693f..ac5eef762d42a78 100644 --- a/templates/sql-pgvector/README.md +++ b/templates/sql-pgvector/README.md @@ -2,7 +2,7 @@ This template enables user to use `pgvector` for combining postgreSQL with semantic search / RAG. -It uses [PGVector](https://github.com/pgvector/pgvector) extension as shown in the [RAG empowered SQL cookbook](cookbook/retrieval_in_sql.ipynb) +It uses [PGVector](https://github.com/pgvector/pgvector) extension as shown in the [RAG empowered SQL cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb) ## Environment Setup From 131c043864c47ef0ae5bbbeaa79062f90cc3dbd8 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:54:04 -0800 Subject: [PATCH 037/157] Fix loading of ImagePromptTemplate (#16868) We didn't override the namespace of the ImagePromptTemplate, so it is listed as being in langchain.schema This updates the mapping to let the loader deserialize. Alternatively, we could make a slight breaking change and update the namespace of the ImagePromptTemplate since we haven't broadly publicized/documented it yet.. --- libs/core/langchain_core/load/mapping.py | 12 ++ libs/core/langchain_core/prompts/image.py | 7 +- .../tests/unit_tests/prompts/test_image.py | 109 ++++++++++++++++++ .../unit_tests/load/test_serializable.py | 15 ++- 4 files changed, 141 insertions(+), 2 deletions(-) create mode 100644 libs/core/tests/unit_tests/prompts/test_image.py diff --git a/libs/core/langchain_core/load/mapping.py b/libs/core/langchain_core/load/mapping.py index 7ac3c7d3e3858a2..ff9834df27b175f 100644 --- a/libs/core/langchain_core/load/mapping.py +++ b/libs/core/langchain_core/load/mapping.py @@ -115,6 +115,12 @@ "chat", "SystemMessagePromptTemplate", ), + ("langchain", "prompts", "image", "ImagePromptTemplate"): ( + "langchain_core", + "prompts", + "image", + "ImagePromptTemplate", + ), ("langchain", "schema", "agent", "AgentActionMessageLog"): ( "langchain_core", "agents", @@ -510,6 +516,12 @@ "system", "SystemMessage", ), + ("langchain", "schema", "prompt_template", "ImagePromptTemplate"): ( + "langchain_core", + "prompts", + "image", + "ImagePromptTemplate", + ), } # Needed for backwards compatibility for a few versions where we serialized diff --git a/libs/core/langchain_core/prompts/image.py b/libs/core/langchain_core/prompts/image.py index 3a3b16117e438e9..d320690924abcd7 100644 --- a/libs/core/langchain_core/prompts/image.py +++ b/libs/core/langchain_core/prompts/image.py @@ -1,4 +1,4 @@ -from typing import Any +from typing import Any, List from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue from langchain_core.prompts.base import BasePromptTemplate @@ -30,6 +30,11 @@ def _prompt_type(self) -> str: """Return the prompt type key.""" return "image-prompt" + @classmethod + def get_lc_namespace(cls) -> List[str]: + """Get the namespace of the langchain object.""" + return ["langchain", "prompts", "image"] + def format_prompt(self, **kwargs: Any) -> PromptValue: """Create Chat Messages.""" return ImagePromptValue(image_url=self.format(**kwargs)) diff --git a/libs/core/tests/unit_tests/prompts/test_image.py b/libs/core/tests/unit_tests/prompts/test_image.py new file mode 100644 index 000000000000000..746c099b80372dd --- /dev/null +++ b/libs/core/tests/unit_tests/prompts/test_image.py @@ -0,0 +1,109 @@ +import json + +from langchain_core.load import dump, loads +from langchain_core.prompts import ChatPromptTemplate + + +def test_image_prompt_template_deserializable() -> None: + """Test that the image prompt template is serializable.""" + loads( + dump.dumps( + ChatPromptTemplate.from_messages( + [("system", [{"type": "image", "image_url": "{img}"}])] + ) + ) + ) + + +def test_image_prompt_template_deserializable_old() -> None: + """Test that the image prompt template is serializable.""" + loads( + json.dumps( + { + "lc": 1, + "type": "constructor", + "id": ["langchain", "prompts", "chat", "ChatPromptTemplate"], + "kwargs": { + "messages": [ + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "SystemMessagePromptTemplate", + ], + "kwargs": { + "prompt": [ + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate", + ], + "kwargs": { + "template": "Foo", + "input_variables": [], + "template_format": "f-string", + "partial_variables": {}, + }, + } + ] + }, + }, + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "HumanMessagePromptTemplate", + ], + "kwargs": { + "prompt": [ + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "image", + "ImagePromptTemplate", + ], + "kwargs": { + "template": { + "url": "data:image/png;base64,{img}" + }, + "input_variables": ["img"], + }, + }, + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate", + ], + "kwargs": { + "template": "{input}", + "input_variables": ["input"], + "template_format": "f-string", + "partial_variables": {}, + }, + }, + ] + }, + }, + ], + "input_variables": ["img", "input"], + }, + } + ) + ) diff --git a/libs/langchain/tests/unit_tests/load/test_serializable.py b/libs/langchain/tests/unit_tests/load/test_serializable.py index 198d8f1d465a05e..64d762035f0ffe4 100644 --- a/libs/langchain/tests/unit_tests/load/test_serializable.py +++ b/libs/langchain/tests/unit_tests/load/test_serializable.py @@ -40,8 +40,21 @@ def import_all_modules(package_name: str) -> dict: def test_serializable_mapping() -> None: + # This should have had a different namespace, as it was never + # exported from the langchain module, but we keep for whoever has + # already serialized it. + to_skip = { + ("langchain", "prompts", "image", "ImagePromptTemplate"): ( + "langchain_core", + "prompts", + "image", + "ImagePromptTemplate", + ), + } serializable_modules = import_all_modules("langchain") - missing = set(SERIALIZABLE_MAPPING).difference(serializable_modules) + missing = set(SERIALIZABLE_MAPPING).difference( + set(serializable_modules).union(to_skip) + ) assert missing == set() extra = set(serializable_modules).difference(SERIALIZABLE_MAPPING) assert extra == set() From e02efd513f475932bb022d5f415af44792353d53 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:55:37 -0800 Subject: [PATCH 038/157] core[patch]: Hide aliases when serializing (#16888) Currently, if you dump an object initialized with an alias, we'll still dump the secret values since they're retained in the kwargs --- libs/core/langchain_core/load/serializable.py | 8 ++ .../tests/unit_tests/load/test_dump.py | 84 +++++++++++++++++++ 2 files changed, 92 insertions(+) diff --git a/libs/core/langchain_core/load/serializable.py b/libs/core/langchain_core/load/serializable.py index 0b54dc7d8a59b57..6b415f78b10ef91 100644 --- a/libs/core/langchain_core/load/serializable.py +++ b/libs/core/langchain_core/load/serializable.py @@ -143,6 +143,14 @@ def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: this = cast(Serializable, self if cls is None else super(cls, self)) secrets.update(this.lc_secrets) + # Now also add the aliases for the secrets + # This ensures known secret aliases are hidden. + # Note: this does NOT hide any other extra kwargs + # that are not present in the fields. + for key in list(secrets): + value = secrets[key] + if key in this.__fields__: + secrets[this.__fields__[key].alias] = value lc_kwargs.update(this.lc_attributes) # include all secrets, even if not specified in kwargs diff --git a/libs/langchain/tests/unit_tests/load/test_dump.py b/libs/langchain/tests/unit_tests/load/test_dump.py index 3e59fbda811861f..5989f609d26de84 100644 --- a/libs/langchain/tests/unit_tests/load/test_dump.py +++ b/libs/langchain/tests/unit_tests/load/test_dump.py @@ -1,5 +1,6 @@ """Test for Serializable base class""" +import json import os from typing import Any, Dict, List from unittest.mock import patch @@ -11,6 +12,7 @@ from langchain_core.load.serializable import Serializable from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate from langchain_core.prompts.prompt import PromptTemplate +from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.tracers.langchain import LangChainTracer from langchain.chains.llm import LLMChain @@ -167,3 +169,85 @@ def test_person_with_invalid_kwargs() -> None: person = Person(secret="hello") with pytest.raises(TypeError): dumps(person, invalid_kwarg="hello") + + +class TestClass(Serializable): + my_favorite_secret: str = Field(alias="my_favorite_secret_alias") + my_other_secret: str = Field() + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + + @root_validator(pre=True) + def get_from_env(cls, values: Dict) -> Dict: + """Get the values from the environment.""" + if "my_favorite_secret" not in values: + values["my_favorite_secret"] = os.getenv("MY_FAVORITE_SECRET") + if "my_other_secret" not in values: + values["my_other_secret"] = os.getenv("MY_OTHER_SECRET") + return values + + @classmethod + def is_lc_serializable(cls) -> bool: + return True + + @classmethod + def get_lc_namespace(cls) -> List[str]: + return ["my", "special", "namespace"] + + @property + def lc_secrets(self) -> Dict[str, str]: + return { + "my_favorite_secret": "MY_FAVORITE_SECRET", + "my_other_secret": "MY_OTHER_SECRET", + } + + +def test_aliases_hidden() -> None: + test_class = TestClass(my_favorite_secret="hello", my_other_secret="world") + dumped = json.loads(dumps(test_class, pretty=True)) + expected_dump = { + "lc": 1, + "type": "constructor", + "id": ["my", "special", "namespace", "TestClass"], + "kwargs": { + "my_favorite_secret": { + "lc": 1, + "type": "secret", + "id": ["MY_FAVORITE_SECRET"], + }, + "my_other_secret": {"lc": 1, "type": "secret", "id": ["MY_OTHER_SECRET"]}, + }, + } + assert dumped == expected_dump + # Check while patching the os environment + with patch.dict( + os.environ, {"MY_FAVORITE_SECRET": "hello", "MY_OTHER_SECRET": "world"} + ): + test_class = TestClass() + dumped = json.loads(dumps(test_class, pretty=True)) + + # Check by alias + test_class = TestClass(my_favorite_secret_alias="hello", my_other_secret="world") + dumped = json.loads(dumps(test_class, pretty=True)) + expected_dump = { + "lc": 1, + "type": "constructor", + "id": ["my", "special", "namespace", "TestClass"], + "kwargs": { + "my_favorite_secret": { + "lc": 1, + "type": "secret", + "id": ["MY_FAVORITE_SECRET"], + }, + "my_favorite_secret_alias": { + "lc": 1, + "type": "secret", + "id": ["MY_FAVORITE_SECRET"], + }, + "my_other_secret": {"lc": 1, "type": "secret", "id": ["MY_OTHER_SECRET"]}, + }, + } + assert dumped == expected_dump From bdacfafa05da613173e4d69b9328a4389f94936d Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Thu, 1 Feb 2024 18:46:05 -0800 Subject: [PATCH 039/157] core[patch]: Remove deep copying of run prior to submitting it to LangChain Tracing (#16904) --- libs/core/langchain_core/tracers/langchain.py | 36 +++++++------------ 1 file changed, 13 insertions(+), 23 deletions(-) diff --git a/libs/core/langchain_core/tracers/langchain.py b/libs/core/langchain_core/tracers/langchain.py index 947e10f7e6be133..ef4defb28b81f01 100644 --- a/libs/core/langchain_core/tracers/langchain.py +++ b/libs/core/langchain_core/tracers/langchain.py @@ -65,16 +65,6 @@ def _get_executor() -> ThreadPoolExecutor: return _EXECUTOR -def _copy(run: Run) -> Run: - """Copy a run.""" - try: - return run.copy(deep=True) - except TypeError: - # Fallback in case the object contains a lock or other - # non-pickleable object - return run.copy() - - class LangChainTracer(BaseTracer): """An implementation of the SharedTracer that POSTS to the langchain endpoint.""" @@ -202,63 +192,63 @@ def _on_llm_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, _copy(run)) + self._submit(self._persist_run_single, run) def _on_chat_model_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, _copy(run)) + self._submit(self._persist_run_single, run) def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_chain_start(self, run: Run) -> None: """Process the Chain Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, _copy(run)) + self._submit(self._persist_run_single, run) def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_tool_start(self, run: Run) -> None: """Process the Tool Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, _copy(run)) + self._submit(self._persist_run_single, run) def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_retriever_start(self, run: Run) -> None: """Process the Retriever Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, _copy(run)) + self._submit(self._persist_run_single, run) def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error.""" - self._submit(self._update_run_single, _copy(run)) + self._submit(self._update_run_single, run) def wait_for_futures(self) -> None: """Wait for the given futures to complete.""" From cc173344737d4ac7ff96a04bfe2e93022b32d8b7 Mon Sep 17 00:00:00 2001 From: hmasdev <73353463+hmasdev@users.noreply.github.com> Date: Fri, 2 Feb 2024 13:09:19 +0900 Subject: [PATCH 040/157] core[minor]: add validation error handler to `BaseTool` (#14007) - **Description:** add a ValidationError handler as a field of [`BaseTool`](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/tools.py#L101) and add unit tests for the code change. - **Issue:** #12721 #13662 - **Dependencies:** None - **Tag maintainer:** - **Twitter handle:** @hmdev3 - **NOTE:** - I'm wondering if the update of document is required. --------- Co-authored-by: Eugene Yurtsev --- libs/core/langchain_core/tools.py | 36 ++++++ libs/core/tests/unit_tests/test_tools.py | 138 ++++++++++++++++++++++- 2 files changed, 170 insertions(+), 4 deletions(-) diff --git a/libs/core/langchain_core/tools.py b/libs/core/langchain_core/tools.py index 10cc95cdebfc735..b1d9e9ead3c341e 100644 --- a/libs/core/langchain_core/tools.py +++ b/libs/core/langchain_core/tools.py @@ -20,6 +20,7 @@ BaseModel, Extra, Field, + ValidationError, create_model, root_validator, validate_arguments, @@ -169,6 +170,11 @@ class ChildTool(BaseTool): ] = False """Handle the content of the ToolException thrown.""" + handle_validation_error: Optional[ + Union[bool, str, Callable[[ValidationError], str]] + ] = False + """Handle the content of the ValidationError thrown.""" + class Config(Serializable.Config): """Configuration for this pydantic object.""" @@ -346,6 +352,21 @@ def run( if new_arg_supported else self._run(*tool_args, **tool_kwargs) ) + except ValidationError as e: + if not self.handle_validation_error: + raise e + elif isinstance(self.handle_validation_error, bool): + observation = "Tool input validation error" + elif isinstance(self.handle_validation_error, str): + observation = self.handle_validation_error + elif callable(self.handle_validation_error): + observation = self.handle_validation_error(e) + else: + raise ValueError( + f"Got unexpected type of `handle_validation_error`. Expected bool, " + f"str or callable. Received: {self.handle_validation_error}" + ) + return observation except ToolException as e: if not self.handle_tool_error: run_manager.on_tool_error(e) @@ -422,6 +443,21 @@ async def arun( if new_arg_supported else await self._arun(*tool_args, **tool_kwargs) ) + except ValidationError as e: + if not self.handle_validation_error: + raise e + elif isinstance(self.handle_validation_error, bool): + observation = "Tool input validation error" + elif isinstance(self.handle_validation_error, str): + observation = self.handle_validation_error + elif callable(self.handle_validation_error): + observation = self.handle_validation_error(e) + else: + raise ValueError( + f"Got unexpected type of `handle_validation_error`. Expected bool, " + f"str or callable. Received: {self.handle_validation_error}" + ) + return observation except ToolException as e: if not self.handle_tool_error: await run_manager.on_tool_error(e) diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py index dda3be342f3a56d..33b5d0ff9b56240 100644 --- a/libs/core/tests/unit_tests/test_tools.py +++ b/libs/core/tests/unit_tests/test_tools.py @@ -3,7 +3,7 @@ from datetime import datetime from enum import Enum from functools import partial -from typing import Any, List, Optional, Type, Union +from typing import Any, Callable, Dict, List, Optional, Type, Union import pytest @@ -11,7 +11,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel +from langchain_core.pydantic_v1 import BaseModel, ValidationError from langchain_core.tools import ( BaseTool, SchemaAnnotationError, @@ -620,7 +620,10 @@ def test_exception_handling_str() -> None: def test_exception_handling_callable() -> None: expected = "foo bar" - handling = lambda _: expected # noqa: E731 + + def handling(e: ToolException) -> str: + return expected # noqa: E731 + _tool = _FakeExceptionTool(handle_tool_error=handling) actual = _tool.run({}) assert expected == actual @@ -648,7 +651,10 @@ async def test_async_exception_handling_str() -> None: async def test_async_exception_handling_callable() -> None: expected = "foo bar" - handling = lambda _: expected # noqa: E731 + + def handling(e: ToolException) -> str: + return expected # noqa: E731 + _tool = _FakeExceptionTool(handle_tool_error=handling) actual = await _tool.arun({}) assert expected == actual @@ -691,3 +697,127 @@ def foo(bar: int, baz: str) -> str: prefix = "foo(bar: int, baz: str) -> str - " assert foo.__doc__ is not None assert structured_tool.description == prefix + foo.__doc__.strip() + + +def test_validation_error_handling_bool() -> None: + """Test that validation errors are handled correctly.""" + expected = "Tool input validation error" + _tool = _MockStructuredTool(handle_validation_error=True) + actual = _tool.run({}) + assert expected == actual + + +def test_validation_error_handling_str() -> None: + """Test that validation errors are handled correctly.""" + expected = "foo bar" + _tool = _MockStructuredTool(handle_validation_error=expected) + actual = _tool.run({}) + assert expected == actual + + +def test_validation_error_handling_callable() -> None: + """Test that validation errors are handled correctly.""" + expected = "foo bar" + + def handling(e: ValidationError) -> str: + return expected # noqa: E731 + + _tool = _MockStructuredTool(handle_validation_error=handling) + actual = _tool.run({}) + assert expected == actual + + +@pytest.mark.parametrize( + "handler", + [ + True, + "foo bar", + lambda _: "foo bar", + ], +) +def test_validation_error_handling_non_validation_error( + handler: Union[bool, str, Callable[[ValidationError], str]] +) -> None: + """Test that validation errors are handled correctly.""" + + class _RaiseNonValidationErrorTool(BaseTool): + name = "raise_non_validation_error_tool" + description = "A tool that raises a non-validation error" + + def _parse_input( + self, + tool_input: Union[str, Dict], + ) -> Union[str, Dict[str, Any]]: + raise NotImplementedError() + + def _run(self) -> str: + return "dummy" + + async def _arun(self) -> str: + return "dummy" + + _tool = _RaiseNonValidationErrorTool(handle_validation_error=handler) + with pytest.raises(NotImplementedError): + _tool.run({}) + + +async def test_async_validation_error_handling_bool() -> None: + """Test that validation errors are handled correctly.""" + expected = "Tool input validation error" + _tool = _MockStructuredTool(handle_validation_error=True) + actual = await _tool.arun({}) + assert expected == actual + + +async def test_async_validation_error_handling_str() -> None: + """Test that validation errors are handled correctly.""" + expected = "foo bar" + _tool = _MockStructuredTool(handle_validation_error=expected) + actual = await _tool.arun({}) + assert expected == actual + + +async def test_async_validation_error_handling_callable() -> None: + """Test that validation errors are handled correctly.""" + expected = "foo bar" + + def handling(e: ValidationError) -> str: + return expected # noqa: E731 + + _tool = _MockStructuredTool(handle_validation_error=handling) + actual = await _tool.arun({}) + assert expected == actual + + +@pytest.mark.parametrize( + "handler", + [ + True, + "foo bar", + lambda _: "foo bar", + ], +) +async def test_async_validation_error_handling_non_validation_error( + handler: Union[bool, str, Callable[[ValidationError], str]] +) -> None: + """Test that validation errors are handled correctly.""" + + class _RaiseNonValidationErrorTool(BaseTool): + name = "raise_non_validation_error_tool" + description = "A tool that raises a non-validation error" + + def _parse_input( + self, + tool_input: Union[str, Dict], + ) -> Union[str, Dict[str, Any]]: + raise NotImplementedError() + + def _run(self) -> str: + return "dummy" + + async def _arun(self) -> str: + return "dummy" + + _tool = _RaiseNonValidationErrorTool(handle_validation_error=handler) + with pytest.raises(NotImplementedError): + await _tool.arun({}) From 3b0fa9079d02e43c32ad5244b65c95d31e7734f1 Mon Sep 17 00:00:00 2001 From: Radhakrishnan Date: Fri, 2 Feb 2024 22:58:06 +0530 Subject: [PATCH 041/157] docs: Updated integration doc for aleph alpha (#16844) Description: Updated doc for llm/aleph_alpha with new functions: invoke. Changed structure of the document to match the required one. Issue: https://github.com/langchain-ai/langchain/issues/15664 Dependencies: None Twitter handle: None --------- Co-authored-by: Radhakrishnan Iyer --- docs/docs/integrations/llms/aleph_alpha.ipynb | 35 +++++++++++-------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/docs/docs/integrations/llms/aleph_alpha.ipynb b/docs/docs/integrations/llms/aleph_alpha.ipynb index 920cadaafa85a7e..7657324d439e561 100644 --- a/docs/docs/integrations/llms/aleph_alpha.ipynb +++ b/docs/docs/integrations/llms/aleph_alpha.ipynb @@ -27,17 +27,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "0cb0f937-b610-42a2-b765-336eed037031", "metadata": { "tags": [] }, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ - " ········\n" + "········\n" ] } ], @@ -51,21 +51,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "6fb585dd", "metadata": { "tags": [] }, "outputs": [], "source": [ - "from langchain.chains import LLMChain\n", "from langchain.prompts import PromptTemplate\n", "from langchain_community.llms import AlephAlpha" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "f81a230d", "metadata": { "tags": [] @@ -81,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "f0d26e48", "metadata": { "tags": [] @@ -98,19 +97,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "6811d621", "metadata": { "tags": [] }, "outputs": [], "source": [ - "llm_chain = LLMChain(prompt=prompt, llm=llm)" + "llm_chain = prompt | llm" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "3058e63f", "metadata": { "tags": [] @@ -119,10 +118,10 @@ { "data": { "text/plain": [ - "' Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.\\n'" + "' Artificial Intelligence is the simulation of human intelligence processes by machines.\\n\\n'" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -130,8 +129,16 @@ "source": [ "question = \"What is AI?\"\n", "\n", - "llm_chain.run(question)" + "llm_chain.invoke({\"question\": question})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3544eff", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -150,7 +157,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.12" }, "vscode": { "interpreter": { From c29e9b641216acf30b5050d399eaa05ae43e6f22 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Fri, 2 Feb 2024 10:23:37 -0800 Subject: [PATCH 042/157] core[patch]: fix chat prompt partial messages placeholder var (#16918) --- libs/core/langchain_core/prompts/base.py | 7 ++---- libs/core/langchain_core/prompts/chat.py | 22 +++++++++---------- .../tests/unit_tests/prompts/test_chat.py | 20 ++++++++++++++++- .../__snapshots__/test_runnable.ambr | 6 +++-- .../load/__snapshots__/test_dump.ambr | 3 ++- 5 files changed, 38 insertions(+), 20 deletions(-) diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py index 07ac72225547c21..9a65e5e32db5a44 100644 --- a/libs/core/langchain_core/prompts/base.py +++ b/libs/core/langchain_core/prompts/base.py @@ -47,9 +47,7 @@ class BasePromptTemplate( If not provided, all variables are assumed to be strings.""" output_parser: Optional[BaseOutputParser] = None """How to parse the output of calling an LLM on this formatted prompt.""" - partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field( - default_factory=dict - ) + partial_variables: Mapping[str, Any] = Field(default_factory=dict) @classmethod def get_lc_namespace(cls) -> List[str]: @@ -143,8 +141,7 @@ def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]: # Get partial params: partial_kwargs = { - k: v if isinstance(v, str) else v() - for k, v in self.partial_variables.items() + k: v if not callable(v) else v() for k, v in self.partial_variables.items() } return {**partial_kwargs, **kwargs} diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py index cfb3192cfadf147..4fbd0666b16ebd1 100644 --- a/libs/core/langchain_core/prompts/chat.py +++ b/libs/core/langchain_core/prompts/chat.py @@ -5,7 +5,6 @@ from pathlib import Path from typing import ( Any, - Callable, Dict, List, Optional, @@ -130,13 +129,7 @@ def format_messages(self, **kwargs: Any) -> List[BaseMessage]: f"variable {self.variable_name} should be a list of base messages, " f"got {value}" ) - for v in convert_to_messages(value): - if not isinstance(v, BaseMessage): - raise ValueError( - f"variable {self.variable_name} should be a list of base messages," - f" got {value}" - ) - return value + return convert_to_messages(value) @property def input_variables(self) -> List[str]: @@ -755,13 +748,20 @@ def from_messages( # Automatically infer input variables from messages input_vars: Set[str] = set() + partial_vars: Dict[str, Any] = {} for _message in _messages: - if isinstance( + if isinstance(_message, MessagesPlaceholder) and _message.optional: + partial_vars[_message.variable_name] = [] + elif isinstance( _message, (BaseChatPromptTemplate, BaseMessagePromptTemplate) ): input_vars.update(_message.input_variables) - return cls(input_variables=sorted(input_vars), messages=_messages) + return cls( + input_variables=sorted(input_vars), + messages=_messages, + partial_variables=partial_vars, + ) def format(self, **kwargs: Any) -> str: """Format the chat template into a string. @@ -799,7 +799,7 @@ def format_messages(self, **kwargs: Any) -> List[BaseMessage]: raise ValueError(f"Unexpected input: {message_template}") return result - def partial(self, **kwargs: Union[str, Callable[[], str]]) -> ChatPromptTemplate: + def partial(self, **kwargs: Any) -> ChatPromptTemplate: """Get a new ChatPromptTemplate with some input variables already filled in. Args: diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py index 029244afe8c2571..0f14605f4095502 100644 --- a/libs/core/tests/unit_tests/prompts/test_chat.py +++ b/libs/core/tests/unit_tests/prompts/test_chat.py @@ -503,9 +503,27 @@ def test_messages_placeholder() -> None: prompt.format_messages() prompt = MessagesPlaceholder("history", optional=True) assert prompt.format_messages() == [] - prompt.format_messages( + assert prompt.format_messages( history=[("system", "You are an AI assistant."), "Hello!"] ) == [ SystemMessage(content="You are an AI assistant."), HumanMessage(content="Hello!"), ] + + +def test_chat_prompt_message_placeholder_partial() -> None: + prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder("history")]) + prompt = prompt.partial(history=[("system", "foo")]) + assert prompt.format_messages() == [SystemMessage(content="foo")] + assert prompt.format_messages(history=[("system", "bar")]) == [ + SystemMessage(content="bar") + ] + + prompt = ChatPromptTemplate.from_messages( + [ + MessagesPlaceholder("history", optional=True), + ] + ) + assert prompt.format_messages() == [] + prompt = prompt.partial(history=[("system", "foo")]) + assert prompt.format_messages() == [SystemMessage(content="foo")] diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr index 051520c0457f664..b149765e88765cd 100644 --- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr +++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr @@ -1544,7 +1544,8 @@ } } } - ] + ], + "partial_variables": {} } }, "middle": [], @@ -1617,7 +1618,8 @@ } } } - ] + ], + "partial_variables": {} } }, "middle": [], diff --git a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr index 0540760d4ce9d5f..826746ac248da36 100644 --- a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr +++ b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr @@ -191,7 +191,8 @@ } } } - ] + ], + "partial_variables": {} } } } From d80c612c923a5aff616e9862fad1568204523933 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Fri, 2 Feb 2024 10:24:02 -0800 Subject: [PATCH 043/157] core[patch]: Message content as positional arg (#16921) --- libs/core/langchain_core/messages/base.py | 6 ++++++ libs/core/langchain_core/prompt_values.py | 4 ++-- libs/core/langchain_core/prompts/chat.py | 2 +- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/libs/core/langchain_core/messages/base.py b/libs/core/langchain_core/messages/base.py index b2fd76b65924f12..bf1c74f490ddbab 100644 --- a/libs/core/langchain_core/messages/base.py +++ b/libs/core/langchain_core/messages/base.py @@ -28,6 +28,12 @@ class BaseMessage(Serializable): class Config: extra = Extra.allow + def __init__( + self, content: Union[str, List[Union[str, Dict]]], **kwargs: Any + ) -> None: + """Pass in content as positional arg.""" + return super().__init__(content=content, **kwargs) + @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" diff --git a/libs/core/langchain_core/prompt_values.py b/libs/core/langchain_core/prompt_values.py index 4c599f9f6a03756..18f62d9f2a55daa 100644 --- a/libs/core/langchain_core/prompt_values.py +++ b/libs/core/langchain_core/prompt_values.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import List, Literal, Sequence +from typing import List, Literal, Sequence, cast from typing_extensions import TypedDict @@ -105,7 +105,7 @@ def to_string(self) -> str: def to_messages(self) -> List[BaseMessage]: """Return prompt as messages.""" - return [HumanMessage(content=[self.image_url])] + return [HumanMessage(content=[cast(dict, self.image_url)])] class ChatPromptValueConcrete(ChatPromptValue): diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py index 4fbd0666b16ebd1..105daba2dfb4762 100644 --- a/libs/core/langchain_core/prompts/chat.py +++ b/libs/core/langchain_core/prompts/chat.py @@ -446,7 +446,7 @@ def format(self, **kwargs: Any) -> BaseMessage: content=text, additional_kwargs=self.additional_kwargs ) else: - content = [] + content: List = [] for prompt in self.prompt: inputs = {var: kwargs[var] for var in prompt.input_variables} if isinstance(prompt, StringPromptTemplate): From 2a510c71a0ccaecef11801b93dc246e61cc9e517 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Fri, 2 Feb 2024 10:24:16 -0800 Subject: [PATCH 044/157] core[patch]: doc init positional args (#16854) --- libs/core/langchain_core/documents/base.py | 6 +++++- .../retrievers/document_compressors/chain_extract.py | 6 ++++-- libs/langchain/langchain/retrievers/ensemble.py | 4 ++-- .../tests/unit_tests/indexes/test_hashed_document.py | 4 ++-- 4 files changed, 13 insertions(+), 7 deletions(-) diff --git a/libs/core/langchain_core/documents/base.py b/libs/core/langchain_core/documents/base.py index f33d7c82b0f884e..c72bfa724fd637c 100644 --- a/libs/core/langchain_core/documents/base.py +++ b/libs/core/langchain_core/documents/base.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import List, Literal +from typing import Any, List, Literal from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Field @@ -17,6 +17,10 @@ class Document(Serializable): """ type: Literal["Document"] = "Document" + def __init__(self, page_content: str, **kwargs: Any) -> None: + """Pass page_content in as positional or named arg.""" + super().__init__(page_content=page_content, **kwargs) + @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" diff --git a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py index 0a04eb5322db252..af8319f1ffc9efb 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py +++ b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py @@ -2,7 +2,7 @@ from __future__ import annotations import asyncio -from typing import Any, Callable, Dict, Optional, Sequence +from typing import Any, Callable, Dict, Optional, Sequence, cast from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel @@ -67,7 +67,9 @@ def compress_documents( output = self.llm_chain.predict_and_parse(**_input, callbacks=callbacks) if len(output) == 0: continue - compressed_docs.append(Document(page_content=output, metadata=doc.metadata)) + compressed_docs.append( + Document(page_content=cast(str, output), metadata=doc.metadata) + ) return compressed_docs async def acompress_documents( diff --git a/libs/langchain/langchain/retrievers/ensemble.py b/libs/langchain/langchain/retrievers/ensemble.py index 050cb88fc4c4e3b..34838f23fed461b 100644 --- a/libs/langchain/langchain/retrievers/ensemble.py +++ b/libs/langchain/langchain/retrievers/ensemble.py @@ -3,7 +3,7 @@ multiple retrievers by using weighted Reciprocal Rank Fusion """ import asyncio -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, cast from langchain_core.callbacks import ( AsyncCallbackManagerForRetrieverRun, @@ -195,7 +195,7 @@ def rank_fusion( # Enforce that retrieved docs are Documents for each list in retriever_docs for i in range(len(retriever_docs)): retriever_docs[i] = [ - Document(page_content=doc) if not isinstance(doc, Document) else doc + Document(page_content=cast(str, doc)) if isinstance(doc, str) else doc for doc in retriever_docs[i] ] diff --git a/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py index 2cf60e3a1a05a66..ff0121967caf485 100644 --- a/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py +++ b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py @@ -13,10 +13,10 @@ def test_hashed_document_hashing() -> None: def test_hashing_with_missing_content() -> None: """Check that ValueError is raised if page_content is missing.""" - with pytest.raises(ValueError): + with pytest.raises(TypeError): _HashedDocument( metadata={"key": "value"}, - ) + ) # type: ignore def test_uid_auto_assigned_to_hash() -> None: From 71f9ea33b693b8dfe5ef8404db94ea2dc9655b43 Mon Sep 17 00:00:00 2001 From: Massimiliano Pronesti Date: Fri, 2 Feb 2024 19:24:49 +0100 Subject: [PATCH 045/157] docs: add quantization to vllm and update API (#16950) - **Description:** Update vLLM docs to include instructions on how to use quantized models, as well as to replace the deprecated methods. --- docs/docs/integrations/llms/vllm.ipynb | 40 ++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/docs/docs/integrations/llms/vllm.ipynb b/docs/docs/integrations/llms/vllm.ipynb index eb4208b97bfdbce..6abf9a1376bc46d 100644 --- a/docs/docs/integrations/llms/vllm.ipynb +++ b/docs/docs/integrations/llms/vllm.ipynb @@ -82,7 +82,7 @@ " temperature=0.8,\n", ")\n", "\n", - "print(llm(\"What is the capital of France ?\"))" + "print(llm.invoke(\"What is the capital of France ?\"))" ] }, { @@ -117,8 +117,7 @@ "1. The first Pokemon game was released in 1996.\n", "2. The president was Bill Clinton.\n", "3. Clinton was president from 1993 to 2001.\n", - "4. The answer is Clinton.\n", - "\n" + "4. The answer is Clinton.\n" ] }, { @@ -142,7 +141,7 @@ "\n", "question = \"Who was the US president in the year the first Pokemon game was released?\"\n", "\n", - "print(llm_chain.run(question))" + "print(llm_chain.invoke(question))" ] }, { @@ -172,7 +171,36 @@ " trust_remote_code=True, # mandatory for hf models\n", ")\n", "\n", - "llm(\"What is the future of AI?\")" + "llm.invoke(\"What is the future of AI?\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6ca8fd911d25faa", + "metadata": { + "collapsed": false + }, + "source": [ + "## Quantization\n", + "\n", + "vLLM supports `awq` quantization. To enable it, pass `quantization` to `vllm_kwargs`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cada3174c46a0ea", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "llm_q = VLLM(\n", + " model=\"TheBloke/Llama-2-7b-Chat-AWQ\",\n", + " trust_remote_code=True,\n", + " max_new_tokens=512,\n", + " vllm_kwargs={\"quantization\": \"awq\"},\n", + ")" ] }, { @@ -216,7 +244,7 @@ " model_name=\"tiiuae/falcon-7b\",\n", " model_kwargs={\"stop\": [\".\"]},\n", ")\n", - "print(llm(\"Rome is\"))" + "print(llm.invoke(\"Rome is\"))" ] } ], From 66adb952847fe1a91a2e7bc081591e5304b82053 Mon Sep 17 00:00:00 2001 From: Ashley Xu <139821907+ashleyxuu@users.noreply.github.com> Date: Fri, 2 Feb 2024 10:26:44 -0800 Subject: [PATCH 046/157] docs: BigQuery Vector Search went public review and updated docs (#16896) Update the docs for BigQuery Vector Search --- docs/docs/integrations/platforms/google.mdx | 6 +----- .../vectorstores/bigquery_vector_search.ipynb | 12 +----------- 2 files changed, 2 insertions(+), 16 deletions(-) diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx index 5d09b18f56b0751..b1ab193a80a0b4d 100644 --- a/docs/docs/integrations/platforms/google.mdx +++ b/docs/docs/integrations/platforms/google.mdx @@ -207,15 +207,11 @@ from langchain_community.vectorstores import MatchingEngine > [Google BigQuery](https://cloud.google.com/bigquery), > BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud. > -> Google BigQuery Vector Search +> [Google BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro) > BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results. > It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance. -> This is a private preview (experimental) feature. Please submit this -> [enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true) -> if you want to enroll BigQuery Vector Search Experimental. - We need to install several python packages. ```bash diff --git a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb index af26dcaf8e02bae..403a8d9fbd2b270 100644 --- a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb @@ -7,22 +7,12 @@ }, "source": [ "# BigQuery Vector Search\n", - "> **BigQueryVectorSearch**:\n", - "BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n", + "> [**BigQuery Vector Search**](https://cloud.google.com/bigquery/docs/vector-search-intro) lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n", "\n", "\n", "This tutorial illustrates how to work with an end-to-end data and embedding management system in LangChain, and provide scalable semantic search in BigQuery." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a **private preview (experimental)** feature. Please submit this\n", - "[enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)\n", - "if you want to enroll BigQuery Vector Search Experimental." - ] - }, { "cell_type": "markdown", "metadata": { From f8943e873947ad11ea6ef0237225ce72b3bffb23 Mon Sep 17 00:00:00 2001 From: Mohammad Mohtashim <45242107+keenborder786@users.noreply.github.com> Date: Sat, 3 Feb 2024 03:11:09 +0500 Subject: [PATCH 047/157] core[patch]: Add doc-string to RunnableEach (#16892) Add doc-string to Runnable Each --------- Co-authored-by: Eugene Yurtsev Co-authored-by: Eugene Yurtsev --- libs/core/langchain_core/runnables/base.py | 31 +++++++++++++++++++--- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py index a5450de38132850..42f5f33c31cdd22 100644 --- a/libs/core/langchain_core/runnables/base.py +++ b/libs/core/langchain_core/runnables/base.py @@ -3730,11 +3730,12 @@ async def input_aiter() -> AsyncIterator[Input]: class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]): - """ - A runnable that delegates calls to another runnable + """A runnable that delegates calls to another runnable with each element of the input sequence. Use only if creating a new RunnableEach subclass with different __init__ args. + + See documentation for RunnableEach for more details. """ bound: Runnable[Input, Output] @@ -3837,9 +3838,31 @@ async def astream_events( class RunnableEach(RunnableEachBase[Input, Output]): - """ - A runnable that delegates calls to another runnable + """A runnable that delegates calls to another runnable with each element of the input sequence. + + It allows you to call multiple inputs with the bounded Runnable. + + RunnableEach makes it easy to run multiple inputs for the runnable. + In the below example, we associate and run three three inputs + with a Runnable: + + .. code-block:: python + + from langchain_core.runnables.base import RunnableEach + from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate + from langchain_core.output_parsers import StrOutputParser + prompt = ChatPromptTemplate.from_template("Tell me a short joke about + {topic}") + model = ChatOpenAI() + output_parser = StrOutputParser() + runnable = prompt | model | output_parser + runnable_each = RunnableEach(bound=runnable) + output = runnable_each.invoke([{'topic':'Computer Science'}, + {'topic':'Art'}, + {'topic':'Biology'}]) + print(output) """ @classmethod From 06660bc78cdd2a110af4e0e9ecd0eff65871a63c Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Fri, 2 Feb 2024 15:05:54 -0800 Subject: [PATCH 048/157] core[patch]: handle some optional cases in tools (#16954) primary problem in pydantic still exists, where `Optional[str]` gets turned to `string` in the jsonschema `.schema()` Also fixes the `SchemaSchema` naming issue --------- Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> --- libs/core/langchain_core/tools.py | 16 +++-- libs/core/tests/unit_tests/test_tools.py | 58 +++++++++++++++++-- .../unit_tests/utils/test_function_calling.py | 26 +++++++-- 3 files changed, 87 insertions(+), 13 deletions(-) diff --git a/libs/core/langchain_core/tools.py b/libs/core/langchain_core/tools.py index b1d9e9ead3c341e..7e1d82e226ed3a3 100644 --- a/libs/core/langchain_core/tools.py +++ b/libs/core/langchain_core/tools.py @@ -39,14 +39,21 @@ class SchemaAnnotationError(TypeError): def _create_subset_model( - name: str, model: BaseModel, field_names: list + name: str, model: Type[BaseModel], field_names: list ) -> Type[BaseModel]: """Create a pydantic model with only a subset of model's fields.""" fields = {} for field_name in field_names: field = model.__fields__[field_name] - fields[field_name] = (field.outer_type_, field.field_info) - return create_model(name, **fields) # type: ignore + t = ( + # this isn't perfect but should work for most functions + field.outer_type_ + if field.required and not field.allow_none + else Optional[field.outer_type_] + ) + fields[field_name] = (t, field.field_info) + rtn = create_model(name, **fields) # type: ignore + return rtn def _get_filtered_args( @@ -764,7 +771,8 @@ def add(a: int, b: int) -> int: description = f"{name}{sig} - {description.strip()}" _args_schema = args_schema if _args_schema is None and infer_schema: - _args_schema = create_schema_from_function(f"{name}Schema", source_function) + # schema name is appended within function + _args_schema = create_schema_from_function(name, source_function) return cls( name=name, func=func, diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py index 33b5d0ff9b56240..d89f8b2657d66e4 100644 --- a/libs/core/tests/unit_tests/test_tools.py +++ b/libs/core/tests/unit_tests/test_tools.py @@ -1,4 +1,5 @@ """Test the base tool implementation.""" + import json from datetime import datetime from enum import Enum @@ -18,6 +19,7 @@ StructuredTool, Tool, ToolException, + _create_subset_model, tool, ) from tests.unit_tests.fake.callbacks import FakeCallbackHandler @@ -321,7 +323,7 @@ def foo(bar: int, baz: str) -> str: "bar": {"title": "Bar", "type": "integer"}, "baz": {"title": "Baz", "type": "string"}, }, - "title": "fooSchemaSchema", + "title": "fooSchema", "type": "object", "required": ["bar", "baz"], } @@ -354,7 +356,7 @@ def foo(bar: int, baz: List[str]) -> str: "bar": {"title": "Bar", "type": "integer"}, "baz": {"title": "Baz", "type": "array", "items": {"type": "string"}}, }, - "title": "fooSchemaSchema", + "title": "fooSchema", "type": "object", "required": ["bar", "baz"], } @@ -454,7 +456,7 @@ def foo( "bar": {"title": "Bar", "type": "integer"}, "baz": {"title": "Baz", "type": "string"}, }, - "title": "fooSchemaSchema", + "title": "fooSchema", "type": "object", "required": ["bar", "baz"], } @@ -685,7 +687,7 @@ def foo(bar: int, baz: str) -> str: } assert structured_tool.args_schema.schema() == { - "title": "fooSchemaSchema", + "title": "fooSchema", "type": "object", "properties": { "bar": {"title": "Bar", "type": "integer"}, @@ -821,3 +823,51 @@ async def _arun(self) -> str: _tool = _RaiseNonValidationErrorTool(handle_validation_error=handler) with pytest.raises(NotImplementedError): await _tool.arun({}) + + +def test_optional_subset_model_rewrite() -> None: + class MyModel(BaseModel): + a: Optional[str] + b: str + c: Optional[List[Optional[str]]] + + model2 = _create_subset_model("model2", MyModel, ["a", "b", "c"]) + + assert "a" not in model2.schema()["required"] # should be optional + assert "b" in model2.schema()["required"] # should be required + assert "c" not in model2.schema()["required"] # should be optional + + +@pytest.mark.parametrize( + "inputs, expected", + [ + # Check not required + ({"bar": "bar"}, {"bar": "bar", "baz": 3, "buzz": "buzz"}), + # Check overwritten + ( + {"bar": "bar", "baz": 4, "buzz": "not-buzz"}, + {"bar": "bar", "baz": 4, "buzz": "not-buzz"}, + ), + # Check validation error when missing + ({}, None), + # Check validation error when wrong type + ({"bar": "bar", "baz": "not-an-int"}, None), + # Check OK when None explicitly passed + ({"bar": "bar", "baz": None}, {"bar": "bar", "baz": None, "buzz": "buzz"}), + ], +) +def test_tool_invoke_optional_args(inputs: dict, expected: Optional[dict]) -> None: + @tool + def foo(bar: str, baz: Optional[int] = 3, buzz: Optional[str] = "buzz") -> dict: + """The foo.""" + return { + "bar": bar, + "baz": baz, + "buzz": buzz, + } + + if expected is not None: + assert foo.invoke(inputs) == expected # type: ignore + else: + with pytest.raises(ValidationError): + foo.invoke(inputs) # type: ignore diff --git a/libs/core/tests/unit_tests/utils/test_function_calling.py b/libs/core/tests/unit_tests/utils/test_function_calling.py index 026338614ce2f0d..bd03abe27579c17 100644 --- a/libs/core/tests/unit_tests/utils/test_function_calling.py +++ b/libs/core/tests/unit_tests/utils/test_function_calling.py @@ -1,9 +1,9 @@ -from typing import Any, Callable, Literal, Type +from typing import Any, Callable, List, Literal, Optional, Type import pytest from langchain_core.pydantic_v1 import BaseModel, Field -from langchain_core.tools import BaseTool +from langchain_core.tools import BaseTool, tool from langchain_core.utils.function_calling import convert_to_openai_function @@ -33,7 +33,7 @@ def dummy_function(arg1: int, arg2: Literal["bar", "baz"]) -> None: @pytest.fixture() -def tool() -> BaseTool: +def dummy_tool() -> BaseTool: class Schema(BaseModel): arg1: int = Field(..., description="foo") arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'") @@ -50,7 +50,7 @@ def _run(self, *args: Any, **kwargs: Any) -> Any: def test_convert_to_openai_function( - pydantic: Type[BaseModel], function: Callable, tool: BaseTool + pydantic: Type[BaseModel], function: Callable, dummy_tool: BaseTool ) -> None: expected = { "name": "dummy_function", @@ -69,6 +69,22 @@ def test_convert_to_openai_function( }, } - for fn in (pydantic, function, tool, expected): + for fn in (pydantic, function, dummy_tool, expected): actual = convert_to_openai_function(fn) # type: ignore assert actual == expected + + +@pytest.mark.xfail(reason="Pydantic converts Optional[str] to str in .schema()") +def test_function_optional_param() -> None: + @tool + def func5( + a: Optional[str], + b: str, + c: Optional[List[Optional[str]]], + ) -> None: + """A test function""" + pass + + func = convert_to_openai_function(func5) + req = func["parameters"]["required"] + assert set(req) == {"b"} From afdd636999838e6f92045607146484238fc27cfc Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Fri, 2 Feb 2024 15:12:21 -0800 Subject: [PATCH 049/157] docs: partner packages (#16960) --- docs/.local_build.sh | 3 +- docs/docs/integrations/chat/mistralai.ipynb | 14 +++- docs/docs/integrations/platforms/index.mdx | 22 ++++++ .../integrations/providers/exa_search.ipynb | 77 ++++++++++++++++++ .../integrations/providers/mistralai.ipynb | 78 +++++++++++++++++++ docs/docs/integrations/providers/nomic.ipynb | 16 ++++ .../integrations/providers/together.ipynb | 78 +++++++++++++++++++ docs/docs/integrations/tools/exa_search.ipynb | 2 +- docs/sidebars.js | 6 +- .../integration_template/docs/provider.ipynb | 6 +- 10 files changed, 293 insertions(+), 9 deletions(-) create mode 100644 docs/docs/integrations/platforms/index.mdx create mode 100644 docs/docs/integrations/providers/exa_search.ipynb create mode 100644 docs/docs/integrations/providers/mistralai.ipynb create mode 100644 docs/docs/integrations/providers/together.ipynb diff --git a/docs/.local_build.sh b/docs/.local_build.sh index 72e15dec90a4a04..a1f181198fd815a 100755 --- a/docs/.local_build.sh +++ b/docs/.local_build.sh @@ -16,7 +16,8 @@ cp ../cookbook/README.md src/pages/cookbook.mdx mkdir -p docs/templates cp ../templates/docs/INDEX.md docs/templates/index.md poetry run python scripts/copy_templates.py -wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md +wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md +wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O docs/langgraph.md yarn diff --git a/docs/docs/integrations/chat/mistralai.ipynb b/docs/docs/integrations/chat/mistralai.ipynb index 17302ca0e122a47..5b4977e87926956 100644 --- a/docs/docs/integrations/chat/mistralai.ipynb +++ b/docs/docs/integrations/chat/mistralai.ipynb @@ -19,7 +19,19 @@ "\n", "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n", "\n", - "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API." + "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n", + "\n", + "You will need the `langchain-mistralai` package to use the API. You can install it via pip:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb978a7e", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-core langchain-mistralai" ] }, { diff --git a/docs/docs/integrations/platforms/index.mdx b/docs/docs/integrations/platforms/index.mdx new file mode 100644 index 000000000000000..9a5fcdf57c98281 --- /dev/null +++ b/docs/docs/integrations/platforms/index.mdx @@ -0,0 +1,22 @@ +# Providers + +LangChain integrates with many providers + +## Partner Packages + +- [OpenAI](/docs/integrations/platforms/openai) +- [Anthropic](/docs/integrations/platforms/anthropic) +- [Google](/docs/integrations/platforms/google) +- [MistralAI](/docs/integrations/providers/mistralai) +- [NVIDIA AI](/docs/integrations/providers/nvidia) +- [Together AI](/docs/integrations/providers/together) +- [Robocorp](/docs/integrations/providers/robocorp) +- [Exa Search](/docs/integrations/providers/exa_search) +- [Nomic](/docs/integrations/providers/nomic) + + +## Featured Community Providers + +- [AWS](/docs/integrations/platforms/aws) +- [Hugging Face](/docs/integrations/platforms/huggingface) +- [Microsoft](/docs/integrations/platforms/microsoft) diff --git a/docs/docs/integrations/providers/exa_search.ipynb b/docs/docs/integrations/providers/exa_search.ipynb new file mode 100644 index 000000000000000..c72dbd1c4a9954e --- /dev/null +++ b/docs/docs/integrations/providers/exa_search.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exa Search\n", + "\n", + "Exa's search integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-exa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to use the package, you will also need to set the `EXA_API_KEY` environment variable to your Exa API key.\n", + "\n", + "## Retriever\n", + "\n", + "You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from langchain_exa import ExaSearchRetriever" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tools\n", + "\n", + "You can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#using-the-exa-sdk-as-langchain-agent-tools).\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/providers/mistralai.ipynb b/docs/docs/integrations/providers/mistralai.ipynb new file mode 100644 index 000000000000000..561751f20c5f807 --- /dev/null +++ b/docs/docs/integrations/providers/mistralai.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MistralAI\n", + "\n", + "Mistral AI is a platform that offers hosting for their powerful open source models.\n", + "\n", + "You can access them via their [API](https://docs.mistral.ai/api/).\n", + "\n", + "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n", + "\n", + "You will also need the `langchain-mistralai` package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-core langchain-mistralai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from langchain_mistralai import ChatMistralAI, MistralAIEmbeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See the docs for their\n", + "\n", + "- [Chat Model](/docs/integrations/chat/mistralai)\n", + "- [Embeddings Model](/docs/integrations/text_embedding/mistralai)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/providers/nomic.ipynb b/docs/docs/integrations/providers/nomic.ipynb index a7475ef36eabda8..b1f4861612addd7 100644 --- a/docs/docs/integrations/providers/nomic.ipynb +++ b/docs/docs/integrations/providers/nomic.ipynb @@ -11,6 +11,22 @@ "- Atlas: their Visual Data Engine\n", "- GPT4All: their Open Source Edge Language Model Ecosystem\n", "\n", + "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-nomic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:" ] }, diff --git a/docs/docs/integrations/providers/together.ipynb b/docs/docs/integrations/providers/together.ipynb new file mode 100644 index 000000000000000..feeb4a022952314 --- /dev/null +++ b/docs/docs/integrations/providers/together.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Together AI\n", + "\n", + "> The Together API makes it easy to fine-tune or run leading open-source models with a couple lines of code. We have integrated the world’s leading open-source models, including Llama-2, RedPajama, Falcon, Alpaca, Stable Diffusion XL, and more. Read more: https://together.ai\n", + "\n", + "To use, you'll need an API key which you can find here:\n", + "https://api.together.xyz/settings/api-keys. This can be passed in as init param\n", + "``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.\n", + "\n", + "Together API reference: https://docs.together.ai/reference/inference\n", + "\n", + "You will also need to install the `langchain-together` integration package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet langchain-together" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from __module_name__ import (\n", + " Together, # LLM\n", + " TogetherEmbeddings,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See the docs for their\n", + "\n", + "- [LLM](/docs/integrations/llms/together)\n", + "- [Embeddings Model](/docs/integrations/text_embedding/together)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/tools/exa_search.ipynb b/docs/docs/integrations/tools/exa_search.ipynb index dc2da3a1d9d1aed..d38087279d5bb44 100644 --- a/docs/docs/integrations/tools/exa_search.ipynb +++ b/docs/docs/integrations/tools/exa_search.ipynb @@ -60,7 +60,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using ExaSearchRetriever\n", + "## Using ExaSearchRetriever\n", "\n", "ExaSearchRetriever is a retriever that uses Exa Search to retrieve relevant documents." ] diff --git a/docs/sidebars.js b/docs/sidebars.js index 46e503542121224..ba924e5a38a5ca8 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -87,11 +87,11 @@ module.exports = { collapsible: false, items: [ { type: "autogenerated", dirName: "integrations/platforms" }, - { type: "category", label: "More", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/providers" }]}, + { type: "category", label: "More", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/providers" }], link: { type: 'generated-index', slug: "integrations/providers", }}, ], link: { - type: 'generated-index', - slug: "integrations/providers", + type: 'doc', + id: 'integrations/platforms/index' }, }, { diff --git a/libs/cli/langchain_cli/integration_template/docs/provider.ipynb b/libs/cli/langchain_cli/integration_template/docs/provider.ipynb index 11f770a42b929fa..a00e11eb2e14e6c 100644 --- a/libs/cli/langchain_cli/integration_template/docs/provider.ipynb +++ b/libs/cli/langchain_cli/integration_template/docs/provider.ipynb @@ -17,9 +17,9 @@ }, "outputs": [], "source": [ - "from __module_name__.chat_models import __ModuleName__Chat\n", - "from __module_name__.llms import __ModuleName__LLM\n", - "from __module_name__.vectorstores import __ModuleName__VectorStore" + "from __module_name__ import Chat__ModuleName__\n", + "from __module_name__ import __ModuleName__LLM\n", + "from __module_name__ import __ModuleName__VectorStore" ] } ], From 2b7e47a66837408e5a83d10742ee75efac2f3b7d Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Fri, 2 Feb 2024 15:59:10 -0800 Subject: [PATCH 050/157] infra: install integration deps for test linting (#16963) --- .github/workflows/_lint.yml | 2 +- poetry.lock | 53 +++++++++++++++++++------------------ pyproject.toml | 1 + 3 files changed, 29 insertions(+), 27 deletions(-) diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index a0c1944a325d2b0..1bb605d22fb17f0 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -105,7 +105,7 @@ jobs: # It doesn't matter how you change it, any change will cause a cache-bust. working-directory: ${{ inputs.working-directory }} run: | - poetry install --with test + poetry install --with test,test_integration - name: Get .mypy_cache_test to speed up mypy uses: actions/cache@v3 diff --git a/poetry.lock b/poetry.lock index 6cb99a0e574664a..d712c5ab7d230b7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "aiohttp" @@ -1694,7 +1694,7 @@ files = [ [[package]] name = "langchain" -version = "0.1.0" +version = "0.1.5" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1706,9 +1706,9 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" -langchain-community = ">=0.0.9,<0.1" -langchain-core = ">=0.1.7,<0.2" -langsmith = "~0.0.77" +langchain-community = ">=0.0.17,<0.1" +langchain-core = ">=0.1.16,<0.2" +langsmith = ">=0.0.83,<0.1" numpy = "^1" pydantic = ">=1,<3" PyYAML = ">=5.3" @@ -1724,7 +1724,7 @@ cli = ["typer (>=0.9.0,<0.10.0)"] cohere = ["cohere (>=4,<5)"] docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", 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"pytest", "pytest-cov"] [[package]] name = "openai" -version = "1.7.0" +version = "1.10.0" description = "The official Python library for the openai API" optional = false python-versions = ">=3.7.1" files = [ - {file = "openai-1.7.0-py3-none-any.whl", hash = "sha256:2282e8e15acb05df79cccba330c025b8e84284c7ec1f3fa31f167a8479066333"}, - {file = "openai-1.7.0.tar.gz", hash = "sha256:f2a8dcb739e8620c9318a2c6304ea72aebb572ba02fa1d586344405e80d567d3"}, + {file = "openai-1.10.0-py3-none-any.whl", hash = "sha256:aa69e97d0223ace9835fbf9c997abe9ee95318f684fd2de6d02c870700c71ebc"}, + {file = "openai-1.10.0.tar.gz", hash = "sha256:208886cb501b930dc63f48d51db9c15e5380380f80516d07332adad67c9f1053"}, ] [package.dependencies] @@ -2946,6 +2946,7 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = 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-content-hash = "0ee5840cf8fda328bd967f6bd3c9be4e698ccfdd4b8b14c970bad7f2d338ec81" +content-hash = "3abf9f2545bcdb66245744f5b5e8f19ae4813b530f4e7e18d77aa783f8e783fd" diff --git a/pyproject.toml b/pyproject.toml index c16f623a37a3029..3044f4ae9100cf5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,7 @@ langchain-openai = { path = "libs/partners/openai", develop = true } [tool.poetry.group.test.dependencies] +[tool.poetry.group.test_integration.dependencies] [tool.poetry.group.typing.dependencies] From 267e71606e8666cb419f35b5d881489504244ce6 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Fri, 2 Feb 2024 16:50:58 -0800 Subject: [PATCH 051/157] docs: Update README.md (#16966) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1f21107f0a43d86..40ac6d9a4768a6b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 🦜️🔗 LangChain -⚡ Building applications with LLMs through composability ⚡ +⚡ Build context-aware reasoning applications ⚡ [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases) [![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml) From 0826d87ecd5f186124af7a26180507c1691c1a72 Mon Sep 17 00:00:00 2001 From: ccurme Date: Sat, 3 Feb 2024 19:30:50 -0500 Subject: [PATCH 052/157] langchain_mistralai[patch]: Invoke callback prior to yielding token (#16986) - **Description:** Invoke callback prior to yielding token in stream and astream methods for ChatMistralAI. - **Issue:** https://github.com/langchain-ai/langchain/issues/16913 --- .../langchain_mistralai/chat_models.py | 4 +- .../tests/unit_tests/test_chat_models.py | 55 +++++++++++++++++++ 2 files changed, 57 insertions(+), 2 deletions(-) diff --git a/libs/partners/mistralai/langchain_mistralai/chat_models.py b/libs/partners/mistralai/langchain_mistralai/chat_models.py index a13308e5d581ce7..d4cd631b9b4f348 100644 --- a/libs/partners/mistralai/langchain_mistralai/chat_models.py +++ b/libs/partners/mistralai/langchain_mistralai/chat_models.py @@ -317,9 +317,9 @@ def _stream( continue chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ - yield ChatGenerationChunk(message=chunk) if run_manager: run_manager.on_llm_new_token(token=chunk.content, chunk=chunk) + yield ChatGenerationChunk(message=chunk) async def _astream( self, @@ -342,9 +342,9 @@ async def _astream( continue chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ - yield ChatGenerationChunk(message=chunk) if run_manager: await run_manager.on_llm_new_token(token=chunk.content, chunk=chunk) + yield ChatGenerationChunk(message=chunk) async def _agenerate( self, diff --git a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py index d965725ba135e9c..8a28a916f591ef2 100644 --- a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py @@ -1,7 +1,10 @@ """Test MistralAI Chat API wrapper.""" import os +from typing import Any, AsyncGenerator, Generator +from unittest.mock import patch import pytest +from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.messages import ( AIMessage, BaseMessage, @@ -12,6 +15,11 @@ # TODO: Remove 'type: ignore' once mistralai has stubs or py.typed marker. from mistralai.models.chat_completion import ( # type: ignore[import] + ChatCompletionResponseStreamChoice, + ChatCompletionStreamResponse, + DeltaMessage, +) +from mistralai.models.chat_completion import ( ChatMessage as MistralChatMessage, ) @@ -63,3 +71,50 @@ def test_convert_message_to_mistral_chat_message( ) -> None: result = _convert_message_to_mistral_chat_message(message) assert result == expected + + +def _make_completion_response_from_token(token: str) -> ChatCompletionStreamResponse: + return ChatCompletionStreamResponse( + id="abc123", + model="fake_model", + choices=[ + ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(content=token), + finish_reason=None, + ) + ], + ) + + +def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator: + for token in ["Hello", " how", " can", " I", " help", "?"]: + yield _make_completion_response_from_token(token) + + +async def mock_chat_astream(*args: Any, **kwargs: Any) -> AsyncGenerator: + for token in ["Hello", " how", " can", " I", " help", "?"]: + yield _make_completion_response_from_token(token) + + +class MyCustomHandler(BaseCallbackHandler): + last_token: str = "" + + def on_llm_new_token(self, token: str, **kwargs: Any) -> None: + self.last_token = token + + +@patch("mistralai.client.MistralClient.chat_stream", new=mock_chat_stream) +def test_stream_with_callback() -> None: + callback = MyCustomHandler() + chat = ChatMistralAI(callbacks=[callback]) + for token in chat.stream("Hello"): + assert callback.last_token == token.content + + +@patch("mistralai.async_client.MistralAsyncClient.chat_stream", new=mock_chat_astream) +async def test_astream_with_callback() -> None: + callback = MyCustomHandler() + chat = ChatMistralAI(callbacks=[callback]) + async for token in chat.astream("Hello"): + assert callback.last_token == token.content From 35446c814e4c31452c2a95c4aa302fa6941e2f62 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Sat, 3 Feb 2024 16:36:57 -0800 Subject: [PATCH 053/157] openai[patch]: rm tiktoken model warning (#16964) --- .../openai/langchain_openai/chat_models/base.py | 10 ---------- .../openai/langchain_openai/embeddings/base.py | 4 +--- .../openai/langchain_openai/llms/base.py | 4 +--- .../tests/unit_tests/chat_models/test_base.py | 16 ++++++++++++++++ .../openai/tests/unit_tests/llms/test_base.py | 16 ++++++++++++---- 5 files changed, 30 insertions(+), 20 deletions(-) diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index b5b7199edba3766..ed33b919bdd7381 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -572,19 +572,9 @@ def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]: model = self.tiktoken_model_name else: model = self.model_name - if model == "gpt-3.5-turbo": - # gpt-3.5-turbo may change over time. - # Returning num tokens assuming gpt-3.5-turbo-0301. - model = "gpt-3.5-turbo-0301" - elif model == "gpt-4": - # gpt-4 may change over time. - # Returning num tokens assuming gpt-4-0314. - model = "gpt-4-0314" - # Returns the number of tokens used by a list of messages. try: encoding = tiktoken.encoding_for_model(model) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) return model, encoding diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py index cd0987ea4317770..d0b815358e39d35 100644 --- a/libs/partners/openai/langchain_openai/embeddings/base.py +++ b/libs/partners/openai/langchain_openai/embeddings/base.py @@ -289,9 +289,7 @@ def _get_len_safe_embeddings( try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") - model = "cl100k_base" - encoding = tiktoken.get_encoding(model) + encoding = tiktoken.get_encoding("cl100k_base") for i, text in enumerate(texts): if self.model.endswith("001"): # See: https://github.com/openai/openai-python/ diff --git a/libs/partners/openai/langchain_openai/llms/base.py b/libs/partners/openai/langchain_openai/llms/base.py index 27c2afd5eef405f..9b0ba87d1f46f73 100644 --- a/libs/partners/openai/langchain_openai/llms/base.py +++ b/libs/partners/openai/langchain_openai/llms/base.py @@ -496,9 +496,7 @@ def get_token_ids(self, text: str) -> List[int]: try: enc = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") - model = "cl100k_base" - enc = tiktoken.get_encoding(model) + enc = tiktoken.get_encoding("cl100k_base") return enc.encode( text, diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py index 767ce0720caf142..b729664e6b81af8 100644 --- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py @@ -118,3 +118,19 @@ def mock_create(*args: Any, **kwargs: Any) -> Any: res = llm.predict("bar") assert res == "Bar Baz" assert completed + + +@pytest.mark.parametrize( + "model", + [ + "gpt-3.5-turbo", + "gpt-4", + "gpt-3.5-0125", + "gpt-4-0125-preview", + "gpt-4-turbo-preview", + "gpt-4-vision-preview", + ], +) +def test__get_encoding_model(model: str) -> None: + ChatOpenAI(model=model)._get_encoding_model() + return diff --git a/libs/partners/openai/tests/unit_tests/llms/test_base.py b/libs/partners/openai/tests/unit_tests/llms/test_base.py index ebcd420995fa386..d05bb0bfe546c3a 100644 --- a/libs/partners/openai/tests/unit_tests/llms/test_base.py +++ b/libs/partners/openai/tests/unit_tests/llms/test_base.py @@ -7,7 +7,6 @@ os.environ["OPENAI_API_KEY"] = "foo" -@pytest.mark.requires("openai") def test_openai_model_param() -> None: llm = OpenAI(model="foo") assert llm.model_name == "foo" @@ -15,19 +14,16 @@ def test_openai_model_param() -> None: assert llm.model_name == "foo" -@pytest.mark.requires("openai") def test_openai_model_kwargs() -> None: llm = OpenAI(model_kwargs={"foo": "bar"}) assert llm.model_kwargs == {"foo": "bar"} -@pytest.mark.requires("openai") def test_openai_invalid_model_kwargs() -> None: with pytest.raises(ValueError): OpenAI(model_kwargs={"model_name": "foo"}) -@pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): llm = OpenAI(foo="bar") @@ -46,3 +42,15 @@ def mock_completion() -> dict: ], "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3}, } + + +@pytest.mark.parametrize( + "model", + [ + "gpt-3.5-turbo-instruct", + "text-davinci-003", + ], +) +def test_get_token_ids(model: str) -> None: + OpenAI(model=model).get_token_ids("foo") + return From 849051102a6e315072e3a1d8dfdcee1527436c92 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Sat, 3 Feb 2024 17:45:44 -0800 Subject: [PATCH 054/157] google-genai[patch]: fix new core typing (#16988) --- .../google-genai/langchain_google_genai/chat_models.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py index 37cc5f342d68968..d30990c5b6e4b0a 100644 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ b/libs/partners/google-genai/langchain_google_genai/chat_models.py @@ -341,7 +341,9 @@ def _parse_chat_history( return messages -def _parts_to_content(parts: List[genai.types.PartType]) -> Union[List[dict], str]: +def _parts_to_content( + parts: List[genai.types.PartType], +) -> Union[str, List[Union[Dict[Any, Any], str]]]: """Converts a list of Gemini API Part objects into a list of LangChain messages.""" if len(parts) == 1 and parts[0].text is not None and not parts[0].inline_data: # Simple text response. The typical response @@ -349,7 +351,7 @@ def _parts_to_content(parts: List[genai.types.PartType]) -> Union[List[dict], st elif not parts: logger.warning("Gemini produced an empty response.") return "" - messages = [] + messages: List[Union[Dict[Any, Any], str]] = [] for part in parts: if part.text is not None: messages.append( From bcfce146d81962715e61e593d2cd7f1df6c2fe12 Mon Sep 17 00:00:00 2001 From: Killinsun - Ryota Takeuchi Date: Mon, 5 Feb 2024 03:45:35 +0900 Subject: [PATCH 055/157] community[patch]: Correct the calling to collection_name in qdrant (#16920) ## Description In #16608, the calling `collection_name` was wrong. I made a fix for it. Sorry for the inconvenience! ## Issue https://github.com/langchain-ai/langchain/issues/16962 ## Dependencies N/A --------- Co-authored-by: Kumar Shivendu Co-authored-by: Harrison Chase --- .../qdrant_sparse_vector_retriever.py | 5 ++- .../vectorstores/qdrant.py | 23 ++++++++--- .../qdrant/async_api/test_from_texts.py | 9 ++++- .../async_api/test_max_marginal_relevance.py | 12 ++++-- .../async_api/test_similarity_search.py | 37 ++++++++++-------- .../vectorstores/qdrant/common.py | 20 ++++++++++ .../vectorstores/qdrant/test_add_texts.py | 3 +- .../vectorstores/qdrant/test_from_texts.py | 9 ++++- .../qdrant/test_max_marginal_relevance.py | 19 ++++++---- .../qdrant/test_similarity_search.py | 38 +++++++++++-------- 10 files changed, 123 insertions(+), 52 deletions(-) diff --git a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py index 1b0c8292bd4636b..daf8917173db82d 100644 --- a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py +++ b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py @@ -120,7 +120,10 @@ def _get_relevant_documents( ) return [ Qdrant._document_from_scored_point( - point, self.content_payload_key, self.metadata_payload_key + point, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ) for point in results ] diff --git a/libs/community/langchain_community/vectorstores/qdrant.py b/libs/community/langchain_community/vectorstores/qdrant.py index d64c5e3b8026df3..47d32cae762a49c 100644 --- a/libs/community/langchain_community/vectorstores/qdrant.py +++ b/libs/community/langchain_community/vectorstores/qdrant.py @@ -620,7 +620,10 @@ def similarity_search_with_score_by_vector( return [ ( self._document_from_scored_point( - result, self.content_payload_key, self.metadata_payload_key + result, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), result.score, ) @@ -713,7 +716,10 @@ async def asimilarity_search_with_score_by_vector( return [ ( self._document_from_scored_point( - result, self.content_payload_key, self.metadata_payload_key + result, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), result.score, ) @@ -1051,7 +1057,10 @@ def max_marginal_relevance_search_with_score_by_vector( return [ ( self._document_from_scored_point( - results[i], self.content_payload_key, self.metadata_payload_key + results[i], + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), results[i].score, ) @@ -1123,7 +1132,10 @@ async def amax_marginal_relevance_search_with_score_by_vector( return [ ( self._document_from_scored_point( - results[i], self.content_payload_key, self.metadata_payload_key + results[i], + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), results[i].score, ) @@ -1938,12 +1950,13 @@ def _build_payloads( def _document_from_scored_point( cls, scored_point: Any, + collection_name: str, content_payload_key: str, metadata_payload_key: str, ) -> Document: metadata = scored_point.payload.get(metadata_payload_key) or {} metadata["_id"] = scored_point.id - metadata["_collection_name"] = scored_point.collection_name + metadata["_collection_name"] = collection_name return Document( page_content=scored_point.payload.get(content_payload_key), metadata=metadata, diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py index dc9f3c9270e7686..1ee99d40bdd38f7 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py @@ -12,7 +12,10 @@ from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) -from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running +from tests.integration_tests.vectorstores.qdrant.common import ( + assert_documents_equals, + qdrant_is_not_running, +) @pytest.mark.parametrize("qdrant_location", qdrant_locations()) @@ -245,4 +248,6 @@ async def test_qdrant_from_texts_stores_metadatas( location=qdrant_location, ) output = await docsearch.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo", metadata={"page": 0})] + assert_documents_equals( + output, [Document(page_content="foo", metadata={"page": 0})] + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py index 59d40c479fb9128..472a66bb0c72f83 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py @@ -10,6 +10,7 @@ from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -41,7 +42,10 @@ async def test_qdrant_max_marginal_relevance_search( output = await docsearch.amax_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0 ) - assert output == [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py index 0d6db192c697072..5ae98ad35aa7821 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py @@ -11,6 +11,7 @@ from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -37,7 +38,7 @@ async def test_qdrant_similarity_search( location=qdrant_location, ) output = await docsearch.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -65,7 +66,7 @@ async def test_qdrant_similarity_search_by_vector( ) embeddings = ConsistentFakeEmbeddings().embed_query("foo") output = await docsearch.asimilarity_search_by_vector(embeddings, k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -95,7 +96,7 @@ async def test_qdrant_similarity_search_with_score_by_vector( output = await docsearch.asimilarity_search_with_score_by_vector(embeddings, k=1) assert len(output) == 1 document, score = output[0] - assert document == Document(page_content="foo") + assert_documents_equals([document], [Document(page_content="foo")]) assert score >= 0 @@ -123,12 +124,15 @@ async def test_qdrant_similarity_search_filters( output = await docsearch.asimilarity_search( "foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}} ) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + output, + [ + Document( + page_content="bar", + metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -262,12 +266,15 @@ async def test_qdrant_similarity_search_filters_with_qdrant_filters( ] ) output = await docsearch.asimilarity_search("foo", k=1, filter=qdrant_filter) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + output, + [ + Document( + page_content="bar", + metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py index 065dddd0a6be273..ec5d14ef3182f2d 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py @@ -1,3 +1,8 @@ +from typing import List + +from langchain_core.documents import Document + + def qdrant_is_not_running() -> bool: """Check if Qdrant is not running.""" import requests @@ -8,3 +13,18 @@ def qdrant_is_not_running() -> bool: return response_json.get("title") != "qdrant - vector search engine" except (requests.exceptions.ConnectionError, requests.exceptions.Timeout): return True + + +def assert_documents_equals(actual: List[Document], expected: List[Document]): + assert len(actual) == len(expected) + + for actual_doc, expected_doc in zip(actual, expected): + assert actual_doc.page_content == expected_doc.page_content + + assert "_id" in actual_doc.metadata + assert "_collection_name" in actual_doc.metadata + + actual_doc.metadata.pop("_id") + actual_doc.metadata.pop("_collection_name") + + assert actual_doc.metadata == expected_doc.metadata diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py index 819216c2807898d..d3d73b3c6d51ad4 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py @@ -8,6 +8,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -33,7 +34,7 @@ def test_qdrant_add_documents_extends_existing_collection( # ConsistentFakeEmbeddings return the same query embedding as the first document # embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the # same as "foobar" embedding - assert output == [Document(page_content="foobar")] + assert_documents_equals(output, [Document(page_content="foobar")]) @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py index e6b97fa703a1e8d..5b00ea9f98f0a69 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py @@ -10,7 +10,10 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) -from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running +from tests.integration_tests.vectorstores.qdrant.common import ( + assert_documents_equals, + qdrant_is_not_running, +) def test_qdrant_from_texts_stores_duplicated_texts() -> None: @@ -257,7 +260,9 @@ def test_qdrant_from_texts_stores_metadatas( batch_size=batch_size, ) output = docsearch.similarity_search("foo", k=1) - assert output == [Document(page_content="foo", metadata={"page": 0})] + assert_documents_equals( + output, [Document(page_content="foo", metadata={"page": 0})] + ) @pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running") diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py index 536003e848112e5..05c7431183360e1 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py @@ -7,6 +7,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -49,14 +50,18 @@ def test_qdrant_max_marginal_relevance_search( output = docsearch.max_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0 ) - assert output == [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) output = docsearch.max_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter ) - assert output == [ - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [Document(page_content="baz", metadata={"page": 2})], + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py index 2df03ada7979469..5b51be93d4edd06 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py @@ -8,6 +8,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -32,7 +33,7 @@ def test_qdrant_similarity_search( vector_name=vector_name, ) output = docsearch.similarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(actual=output, expected=[Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -58,7 +59,7 @@ def test_qdrant_similarity_search_by_vector( ) embeddings = ConsistentFakeEmbeddings().embed_query("foo") output = docsearch.similarity_search_by_vector(embeddings, k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -86,7 +87,7 @@ def test_qdrant_similarity_search_with_score_by_vector( output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1) assert len(output) == 1 document, score = output[0] - assert document == Document(page_content="foo") + assert_documents_equals(actual=[document], expected=[Document(page_content="foo")]) assert score >= 0 @@ -113,12 +114,16 @@ def test_qdrant_similarity_search_filters( output = docsearch.similarity_search( "foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}} ) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, - ) - ] + + assert_documents_equals( + actual=output, + expected=[ + Document( + page_content="bar", + metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -240,12 +245,15 @@ def test_qdrant_similarity_search_filters_with_qdrant_filters( ] ) output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + actual=output, + expected=[ + Document( + page_content="bar", + metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("batch_size", [1, 64]) From 54fcd476bb3ce089eac92aa1e6838ac31215150a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicolas=20Greni=C3=A9?= Date: Sun, 4 Feb 2024 15:13:29 -0800 Subject: [PATCH 056/157] docs: Update ollama examples with new community libraries (#17007) - **Description:** Updating one line code sample for Ollama with new **langchain_community** package - **Issue:** - **Dependencies:** none - **Twitter handle:** @picsoung --- docs/docs/integrations/providers/ollama.mdx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/docs/integrations/providers/ollama.mdx b/docs/docs/integrations/providers/ollama.mdx index da10174a168508d..ba00e2a24693edd 100644 --- a/docs/docs/integrations/providers/ollama.mdx +++ b/docs/docs/integrations/providers/ollama.mdx @@ -21,7 +21,7 @@ To use, you should set up the environment variables `ANYSCALE_API_BASE` and ## LLM ```python -from langchain.llms import Ollama +from langchain_community.llms import Ollama ``` See the notebook example [here](/docs/integrations/llms/ollama). @@ -31,7 +31,7 @@ See the notebook example [here](/docs/integrations/llms/ollama). ### Chat Ollama ```python -from langchain.chat_models import ChatOllama +from langchain_community.chat_models import ChatOllama ``` See the notebook example [here](/docs/integrations/chat/ollama). @@ -47,7 +47,7 @@ See the notebook example [here](/docs/integrations/chat/ollama_functions). ## Embedding models ```python -from langchain.embeddings import OllamaEmbeddings +from langchain_community.embeddings import OllamaEmbeddings ``` See the notebook example [here](/docs/integrations/text_embedding/ollama). From b3c3b58f2c85ebdfd380c0c9b85d661757236887 Mon Sep 17 00:00:00 2001 From: Ryan Kraus Date: Mon, 5 Feb 2024 13:13:25 -0500 Subject: [PATCH 057/157] core[patch]: Fixed bug in dict to message conversion. (#17023) - **Description**: We discovered a bug converting dictionaries to messages where the ChatMessageChunk message type isn't handled. This PR adds support for that message type. - **Issue**: #17022 - **Dependencies**: None - **Twitter handle**: None --- libs/core/langchain_core/messages/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/libs/core/langchain_core/messages/__init__.py b/libs/core/langchain_core/messages/__init__.py index c35dac72b98f06c..86bea028051fb1a 100644 --- a/libs/core/langchain_core/messages/__init__.py +++ b/libs/core/langchain_core/messages/__init__.py @@ -92,6 +92,8 @@ def _message_from_dict(message: dict) -> BaseMessage: return ToolMessageChunk(**message["data"]) elif _type == "SystemMessageChunk": return SystemMessageChunk(**message["data"]) + elif _type == "ChatMessageChunk": + return ChatMessageChunk(**message["data"]) else: raise ValueError(f"Got unexpected message type: {_type}") From 2ef69fe11bb33dfe1419741d477e2dc0b82a5d37 Mon Sep 17 00:00:00 2001 From: Christophe Bornet Date: Mon, 5 Feb 2024 10:20:28 -0800 Subject: [PATCH 058/157] Add async methods to BaseChatMessageHistory and BaseMemory (#16728) Adds: * async methods to BaseChatMessageHistory * async methods to ChatMessageHistory * async methods to BaseMemory * async methods to BaseChatMemory * async methods to ConversationBufferMemory * tests of ConversationBufferMemory's async methods **Twitter handle:** cbornet_ --- .../chat_message_histories/in_memory.py | 12 +++- libs/core/langchain_core/chat_history.py | 55 +++++++++++++++++-- libs/core/langchain_core/memory.py | 15 +++++ .../chat_history/test_chat_history.py | 33 +++++++++++ libs/langchain/langchain/memory/buffer.py | 46 ++++++++++++++-- .../langchain/langchain/memory/chat_memory.py | 19 ++++++- .../unit_tests/chains/test_conversation.py | 32 ++++++++++- 7 files changed, 197 insertions(+), 15 deletions(-) diff --git a/libs/community/langchain_community/chat_message_histories/in_memory.py b/libs/community/langchain_community/chat_message_histories/in_memory.py index 8c76e850dd917e9..fe6c6406524c598 100644 --- a/libs/community/langchain_community/chat_message_histories/in_memory.py +++ b/libs/community/langchain_community/chat_message_histories/in_memory.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Sequence from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.messages import BaseMessage @@ -13,9 +13,19 @@ class ChatMessageHistory(BaseChatMessageHistory, BaseModel): messages: List[BaseMessage] = Field(default_factory=list) + async def aget_messages(self) -> List[BaseMessage]: + return self.messages + def add_message(self, message: BaseMessage) -> None: """Add a self-created message to the store""" self.messages.append(message) + async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None: + """Add messages to the store""" + self.add_messages(messages) + def clear(self) -> None: self.messages = [] + + async def aclear(self) -> None: + self.clear() diff --git a/libs/core/langchain_core/chat_history.py b/libs/core/langchain_core/chat_history.py index e558c1479ed0ccb..1042e2e8ef85c5c 100644 --- a/libs/core/langchain_core/chat_history.py +++ b/libs/core/langchain_core/chat_history.py @@ -9,16 +9,32 @@ HumanMessage, get_buffer_string, ) +from langchain_core.runnables import run_in_executor class BaseChatMessageHistory(ABC): """Abstract base class for storing chat message history. - Implementations should over-ride the add_messages method to handle bulk addition - of messages. + Implementations guidelines: - The default implementation of add_message will correctly call add_messages, so - it is not necessary to implement both methods. + Implementations are expected to over-ride all or some of the following methods: + + * add_messages: sync variant for bulk addition of messages + * aadd_messages: async variant for bulk addition of messages + * messages: sync variant for getting messages + * aget_messages: async variant for getting messages + * clear: sync variant for clearing messages + * aclear: async variant for clearing messages + + add_messages contains a default implementation that calls add_message + for each message in the sequence. This is provided for backwards compatibility + with existing implementations which only had add_message. + + Async variants all have default implementations that call the sync variants. + Implementers can choose to over-ride the async implementations to provide + truly async implementations. + + Usage guidelines: When used for updating history, users should favor usage of `add_messages` over `add_message` or other variants like `add_user_message` and `add_ai_message` @@ -54,7 +70,22 @@ def clear(self): """ messages: List[BaseMessage] - """A list of Messages stored in-memory.""" + """A property or attribute that returns a list of messages. + + In general, getting the messages may involve IO to the underlying + persistence layer, so this operation is expected to incur some + latency. + """ + + async def aget_messages(self) -> List[BaseMessage]: + """Async version of getting messages. + + Can over-ride this method to provide an efficient async implementation. + + In general, fetching messages may involve IO to the underlying + persistence layer. + """ + return await run_in_executor(None, lambda: self.messages) def add_user_message(self, message: Union[HumanMessage, str]) -> None: """Convenience method for adding a human message string to the store. @@ -98,7 +129,7 @@ def add_message(self, message: BaseMessage) -> None: """ if type(self).add_messages != BaseChatMessageHistory.add_messages: # This means that the sub-class has implemented an efficient add_messages - # method, so we should usage of add_message to that. + # method, so we should use it. self.add_messages([message]) else: raise NotImplementedError( @@ -118,10 +149,22 @@ def add_messages(self, messages: Sequence[BaseMessage]) -> None: for message in messages: self.add_message(message) + async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None: + """Add a list of messages. + + Args: + messages: A list of BaseMessage objects to store. + """ + await run_in_executor(None, self.add_messages, messages) + @abstractmethod def clear(self) -> None: """Remove all messages from the store""" + async def aclear(self) -> None: + """Remove all messages from the store""" + await run_in_executor(None, self.clear) + def __str__(self) -> str: """Return a string representation of the chat history.""" return get_buffer_string(self.messages) diff --git a/libs/core/langchain_core/memory.py b/libs/core/langchain_core/memory.py index 0b362661cfde80f..ad61e90fdad4ff4 100644 --- a/libs/core/langchain_core/memory.py +++ b/libs/core/langchain_core/memory.py @@ -4,6 +4,7 @@ from typing import Any, Dict, List from langchain_core.load.serializable import Serializable +from langchain_core.runnables import run_in_executor class BaseMemory(Serializable, ABC): @@ -50,10 +51,24 @@ def memory_variables(self) -> List[str]: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: + """Return key-value pairs given the text input to the chain.""" + return await run_in_executor(None, self.load_memory_variables, inputs) + @abstractmethod def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save the context of this chain run to memory.""" + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save the context of this chain run to memory.""" + await run_in_executor(None, self.save_context, inputs, outputs) + @abstractmethod def clear(self) -> None: """Clear memory contents.""" + + async def aclear(self) -> None: + """Clear memory contents.""" + await run_in_executor(None, self.clear) diff --git a/libs/core/tests/unit_tests/chat_history/test_chat_history.py b/libs/core/tests/unit_tests/chat_history/test_chat_history.py index 0f6b696e8fbcbdd..e7d2b724f7efaa7 100644 --- a/libs/core/tests/unit_tests/chat_history/test_chat_history.py +++ b/libs/core/tests/unit_tests/chat_history/test_chat_history.py @@ -66,3 +66,36 @@ def clear(self) -> None: assert len(store) == 4 assert store[2] == HumanMessage(content="Hello") assert store[3] == HumanMessage(content="World") + + +async def test_async_interface() -> None: + """Test async interface for BaseChatMessageHistory.""" + + class BulkAddHistory(BaseChatMessageHistory): + def __init__(self) -> None: + self.messages = [] + + def add_messages(self, message: Sequence[BaseMessage]) -> None: + """Add a message to the store.""" + self.messages.extend(message) + + def clear(self) -> None: + """Clear the store.""" + self.messages.clear() + + chat_history = BulkAddHistory() + await chat_history.aadd_messages( + [HumanMessage(content="Hello"), HumanMessage(content="World")] + ) + assert await chat_history.aget_messages() == [ + HumanMessage(content="Hello"), + HumanMessage(content="World"), + ] + await chat_history.aadd_messages([HumanMessage(content="!")]) + assert await chat_history.aget_messages() == [ + HumanMessage(content="Hello"), + HumanMessage(content="World"), + HumanMessage(content="!"), + ] + await chat_history.aclear() + assert await chat_history.aget_messages() == [] diff --git a/libs/langchain/langchain/memory/buffer.py b/libs/langchain/langchain/memory/buffer.py index c8b819fb7543d77..3cc9b162dabc872 100644 --- a/libs/langchain/langchain/memory/buffer.py +++ b/libs/langchain/langchain/memory/buffer.py @@ -19,20 +19,40 @@ def buffer(self) -> Any: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str - @property - def buffer_as_str(self) -> str: - """Exposes the buffer as a string in case return_messages is True.""" + async def abuffer(self) -> Any: + """String buffer of memory.""" + return ( + await self.abuffer_as_messages() + if self.return_messages + else await self.abuffer_as_str() + ) + + def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( - self.chat_memory.messages, + messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) + @property + def buffer_as_str(self) -> str: + """Exposes the buffer as a string in case return_messages is True.""" + return self._buffer_as_str(self.chat_memory.messages) + + async def abuffer_as_str(self) -> str: + """Exposes the buffer as a string in case return_messages is True.""" + messages = await self.chat_memory.aget_messages() + return self._buffer_as_str(messages) + @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages + async def abuffer_as_messages(self) -> List[BaseMessage]: + """Exposes the buffer as a list of messages in case return_messages is False.""" + return await self.chat_memory.aget_messages() + @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. @@ -45,6 +65,11 @@ def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer} + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: + """Return key-value pairs given the text input to the chain.""" + buffer = await self.abuffer() + return {self.memory_key: buffer} + class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" @@ -77,6 +102,10 @@ def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return {self.memory_key: self.buffer} + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: + """Return history buffer.""" + return self.load_memory_variables(inputs) + def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: @@ -93,6 +122,15 @@ def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save context from this conversation to buffer.""" + return self.save_context(inputs, outputs) + def clear(self) -> None: """Clear memory contents.""" self.buffer = "" + + async def aclear(self) -> None: + self.clear() diff --git a/libs/langchain/langchain/memory/chat_memory.py b/libs/langchain/langchain/memory/chat_memory.py index 7808264209b1c15..ad030c3f71a3915 100644 --- a/libs/langchain/langchain/memory/chat_memory.py +++ b/libs/langchain/langchain/memory/chat_memory.py @@ -4,6 +4,7 @@ from langchain_community.chat_message_histories.in_memory import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.memory import BaseMemory +from langchain_core.messages import AIMessage, HumanMessage from langchain_core.pydantic_v1 import Field from langchain.memory.utils import get_prompt_input_key @@ -35,9 +36,23 @@ def _get_input_output( def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" input_str, output_str = self._get_input_output(inputs, outputs) - self.chat_memory.add_user_message(input_str) - self.chat_memory.add_ai_message(output_str) + self.chat_memory.add_messages( + [HumanMessage(content=input_str), AIMessage(content=output_str)] + ) + + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save context from this conversation to buffer.""" + input_str, output_str = self._get_input_output(inputs, outputs) + await self.chat_memory.aadd_messages( + [HumanMessage(content=input_str), AIMessage(content=output_str)] + ) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() + + async def aclear(self) -> None: + """Clear memory contents.""" + await self.chat_memory.aclear() diff --git a/libs/langchain/tests/unit_tests/chains/test_conversation.py b/libs/langchain/tests/unit_tests/chains/test_conversation.py index 86ecd647e8d45df..d00b1e4bc6ef1e6 100644 --- a/libs/langchain/tests/unit_tests/chains/test_conversation.py +++ b/libs/langchain/tests/unit_tests/chains/test_conversation.py @@ -14,14 +14,22 @@ def test_memory_ai_prefix() -> None: """Test that ai_prefix in the memory component works.""" memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant") memory.save_context({"input": "bar"}, {"output": "foo"}) - assert memory.buffer == "Human: bar\nAssistant: foo" + assert memory.load_memory_variables({}) == {"foo": "Human: bar\nAssistant: foo"} def test_memory_human_prefix() -> None: """Test that human_prefix in the memory component works.""" memory = ConversationBufferMemory(memory_key="foo", human_prefix="Friend") memory.save_context({"input": "bar"}, {"output": "foo"}) - assert memory.buffer == "Friend: bar\nAI: foo" + assert memory.load_memory_variables({}) == {"foo": "Friend: bar\nAI: foo"} + + +async def test_memory_async() -> None: + memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant") + await memory.asave_context({"input": "bar"}, {"output": "foo"}) + assert await memory.aload_memory_variables({}) == { + "foo": "Human: bar\nAssistant: foo" + } def test_conversation_chain_works() -> None: @@ -100,3 +108,23 @@ def test_clearing_conversation_memory(memory: BaseMemory) -> None: memory.clear() assert memory.load_memory_variables({}) == {"baz": ""} + + +@pytest.mark.parametrize( + "memory", + [ + ConversationBufferMemory(memory_key="baz"), + ConversationSummaryMemory(llm=FakeLLM(), memory_key="baz"), + ConversationBufferWindowMemory(memory_key="baz"), + ], +) +async def test_clearing_conversation_memory_async(memory: BaseMemory) -> None: + """Test clearing the conversation memory.""" + # This is a good input because the input is not the same as baz. + good_inputs = {"foo": "bar", "baz": "foo"} + # This is a good output because there is one variable. + good_outputs = {"bar": "foo"} + await memory.asave_context(good_inputs, good_outputs) + + await memory.aclear() + assert await memory.aload_memory_variables({}) == {"baz": ""} From 2145636f1d7af49cdecdde139377cc6c37e6765f Mon Sep 17 00:00:00 2001 From: Mikhail Khludnev Date: Mon, 5 Feb 2024 21:45:06 +0300 Subject: [PATCH 059/157] Nvidia trt model name for stop_stream() (#16997) just removing some legacy leftover. --- libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py index f56eeb9a1dad08d..36e1e6e5ca936da 100644 --- a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py +++ b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py @@ -377,14 +377,14 @@ class StreamingResponseGenerator(queue.Queue): def __init__( self, - client: grpcclient.InferenceServerClient, + llm: TritonTensorRTLLM, request_id: str, force_batch: bool, stop_words: Sequence[str], ) -> None: """Instantiate the generator class.""" super().__init__() - self.client = client + self.llm = llm self.request_id = request_id self._batch = force_batch self._stop_words = stop_words @@ -397,8 +397,8 @@ def __next__(self) -> str: """Return the next retrieved token.""" val = self.get() if val is None or val in self._stop_words: - self.client.stop_stream( - "tensorrt_llm", self.request_id, signal=not self._batch + self.llm.stop_stream( + self.llm.model_name, self.request_id, signal=not self._batch ) raise StopIteration() return val From fb245451d2347b080eda9b3a41f8f9947712b04b Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Mon, 5 Feb 2024 14:13:30 -0500 Subject: [PATCH 060/157] core[patch]: Add langsmith to printed sys information (#16899) --- libs/core/langchain_core/sys_info.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/libs/core/langchain_core/sys_info.py b/libs/core/langchain_core/sys_info.py index 467d125616fa681..bdda43f3f4f101b 100644 --- a/libs/core/langchain_core/sys_info.py +++ b/libs/core/langchain_core/sys_info.py @@ -13,6 +13,7 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: other_langchain_packages = [ "langserve", "langgraph", + "langsmith", ] langchain_pkgs = [ @@ -24,7 +25,7 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: ) # Always surface these packages to the top - order_by = ["langchain_core", "langchain", "langchain_community"] + order_by = ["langchain_core", "langchain", "langchain_community", "langsmith"] for pkg in reversed(order_by): if pkg in all_packages: From db6af2139592b0f21e72c6221bb445767e1a0c7d Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 5 Feb 2024 11:15:06 -0800 Subject: [PATCH 061/157] docs: exa contents (#16555) --- docs/docs/integrations/tools/exa_search.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/docs/integrations/tools/exa_search.ipynb b/docs/docs/integrations/tools/exa_search.ipynb index d38087279d5bb44..5043225331399c9 100644 --- a/docs/docs/integrations/tools/exa_search.ipynb +++ b/docs/docs/integrations/tools/exa_search.ipynb @@ -345,7 +345,7 @@ " Set the optional include_domains (list[str]) parameter to restrict the search to a list of domains.\n", " Set the optional start_published_date (str) parameter to restrict the search to documents published after the date (YYYY-MM-DD).\n", " \"\"\"\n", - " return exa.search(\n", + " return exa.search_and_contents(\n", " f\"{query}\",\n", " use_autoprompt=True,\n", " num_results=5,\n", @@ -359,7 +359,7 @@ " \"\"\"Search for webpages similar to a given URL.\n", " The url passed in should be a URL returned from `search`.\n", " \"\"\"\n", - " return exa.find_similar(url, num_results=5)\n", + " return exa.find_similar_and_contents(url, num_results=5)\n", "\n", "\n", "@tool\n", From 4eda647fdde5ec2d43594bff2c97be35268987b2 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Mon, 5 Feb 2024 11:22:06 -0800 Subject: [PATCH 062/157] infra: add -p to mkdir in lint steps (#17013) Previously, if this did not find a mypy cache then it wouldnt run this makes it always run adding mypy ignore comments with existing uncaught issues to unblock other prs --------- Co-authored-by: Erick Friis Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> --- .github/workflows/_lint.yml | 6 +-- libs/community/Makefile | 2 +- .../agent_toolkits/openapi/planner.py | 10 ++--- .../agent_toolkits/sql/base.py | 8 ++-- .../callbacks/mlflow_callback.py | 2 +- .../chat_message_histories/elasticsearch.py | 2 +- .../chat_message_histories/tidb.py | 4 +- .../chat_message_histories/zep.py | 4 +- .../chat_models/azureml_endpoint.py | 12 +++--- .../langchain_community/chat_models/edenai.py | 8 ++-- .../langchain_community/chat_models/ernie.py | 2 +- .../chat_models/gpt_router.py | 10 ++--- .../chat_models/huggingface.py | 2 +- .../langchain_community/chat_models/konko.py | 2 +- .../langchain_community/chat_models/ollama.py | 22 +++++------ .../langchain_community/chat_models/tongyi.py | 2 +- .../chat_models/vertexai.py | 2 +- .../langchain_community/chat_models/yandex.py | 9 +++-- .../chat_models/zhipuai.py | 19 ++++++---- .../document_loaders/assemblyai.py | 2 +- .../document_loaders/astradb.py | 8 ++-- .../document_loaders/base.py | 4 +- .../document_loaders/cassandra.py | 10 ++--- .../document_loaders/chm.py | 6 +-- .../document_loaders/doc_intelligence.py | 2 +- .../document_loaders/mediawikidump.py | 4 +- .../document_loaders/parsers/vsdx.py | 14 +++---- .../embeddings/baichuan.py | 4 +- .../langchain_community/embeddings/edenai.py | 2 +- .../langchain_community/embeddings/embaas.py | 2 +- .../embeddings/gradient_ai.py | 2 +- .../embeddings/llm_rails.py | 2 +- .../langchain_community/embeddings/minimax.py | 2 +- .../langchain_community/embeddings/mlflow.py | 3 +- .../embeddings/oci_generative_ai.py | 8 ++-- .../embeddings/spacy_embeddings.py | 4 +- .../langchain_community/embeddings/yandex.py | 8 ++-- .../langchain_community/graphs/neo4j_graph.py | 2 +- .../graphs/ontotext_graphdb_graph.py | 11 +++--- .../graphs/tigergraph_graph.py | 6 +-- .../llms/azureml_endpoint.py | 36 +++++++++--------- .../langchain_community/llms/baichuan.py | 4 +- .../langchain_community/llms/bedrock.py | 16 ++++---- .../llms/oci_generative_ai.py | 10 ++--- .../langchain_community/llms/ollama.py | 8 ++-- .../langchain_community/llms/pipelineai.py | 2 +- .../langchain_community/llms/stochasticai.py | 4 +- .../langchain_community/llms/vertexai.py | 6 +-- .../langchain_community/llms/watsonxllm.py | 4 +- .../langchain_community/llms/yandex.py | 11 +++--- .../tools/amadeus/closest_airport.py | 2 +- .../langchain_community/tools/shell/tool.py | 4 +- .../utilities/brave_search.py | 2 +- .../langchain_community/utilities/requests.py | 14 +++---- .../utilities/sql_database.py | 4 +- .../vectorstores/alibabacloud_opensearch.py | 2 +- .../vectorstores/astradb.py | 38 +++++++++---------- .../vectorstores/azuresearch.py | 2 +- .../vectorstores/bigquery_vector_search.py | 6 +-- .../vectorstores/deeplake.py | 6 +-- .../langchain_community/vectorstores/faiss.py | 4 +- .../vectorstores/hanavector.py | 34 ++++++++--------- .../vectorstores/jaguar.py | 6 +-- .../vectorstores/milvus.py | 8 ++-- .../vectorstores/pgembedding.py | 2 +- .../vectorstores/pgvecto_rs.py | 4 +- .../vectorstores/pgvector.py | 20 +++++----- .../vectorstores/surrealdb.py | 2 +- .../vectorstores/tencentvectordb.py | 4 +- .../vectorstores/thirdai_neuraldb.py | 32 ++++++++-------- .../vectorstores/vectara.py | 6 +-- .../vectorstores/vikngdb.py | 34 ++++++++--------- libs/community/poetry.lock | 6 +-- libs/community/pyproject.toml | 2 +- .../chat_message_histories/test_tidb.py | 2 +- .../chat_models/test_konko.py | 4 +- .../chat_models/test_llama_edge.py | 2 +- .../document_loaders/test_astradb.py | 2 +- .../document_loaders/test_cassandra.py | 2 +- .../embeddings/test_baichuan.py | 6 +-- .../integration_tests/graphs/test_neo4j.py | 2 +- .../llms/test_azureml_endpoint.py | 8 ++-- .../integration_tests/llms/test_bedrock.py | 14 +++---- .../integration_tests/storage/test_astradb.py | 18 ++++----- .../utilities/test_google_trends.py | 2 +- .../vectorstores/qdrant/common.py | 2 +- .../test_bigquery_vector_search.py | 2 +- .../vectorstores/test_deeplake.py | 6 +-- .../vectorstores/test_hanavector.py | 22 +++++------ .../vectorstores/test_lantern.py | 2 +- .../vectorstores/test_thirdai_neuraldb.py | 12 +++--- .../vectorstores/test_vectara.py | 10 ++--- .../unit_tests/chat_models/test_edenai.py | 2 +- .../unit_tests/embeddings/test_gradient_ai.py | 6 +-- .../embeddings/test_oci_gen_ai_embedding.py | 4 +- .../unit_tests/graphs/test_neo4j_graph.py | 8 ++-- .../graphs/test_ontotext_graphdb_graph.py | 2 +- .../unit_tests/llms/test_oci_generative_ai.py | 4 +- .../tests/unit_tests/llms/test_ollama.py | 14 +++---- libs/core/Makefile | 2 +- libs/langchain/Makefile | 2 +- .../langchain/agents/openai_assistant/base.py | 4 +- libs/langchain/langchain/chains/moderation.py | 2 +- 103 files changed, 378 insertions(+), 369 deletions(-) diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index 1bb605d22fb17f0..e2e3877c5542c89 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -86,7 +86,7 @@ jobs: with: path: | ${{ env.WORKDIR }}/.mypy_cache - key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }} - name: Analysing the code with our lint @@ -105,7 +105,7 @@ jobs: # It doesn't matter how you change it, any change will cause a cache-bust. working-directory: ${{ inputs.working-directory }} run: | - poetry install --with test,test_integration + poetry install --with test - name: Get .mypy_cache_test to speed up mypy uses: actions/cache@v3 @@ -114,7 +114,7 @@ jobs: with: path: | ${{ env.WORKDIR }}/.mypy_cache_test - key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }} - name: Analysing the code with our lint working-directory: ${{ inputs.working-directory }} diff --git a/libs/community/Makefile b/libs/community/Makefile index 900deed91ab7a26..d7bda9945aa4ac5 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -41,7 +41,7 @@ lint lint_diff lint_package lint_tests: poetry run ruff . [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py index e95e011e395e36d..7b561a930393963 100644 --- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py +++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py @@ -84,7 +84,7 @@ def _run(self, text: str) -> str: raise e data_params = data.get("params") response = self.requests_wrapper.get(data["url"], params=data_params) - response = response[: self.response_length] + response = response[: self.response_length] # type: ignore[index] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -115,7 +115,7 @@ def _run(self, text: str) -> str: except json.JSONDecodeError as e: raise e response = self.requests_wrapper.post(data["url"], data["data"]) - response = response[: self.response_length] + response = response[: self.response_length] # type: ignore[index] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -146,7 +146,7 @@ def _run(self, text: str) -> str: except json.JSONDecodeError as e: raise e response = self.requests_wrapper.patch(data["url"], data["data"]) - response = response[: self.response_length] + response = response[: self.response_length] # type: ignore[index] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -177,7 +177,7 @@ def _run(self, text: str) -> str: except json.JSONDecodeError as e: raise e response = self.requests_wrapper.put(data["url"], data["data"]) - response = response[: self.response_length] + response = response[: self.response_length] # type: ignore[index] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -209,7 +209,7 @@ def _run(self, text: str) -> str: except json.JSONDecodeError as e: raise e response = self.requests_wrapper.delete(data["url"]) - response = response[: self.response_length] + response = response[: self.response_length] # type: ignore[index] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() diff --git a/libs/community/langchain_community/agent_toolkits/sql/base.py b/libs/community/langchain_community/agent_toolkits/sql/base.py index 73915732cf18c53..65294cf094be408 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/base.py +++ b/libs/community/langchain_community/agent_toolkits/sql/base.py @@ -177,12 +177,12 @@ def create_sql_agent( elif agent_type == AgentType.OPENAI_FUNCTIONS: if prompt is None: messages = [ - SystemMessage(content=prefix), + SystemMessage(content=prefix), # type: ignore[arg-type] HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), ] - prompt = ChatPromptTemplate.from_messages(messages) + prompt = ChatPromptTemplate.from_messages(messages) # type: ignore[arg-type] agent = RunnableAgent( runnable=create_openai_functions_agent(llm, tools, prompt), input_keys_arg=["input"], @@ -191,12 +191,12 @@ def create_sql_agent( elif agent_type == "openai-tools": if prompt is None: messages = [ - SystemMessage(content=prefix), + SystemMessage(content=prefix), # type: ignore[arg-type] HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), ] - prompt = ChatPromptTemplate.from_messages(messages) + prompt = ChatPromptTemplate.from_messages(messages) # type: ignore[arg-type] agent = RunnableMultiActionAgent( runnable=create_openai_tools_agent(llm, tools, prompt), input_keys_arg=["input"], diff --git a/libs/community/langchain_community/callbacks/mlflow_callback.py b/libs/community/langchain_community/callbacks/mlflow_callback.py index bda5aa4054d3afe..ea862c23fd21653 100644 --- a/libs/community/langchain_community/callbacks/mlflow_callback.py +++ b/libs/community/langchain_community/callbacks/mlflow_callback.py @@ -723,7 +723,7 @@ def _create_session_analysis_df(self) -> Any: ) return session_analysis_df - def _contain_llm_records(self): + def _contain_llm_records(self): # type: ignore[no-untyped-def] return bool(self.records["on_llm_start_records"]) def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None: diff --git a/libs/community/langchain_community/chat_message_histories/elasticsearch.py b/libs/community/langchain_community/chat_message_histories/elasticsearch.py index f98515e99dc1771..a9e076563e9acde 100644 --- a/libs/community/langchain_community/chat_message_histories/elasticsearch.py +++ b/libs/community/langchain_community/chat_message_histories/elasticsearch.py @@ -47,7 +47,7 @@ def __init__( ): self.index: str = index self.session_id: str = session_id - self.ensure_ascii: bool = esnsure_ascii + self.ensure_ascii: bool = esnsure_ascii # type: ignore[assignment] # Initialize Elasticsearch client from passed client arg or connection info if es_connection is not None: diff --git a/libs/community/langchain_community/chat_message_histories/tidb.py b/libs/community/langchain_community/chat_message_histories/tidb.py index bfa36ad06ffa515..973e97820a2e4a4 100644 --- a/libs/community/langchain_community/chat_message_histories/tidb.py +++ b/libs/community/langchain_community/chat_message_histories/tidb.py @@ -40,7 +40,7 @@ def __init__( self.session_id = session_id self.table_name = table_name self.earliest_time = earliest_time - self.cache = [] + self.cache = [] # type: ignore[var-annotated] # Set up SQLAlchemy engine and session self.engine = create_engine(connection_string) @@ -102,7 +102,7 @@ def _load_messages_to_cache(self) -> None: logger.error(f"Error loading messages to cache: {e}") @property - def messages(self) -> List[BaseMessage]: + def messages(self) -> List[BaseMessage]: # type: ignore[override] """returns all messages""" if len(self.cache) == 0: self.reload_cache() diff --git a/libs/community/langchain_community/chat_message_histories/zep.py b/libs/community/langchain_community/chat_message_histories/zep.py index 45de39f150e8e7d..b38b1e9b49d2202 100644 --- a/libs/community/langchain_community/chat_message_histories/zep.py +++ b/libs/community/langchain_community/chat_message_histories/zep.py @@ -149,7 +149,7 @@ def _get_memory(self) -> Optional[Memory]: return None return zep_memory - def add_user_message( + def add_user_message( # type: ignore[override] self, message: str, metadata: Optional[Dict[str, Any]] = None ) -> None: """Convenience method for adding a human message string to the store. @@ -160,7 +160,7 @@ def add_user_message( """ self.add_message(HumanMessage(content=message), metadata=metadata) - def add_ai_message( + def add_ai_message( # type: ignore[override] self, message: str, metadata: Optional[Dict[str, Any]] = None ) -> None: """Convenience method for adding an AI message string to the store. diff --git a/libs/community/langchain_community/chat_models/azureml_endpoint.py b/libs/community/langchain_community/chat_models/azureml_endpoint.py index 58192d6cdce70c4..36229582723ce9c 100644 --- a/libs/community/langchain_community/chat_models/azureml_endpoint.py +++ b/libs/community/langchain_community/chat_models/azureml_endpoint.py @@ -20,7 +20,7 @@ class LlamaContentFormatter(ContentFormatterBase): - def __init__(self): + def __init__(self): # type: ignore[no-untyped-def] raise TypeError( "`LlamaContentFormatter` is deprecated for chat models. Use " "`LlamaChatContentFormatter` instead." @@ -72,7 +72,7 @@ def _convert_message_to_dict(message: BaseMessage) -> Dict: def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, messages: List[BaseMessage], model_kwargs: Dict, @@ -98,9 +98,9 @@ def format_request_payload( raise ValueError( f"`api_type` {api_type} is not supported by this formatter" ) - return str.encode(request_payload) + return str.encode(request_payload) # type: ignore[return-value] - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> ChatGeneration: """Formats response""" @@ -108,7 +108,7 @@ def format_response_payload( try: choice = json.loads(output)["output"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return ChatGeneration( message=BaseMessage( content=choice.strip(), @@ -125,7 +125,7 @@ def format_response_payload( "model. Expected `dict` but `{type(choice)}` was received." ) except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return ChatGeneration( message=BaseMessage( content=choice["message"]["content"].strip(), diff --git a/libs/community/langchain_community/chat_models/edenai.py b/libs/community/langchain_community/chat_models/edenai.py index 8c72f1d791d641e..a97dbf28b9c7d54 100644 --- a/libs/community/langchain_community/chat_models/edenai.py +++ b/libs/community/langchain_community/chat_models/edenai.py @@ -175,7 +175,7 @@ def _stream( """Call out to EdenAI's chat endpoint.""" url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -216,7 +216,7 @@ async def _astream( ) -> AsyncIterator[ChatGenerationChunk]: url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -265,7 +265,7 @@ def _generate( url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -323,7 +323,7 @@ async def _agenerate( url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) diff --git a/libs/community/langchain_community/chat_models/ernie.py b/libs/community/langchain_community/chat_models/ernie.py index 9954ef4272fc9f2..6f2a4d649bfa8bf 100644 --- a/libs/community/langchain_community/chat_models/ernie.py +++ b/libs/community/langchain_community/chat_models/ernie.py @@ -214,7 +214,7 @@ def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult: generations = [ ChatGeneration( message=AIMessage( - content=response.get("result"), + content=response.get("result"), # type: ignore[arg-type] additional_kwargs={**additional_kwargs}, ) ) diff --git a/libs/community/langchain_community/chat_models/gpt_router.py b/libs/community/langchain_community/chat_models/gpt_router.py index 498d8542c8db9d2..571d458e4b3d577 100644 --- a/libs/community/langchain_community/chat_models/gpt_router.py +++ b/libs/community/langchain_community/chat_models/gpt_router.py @@ -56,7 +56,7 @@ class GPTRouterModel(BaseModel): provider_name: str -def get_ordered_generation_requests( +def get_ordered_generation_requests( # type: ignore[no-untyped-def, no-untyped-def] models_priority_list: List[GPTRouterModel], **kwargs ): """ @@ -100,7 +100,7 @@ def completion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]: +) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]: # type: ignore[type-arg] """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -122,7 +122,7 @@ async def acompletion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]: +) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]: # type: ignore[type-arg] """Use tenacity to retry the async completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -282,7 +282,7 @@ async def _agenerate( ) return self._create_chat_result(response) - def _create_chat_generation_chunk( + def _create_chat_generation_chunk( # type: ignore[no-untyped-def, no-untyped-def] self, data: Mapping[str, Any], default_chunk_class ): chunk = _convert_delta_to_message_chunk( @@ -293,7 +293,7 @@ def _create_chat_generation_chunk( dict(finish_reason=finish_reason) if finish_reason is not None else None ) default_chunk_class = chunk.__class__ - chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) + chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) # type: ignore[assignment] return chunk, default_chunk_class def _stream( diff --git a/libs/community/langchain_community/chat_models/huggingface.py b/libs/community/langchain_community/chat_models/huggingface.py index b83184ac6e5fdc5..bb52c7795d3ebc3 100644 --- a/libs/community/langchain_community/chat_models/huggingface.py +++ b/libs/community/langchain_community/chat_models/huggingface.py @@ -144,7 +144,7 @@ def _resolve_model_id(self) -> None: elif isinstance(self.llm, HuggingFaceHub): # no need to look up model_id for HuggingFaceHub LLM - self.model_id = self.llm.repo_id + self.model_id = self.llm.repo_id # type: ignore[assignment] return else: diff --git a/libs/community/langchain_community/chat_models/konko.py b/libs/community/langchain_community/chat_models/konko.py index 8492a5f8c56f311..9a977a1728a932c 100644 --- a/libs/community/langchain_community/chat_models/konko.py +++ b/libs/community/langchain_community/chat_models/konko.py @@ -169,7 +169,7 @@ def get_available_models( } if openai_api_key: - headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value() + headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value() # type: ignore[union-attr] models_response = requests.get(models_url, headers=headers) diff --git a/libs/community/langchain_community/chat_models/ollama.py b/libs/community/langchain_community/chat_models/ollama.py index e0327e7e50cdc6c..a6bb1bb69880dd4 100644 --- a/libs/community/langchain_community/chat_models/ollama.py +++ b/libs/community/langchain_community/chat_models/ollama.py @@ -74,10 +74,10 @@ def _format_message_as_text(self, message: BaseMessage) -> str: if isinstance(message, ChatMessage): message_text = f"\n\n{message.role.capitalize()}: {message.content}" elif isinstance(message, HumanMessage): - if message.content[0].get("type") == "text": - message_text = f"[INST] {message.content[0]['text']} [/INST]" - elif message.content[0].get("type") == "image_url": - message_text = message.content[0]["image_url"]["url"] + if message.content[0].get("type") == "text": # type: ignore[union-attr] + message_text = f"[INST] {message.content[0]['text']} [/INST]" # type: ignore[index] + elif message.content[0].get("type") == "image_url": # type: ignore[union-attr] + message_text = message.content[0]["image_url"]["url"] # type: ignore[index, index] elif isinstance(message, AIMessage): message_text = f"{message.content}" elif isinstance(message, SystemMessage): @@ -112,11 +112,11 @@ def _convert_messages_to_ollama_messages( content = message.content else: for content_part in message.content: - if content_part.get("type") == "text": - content += f"\n{content_part['text']}" - elif content_part.get("type") == "image_url": - if isinstance(content_part.get("image_url"), str): - image_url_components = content_part["image_url"].split(",") + if content_part.get("type") == "text": # type: ignore[union-attr] + content += f"\n{content_part['text']}" # type: ignore[index] + elif content_part.get("type") == "image_url": # type: ignore[union-attr] + if isinstance(content_part.get("image_url"), str): # type: ignore[union-attr] + image_url_components = content_part["image_url"].split(",") # type: ignore[index] # Support data:image/jpeg;base64, format # and base64 strings if len(image_url_components) > 1: @@ -142,7 +142,7 @@ def _convert_messages_to_ollama_messages( } ) - return ollama_messages + return ollama_messages # type: ignore[return-value] def _create_chat_stream( self, @@ -337,7 +337,7 @@ async def _astream( verbose=self.verbose, ) except OllamaEndpointNotFoundError: - async for chunk in self._legacy_astream(messages, stop, **kwargs): + async for chunk in self._legacy_astream(messages, stop, **kwargs): # type: ignore[attr-defined] yield chunk @deprecated("0.0.3", alternative="_stream") diff --git a/libs/community/langchain_community/chat_models/tongyi.py b/libs/community/langchain_community/chat_models/tongyi.py index 004ffdd3b12bfe2..4623864c8513976 100644 --- a/libs/community/langchain_community/chat_models/tongyi.py +++ b/libs/community/langchain_community/chat_models/tongyi.py @@ -197,7 +197,7 @@ def _default_params(self) -> Dict[str, Any]: return { "model": self.model_name, "top_p": self.top_p, - "api_key": self.dashscope_api_key.get_secret_value(), + "api_key": self.dashscope_api_key.get_secret_value(), # type: ignore[union-attr] "result_format": "message", **self.model_kwargs, } diff --git a/libs/community/langchain_community/chat_models/vertexai.py b/libs/community/langchain_community/chat_models/vertexai.py index cecd63ac41f6a3d..c5a81ea32a9fbba 100644 --- a/libs/community/langchain_community/chat_models/vertexai.py +++ b/libs/community/langchain_community/chat_models/vertexai.py @@ -121,7 +121,7 @@ def _convert_to_prompt(part: Union[str, Dict]) -> Part: elif path.startswith("data:image/"): # extract base64 component from image uri try: - encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group( + encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group( # type: ignore[union-attr] 1 ) except AttributeError: diff --git a/libs/community/langchain_community/chat_models/yandex.py b/libs/community/langchain_community/chat_models/yandex.py index 61de024089edfbf..94e05803405f89c 100644 --- a/libs/community/langchain_community/chat_models/yandex.py +++ b/libs/community/langchain_community/chat_models/yandex.py @@ -52,7 +52,7 @@ def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]: return chat_history -class ChatYandexGPT(_BaseYandexGPT, BaseChatModel): +class ChatYandexGPT(_BaseYandexGPT, BaseChatModel): # type: ignore[misc] """Wrapper around YandexGPT large language models. There are two authentication options for the service account @@ -156,7 +156,7 @@ def _make_request( messages=[Message(**message) for message in message_history], ) stub = TextGenerationServiceStub(channel) - res = stub.Completion(request, metadata=self._grpc_metadata) + res = stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] return list(res)[0].alternatives[0].message.text @@ -201,7 +201,7 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st messages=[Message(**message) for message in message_history], ) stub = TextGenerationAsyncServiceStub(channel) - operation = await stub.Completion(request, metadata=self._grpc_metadata) + operation = await stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] async with grpc.aio.secure_channel( operation_api_url, channel_credentials ) as operation_channel: @@ -210,7 +210,8 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st await asyncio.sleep(1) operation_request = GetOperationRequest(operation_id=operation.id) operation = await operation_stub.Get( - operation_request, metadata=self._grpc_metadata + operation_request, + metadata=self._grpc_metadata, # type: ignore[attr-defined] ) completion_response = CompletionResponse() diff --git a/libs/community/langchain_community/chat_models/zhipuai.py b/libs/community/langchain_community/chat_models/zhipuai.py index e8e56f0b95c27d0..113c2be4a1e747a 100644 --- a/libs/community/langchain_community/chat_models/zhipuai.py +++ b/libs/community/langchain_community/chat_models/zhipuai.py @@ -161,7 +161,7 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - def __init__(self, *args, **kwargs): + def __init__(self, *args, **kwargs): # type: ignore[no-untyped-def] super().__init__(*args, **kwargs) try: import zhipuai @@ -174,7 +174,7 @@ def __init__(self, *args, **kwargs): "Please install it via 'pip install zhipuai'" ) - def invoke(self, prompt): + def invoke(self, prompt): # type: ignore[no-untyped-def] if self.model == "chatglm_turbo": return self.zhipuai.model_api.invoke( model=self.model, @@ -195,7 +195,7 @@ def invoke(self, prompt): ) return None - def sse_invoke(self, prompt): + def sse_invoke(self, prompt): # type: ignore[no-untyped-def] if self.model == "chatglm_turbo": return self.zhipuai.model_api.sse_invoke( model=self.model, @@ -218,7 +218,7 @@ def sse_invoke(self, prompt): ) return None - async def async_invoke(self, prompt): + async def async_invoke(self, prompt): # type: ignore[no-untyped-def] loop = asyncio.get_running_loop() partial_func = partial( self.zhipuai.model_api.async_invoke, model=self.model, prompt=prompt @@ -229,7 +229,7 @@ async def async_invoke(self, prompt): ) return response - async def async_invoke_result(self, task_id): + async def async_invoke_result(self, task_id): # type: ignore[no-untyped-def] loop = asyncio.get_running_loop() response = await loop.run_in_executor( None, @@ -270,11 +270,14 @@ def _generate( else: stream_iter = self._stream( - prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + prompt=prompt, # type: ignore[arg-type] + stop=stop, + run_manager=run_manager, + **kwargs, ) return generate_from_stream(stream_iter) - async def _agenerate( + async def _agenerate( # type: ignore[override] self, messages: List[BaseMessage], stop: Optional[List[str]] = None, @@ -307,7 +310,7 @@ async def _agenerate( generations=[ChatGeneration(message=AIMessage(content=content))] ) - def _stream( + def _stream( # type: ignore[override] self, prompt: List[Dict[str, str]], stop: Optional[List[str]] = None, diff --git a/libs/community/langchain_community/document_loaders/assemblyai.py b/libs/community/langchain_community/document_loaders/assemblyai.py index d3947d9f71b2f45..eb4671c0d23b6bb 100644 --- a/libs/community/langchain_community/document_loaders/assemblyai.py +++ b/libs/community/langchain_community/document_loaders/assemblyai.py @@ -123,7 +123,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): """ - def __init__(self, transcript_id, api_key, transcript_format): + def __init__(self, transcript_id, api_key, transcript_format): # type: ignore[no-untyped-def] """ Initializes the AssemblyAI AssemblyAIAudioLoaderById. diff --git a/libs/community/langchain_community/document_loaders/astradb.py b/libs/community/langchain_community/document_loaders/astradb.py index 2af1e77fde189a5..4cc1621a39d647d 100644 --- a/libs/community/langchain_community/document_loaders/astradb.py +++ b/libs/community/langchain_community/document_loaders/astradb.py @@ -65,7 +65,7 @@ def load(self) -> List[Document]: return list(self.lazy_load()) def lazy_load(self) -> Iterator[Document]: - queue = Queue(self.nb_prefetched) + queue = Queue(self.nb_prefetched) # type: ignore[var-annotated] t = threading.Thread(target=self.fetch_results, args=(queue,)) t.start() while True: @@ -95,7 +95,7 @@ async def alazy_load(self) -> AsyncIterator[Document]: item = await run_in_executor(None, lambda it: next(it, done), iterator) if item is done: break - yield item + yield item # type: ignore[misc] return async_collection = await self.astra_env.async_astra_db.collection( self.collection_name @@ -116,13 +116,13 @@ async def alazy_load(self) -> AsyncIterator[Document]: }, ) - def fetch_results(self, queue: Queue): + def fetch_results(self, queue: Queue): # type: ignore[no-untyped-def] self.fetch_page_result(queue) while self.find_options.get("pageState"): self.fetch_page_result(queue) queue.put(None) - def fetch_page_result(self, queue: Queue): + def fetch_page_result(self, queue: Queue): # type: ignore[no-untyped-def] res = self.collection.find( filter=self.filter, options=self.find_options, diff --git a/libs/community/langchain_community/document_loaders/base.py b/libs/community/langchain_community/document_loaders/base.py index 7a3e5a2706c8039..fc266ff51d68337 100644 --- a/libs/community/langchain_community/document_loaders/base.py +++ b/libs/community/langchain_community/document_loaders/base.py @@ -64,10 +64,10 @@ async def alazy_load(self) -> AsyncIterator[Document]: iterator = await run_in_executor(None, self.lazy_load) done = object() while True: - doc = await run_in_executor(None, next, iterator, done) + doc = await run_in_executor(None, next, iterator, done) # type: ignore[call-arg, arg-type] if doc is done: break - yield doc + yield doc # type: ignore[misc] class BaseBlobParser(ABC): diff --git a/libs/community/langchain_community/document_loaders/cassandra.py b/libs/community/langchain_community/document_loaders/cassandra.py index 3167711228ac8b9..a3b7732c131cb79 100644 --- a/libs/community/langchain_community/document_loaders/cassandra.py +++ b/libs/community/langchain_community/document_loaders/cassandra.py @@ -33,14 +33,14 @@ def __init__( page_content_mapper: Callable[[Any], str] = str, metadata_mapper: Callable[[Any], dict] = lambda _: {}, *, - query_parameters: Union[dict, Sequence] = None, - query_timeout: Optional[float] = _NOT_SET, + query_parameters: Union[dict, Sequence] = None, # type: ignore[assignment] + query_timeout: Optional[float] = _NOT_SET, # type: ignore[assignment] query_trace: bool = False, - query_custom_payload: dict = None, + query_custom_payload: dict = None, # type: ignore[assignment] query_execution_profile: Any = _NOT_SET, query_paging_state: Any = None, query_host: Host = None, - query_execute_as: str = None, + query_execute_as: str = None, # type: ignore[assignment] ) -> None: """ Document Loader for Apache Cassandra. @@ -85,7 +85,7 @@ def __init__( self.query = f"SELECT * FROM {_keyspace}.{table};" self.metadata = {"table": table, "keyspace": _keyspace} else: - self.query = query + self.query = query # type: ignore[assignment] self.metadata = {} self.session = session or check_resolve_session(session) diff --git a/libs/community/langchain_community/document_loaders/chm.py b/libs/community/langchain_community/document_loaders/chm.py index c036ee8b8f364a7..381087d9917e5e8 100644 --- a/libs/community/langchain_community/document_loaders/chm.py +++ b/libs/community/langchain_community/document_loaders/chm.py @@ -27,7 +27,7 @@ class UnstructuredCHMLoader(UnstructuredFileLoader): def _get_elements(self) -> List: from unstructured.partition.html import partition_html - with CHMParser(self.file_path) as f: + with CHMParser(self.file_path) as f: # type: ignore[arg-type] return [ partition_html(text=item["content"], **self.unstructured_kwargs) for item in f.load_all() @@ -45,10 +45,10 @@ def __init__(self, path: str): self.file = chm.CHMFile() self.file.LoadCHM(path) - def __enter__(self): + def __enter__(self): # type: ignore[no-untyped-def] return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback): # type: ignore[no-untyped-def] if self.file: self.file.CloseCHM() diff --git a/libs/community/langchain_community/document_loaders/doc_intelligence.py b/libs/community/langchain_community/document_loaders/doc_intelligence.py index d1326afdbe3aadc..2aae5212a594453 100644 --- a/libs/community/langchain_community/document_loaders/doc_intelligence.py +++ b/libs/community/langchain_community/document_loaders/doc_intelligence.py @@ -89,4 +89,4 @@ def lazy_load( blob = Blob.from_path(self.file_path) yield from self.parser.parse(blob) else: - yield from self.parser.parse_url(self.url_path) + yield from self.parser.parse_url(self.url_path) # type: ignore[arg-type] diff --git a/libs/community/langchain_community/document_loaders/mediawikidump.py b/libs/community/langchain_community/document_loaders/mediawikidump.py index 360420c536dd7c7..4868e5f89cd3545 100644 --- a/libs/community/langchain_community/document_loaders/mediawikidump.py +++ b/libs/community/langchain_community/document_loaders/mediawikidump.py @@ -60,7 +60,7 @@ def __init__( self.skip_redirects = skip_redirects self.stop_on_error = stop_on_error - def _load_dump_file(self): + def _load_dump_file(self): # type: ignore[no-untyped-def] try: import mwxml except ImportError as e: @@ -70,7 +70,7 @@ def _load_dump_file(self): return mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding)) - def _load_single_page_from_dump(self, page) -> Document: + def _load_single_page_from_dump(self, page) -> Document: # type: ignore[no-untyped-def, return] """Parse a single page.""" try: import mwparserfromhell diff --git a/libs/community/langchain_community/document_loaders/parsers/vsdx.py b/libs/community/langchain_community/document_loaders/parsers/vsdx.py index 109521e48cc9fa9..d4dde56de0c99a8 100644 --- a/libs/community/langchain_community/document_loaders/parsers/vsdx.py +++ b/libs/community/langchain_community/document_loaders/parsers/vsdx.py @@ -11,7 +11,7 @@ class VsdxParser(BaseBlobParser, ABC): - def parse(self, blob: Blob) -> Iterator[Document]: + def parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[override] """Parse a vsdx file.""" return self.lazy_parse(blob) @@ -21,7 +21,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]: with blob.as_bytes_io() as pdf_file_obj: with zipfile.ZipFile(pdf_file_obj, "r") as zfile: - pages = self.get_pages_content(zfile, blob.source) + pages = self.get_pages_content(zfile, blob.source) # type: ignore[arg-type] yield from [ Document( @@ -60,13 +60,13 @@ def get_pages_content( if "visio/pages/pages.xml" not in zfile.namelist(): print("WARNING - No pages.xml file found in {}".format(source)) - return + return # type: ignore[return-value] if "visio/pages/_rels/pages.xml.rels" not in zfile.namelist(): print("WARNING - No pages.xml.rels file found in {}".format(source)) - return + return # type: ignore[return-value] if "docProps/app.xml" not in zfile.namelist(): print("WARNING - No app.xml file found in {}".format(source)) - return + return # type: ignore[return-value] pagesxml_content: dict = xmltodict.parse(zfile.read("visio/pages/pages.xml")) appxml_content: dict = xmltodict.parse(zfile.read("docProps/app.xml")) @@ -79,7 +79,7 @@ def get_pages_content( rel["@Name"].strip() for rel in pagesxml_content["Pages"]["Page"] ] else: - disordered_names: List[str] = [ + disordered_names: List[str] = [ # type: ignore[no-redef] pagesxml_content["Pages"]["Page"]["@Name"].strip() ] if isinstance(pagesxmlrels_content["Relationships"]["Relationship"], list): @@ -88,7 +88,7 @@ def get_pages_content( for rel in pagesxmlrels_content["Relationships"]["Relationship"] ] else: - disordered_paths: List[str] = [ + disordered_paths: List[str] = [ # type: ignore[no-redef] "visio/pages/" + pagesxmlrels_content["Relationships"]["Relationship"]["@Target"] ] diff --git a/libs/community/langchain_community/embeddings/baichuan.py b/libs/community/langchain_community/embeddings/baichuan.py index b31e6092d592527..9f0bf92ea8a45b8 100644 --- a/libs/community/langchain_community/embeddings/baichuan.py +++ b/libs/community/langchain_community/embeddings/baichuan.py @@ -89,7 +89,7 @@ def _embed(self, texts: List[str]) -> Optional[List[List[float]]]: print(f"Exception occurred while trying to get embeddings: {str(e)}") return None - def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: + def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: # type: ignore[override] """Public method to get embeddings for a list of documents. Args: @@ -100,7 +100,7 @@ def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: """ return self._embed(texts) - def embed_query(self, text: str) -> Optional[List[float]]: + def embed_query(self, text: str) -> Optional[List[float]]: # type: ignore[override] """Public method to get embedding for a single query text. Args: diff --git a/libs/community/langchain_community/embeddings/edenai.py b/libs/community/langchain_community/embeddings/edenai.py index 3d6b1ec16d039c9..9446969d332c51b 100644 --- a/libs/community/langchain_community/embeddings/edenai.py +++ b/libs/community/langchain_community/embeddings/edenai.py @@ -56,7 +56,7 @@ def _generate_embeddings(self, texts: List[str]) -> List[List[float]]: headers = { "accept": "application/json", "content-type": "application/json", - "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } diff --git a/libs/community/langchain_community/embeddings/embaas.py b/libs/community/langchain_community/embeddings/embaas.py index 2f0b31f4439229a..00f23116bf4c0d2 100644 --- a/libs/community/langchain_community/embeddings/embaas.py +++ b/libs/community/langchain_community/embeddings/embaas.py @@ -85,7 +85,7 @@ def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload: def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]: """Sends a request to the Embaas API and handles the response.""" headers = { - "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } diff --git a/libs/community/langchain_community/embeddings/gradient_ai.py b/libs/community/langchain_community/embeddings/gradient_ai.py index 6fd05d3c699dfee..c0133d023863a7c 100644 --- a/libs/community/langchain_community/embeddings/gradient_ai.py +++ b/libs/community/langchain_community/embeddings/gradient_ai.py @@ -162,5 +162,5 @@ class TinyAsyncGradientEmbeddingClient: #: :meta private: It might be entirely removed in the future. """ - def __init__(self, *args, **kwargs) -> None: + def __init__(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def] raise ValueError("Deprecated,TinyAsyncGradientEmbeddingClient was removed.") diff --git a/libs/community/langchain_community/embeddings/llm_rails.py b/libs/community/langchain_community/embeddings/llm_rails.py index 44bc0171803ae9b..c8afa11b491debd 100644 --- a/libs/community/langchain_community/embeddings/llm_rails.py +++ b/libs/community/langchain_community/embeddings/llm_rails.py @@ -56,7 +56,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: """ response = requests.post( "https://api.llmrails.com/v1/embeddings", - headers={"X-API-KEY": self.api_key.get_secret_value()}, + headers={"X-API-KEY": self.api_key.get_secret_value()}, # type: ignore[union-attr] json={"input": texts, "model": self.model}, timeout=60, ) diff --git a/libs/community/langchain_community/embeddings/minimax.py b/libs/community/langchain_community/embeddings/minimax.py index a13eb98367d6597..38e56c553b87d33 100644 --- a/libs/community/langchain_community/embeddings/minimax.py +++ b/libs/community/langchain_community/embeddings/minimax.py @@ -110,7 +110,7 @@ def embed( # HTTP headers for authorization headers = { - "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } diff --git a/libs/community/langchain_community/embeddings/mlflow.py b/libs/community/langchain_community/embeddings/mlflow.py index 6b24dacb0257549..1b1abb410323728 100644 --- a/libs/community/langchain_community/embeddings/mlflow.py +++ b/libs/community/langchain_community/embeddings/mlflow.py @@ -71,7 +71,8 @@ def embed(self, texts: List[str], params: Dict[str, str]) -> List[List[float]]: embeddings: List[List[float]] = [] for txt in _chunk(texts, 20): resp = self._client.predict( - endpoint=self.endpoint, inputs={"input": txt, **params} + endpoint=self.endpoint, + inputs={"input": txt, **params}, # type: ignore[arg-type] ) embeddings.extend(r["embedding"] for r in resp["data"]) return embeddings diff --git a/libs/community/langchain_community/embeddings/oci_generative_ai.py b/libs/community/langchain_community/embeddings/oci_generative_ai.py index 6d47fec6f32f9f8..afcbb62024a02e6 100644 --- a/libs/community/langchain_community/embeddings/oci_generative_ai.py +++ b/libs/community/langchain_community/embeddings/oci_generative_ai.py @@ -63,16 +63,16 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): If not specified , DEFAULT will be used """ - model_id: str = None + model_id: str = None # type: ignore[assignment] """Id of the model to call, e.g., cohere.embed-english-light-v2.0""" model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None + service_endpoint: str = None # type: ignore[assignment] """service endpoint url""" - compartment_id: str = None + compartment_id: str = None # type: ignore[assignment] """OCID of compartment""" truncate: Optional[str] = "END" @@ -109,7 +109,7 @@ def validate_environment(cls, values: Dict) -> Dict: # pylint: disable=no-self- client_kwargs.pop("signer", None) elif values["auth_type"] == OCIAuthType(2).name: - def make_security_token_signer(oci_config): + def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] pk = oci.signer.load_private_key_from_file( oci_config.get("key_file"), None ) diff --git a/libs/community/langchain_community/embeddings/spacy_embeddings.py b/libs/community/langchain_community/embeddings/spacy_embeddings.py index 645d5afc96398cc..954b2550fb41aff 100644 --- a/libs/community/langchain_community/embeddings/spacy_embeddings.py +++ b/libs/community/langchain_community/embeddings/spacy_embeddings.py @@ -78,7 +78,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: Returns: A list of embeddings, one for each document. """ - return [self.nlp(text).vector.tolist() for text in texts] + return [self.nlp(text).vector.tolist() for text in texts] # type: ignore[misc] def embed_query(self, text: str) -> List[float]: """ @@ -90,7 +90,7 @@ def embed_query(self, text: str) -> List[float]: Returns: The embedding for the text. """ - return self.nlp(text).vector.tolist() + return self.nlp(text).vector.tolist() # type: ignore[misc] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """ diff --git a/libs/community/langchain_community/embeddings/yandex.py b/libs/community/langchain_community/embeddings/yandex.py index d3e6ed69e456b66..34f71feafae8896 100644 --- a/libs/community/langchain_community/embeddings/yandex.py +++ b/libs/community/langchain_community/embeddings/yandex.py @@ -42,10 +42,10 @@ class YandexGPTEmbeddings(BaseModel, Embeddings): embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb:///text-search-query/latest") """ - iam_token: SecretStr = "" + iam_token: SecretStr = "" # type: ignore[assignment] """Yandex Cloud IAM token for service account with the `ai.languageModels.user` role""" - api_key: SecretStr = "" + api_key: SecretStr = "" # type: ignore[assignment] """Yandex Cloud Api Key for service account with the `ai.languageModels.user` role""" model_uri: str = "" @@ -146,7 +146,7 @@ def _completion_with_retry(**_kwargs: Any) -> Any: return _completion_with_retry(**kwargs) -def _make_request(self: YandexGPTEmbeddings, texts: List[str]): +def _make_request(self: YandexGPTEmbeddings, texts: List[str]): # type: ignore[no-untyped-def] try: import grpc from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501 @@ -167,7 +167,7 @@ def _make_request(self: YandexGPTEmbeddings, texts: List[str]): for text in texts: request = TextEmbeddingRequest(model_uri=self.model_uri, text=text) stub = EmbeddingsServiceStub(channel) - res = stub.TextEmbedding(request, metadata=self._grpc_metadata) + res = stub.TextEmbedding(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] result.append(list(res.embedding)) time.sleep(self.sleep_interval) diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py index b8970c06ecce53a..125fd96b151c5d9 100644 --- a/libs/community/langchain_community/graphs/neo4j_graph.py +++ b/libs/community/langchain_community/graphs/neo4j_graph.py @@ -56,7 +56,7 @@ def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]: cleaned_list.append(value_sanitize(item)) else: cleaned_list.append(item) - new_dict[key] = cleaned_list + new_dict[key] = cleaned_list # type: ignore[assignment] else: new_dict[key] = value return new_dict diff --git a/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py b/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py index c1072a97f81b00b..bf5d1a7177747b4 100644 --- a/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py +++ b/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py @@ -95,12 +95,13 @@ def __init__( if local_file: ontology_schema_graph = self._load_ontology_schema_from_file( - local_file, local_file_format + local_file, + local_file_format, # type: ignore[arg-type] ) else: - self._validate_user_query(query_ontology) + self._validate_user_query(query_ontology) # type: ignore[arg-type] ontology_schema_graph = self._load_ontology_schema_with_query( - query_ontology + query_ontology # type: ignore[arg-type] ) self.schema = ontology_schema_graph.serialize(format="turtle") @@ -139,7 +140,7 @@ def _check_connectivity(self) -> None: ) @staticmethod - def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None): + def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None): # type: ignore[no-untyped-def, assignment] """ Parse the ontology schema statements from the provided file """ @@ -176,7 +177,7 @@ def _validate_user_query(query_ontology: str) -> None: "Invalid query type. Only CONSTRUCT queries are supported." ) - def _load_ontology_schema_with_query(self, query: str): + def _load_ontology_schema_with_query(self, query: str): # type: ignore[no-untyped-def] """ Execute the query for collecting the ontology schema statements """ diff --git a/libs/community/langchain_community/graphs/tigergraph_graph.py b/libs/community/langchain_community/graphs/tigergraph_graph.py index cff2f4e2ce7fa74..f32d43e6e5e2cdf 100644 --- a/libs/community/langchain_community/graphs/tigergraph_graph.py +++ b/libs/community/langchain_community/graphs/tigergraph_graph.py @@ -31,7 +31,7 @@ def conn(self) -> Any: def schema(self) -> Dict[str, Any]: return self._schema - def get_schema(self) -> str: + def get_schema(self) -> str: # type: ignore[override] if self._schema: return str(self._schema) else: @@ -71,10 +71,10 @@ def generate_schema( """ return self._conn.getSchema(force=True) - def refresh_schema(self): + def refresh_schema(self): # type: ignore[no-untyped-def] self.generate_schema() - def query(self, query: str) -> Dict[str, Any]: + def query(self, query: str) -> Dict[str, Any]: # type: ignore[override] """Query the TigerGraph database.""" answer = self._conn.ai.query(query) return answer diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py index 3480a801f96f691..3601a7629a22611 100644 --- a/libs/community/langchain_community/llms/azureml_endpoint.py +++ b/libs/community/langchain_community/llms/azureml_endpoint.py @@ -165,7 +165,7 @@ class GPT2ContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: prompt = ContentFormatterBase.escape_special_characters(prompt) @@ -174,13 +174,13 @@ def format_request_payload( ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0]["0"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -207,7 +207,7 @@ class HFContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: ContentFormatterBase.escape_special_characters(prompt) @@ -216,13 +216,13 @@ def format_request_payload( ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0]["0"]["generated_text"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -233,7 +233,7 @@ class DollyContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: prompt = ContentFormatterBase.escape_special_characters(prompt) @@ -245,13 +245,13 @@ def format_request_payload( ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -262,7 +262,7 @@ class LlamaContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: """Formats the request according to the chosen api""" @@ -284,7 +284,7 @@ def format_request_payload( ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: """Formats response""" @@ -292,7 +292,7 @@ def format_response_payload( try: choice = json.loads(output)[0]["0"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) if api_type == AzureMLEndpointApiType.serverless: try: @@ -304,7 +304,7 @@ def format_response_payload( "received." ) except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation( text=choice["text"].strip(), generation_info=dict( @@ -397,7 +397,7 @@ def validate_endpoint_api_type( ) -> AzureMLEndpointApiType: """Validate that endpoint api type is compatible with the URL format.""" endpoint_url = values.get("endpoint_url") - if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith( + if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith( # type: ignore[union-attr] "/score" ): raise ValueError( @@ -407,8 +407,8 @@ def validate_endpoint_api_type( "`/v1/chat/completions`, use `endpoint_api_type='serverless'` instead." ) if field_value == AzureMLEndpointApiType.serverless and not ( - endpoint_url.endswith("/v1/completions") - or endpoint_url.endswith("/v1/chat/completions") + endpoint_url.endswith("/v1/completions") # type: ignore[union-attr] + or endpoint_url.endswith("/v1/chat/completions") # type: ignore[union-attr] ): raise ValueError( "Endpoints of type `serverless` should follow the format " @@ -426,7 +426,9 @@ def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClien deployment_name = values.get("deployment_name") http_client = AzureMLEndpointClient( - endpoint_url, endpoint_key.get_secret_value(), deployment_name + endpoint_url, # type: ignore + endpoint_key.get_secret_value(), # type: ignore + deployment_name, # type: ignore ) return http_client diff --git a/libs/community/langchain_community/llms/baichuan.py b/libs/community/langchain_community/llms/baichuan.py index 2627b81bd243530..293c95d2c84a8fe 100644 --- a/libs/community/langchain_community/llms/baichuan.py +++ b/libs/community/langchain_community/llms/baichuan.py @@ -56,11 +56,11 @@ def _default_params(self) -> Dict[str, Any]: def _post(self, request: Any) -> Any: headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", # type: ignore[union-attr] } try: response = requests.post( - self.baichuan_api_host, + self.baichuan_api_host, # type: ignore[arg-type] headers=headers, json=request, timeout=self.timeout, diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py index c66d0d071a84367..002eb90fc0b9d8d 100644 --- a/libs/community/langchain_community/llms/bedrock.py +++ b/libs/community/langchain_community/llms/bedrock.py @@ -395,8 +395,8 @@ def _get_guardrails_canonical(self) -> Dict[str, Any]: """ return { "amazon-bedrock-guardrailDetails": { - "guardrailId": self.guardrails.get("id"), - "guardrailVersion": self.guardrails.get("version"), + "guardrailId": self.guardrails.get("id"), # type: ignore[union-attr] + "guardrailVersion": self.guardrails.get("version"), # type: ignore[union-attr] } } @@ -427,7 +427,7 @@ def _prepare_input_and_invoke( if self._guardrails_enabled: request_options["guardrail"] = "ENABLED" - if self.guardrails.get("trace"): + if self.guardrails.get("trace"): # type: ignore[union-attr] request_options["trace"] = "ENABLED" try: @@ -446,7 +446,7 @@ def _prepare_input_and_invoke( # Verify and raise a callback error if any intervention occurs or a signal is # sent from a Bedrock service, # such as when guardrails are triggered. - services_trace = self._get_bedrock_services_signal(body) + services_trace = self._get_bedrock_services_signal(body) # type: ignore[arg-type] if services_trace.get("signal") and run_manager is not None: run_manager.on_llm_error( @@ -468,7 +468,7 @@ def _get_bedrock_services_signal(self, body: dict) -> dict: if ( self._guardrails_enabled - and self.guardrails.get("trace") + and self.guardrails.get("trace") # type: ignore[union-attr] and self._is_guardrails_intervention(body) ): return { @@ -526,7 +526,7 @@ def _prepare_input_and_invoke_stream( if self._guardrails_enabled: request_options["guardrail"] = "ENABLED" - if self.guardrails.get("trace"): + if self.guardrails.get("trace"): # type: ignore[union-attr] request_options["trace"] = "ENABLED" try: @@ -540,7 +540,7 @@ def _prepare_input_and_invoke_stream( ): yield chunk # verify and raise callback error if any middleware intervened - self._get_bedrock_services_signal(chunk.generation_info) + self._get_bedrock_services_signal(chunk.generation_info) # type: ignore[arg-type] if run_manager is not None: run_manager.on_llm_new_token(chunk.text, chunk=chunk) @@ -588,7 +588,7 @@ async def _aprepare_input_and_invoke_stream( ): await run_manager.on_llm_new_token(chunk.text, chunk=chunk) elif run_manager is not None: - run_manager.on_llm_new_token(chunk.text, chunk=chunk) + run_manager.on_llm_new_token(chunk.text, chunk=chunk) # type: ignore[unused-coroutine] class Bedrock(LLM, BedrockBase): diff --git a/libs/community/langchain_community/llms/oci_generative_ai.py b/libs/community/langchain_community/llms/oci_generative_ai.py index 092cbda554857e8..0ed977e24b035db 100644 --- a/libs/community/langchain_community/llms/oci_generative_ai.py +++ b/libs/community/langchain_community/llms/oci_generative_ai.py @@ -42,10 +42,10 @@ class OCIGenAIBase(BaseModel, ABC): If not specified , DEFAULT will be used """ - model_id: str = None + model_id: str = None # type: ignore[assignment] """Id of the model to call, e.g., cohere.command""" - provider: str = None + provider: str = None # type: ignore[assignment] """Provider name of the model. Default to None, will try to be derived from the model_id otherwise, requires user input @@ -54,10 +54,10 @@ class OCIGenAIBase(BaseModel, ABC): model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None + service_endpoint: str = None # type: ignore[assignment] """service endpoint url""" - compartment_id: str = None + compartment_id: str = None # type: ignore[assignment] """OCID of compartment""" is_stream: bool = False @@ -94,7 +94,7 @@ def validate_environment(cls, values: Dict) -> Dict: client_kwargs.pop("signer", None) elif values["auth_type"] == OCIAuthType(2).name: - def make_security_token_signer(oci_config): + def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] pk = oci.signer.load_private_key_from_file( oci_config.get("key_file"), None ) diff --git a/libs/community/langchain_community/llms/ollama.py b/libs/community/langchain_community/llms/ollama.py index a06ab72641b13c3..c6aba99accf72c3 100644 --- a/libs/community/langchain_community/llms/ollama.py +++ b/libs/community/langchain_community/llms/ollama.py @@ -297,7 +297,7 @@ async def _acreate_stream( "Ollama call failed with status code 404." ) else: - optional_detail = await response.json().get("error") + optional_detail = await response.json().get("error") # type: ignore[attr-defined] raise ValueError( f"Ollama call failed with status code {response.status}." f" Details: {optional_detail}" @@ -380,7 +380,7 @@ def _llm_type(self) -> str: """Return type of llm.""" return "ollama-llm" - def _generate( + def _generate( # type: ignore[override] self, prompts: List[str], stop: Optional[List[str]] = None, @@ -416,7 +416,7 @@ def _generate( generations.append([final_chunk]) return LLMResult(generations=generations) - async def _agenerate( + async def _agenerate( # type: ignore[override] self, prompts: List[str], stop: Optional[List[str]] = None, @@ -445,7 +445,7 @@ async def _agenerate( prompt, stop=stop, images=images, - run_manager=run_manager, + run_manager=run_manager, # type: ignore[arg-type] verbose=self.verbose, **kwargs, ) diff --git a/libs/community/langchain_community/llms/pipelineai.py b/libs/community/langchain_community/llms/pipelineai.py index d309c9cab55b75e..170722d920a532d 100644 --- a/libs/community/langchain_community/llms/pipelineai.py +++ b/libs/community/langchain_community/llms/pipelineai.py @@ -102,7 +102,7 @@ def _call( "Could not import pipeline-ai python package. " "Please install it with `pip install pipeline-ai`." ) - client = PipelineCloud(token=self.pipeline_api_key.get_secret_value()) + client = PipelineCloud(token=self.pipeline_api_key.get_secret_value()) # type: ignore[union-attr] params = self.pipeline_kwargs or {} params = {**params, **kwargs} diff --git a/libs/community/langchain_community/llms/stochasticai.py b/libs/community/langchain_community/llms/stochasticai.py index d645a019da76475..4cfc6d5ee9482da 100644 --- a/libs/community/langchain_community/llms/stochasticai.py +++ b/libs/community/langchain_community/llms/stochasticai.py @@ -107,7 +107,7 @@ def _call( url=self.api_url, json={"prompt": prompt, "params": params}, headers={ - "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", + "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", # type: ignore[union-attr] "Accept": "application/json", "Content-Type": "application/json", }, @@ -119,7 +119,7 @@ def _call( response_get = requests.get( url=response_post_json["data"]["responseUrl"], headers={ - "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", + "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", # type: ignore[union-attr] "Accept": "application/json", "Content-Type": "application/json", }, diff --git a/libs/community/langchain_community/llms/vertexai.py b/libs/community/langchain_community/llms/vertexai.py index 3833bbd785c7d66..58e209f7665901a 100644 --- a/libs/community/langchain_community/llms/vertexai.py +++ b/libs/community/langchain_community/llms/vertexai.py @@ -49,7 +49,7 @@ def is_gemini_model(model_name: str) -> bool: return model_name is not None and "gemini" in model_name -def completion_with_retry( +def completion_with_retry( # type: ignore[no-redef] llm: VertexAI, prompt: List[Union[str, "Image"]], stream: bool = False, @@ -330,7 +330,7 @@ def _generate( generation += chunk generations.append([generation]) else: - res = completion_with_retry( + res = completion_with_retry( # type: ignore[misc] self, [prompt], stream=should_stream, @@ -373,7 +373,7 @@ def _stream( **kwargs: Any, ) -> Iterator[GenerationChunk]: params = self._prepare_params(stop=stop, stream=True, **kwargs) - for stream_resp in completion_with_retry( + for stream_resp in completion_with_retry( # type: ignore[misc] self, [prompt], stream=True, diff --git a/libs/community/langchain_community/llms/watsonxllm.py b/libs/community/langchain_community/llms/watsonxllm.py index d60c72846005964..d3225730c39a879 100644 --- a/libs/community/langchain_community/llms/watsonxllm.py +++ b/libs/community/langchain_community/llms/watsonxllm.py @@ -250,9 +250,9 @@ def get_count_value(key: str, result: Dict[str, Any]) -> int: } def _get_chat_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]: - params: Dict[str, Any] = {**self.params} if self.params else None + params: Dict[str, Any] = {**self.params} if self.params else {} if stop is not None: - params = (params or {}) | {"stop_sequences": stop} + params["stop_sequences"] = stop return params def _create_llm_result(self, response: List[dict]) -> LLMResult: diff --git a/libs/community/langchain_community/llms/yandex.py b/libs/community/langchain_community/llms/yandex.py index f8e053733181230..d4cf5840ca189c4 100644 --- a/libs/community/langchain_community/llms/yandex.py +++ b/libs/community/langchain_community/llms/yandex.py @@ -25,10 +25,10 @@ class _BaseYandexGPT(Serializable): - iam_token: SecretStr = "" + iam_token: SecretStr = "" # type: ignore[assignment] """Yandex Cloud IAM token for service or user account with the `ai.languageModels.user` role""" - api_key: SecretStr = "" + api_key: SecretStr = "" # type: ignore[assignment] """Yandex Cloud Api Key for service account with the `ai.languageModels.user` role""" folder_id: str = "" @@ -211,7 +211,7 @@ def _make_request( messages=[Message(role="user", text=prompt)], ) stub = TextGenerationServiceStub(channel) - res = stub.Completion(request, metadata=self._grpc_metadata) + res = stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] return list(res)[0].alternatives[0].message.text @@ -253,7 +253,7 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str: messages=[Message(role="user", text=prompt)], ) stub = TextGenerationAsyncServiceStub(channel) - operation = await stub.Completion(request, metadata=self._grpc_metadata) + operation = await stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] async with grpc.aio.secure_channel( operation_api_url, channel_credentials ) as operation_channel: @@ -262,7 +262,8 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str: await asyncio.sleep(1) operation_request = GetOperationRequest(operation_id=operation.id) operation = await operation_stub.Get( - operation_request, metadata=self._grpc_metadata + operation_request, + metadata=self._grpc_metadata, # type: ignore[attr-defined] ) completion_response = CompletionResponse() diff --git a/libs/community/langchain_community/tools/amadeus/closest_airport.py b/libs/community/langchain_community/tools/amadeus/closest_airport.py index cf108f3b11d9910..1a3f4c4da2cb284 100644 --- a/libs/community/langchain_community/tools/amadeus/closest_airport.py +++ b/libs/community/langchain_community/tools/amadeus/closest_airport.py @@ -58,4 +58,4 @@ def _run( ' Location Identifier" ' ) - return self.llm.invoke(content) + return self.llm.invoke(content) # type: ignore[union-attr] diff --git a/libs/community/langchain_community/tools/shell/tool.py b/libs/community/langchain_community/tools/shell/tool.py index e26deb365dcfc0f..15445441a30250b 100644 --- a/libs/community/langchain_community/tools/shell/tool.py +++ b/libs/community/langchain_community/tools/shell/tool.py @@ -93,10 +93,10 @@ def _run( return self.process.run(commands) else: logger.info("Invalid input. User aborted command execution.") - return None + return None # type: ignore[return-value] else: return self.process.run(commands) except Exception as e: logger.error(f"Error during command execution: {e}") - return None + return None # type: ignore[return-value] diff --git a/libs/community/langchain_community/utilities/brave_search.py b/libs/community/langchain_community/utilities/brave_search.py index 8f3df0666c6af67..fd282fc3465a52a 100644 --- a/libs/community/langchain_community/utilities/brave_search.py +++ b/libs/community/langchain_community/utilities/brave_search.py @@ -48,7 +48,7 @@ def download_documents(self, query: str) -> List[Document]: results = self._search_request(query) return [ Document( - page_content=item.get("description"), + page_content=item.get("description"), # type: ignore[arg-type] metadata={"title": item.get("title"), "link": item.get("url")}, ) for item in results diff --git a/libs/community/langchain_community/utilities/requests.py b/libs/community/langchain_community/utilities/requests.py index 673df3d5e57fe81..fd183b4cfad1ea7 100644 --- a/libs/community/langchain_community/utilities/requests.py +++ b/libs/community/langchain_community/utilities/requests.py @@ -141,9 +141,9 @@ def _aget_resp_content( self, response: aiohttp.ClientResponse ) -> Union[str, Dict[str, Any]]: if self.response_content_type == "text": - return response.text() + return response.text() # type: ignore[return-value] elif self.response_content_type == "json": - return response.json() + return response.json() # type: ignore[return-value] else: raise ValueError(f"Invalid return type: {self.response_content_type}") @@ -176,33 +176,33 @@ def delete(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]: async def aget(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]: """GET the URL and return the text asynchronously.""" async with self.requests.aget(url, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def apost( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """POST to the URL and return the text asynchronously.""" async with self.requests.apost(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def apatch( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """PATCH the URL and return the text asynchronously.""" async with self.requests.apatch(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def aput( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """PUT the URL and return the text asynchronously.""" async with self.requests.aput(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def adelete(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]: """DELETE the URL and return the text asynchronously.""" async with self.requests.adelete(url, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] class JsonRequestsWrapper(GenericRequestsWrapper): diff --git a/libs/community/langchain_community/utilities/sql_database.py b/libs/community/langchain_community/utilities/sql_database.py index e56423733cb2969..298b8ca7e224c98 100644 --- a/libs/community/langchain_community/utilities/sql_database.py +++ b/libs/community/langchain_community/utilities/sql_database.py @@ -381,7 +381,7 @@ def _execute( If the statement returns no rows, an empty list is returned. """ - with self._engine.begin() as connection: # type: Connection + with self._engine.begin() as connection: # type: Connection # type: ignore[name-defined] if self._schema is not None: if self.dialect == "snowflake": connection.exec_driver_sql( @@ -444,7 +444,7 @@ def run( ] if not include_columns: - res = [tuple(row.values()) for row in res] + res = [tuple(row.values()) for row in res] # type: ignore[misc] if not res: return "" diff --git a/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py b/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py index c9d5b85a6bd4cb1..12d02ae19d36233 100644 --- a/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py +++ b/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py @@ -356,7 +356,7 @@ def create_results(self, json_result: Dict[str, Any]) -> List[Document]: "fields" not in item or self.config.field_name_mapping["document"] not in item["fields"] ): - query_result_list.append(Document()) + query_result_list.append(Document()) # type: ignore[call-arg] else: fields = item["fields"] query_result_list.append( diff --git a/libs/community/langchain_community/vectorstores/astradb.py b/libs/community/langchain_community/vectorstores/astradb.py index 7d59bc91ebd0088..e6d1a5e010072f4 100644 --- a/libs/community/langchain_community/vectorstores/astradb.py +++ b/libs/community/langchain_community/vectorstores/astradb.py @@ -140,7 +140,7 @@ def _filter_to_metadata(filter_dict: Optional[Dict[str, Any]]) -> Dict[str, Any] if isinstance(v, list): metadata_filter[k] = [AstraDB._filter_to_metadata(f) for f in v] else: - metadata_filter[k] = AstraDB._filter_to_metadata(v) + metadata_filter[k] = AstraDB._filter_to_metadata(v) # type: ignore[assignment] else: metadata_filter[f"metadata.{k}"] = v @@ -253,13 +253,13 @@ def __init__( else: self.clear() - def _ensure_astra_db_client(self): + def _ensure_astra_db_client(self): # type: ignore[no-untyped-def] if not self.astra_db: raise ValueError("Missing AstraDB client") async def _setup_db(self, pre_delete_collection: bool) -> None: if pre_delete_collection: - await self.async_astra_db.delete_collection( + await self.async_astra_db.delete_collection( # type: ignore[union-attr] collection_name=self.collection_name, ) await self._aprovision_collection() @@ -282,7 +282,7 @@ def _provision_collection(self) -> None: Internal-usage method, no object members are set, other than working on the underlying actual storage. """ - self.astra_db.create_collection( + self.astra_db.create_collection( # type: ignore[union-attr] dimension=self._get_embedding_dimension(), collection_name=self.collection_name, metric=self.metric, @@ -295,7 +295,7 @@ async def _aprovision_collection(self) -> None: Internal-usage method, no object members are set, other than working on the underlying actual storage. """ - await self.async_astra_db.create_collection( + await self.async_astra_db.create_collection( # type: ignore[union-attr] dimension=self._get_embedding_dimension(), collection_name=self.collection_name, metric=self.metric, @@ -328,7 +328,7 @@ async def aclear(self) -> None: await self._ensure_db_setup() if not self.async_astra_db: await run_in_executor(None, self.clear) - await self.async_collection.delete_many({}) + await self.async_collection.delete_many({}) # type: ignore[union-attr] def delete_by_document_id(self, document_id: str) -> bool: """ @@ -336,7 +336,7 @@ def delete_by_document_id(self, document_id: str) -> bool: Return True if a document has indeed been deleted, False if ID not found. """ self._ensure_astra_db_client() - deletion_response = self.collection.delete_one(document_id) + deletion_response = self.collection.delete_one(document_id) # type: ignore[union-attr] return ((deletion_response or {}).get("status") or {}).get( "deletedCount", 0 ) == 1 @@ -434,7 +434,7 @@ def delete_collection(self) -> None: Use with caution. """ self._ensure_astra_db_client() - self.astra_db.delete_collection( + self.astra_db.delete_collection( # type: ignore[union-attr] collection_name=self.collection_name, ) @@ -448,7 +448,7 @@ async def adelete_collection(self) -> None: await self._ensure_db_setup() if not self.async_astra_db: await run_in_executor(None, self.delete_collection) - await self.async_astra_db.delete_collection( + await self.async_astra_db.delete_collection( # type: ignore[union-attr] collection_name=self.collection_name, ) @@ -571,7 +571,7 @@ def add_texts( ) def _handle_batch(document_batch: List[DocDict]) -> List[str]: - im_result = self.collection.insert_many( + im_result = self.collection.insert_many( # type: ignore[union-attr] documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, @@ -581,7 +581,7 @@ def _handle_batch(document_batch: List[DocDict]) -> List[str]: ) def _handle_missing_document(missing_document: DocDict) -> str: - replacement_result = self.collection.find_one_and_replace( + replacement_result = self.collection.find_one_and_replace( # type: ignore[union-attr] filter={"_id": missing_document["_id"]}, replacement=missing_document, ) @@ -672,7 +672,7 @@ async def aadd_texts( ) async def _handle_batch(document_batch: List[DocDict]) -> List[str]: - im_result = await self.async_collection.insert_many( + im_result = await self.async_collection.insert_many( # type: ignore[union-attr] documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, @@ -682,7 +682,7 @@ async def _handle_batch(document_batch: List[DocDict]) -> List[str]: ) async def _handle_missing_document(missing_document: DocDict) -> str: - replacement_result = await self.async_collection.find_one_and_replace( + replacement_result = await self.async_collection.find_one_and_replace( # type: ignore[union-attr] filter={"_id": missing_document["_id"]}, replacement=missing_document, ) @@ -729,7 +729,7 @@ def similarity_search_with_score_id_by_vector( metadata_parameter = self._filter_to_metadata(filter) # hits = list( - self.collection.paginated_find( + self.collection.paginated_find( # type: ignore[union-attr] filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": k, "includeSimilarity": True}, @@ -771,7 +771,7 @@ async def asimilarity_search_with_score_id_by_vector( if not self.async_collection: return await run_in_executor( None, - self.asimilarity_search_with_score_id_by_vector, + self.asimilarity_search_with_score_id_by_vector, # type: ignore[arg-type] embedding, k, filter, @@ -962,7 +962,7 @@ async def asimilarity_search_with_score( ) @staticmethod - def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits): + def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits): # type: ignore[no-untyped-def] mmr_chosen_indices = maximal_marginal_relevance( np.array(embedding, dtype=np.float32), [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits], @@ -1008,7 +1008,7 @@ def max_marginal_relevance_search_by_vector( metadata_parameter = self._filter_to_metadata(filter) prefetch_hits = list( - self.collection.paginated_find( + self.collection.paginated_find( # type: ignore[union-attr] filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": fetch_k, "includeSimilarity": True}, @@ -1228,7 +1228,7 @@ def from_texts( batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) - return astra_db_store + return astra_db_store # type: ignore[return-value] @classmethod async def afrom_texts( @@ -1263,7 +1263,7 @@ async def afrom_texts( batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) - return astra_db_store + return astra_db_store # type: ignore[return-value] @classmethod def from_documents( diff --git a/libs/community/langchain_community/vectorstores/azuresearch.py b/libs/community/langchain_community/vectorstores/azuresearch.py index 992db81f227b086..9006985fa2db2e6 100644 --- a/libs/community/langchain_community/vectorstores/azuresearch.py +++ b/libs/community/langchain_community/vectorstores/azuresearch.py @@ -339,7 +339,7 @@ def add_texts( # batching support if embedding function is an Embeddings object if isinstance(self.embedding_function, Embeddings): try: - embeddings = self.embedding_function.embed_documents(texts) + embeddings = self.embedding_function.embed_documents(texts) # type: ignore[arg-type] except NotImplementedError: embeddings = [self.embedding_function.embed_query(x) for x in texts] else: diff --git a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py index 64a1f4b76551c44..28edc6536ce5918 100644 --- a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py +++ b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py @@ -222,7 +222,7 @@ def _initialize_vector_index(self) -> Any: self._logger.debug("Vector index already exists.") self._have_index = True - def _create_index_in_background(self): + def _create_index_in_background(self): # type: ignore[no-untyped-def] if self._have_index or self._creating_index: # Already have an index or in the process of creating one. return @@ -231,7 +231,7 @@ def _create_index_in_background(self): thread = Thread(target=self._create_index, daemon=True) thread.start() - def _create_index(self): + def _create_index(self): # type: ignore[no-untyped-def] from google.api_core.exceptions import ClientError table = self.bq_client.get_table(self.vectors_table) @@ -289,7 +289,7 @@ def embeddings(self) -> Optional[Embeddings]: def full_table_id(self) -> str: return self._full_table_id - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, diff --git a/libs/community/langchain_community/vectorstores/deeplake.py b/libs/community/langchain_community/vectorstores/deeplake.py index 52fe55fa7cedba2..659c24c6ca8e21a 100644 --- a/libs/community/langchain_community/vectorstores/deeplake.py +++ b/libs/community/langchain_community/vectorstores/deeplake.py @@ -905,7 +905,7 @@ def ds(self) -> Any: return self.vectorstore.dataset @classmethod - def _validate_kwargs(cls, kwargs, method_name): + def _validate_kwargs(cls, kwargs, method_name): # type: ignore[no-untyped-def] if kwargs: valid_items = cls._get_valid_args(method_name) unsupported_items = cls._get_unsupported_items(kwargs, valid_items) @@ -917,14 +917,14 @@ def _validate_kwargs(cls, kwargs, method_name): ) @classmethod - def _get_valid_args(cls, method_name): + def _get_valid_args(cls, method_name): # type: ignore[no-untyped-def] if method_name == "search": return cls._valid_search_kwargs else: return [] @staticmethod - def _get_unsupported_items(kwargs, valid_items): + def _get_unsupported_items(kwargs, valid_items): # type: ignore[no-untyped-def] kwargs = {k: v for k, v in kwargs.items() if k not in valid_items} unsupported_items = None if kwargs: diff --git a/libs/community/langchain_community/vectorstores/faiss.py b/libs/community/langchain_community/vectorstores/faiss.py index 044209add232c84..0341756fc5f954b 100644 --- a/libs/community/langchain_community/vectorstores/faiss.py +++ b/libs/community/langchain_community/vectorstores/faiss.py @@ -305,7 +305,7 @@ def similarity_search_with_score_by_vector( if filter is not None: if isinstance(filter, dict): - def filter_func(metadata): + def filter_func(metadata): # type: ignore[no-untyped-def] if all( metadata.get(key) in value if isinstance(value, list) @@ -607,7 +607,7 @@ def max_marginal_relevance_search_with_score_by_vector( filtered_indices = [] if isinstance(filter, dict): - def filter_func(metadata): + def filter_func(metadata): # type: ignore[no-untyped-def] if all( metadata.get(key) in value if isinstance(value, list) diff --git a/libs/community/langchain_community/vectorstores/hanavector.py b/libs/community/langchain_community/vectorstores/hanavector.py index 04eec65a44868e0..5aba9334828697e 100644 --- a/libs/community/langchain_community/vectorstores/hanavector.py +++ b/libs/community/langchain_community/vectorstores/hanavector.py @@ -117,7 +117,7 @@ def __init__( self.vector_column_length, ) - def _table_exists(self, table_name) -> bool: + def _table_exists(self, table_name) -> bool: # type: ignore[no-untyped-def] sql_str = ( "SELECT COUNT(*) FROM SYS.TABLES WHERE SCHEMA_NAME = CURRENT_SCHEMA" " AND TABLE_NAME = ?" @@ -133,7 +133,7 @@ def _table_exists(self, table_name) -> bool: cur.close() return False - def _check_column(self, table_name, column_name, column_type, column_length=None): + def _check_column(self, table_name, column_name, column_type, column_length=None): # type: ignore[no-untyped-def] sql_str = ( "SELECT DATA_TYPE_NAME, LENGTH FROM SYS.TABLE_COLUMNS WHERE " "SCHEMA_NAME = CURRENT_SCHEMA " @@ -166,17 +166,17 @@ def _check_column(self, table_name, column_name, column_type, column_length=None def embeddings(self) -> Embeddings: return self.embedding - def _sanitize_name(input_str: str) -> str: + def _sanitize_name(input_str: str) -> str: # type: ignore[misc] # Remove characters that are not alphanumeric or underscores return re.sub(r"[^a-zA-Z0-9_]", "", input_str) - def _sanitize_int(input_int: any) -> int: + def _sanitize_int(input_int: any) -> int: # type: ignore[valid-type] value = int(str(input_int)) if value < -1: raise ValueError(f"Value ({value}) must not be smaller than -1") return int(str(input_int)) - def _sanitize_list_float(embedding: List[float]) -> List[float]: + def _sanitize_list_float(embedding: List[float]) -> List[float]: # type: ignore[misc] for value in embedding: if not isinstance(value, float): raise ValueError(f"Value ({value}) does not have type float") @@ -185,14 +185,14 @@ def _sanitize_list_float(embedding: List[float]) -> List[float]: # Compile pattern only once, for better performance _compiled_pattern = re.compile("^[_a-zA-Z][_a-zA-Z0-9]*$") - def _sanitize_metadata_keys(metadata: dict) -> dict: + def _sanitize_metadata_keys(metadata: dict) -> dict: # type: ignore[misc] for key in metadata.keys(): if not HanaDB._compiled_pattern.match(key): raise ValueError(f"Invalid metadata key {key}") return metadata - def add_texts( + def add_texts( # type: ignore[override] self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, @@ -243,7 +243,7 @@ def add_texts( return [] @classmethod - def from_texts( + def from_texts( # type: ignore[no-untyped-def, override] cls: Type[HanaDB], texts: List[str], embedding: Embeddings, @@ -277,7 +277,7 @@ def from_texts( instance.add_texts(texts, metadatas) return instance - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, k: int = 4, filter: Optional[dict] = None ) -> List[Document]: """Return docs most similar to query. @@ -382,7 +382,7 @@ def similarity_search_with_score_by_vector( ) return [(result_item[0], result_item[1]) for result_item in whole_result] - def similarity_search_by_vector( + def similarity_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, filter: Optional[dict] = None ) -> List[Document]: """Return docs most similar to embedding vector. @@ -401,7 +401,7 @@ def similarity_search_by_vector( ) return [doc for doc, _ in docs_and_scores] - def _create_where_by_filter(self, filter): + def _create_where_by_filter(self, filter): # type: ignore[no-untyped-def] query_tuple = [] where_str = "" if filter: @@ -427,7 +427,7 @@ def _create_where_by_filter(self, filter): return where_str, query_tuple - def delete( + def delete( # type: ignore[override] self, ids: Optional[List[str]] = None, filter: Optional[dict] = None ) -> Optional[bool]: """Delete entries by filter with metadata values @@ -459,7 +459,7 @@ def delete( return True - async def adelete( + async def adelete( # type: ignore[override] self, ids: Optional[List[str]] = None, filter: Optional[dict] = None ) -> Optional[bool]: """Delete by vector ID or other criteria. @@ -473,7 +473,7 @@ async def adelete( """ return await run_in_executor(None, self.delete, ids=ids, filter=filter) - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, k: int = 4, @@ -511,11 +511,11 @@ def max_marginal_relevance_search( filter=filter, ) - def _parse_float_array_from_string(array_as_string: str) -> List[float]: + def _parse_float_array_from_string(array_as_string: str) -> List[float]: # type: ignore[misc] array_wo_brackets = array_as_string[1:-1] return [float(x) for x in array_wo_brackets.split(",")] - def max_marginal_relevance_search_by_vector( + def max_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, @@ -533,7 +533,7 @@ def max_marginal_relevance_search_by_vector( return [whole_result[i][0] for i in mmr_doc_indexes] - async def amax_marginal_relevance_search_by_vector( + async def amax_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, diff --git a/libs/community/langchain_community/vectorstores/jaguar.py b/libs/community/langchain_community/vectorstores/jaguar.py index 2771530d66dfb67..a42cdf3641e376c 100644 --- a/libs/community/langchain_community/vectorstores/jaguar.py +++ b/libs/community/langchain_community/vectorstores/jaguar.py @@ -135,7 +135,7 @@ def run(self, query: str, withFile: bool = False) -> dict: def embeddings(self) -> Optional[Embeddings]: return self._embedding - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, @@ -351,7 +351,7 @@ def is_anomalous( return False @classmethod - def from_texts( + def from_texts( # type: ignore[override] cls, texts: List[str], embedding: Embeddings, @@ -383,7 +383,7 @@ def clear(self) -> None: q = "truncate store " + podstore self.run(q) - def delete(self, zids: List[str], **kwargs: Any) -> None: + def delete(self, zids: List[str], **kwargs: Any) -> None: # type: ignore[override] """ Delete records in jaguardb by a list of zero-ids Args: diff --git a/libs/community/langchain_community/vectorstores/milvus.py b/libs/community/langchain_community/vectorstores/milvus.py index e72a82f2bd21f5a..28244621ca7a499 100644 --- a/libs/community/langchain_community/vectorstores/milvus.py +++ b/libs/community/langchain_community/vectorstores/milvus.py @@ -554,10 +554,10 @@ def add_texts( } if not self.auto_id: - insert_dict[self._primary_field] = ids + insert_dict[self._primary_field] = ids # type: ignore[assignment] if self._metadata_field is not None: - for d in metadatas: + for d in metadatas: # type: ignore[union-attr] insert_dict.setdefault(self._metadata_field, []).append(d) else: # Collect the metadata into the insert dict. @@ -901,7 +901,7 @@ def max_marginal_relevance_search_by_vector( ret.append(documents[x]) return ret - def delete( + def delete( # type: ignore[no-untyped-def] self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str ): """Delete by vector ID or boolean expression. @@ -923,7 +923,7 @@ def delete( assert isinstance( expr, str ), "Either ids list or expr string must be provided." - return self.col.delete(expr=expr, **kwargs) + return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr] @classmethod def from_texts( diff --git a/libs/community/langchain_community/vectorstores/pgembedding.py b/libs/community/langchain_community/vectorstores/pgembedding.py index d5c37d5942ee2fb..cbdf65c6c4f55f1 100644 --- a/libs/community/langchain_community/vectorstores/pgembedding.py +++ b/libs/community/langchain_community/vectorstores/pgembedding.py @@ -398,7 +398,7 @@ def similarity_search_with_score_by_vector( docs = [ ( Document( - page_content=result.EmbeddingStore.document, + page_content=result.EmbeddingStore.document, # type: ignore[arg-type] metadata=result.EmbeddingStore.cmetadata, ), result.distance if self.embedding_function is not None else 0.0, diff --git a/libs/community/langchain_community/vectorstores/pgvecto_rs.py b/libs/community/langchain_community/vectorstores/pgvecto_rs.py index 2b14cd8036ea7ad..2f1dbf427202236 100644 --- a/libs/community/langchain_community/vectorstores/pgvecto_rs.py +++ b/libs/community/langchain_community/vectorstores/pgvecto_rs.py @@ -133,7 +133,7 @@ def add_texts( Record.from_text(text, embedding, meta) for text, embedding, meta in zip(texts, embeddings, metadatas or []) ] - self._store.insert(records) + self._store.insert(records) # type: ignore[union-attr] return [str(record.id) for record in records] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: @@ -177,7 +177,7 @@ def similarity_search_with_score_by_vector( real_filter = meta_contains(filter) else: real_filter = filter - results = self._store.search( + results = self._store.search( # type: ignore[union-attr] query_vector, distance_func_map[distance_func], k, diff --git a/libs/community/langchain_community/vectorstores/pgvector.py b/libs/community/langchain_community/vectorstores/pgvector.py index fe439d86c5a7e89..755c72bb4dc722a 100644 --- a/libs/community/langchain_community/vectorstores/pgvector.py +++ b/libs/community/langchain_community/vectorstores/pgvector.py @@ -238,7 +238,7 @@ def _create_engine(self) -> sqlalchemy.engine.Engine: def create_vector_extension(self) -> None: try: - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] # The advisor lock fixes issue arising from concurrent # creation of the vector extension. # https://github.com/langchain-ai/langchain/issues/12933 @@ -256,24 +256,24 @@ def create_vector_extension(self) -> None: raise Exception(f"Failed to create vector extension: {e}") from e def create_tables_if_not_exists(self) -> None: - with Session(self._bind) as session, session.begin(): + with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.create_all(session.get_bind()) def drop_tables(self) -> None: - with Session(self._bind) as session, session.begin(): + with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.drop_all(session.get_bind()) def create_collection(self) -> None: if self.pre_delete_collection: self.delete_collection() - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] self.CollectionStore.get_or_create( session, self.collection_name, cmetadata=self.collection_metadata ) def delete_collection(self) -> None: self.logger.debug("Trying to delete collection") - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: self.logger.warning("Collection not found") @@ -284,7 +284,7 @@ def delete_collection(self) -> None: @contextlib.contextmanager def _make_session(self) -> Generator[Session, None, None]: """Create a context manager for the session, bind to _conn string.""" - yield Session(self._bind) + yield Session(self._bind) # type: ignore[arg-type] def delete( self, @@ -298,7 +298,7 @@ def delete( ids: List of ids to delete. collection_only: Only delete ids in the collection. """ - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] if ids is not None: self.logger.debug( "Trying to delete vectors by ids (represented by the model " @@ -383,7 +383,7 @@ def add_embeddings( if not metadatas: metadatas = [{} for _ in texts] - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") @@ -508,7 +508,7 @@ def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, floa ] return docs - def _create_filter_clause(self, key, value): + def _create_filter_clause(self, key, value): # type: ignore[no-untyped-def] IN, NIN, BETWEEN, GT, LT, NE = "in", "nin", "between", "gt", "lt", "ne" EQ, LIKE, CONTAINS, OR, AND = "eq", "like", "contains", "or", "and" @@ -575,7 +575,7 @@ def __query_collection( filter: Optional[Dict[str, str]] = None, ) -> List[Any]: """Query the collection.""" - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") diff --git a/libs/community/langchain_community/vectorstores/surrealdb.py b/libs/community/langchain_community/vectorstores/surrealdb.py index d21f5bf0e020016..ef65c5ec6b00219 100644 --- a/libs/community/langchain_community/vectorstores/surrealdb.py +++ b/libs/community/langchain_community/vectorstores/surrealdb.py @@ -115,7 +115,7 @@ async def aadd_texts( for idx, text in enumerate(texts): data = {"text": text, "embedding": embeddings[idx]} if metadatas is not None and idx < len(metadatas): - data["metadata"] = metadatas[idx] + data["metadata"] = metadatas[idx] # type: ignore[assignment] record = await self.sdb.create( self.collection, data, diff --git a/libs/community/langchain_community/vectorstores/tencentvectordb.py b/libs/community/langchain_community/vectorstores/tencentvectordb.py index d1c05b647d4e860..e185a3ab123ddd7 100644 --- a/libs/community/langchain_community/vectorstores/tencentvectordb.py +++ b/libs/community/langchain_community/vectorstores/tencentvectordb.py @@ -316,7 +316,7 @@ def similarity_search_with_score_by_vector( meta = result.get(self.field_metadata) if meta is not None: meta = json.loads(meta) - doc = Document(page_content=result.get(self.field_text), metadata=meta) + doc = Document(page_content=result.get(self.field_text), metadata=meta) # type: ignore[arg-type] pair = (doc, result.get("score", 0.0)) ret.append(pair) return ret @@ -374,7 +374,7 @@ def max_marginal_relevance_search_by_vector( meta = result.get(self.field_metadata) if meta is not None: meta = json.loads(meta) - doc = Document(page_content=result.get(self.field_text), metadata=meta) + doc = Document(page_content=result.get(self.field_text), metadata=meta) # type: ignore[arg-type] documents.append(doc) ordered_result_embeddings.append(result.get(self.field_vector)) # Get the new order of results. diff --git a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py index f447d73745c55d9..444f7d14f8d2330 100644 --- a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py +++ b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py @@ -24,7 +24,7 @@ class Config: underscore_attrs_are_private = True @staticmethod - def _verify_thirdai_library(thirdai_key: Optional[str] = None): + def _verify_thirdai_library(thirdai_key: Optional[str] = None): # type: ignore[no-untyped-def] try: from thirdai import licensing @@ -38,7 +38,7 @@ def _verify_thirdai_library(thirdai_key: Optional[str] = None): ) @classmethod - def from_scratch( + def from_scratch( # type: ignore[no-untyped-def, no-untyped-def] cls, thirdai_key: Optional[str] = None, **model_kwargs, @@ -69,10 +69,10 @@ def from_scratch( NeuralDBVectorStore._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb - return cls(db=ndb.NeuralDB(**model_kwargs)) + return cls(db=ndb.NeuralDB(**model_kwargs)) # type: ignore[call-arg] @classmethod - def from_bazaar( + def from_bazaar( # type: ignore[no-untyped-def] cls, base: str, bazaar_cache: Optional[str] = None, @@ -111,10 +111,10 @@ def from_bazaar( os.mkdir(cache) model_bazaar = ndb.Bazaar(cache) model_bazaar.fetch() - return cls(db=model_bazaar.get_model(base)) + return cls(db=model_bazaar.get_model(base)) # type: ignore[call-arg] @classmethod - def from_checkpoint( + def from_checkpoint( # type: ignore[no-untyped-def] cls, checkpoint: Union[str, Path], thirdai_key: Optional[str] = None, @@ -146,7 +146,7 @@ def from_checkpoint( NeuralDBVectorStore._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb - return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint)) + return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint)) # type: ignore[call-arg] @classmethod def from_texts( @@ -187,11 +187,11 @@ def add_texts( df = pd.DataFrame({"texts": texts}) if metadatas: df = pd.concat([df, pd.DataFrame.from_records(metadatas)], axis=1) - temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False) + temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False) # type: ignore[call-overload] df.to_csv(temp) source_id = self.insert([ndb.CSV(temp.name)], **kwargs)[0] offset = self.db._savable_state.documents.get_source_by_id(source_id)[1] - return [str(offset + i) for i in range(len(texts))] + return [str(offset + i) for i in range(len(texts))] # type: ignore[arg-type] @root_validator() def validate_environments(cls, values: Dict) -> Dict: @@ -205,7 +205,7 @@ def validate_environments(cls, values: Dict) -> Dict: ) return values - def insert( + def insert( # type: ignore[no-untyped-def, no-untyped-def] self, sources: List[Any], train: bool = True, @@ -229,7 +229,7 @@ def insert( **kwargs, ) - def _preprocess_sources(self, sources): + def _preprocess_sources(self, sources): # type: ignore[no-untyped-def] """Checks if the provided sources are string paths. If they are, convert to NeuralDB document objects. @@ -261,7 +261,7 @@ def _preprocess_sources(self, sources): ) return preprocessed_sources - def upvote(self, query: str, document_id: Union[int, str]): + def upvote(self, query: str, document_id: Union[int, str]): # type: ignore[no-untyped-def] """The vectorstore upweights the score of a document for a specific query. This is useful for fine-tuning the vectorstore to user behavior. @@ -271,7 +271,7 @@ def upvote(self, query: str, document_id: Union[int, str]): """ self.db.text_to_result(query, int(document_id)) - def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]): + def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]): # type: ignore[no-untyped-def] """Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the vectorstore to user behavior. @@ -284,7 +284,7 @@ def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]): [(query, int(doc_id)) for query, doc_id in query_id_pairs] ) - def associate(self, source: str, target: str): + def associate(self, source: str, target: str): # type: ignore[no-untyped-def] """The vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase. @@ -295,7 +295,7 @@ def associate(self, source: str, target: str): """ self.db.associate(source, target) - def associate_batch(self, text_pairs: List[Tuple[str, str]]): + def associate_batch(self, text_pairs: List[Tuple[str, str]]): # type: ignore[no-untyped-def] """Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase. @@ -334,7 +334,7 @@ def similarity_search( except Exception as e: raise ValueError(f"Error while retrieving documents: {e}") from e - def save(self, path: str): + def save(self, path: str): # type: ignore[no-untyped-def] """Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path) diff --git a/libs/community/langchain_community/vectorstores/vectara.py b/libs/community/langchain_community/vectorstores/vectara.py index 0fc910042168f8b..3e0ecd3bfe76e24 100644 --- a/libs/community/langchain_community/vectorstores/vectara.py +++ b/libs/community/langchain_community/vectorstores/vectara.py @@ -384,7 +384,7 @@ def vectara_query( f"(code {response.status_code}, reason {response.reason}, details " f"{response.text})", ) - return [], "" + return [], "" # type: ignore[return-value] result = response.json() @@ -454,7 +454,7 @@ def similarity_search_with_score( docs = self.vectara_query(query, config) return docs - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, **kwargs: Any, @@ -474,7 +474,7 @@ def similarity_search( ) return [doc for doc, _ in docs_and_scores] - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, fetch_k: int = 50, diff --git a/libs/community/langchain_community/vectorstores/vikngdb.py b/libs/community/langchain_community/vectorstores/vikngdb.py index 2f235f0bf4cbd4c..942f4b9591a1998 100644 --- a/libs/community/langchain_community/vectorstores/vikngdb.py +++ b/libs/community/langchain_community/vectorstores/vikngdb.py @@ -15,7 +15,7 @@ class VikingDBConfig(object): - def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"): + def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"): # type: ignore[no-untyped-def] self.host = host self.region = region self.ak = ak @@ -47,11 +47,11 @@ def __init__( self.index_params = index_params self.drop_old = drop_old self.service = VikingDBService( - connection_args.host, - connection_args.region, - connection_args.ak, - connection_args.sk, - connection_args.scheme, + connection_args.host, # type: ignore[union-attr] + connection_args.region, # type: ignore[union-attr] + connection_args.ak, # type: ignore[union-attr] + connection_args.sk, # type: ignore[union-attr] + connection_args.scheme, # type: ignore[union-attr] ) try: @@ -143,7 +143,7 @@ def _create_index(self) -> None: scalar_index=scalar_index, ) - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, @@ -183,7 +183,7 @@ def add_texts( if metadatas is not None and index < len(metadatas): names = list(metadatas[index].keys()) for name in names: - field[name] = metadatas[index].get(name) + field[name] = metadatas[index].get(name) # type: ignore[assignment] data.append(Data(field)) total_count = len(data) @@ -191,10 +191,10 @@ def add_texts( end = min(i + batch_size, total_count) insert_data = data[i:end] # print(insert_data) - self.collection.upsert_data(insert_data) + self.collection.upsert_data(insert_data) # type: ignore[union-attr] return pks - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, params: Optional[dict] = None, @@ -216,7 +216,7 @@ def similarity_search_with_score( ) return res - def similarity_search_by_vector( + def similarity_search_by_vector( # type: ignore[override] self, embedding: List[float], params: Optional[dict] = None, @@ -251,7 +251,7 @@ def similarity_search_with_score_by_vector( if params.get("partition") is not None: partition = params["partition"] - res = self.index.search_by_vector( + res = self.index.search_by_vector( # type: ignore[union-attr] embedding, filter=filter, limit=limit, @@ -269,7 +269,7 @@ def similarity_search_with_score_by_vector( ret.append(pair) return ret - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, k: int = 4, @@ -286,7 +286,7 @@ def max_marginal_relevance_search( **kwargs, ) - def max_marginal_relevance_search_by_vector( + def max_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, @@ -311,7 +311,7 @@ def max_marginal_relevance_search_by_vector( if params.get("partition") is not None: partition = params["partition"] - res = self.index.search_by_vector( + res = self.index.search_by_vector( # type: ignore[union-attr] embedding, filter=filter, limit=limit, @@ -347,10 +347,10 @@ def delete( ) -> None: if self.collection is None: logger.debug("No existing collection to search.") - self.collection.delete_data(ids) + self.collection.delete_data(ids) # type: ignore[union-attr] @classmethod - def from_texts( + def from_texts( # type: ignore[no-untyped-def, override] cls, texts: List[str], embedding: Embeddings, diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index f674b1d20a60bfa..70d943ea09443ca 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "aenum" @@ -3944,7 +3944,7 @@ files = [ [[package]] name = "langchain-core" -version = "0.1.17" +version = "0.1.18" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -9252,4 +9252,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "6e1aabbf689bf7294ffc3f9215559157b95868275421d776862ddb1499969c79" +content-hash = "1ab63edcddcef2deb01e6fff5c376f7b0773435bb9d5b55bc1d50d19a8f1dee2" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 4080d8c7ee72706..14a0117adb76849 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -101,7 +101,7 @@ optional = true # dependencies used for running tests (e.g., pytest, freezegun, response). # Any dependencies that do not meet that criteria will be removed. pytest = "^7.3.0" -pytest-cov = "^4.0.0" +pytest-cov = "^4.1.0" pytest-dotenv = "^0.5.2" duckdb-engine = "^0.9.2" pytest-watcher = "^0.2.6" diff --git a/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py b/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py index 17601af48b55750..e6a3d2303dc1b6d 100644 --- a/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py +++ b/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py @@ -57,7 +57,7 @@ def test_add_messages() -> None: assert len(message_store_another.messages) == 0 -def test_tidb_recent_chat_message(): +def test_tidb_recent_chat_message(): # type: ignore[no-untyped-def] """Test the TiDBChatMessageHistory with earliest_time parameter.""" import time from datetime import datetime diff --git a/libs/community/tests/integration_tests/chat_models/test_konko.py b/libs/community/tests/integration_tests/chat_models/test_konko.py index 9f38f740eb45ab7..7287d5b11dd64b1 100644 --- a/libs/community/tests/integration_tests/chat_models/test_konko.py +++ b/libs/community/tests/integration_tests/chat_models/test_konko.py @@ -40,7 +40,7 @@ def test_konko_key_masked_when_passed_via_constructor( captured = capsys.readouterr() assert captured.out == "**********" - print(chat.konko_secret_key, end="") + print(chat.konko_secret_key, end="") # type: ignore[attr-defined] captured = capsys.readouterr() assert captured.out == "**********" @@ -49,7 +49,7 @@ def test_uses_actual_secret_value_from_secret_str() -> None: """Test that actual secret is retrieved using `.get_secret_value()`.""" chat = ChatKonko(openai_api_key="test-openai-key", konko_api_key="test-konko-key") assert cast(SecretStr, chat.konko_api_key).get_secret_value() == "test-openai-key" - assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key" + assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key" # type: ignore[attr-defined] def test_konko_chat_test() -> None: diff --git a/libs/community/tests/integration_tests/chat_models/test_llama_edge.py b/libs/community/tests/integration_tests/chat_models/test_llama_edge.py index 50919902dd4255b..92e674c0fbf1ce3 100644 --- a/libs/community/tests/integration_tests/chat_models/test_llama_edge.py +++ b/libs/community/tests/integration_tests/chat_models/test_llama_edge.py @@ -47,6 +47,6 @@ def test_chat_wasm_service_streaming() -> None: output = "" for chunk in chat.stream(messages): print(chunk.content, end="", flush=True) - output += chunk.content + output += chunk.content # type: ignore[operator] assert "Paris" in output diff --git a/libs/community/tests/integration_tests/document_loaders/test_astradb.py b/libs/community/tests/integration_tests/document_loaders/test_astradb.py index 0a8518885ae4ba1..8f9146aacb51fe6 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_astradb.py +++ b/libs/community/tests/integration_tests/document_loaders/test_astradb.py @@ -167,5 +167,5 @@ async def test_extraction_function_async( find_options={"limit": 30}, extraction_function=lambda x: x["foo"], ) - doc = await anext(loader.alazy_load()) + doc = await anext(loader.alazy_load()) # type: ignore[name-defined] assert doc.page_content == "bar" diff --git a/libs/community/tests/integration_tests/document_loaders/test_cassandra.py b/libs/community/tests/integration_tests/document_loaders/test_cassandra.py index 037ee0a34703dca..5562188eced86a5 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_cassandra.py +++ b/libs/community/tests/integration_tests/document_loaders/test_cassandra.py @@ -14,7 +14,7 @@ @pytest.fixture(autouse=True, scope="session") -def keyspace() -> str: +def keyspace() -> str: # type: ignore[misc] import cassio from cassandra.cluster import Cluster from cassio.config import check_resolve_session, resolve_keyspace diff --git a/libs/community/tests/integration_tests/embeddings/test_baichuan.py b/libs/community/tests/integration_tests/embeddings/test_baichuan.py index 008dc0f97dfeea8..7512b060580825a 100644 --- a/libs/community/tests/integration_tests/embeddings/test_baichuan.py +++ b/libs/community/tests/integration_tests/embeddings/test_baichuan.py @@ -7,8 +7,8 @@ def test_baichuan_embedding_documents() -> None: documents = ["今天天气不错", "今天阳光灿烂"] embedding = BaichuanTextEmbeddings() output = embedding.embed_documents(documents) - assert len(output) == 2 - assert len(output[0]) == 1024 + assert len(output) == 2 # type: ignore[arg-type] + assert len(output[0]) == 1024 # type: ignore[index] def test_baichuan_embedding_query() -> None: @@ -16,4 +16,4 @@ def test_baichuan_embedding_query() -> None: document = "所有的小学生都会学过只因兔同笼问题。" embedding = BaichuanTextEmbeddings() output = embedding.embed_query(document) - assert len(output) == 1024 + assert len(output) == 1024 # type: ignore[arg-type] diff --git a/libs/community/tests/integration_tests/graphs/test_neo4j.py b/libs/community/tests/integration_tests/graphs/test_neo4j.py index e4b47f907ead864..a209c56d099b822 100644 --- a/libs/community/tests/integration_tests/graphs/test_neo4j.py +++ b/libs/community/tests/integration_tests/graphs/test_neo4j.py @@ -85,7 +85,7 @@ def test_neo4j_timeout() -> None: graph.query("UNWIND range(0,100000,1) AS i MERGE (:Foo {id:i})") except Exception as e: assert ( - e.code + e.code # type: ignore[attr-defined] == "Neo.ClientError.Transaction.TransactionTimedOutClientConfiguration" ) diff --git a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py index 4d0e86b510257a0..3c4e61eb4448987 100644 --- a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py @@ -62,7 +62,7 @@ class CustomFormatter(ContentFormatterBase): content_type = "application/json" accepts = "application/json" - def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: + def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: # type: ignore[override] input_str = json.dumps( { "inputs": [prompt], @@ -72,7 +72,7 @@ def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: ) return input_str.encode("utf-8") - def format_response_payload(self, output: bytes) -> str: + def format_response_payload(self, output: bytes) -> str: # type: ignore[override] response_json = json.loads(output) return response_json[0]["summary_text"] @@ -104,7 +104,7 @@ class CustomContentFormatter(ContentFormatterBase): content_type = "application/json" accepts = "application/json" - def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: + def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: # type: ignore[override] input_str = json.dumps( { "incorrect_input": {"input_string": [prompt]}, @@ -113,7 +113,7 @@ def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: ) return str.encode(input_str) - def format_response_payload(self, output: bytes) -> str: + def format_response_payload(self, output: bytes) -> str: # type: ignore[override] response_json = json.loads(output) return response_json[0]["0"] diff --git a/libs/community/tests/integration_tests/llms/test_bedrock.py b/libs/community/tests/integration_tests/llms/test_bedrock.py index 45a7bcd0bfadcf9..97dbf4f4e8ae231 100644 --- a/libs/community/tests/integration_tests/llms/test_bedrock.py +++ b/libs/community/tests/integration_tests/llms/test_bedrock.py @@ -37,12 +37,12 @@ async def on_llm_error( if reason == "GUARDRAIL_INTERVENED": self.guardrails_intervened = True - def get_response(self): + def get_response(self): # type: ignore[no-untyped-def] return self.guardrails_intervened @pytest.fixture(autouse=True) -def bedrock_runtime_client(): +def bedrock_runtime_client(): # type: ignore[no-untyped-def] import boto3 try: @@ -56,7 +56,7 @@ def bedrock_runtime_client(): @pytest.fixture(autouse=True) -def bedrock_client(): +def bedrock_client(): # type: ignore[no-untyped-def] import boto3 try: @@ -70,7 +70,7 @@ def bedrock_client(): @pytest.fixture -def bedrock_models(bedrock_client): +def bedrock_models(bedrock_client): # type: ignore[no-untyped-def] """List bedrock models.""" response = bedrock_client.list_foundation_models().get("modelSummaries") models = {} @@ -79,7 +79,7 @@ def bedrock_models(bedrock_client): return models -def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): +def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): # type: ignore[no-untyped-def] try: llm = Bedrock( model_id="anthropic.claude-instant-v1", @@ -92,7 +92,7 @@ def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False) -def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( +def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( # type: ignore[no-untyped-def] bedrock_runtime_client, bedrock_models ): try: @@ -112,7 +112,7 @@ def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False) -def test_amazon_bedrock_guardrails_intervention_for_invalid_query( +def test_amazon_bedrock_guardrails_intervention_for_invalid_query( # type: ignore[no-untyped-def] bedrock_runtime_client, bedrock_models ): try: diff --git a/libs/community/tests/integration_tests/storage/test_astradb.py b/libs/community/tests/integration_tests/storage/test_astradb.py index 37a955ad31e2dc3..643b4e93a3185e3 100644 --- a/libs/community/tests/integration_tests/storage/test_astradb.py +++ b/libs/community/tests/integration_tests/storage/test_astradb.py @@ -16,7 +16,7 @@ def _has_env_vars() -> bool: @pytest.fixture -def astra_db(): +def astra_db(): # type: ignore[no-untyped-def] from astrapy.db import AstraDB return AstraDB( @@ -26,14 +26,14 @@ def astra_db(): ) -def init_store(astra_db, collection_name: str): +def init_store(astra_db, collection_name: str): # type: ignore[no-untyped-def, no-untyped-def] astra_db.create_collection(collection_name) store = AstraDBStore(collection_name=collection_name, astra_db_client=astra_db) store.mset([("key1", [0.1, 0.2]), ("key2", "value2")]) return store -def init_bytestore(astra_db, collection_name: str): +def init_bytestore(astra_db, collection_name: str): # type: ignore[no-untyped-def, no-untyped-def] astra_db.create_collection(collection_name) store = AstraDBByteStore(collection_name=collection_name, astra_db_client=astra_db) store.mset([("key1", b"value1"), ("key2", b"value2")]) @@ -43,7 +43,7 @@ def init_bytestore(astra_db, collection_name: str): @pytest.mark.requires("astrapy") @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") class TestAstraDBStore: - def test_mget(self, astra_db) -> None: + def test_mget(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test AstraDBStore mget method.""" collection_name = "lc_test_store_mget" try: @@ -52,7 +52,7 @@ def test_mget(self, astra_db) -> None: finally: astra_db.delete_collection(collection_name) - def test_mset(self, astra_db) -> None: + def test_mset(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that multiple keys can be set with AstraDBStore.""" collection_name = "lc_test_store_mset" try: @@ -64,7 +64,7 @@ def test_mset(self, astra_db) -> None: finally: astra_db.delete_collection(collection_name) - def test_mdelete(self, astra_db) -> None: + def test_mdelete(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that deletion works as expected.""" collection_name = "lc_test_store_mdelete" try: @@ -75,7 +75,7 @@ def test_mdelete(self, astra_db) -> None: finally: astra_db.delete_collection(collection_name) - def test_yield_keys(self, astra_db) -> None: + def test_yield_keys(self, astra_db) -> None: # type: ignore[no-untyped-def] collection_name = "lc_test_store_yield_keys" try: store = init_store(astra_db, collection_name) @@ -85,7 +85,7 @@ def test_yield_keys(self, astra_db) -> None: finally: astra_db.delete_collection(collection_name) - def test_bytestore_mget(self, astra_db) -> None: + def test_bytestore_mget(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test AstraDBByteStore mget method.""" collection_name = "lc_test_bytestore_mget" try: @@ -94,7 +94,7 @@ def test_bytestore_mget(self, astra_db) -> None: finally: astra_db.delete_collection(collection_name) - def test_bytestore_mset(self, astra_db) -> None: + def test_bytestore_mset(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that multiple keys can be set with AstraDBByteStore.""" collection_name = "lc_test_bytestore_mset" try: diff --git a/libs/community/tests/integration_tests/utilities/test_google_trends.py b/libs/community/tests/integration_tests/utilities/test_google_trends.py index 0455f16a5806059..c2583d263395ac6 100644 --- a/libs/community/tests/integration_tests/utilities/test_google_trends.py +++ b/libs/community/tests/integration_tests/utilities/test_google_trends.py @@ -6,7 +6,7 @@ @patch("serpapi.SerpApiClient.get_json") -def test_unexpected_response(mocked_serpapiclient): +def test_unexpected_response(mocked_serpapiclient): # type: ignore[no-untyped-def] os.environ["SERPAPI_API_KEY"] = "123abcd" resp = { "search_metadata": { diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py index ec5d14ef3182f2d..c09fcc6a7a30003 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py @@ -15,7 +15,7 @@ def qdrant_is_not_running() -> bool: return True -def assert_documents_equals(actual: List[Document], expected: List[Document]): +def assert_documents_equals(actual: List[Document], expected: List[Document]): # type: ignore[no-untyped-def] assert len(actual) == len(expected) for actual_doc, expected_doc in zip(actual, expected): diff --git a/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py b/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py index 57da87d669655ea..d5146258c5ea25b 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py +++ b/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py @@ -32,7 +32,7 @@ def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch: TestBigQueryVectorStore.dataset_name, exists_ok=True ) TestBigQueryVectorStore.store = BigQueryVectorSearch( - project_id=os.environ.get("PROJECT", None), + project_id=os.environ.get("PROJECT", None), # type: ignore[arg-type] embedding=FakeEmbeddings(), dataset_name=TestBigQueryVectorStore.dataset_name, table_name=TEST_TABLE_NAME, diff --git a/libs/community/tests/integration_tests/vectorstores/test_deeplake.py b/libs/community/tests/integration_tests/vectorstores/test_deeplake.py index 99d4d6def2e631b..7f86795d972efb9 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_deeplake.py +++ b/libs/community/tests/integration_tests/vectorstores/test_deeplake.py @@ -52,7 +52,7 @@ def test_deeplake_with_metadatas() -> None: assert output == [Document(page_content="foo", metadata={"page": "0"})] -def test_deeplake_with_persistence(deeplake_datastore) -> None: +def test_deeplake_with_persistence(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test end to end construction and search, with persistence.""" output = deeplake_datastore.similarity_search("foo", k=1) assert output == [Document(page_content="foo", metadata={"page": "0"})] @@ -72,7 +72,7 @@ def test_deeplake_with_persistence(deeplake_datastore) -> None: # Or on program exit -def test_deeplake_overwrite_flag(deeplake_datastore) -> None: +def test_deeplake_overwrite_flag(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test overwrite behavior""" dataset_path = deeplake_datastore.vectorstore.dataset_handler.path @@ -108,7 +108,7 @@ def test_deeplake_overwrite_flag(deeplake_datastore) -> None: output = docsearch.similarity_search("foo", k=1) -def test_similarity_search(deeplake_datastore) -> None: +def test_similarity_search(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test similarity search.""" distance_metric = "cos" output = deeplake_datastore.similarity_search( diff --git a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py index dfcdb8c7040e9e4..6e6f5b44a6edd36 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py +++ b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py @@ -38,7 +38,7 @@ def embed_query(self, text: str) -> List[float]: class ConfigData: - def __init__(self): + def __init__(self): # type: ignore[no-untyped-def] self.conn = None self.schema_name = "" @@ -46,7 +46,7 @@ def __init__(self): test_setup = ConfigData() -def generateSchemaName(cursor): +def generateSchemaName(cursor): # type: ignore[no-untyped-def] cursor.execute( "SELECT REPLACE(CURRENT_UTCDATE, '-', '') || '_' || BINTOHEX(SYSUUID) FROM " "DUMMY;" @@ -59,7 +59,7 @@ def generateSchemaName(cursor): return f"VEC_{uid}" -def setup_module(module): +def setup_module(module): # type: ignore[no-untyped-def] test_setup.conn = dbapi.connect( address=os.environ.get("HANA_DB_ADDRESS"), port=os.environ.get("HANA_DB_PORT"), @@ -81,7 +81,7 @@ def setup_module(module): cur.close() -def teardown_module(module): +def teardown_module(module): # type: ignore[no-untyped-def] try: cur = test_setup.conn.cursor() sql_str = f"DROP SCHEMA {test_setup.schema_name} CASCADE" @@ -100,13 +100,13 @@ def texts() -> List[str]: @pytest.fixture def metadatas() -> List[str]: return [ - {"start": 0, "end": 100, "quality": "good", "ready": True}, - {"start": 100, "end": 200, "quality": "bad", "ready": False}, - {"start": 200, "end": 300, "quality": "ugly", "ready": True}, + {"start": 0, "end": 100, "quality": "good", "ready": True}, # type: ignore[list-item] + {"start": 100, "end": 200, "quality": "bad", "ready": False}, # type: ignore[list-item] + {"start": 200, "end": 300, "quality": "ugly", "ready": True}, # type: ignore[list-item] ] -def drop_table(connection, table_name): +def drop_table(connection, table_name): # type: ignore[no-untyped-def] try: cur = connection.cursor() sql_str = f"DROP TABLE {table_name}" @@ -825,7 +825,7 @@ def test_hanavector_filter_prepared_statement_params( rows = cur.fetchall() assert len(rows) == 1 - query_value = "good" + query_value = "good" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.quality') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() @@ -839,14 +839,14 @@ def test_hanavector_filter_prepared_statement_params( assert len(rows) == 1 # query_value = True - query_value = "true" + query_value = "true" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() assert len(rows) == 2 # query_value = False - query_value = "false" + query_value = "false" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() diff --git a/libs/community/tests/integration_tests/vectorstores/test_lantern.py b/libs/community/tests/integration_tests/vectorstores/test_lantern.py index 8de7d8036443d07..bde3c5b69650263 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_lantern.py +++ b/libs/community/tests/integration_tests/vectorstores/test_lantern.py @@ -31,7 +31,7 @@ def fix_distance_precision( class FakeEmbeddingsWithAdaDimension(FakeEmbeddings): """Fake embeddings functionality for testing.""" - def __init__(self): + def __init__(self): # type: ignore[no-untyped-def] super(FakeEmbeddingsWithAdaDimension, self).__init__(size=ADA_TOKEN_COUNT) def embed_documents(self, texts: List[str]) -> List[List[float]]: diff --git a/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py b/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py index bd5eafca00d1895..370e8ff54fa3f96 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py @@ -7,7 +7,7 @@ @pytest.fixture(scope="session") -def test_csv(): +def test_csv(): # type: ignore[no-untyped-def] csv = "thirdai-test.csv" with open(csv, "w") as o: o.write("column_1,column_2\n") @@ -16,13 +16,13 @@ def test_csv(): os.remove(csv) -def assert_result_correctness(documents): +def assert_result_correctness(documents): # type: ignore[no-untyped-def] assert len(documents) == 1 assert documents[0].page_content == "column_1: column one\n\ncolumn_2: column two" @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_scratch(test_csv): +def test_neuraldb_retriever_from_scratch(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_scratch() retriever.insert([test_csv]) documents = retriever.similarity_search("column") @@ -30,7 +30,7 @@ def test_neuraldb_retriever_from_scratch(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_checkpoint(test_csv): +def test_neuraldb_retriever_from_checkpoint(test_csv): # type: ignore[no-untyped-def] checkpoint = "thirdai-test-save.ndb" if os.path.exists(checkpoint): shutil.rmtree(checkpoint) @@ -47,7 +47,7 @@ def test_neuraldb_retriever_from_checkpoint(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_bazaar(test_csv): +def test_neuraldb_retriever_from_bazaar(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_bazaar("General QnA") retriever.insert([test_csv]) documents = retriever.similarity_search("column") @@ -55,7 +55,7 @@ def test_neuraldb_retriever_from_bazaar(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_other_methods(test_csv): +def test_neuraldb_retriever_other_methods(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_scratch() retriever.insert([test_csv]) # Make sure they don't throw an error. diff --git a/libs/community/tests/integration_tests/vectorstores/test_vectara.py b/libs/community/tests/integration_tests/vectorstores/test_vectara.py index 1aaf4856571eef0..6e2f4f2900a2de6 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_vectara.py +++ b/libs/community/tests/integration_tests/vectorstores/test_vectara.py @@ -25,7 +25,7 @@ def get_abbr(s: str) -> str: @pytest.fixture(scope="function") -def vectara1(): +def vectara1(): # type: ignore[no-untyped-def] # Set up code # create a new Vectara instance vectara1: Vectara = Vectara() @@ -54,7 +54,7 @@ def vectara1(): vectara1._delete_doc(doc_id) -def test_vectara_add_documents(vectara1) -> None: +def test_vectara_add_documents(vectara1) -> None: # type: ignore[no-untyped-def] """Test add_documents.""" # test without filter @@ -164,7 +164,7 @@ def test_vectara_from_files() -> None: @pytest.fixture(scope="function") -def vectara3(): +def vectara3(): # type: ignore[no-untyped-def] # Set up code vectara3: Vectara = Vectara() @@ -210,7 +210,7 @@ def vectara3(): vectara3._delete_doc(doc_id) -def test_vectara_mmr(vectara3) -> None: +def test_vectara_mmr(vectara3) -> None: # type: ignore[no-untyped-def] # test max marginal relevance output1 = vectara3.max_marginal_relevance_search( "generative AI", @@ -241,7 +241,7 @@ def test_vectara_mmr(vectara3) -> None: ) -def test_vectara_with_summary(vectara3) -> None: +def test_vectara_with_summary(vectara3) -> None: # type: ignore[no-untyped-def] """Test vectara summary.""" # test summarization num_results = 10 diff --git a/libs/community/tests/unit_tests/chat_models/test_edenai.py b/libs/community/tests/unit_tests/chat_models/test_edenai.py index 2fa85512c012b79..dfafc5af988d4d2 100644 --- a/libs/community/tests/unit_tests/chat_models/test_edenai.py +++ b/libs/community/tests/unit_tests/chat_models/test_edenai.py @@ -35,6 +35,6 @@ def test_edenai_messages_formatting(messages: List[BaseMessage], expected: str) ("role", "role_response"), [("ai", "assistant"), ("human", "user"), ("chat", "user")], ) -def test_edenai_message_role(role: str, role_response) -> None: +def test_edenai_message_role(role: str, role_response) -> None: # type: ignore[no-untyped-def] role = _message_role(role) assert role == role_response diff --git a/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py b/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py index fa57109ef02cba2..1bec4a7bf63c0ed 100644 --- a/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py +++ b/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py @@ -29,7 +29,7 @@ def embed(self, inputs: List[Dict[str, str]]) -> Any: embeddings = [] for i, inp in enumerate(inputs): # verify correct ordering - inp = inp["input"] + inp = inp["input"] # type: ignore[assignment] if "pizza" in inp: v = [1.0, 0.0, 0.0] elif "document" in inp: @@ -45,14 +45,14 @@ def embed(self, inputs: List[Dict[str, str]]) -> Any: output.embeddings = embeddings return output - async def aembed(self, *args) -> Any: + async def aembed(self, *args) -> Any: # type: ignore[no-untyped-def] return self.embed(*args) class MockGradient(MagicMock): """Mock Gradient package.""" - def __init__(self, access_token: str, workspace_id, host): + def __init__(self, access_token: str, workspace_id, host): # type: ignore[no-untyped-def] assert access_token == _GRADIENT_SECRET assert workspace_id == _GRADIENT_WORKSPACE_ID assert host == _GRADIENT_BASE_URL diff --git a/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py b/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py index 12d9c447d5dc190..d43f3108b9cf7ed 100644 --- a/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py +++ b/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py @@ -8,7 +8,7 @@ class MockResponseDict(dict): - def __getattr__(self, val): + def __getattr__(self, val): # type: ignore[no-untyped-def] return self[val] @@ -25,7 +25,7 @@ def test_embedding_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None: client=oci_gen_ai_client, ) - def mocked_response(invocation_obj): + def mocked_response(invocation_obj): # type: ignore[no-untyped-def] docs = invocation_obj.inputs embeddings = [] diff --git a/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py b/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py index b352529ba67348f..eebef74ef16e4be 100644 --- a/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py +++ b/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py @@ -1,14 +1,14 @@ from langchain_community.graphs.neo4j_graph import value_sanitize -def test_value_sanitize_with_small_list(): +def test_value_sanitize_with_small_list(): # type: ignore[no-untyped-def] small_list = list(range(15)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "small_list": small_list} expected_output = {"key1": "value1", "small_list": small_list} assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_oversized_list(): +def test_value_sanitize_with_oversized_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": oversized_list} expected_output = { @@ -18,14 +18,14 @@ def test_value_sanitize_with_oversized_list(): assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_nested_oversized_list(): +def test_value_sanitize_with_nested_oversized_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": {"key": oversized_list}} expected_output = {"key1": "value1", "oversized_list": {}} assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_dict_in_list(): +def test_value_sanitize_with_dict_in_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": [1, 2, {"key": oversized_list}]} expected_output = {"key1": "value1", "oversized_list": [1, 2, {}]} diff --git a/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py b/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py index 8b025fed36fb13c..9beb2da271ded1e 100644 --- a/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py +++ b/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py @@ -15,7 +15,7 @@ def test_validate_user_query_wrong_type(self) -> None: with self.assertRaises(TypeError) as e: OntotextGraphDBGraph._validate_user_query( - [ + [ # type: ignore[arg-type] "PREFIX starwars: " "PREFIX rdfs: " "DESCRIBE starwars: ?term " diff --git a/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py b/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py index 694d88f0c24edfa..b1c36ec7a5fedff 100644 --- a/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py +++ b/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py @@ -8,7 +8,7 @@ class MockResponseDict(dict): - def __getattr__(self, val): + def __getattr__(self, val): # type: ignore[no-untyped-def] return self[val] @@ -23,7 +23,7 @@ def test_llm_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None: provider = llm._get_provider() - def mocked_response(*args): + def mocked_response(*args): # type: ignore[no-untyped-def] response_text = "This is the completion." if provider == "cohere": diff --git a/libs/community/tests/unit_tests/llms/test_ollama.py b/libs/community/tests/unit_tests/llms/test_ollama.py index 63a323eb35efd6c..6dcb0bd38e3678c 100644 --- a/libs/community/tests/unit_tests/llms/test_ollama.py +++ b/libs/community/tests/unit_tests/llms/test_ollama.py @@ -4,11 +4,11 @@ from langchain_community.llms.ollama import Ollama -def mock_response_stream(): +def mock_response_stream(): # type: ignore[no-untyped-def] mock_response = [b'{ "response": "Response chunk 1" }'] class MockRaw: - def read(self, chunk_size): + def read(self, chunk_size): # type: ignore[no-untyped-def] try: return mock_response.pop() except IndexError: @@ -31,7 +31,7 @@ def test_pass_headers_if_provided(monkeypatch: MonkeyPatch) -> None: timeout=300, ) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -52,7 +52,7 @@ def mock_post(url, headers, json, stream, timeout): def test_handle_if_headers_not_provided(monkeypatch: MonkeyPatch) -> None: llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -72,7 +72,7 @@ def test_handle_kwargs_top_level_parameters(monkeypatch: MonkeyPatch) -> None: """Test that top level params are sent to the endpoint as top level params""" llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -118,7 +118,7 @@ def test_handle_kwargs_with_unknown_param(monkeypatch: MonkeyPatch) -> None: """ llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -165,7 +165,7 @@ def test_handle_kwargs_with_options(monkeypatch: MonkeyPatch) -> None: """ llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", diff --git a/libs/core/Makefile b/libs/core/Makefile index 2c685dfd44903f2..ab8e9cadf03b320 100644 --- a/libs/core/Makefile +++ b/libs/core/Makefile @@ -42,7 +42,7 @@ lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) diff --git a/libs/langchain/Makefile b/libs/langchain/Makefile index 50f1e423c0feb46..cff2f348546a18b 100644 --- a/libs/langchain/Makefile +++ b/libs/langchain/Makefile @@ -62,7 +62,7 @@ lint lint_diff lint_package lint_tests: poetry run ruff . [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) diff --git a/libs/langchain/langchain/agents/openai_assistant/base.py b/libs/langchain/langchain/agents/openai_assistant/base.py index cd4e14429187bf1..99620322d8d7d45 100644 --- a/libs/langchain/langchain/agents/openai_assistant/base.py +++ b/libs/langchain/langchain/agents/openai_assistant/base.py @@ -210,7 +210,7 @@ def create_assistant( assistant = client.beta.assistants.create( name=name, instructions=instructions, - tools=[convert_to_openai_tool(tool) for tool in tools], + tools=[convert_to_openai_tool(tool) for tool in tools], # type: ignore model=model, ) return cls(assistant_id=assistant.id, client=client, **kwargs) @@ -331,7 +331,7 @@ async def acreate_assistant( assistant = await async_client.beta.assistants.create( name=name, instructions=instructions, - tools=openai_tools, + tools=openai_tools, # type: ignore model=model, ) return cls(assistant_id=assistant.id, async_client=async_client, **kwargs) diff --git a/libs/langchain/langchain/chains/moderation.py b/libs/langchain/langchain/chains/moderation.py index 00d6cbd3f3528c0..6deb63ed0629c61 100644 --- a/libs/langchain/langchain/chains/moderation.py +++ b/libs/langchain/langchain/chains/moderation.py @@ -52,7 +52,7 @@ def validate_environment(cls, values: Dict) -> Dict: openai.api_key = openai_api_key if openai_organization: openai.organization = openai_organization - values["client"] = openai.Moderation + values["client"] = openai.Moderation # type: ignore except ImportError: raise ImportError( "Could not import openai python package. " From 1183769cf79bc864d7768b8108ec3dc09da1b619 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 5 Feb 2024 11:50:17 -0800 Subject: [PATCH 063/157] template: tool-retrieval-fireworks (#17052) - Initial commit oss-tool-retrieval-agent - README update - lint - lock - format imports - Rename to retrieval-agent-fireworks - cr --------- Co-authored-by: Taqi Jaffri --- .../retrieval-agent-fireworks/.gitignore | 1 + templates/retrieval-agent-fireworks/LICENSE | 21 + templates/retrieval-agent-fireworks/README.md | 73 + .../retrieval-agent-fireworks/poetry.lock | 1840 +++++++++++++++++ .../retrieval-agent-fireworks/pyproject.toml | 34 + .../retrieval_agent_fireworks/__init__.py | 3 + .../retrieval_agent_fireworks/chain.py | 109 + .../tests/__init__.py | 0 8 files changed, 2081 insertions(+) create mode 100644 templates/retrieval-agent-fireworks/.gitignore create mode 100644 templates/retrieval-agent-fireworks/LICENSE create mode 100644 templates/retrieval-agent-fireworks/README.md create mode 100644 templates/retrieval-agent-fireworks/poetry.lock create mode 100644 templates/retrieval-agent-fireworks/pyproject.toml create mode 100644 templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py create mode 100644 templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py create mode 100644 templates/retrieval-agent-fireworks/tests/__init__.py diff --git a/templates/retrieval-agent-fireworks/.gitignore b/templates/retrieval-agent-fireworks/.gitignore new file mode 100644 index 000000000000000..bee8a64b79a9959 --- /dev/null +++ b/templates/retrieval-agent-fireworks/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/templates/retrieval-agent-fireworks/LICENSE b/templates/retrieval-agent-fireworks/LICENSE new file mode 100644 index 000000000000000..426b65090341f37 --- /dev/null +++ b/templates/retrieval-agent-fireworks/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/retrieval-agent-fireworks/README.md b/templates/retrieval-agent-fireworks/README.md new file mode 100644 index 000000000000000..5a796c957c29c2f --- /dev/null +++ b/templates/retrieval-agent-fireworks/README.md @@ -0,0 +1,73 @@ +# retrieval-agent-fireworks + +This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv. + +We will use `Mixtral8x7b-instruct-v0.1`, which is shown in this blog to yield reasonable +results with function calling even though it is not fine tuned for this task: https://huggingface.co/blog/open-source-llms-as-agents + + +## Environment Setup + +There are various great ways to run OSS models. We will use FireworksAI as an easy way to run the models. See [here](https://python.langchain.com/docs/integrations/providers/fireworks) for more information. + +Set the `FIREWORKS_API_KEY` environment variable to access Fireworks. + + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package retrieval-agent-fireworks +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add retrieval-agent-fireworks +``` + +And add the following code to your `server.py` file: +```python +from retrieval_agent_fireworks import chain as retrieval_agent_fireworks_chain + +add_routes(app, retrieval_agent_fireworks_chain, path="/retrieval-agent-fireworks") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/retrieval-agent-fireworks/playground](http://127.0.0.1:8000/retrieval-agent-fireworks/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/retrieval-agent-fireworks") +``` \ No newline at end of file diff --git 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+langchainhub = "^0.1.14" +fireworks-ai = "^0.11.2" + + +[tool.poetry.group.dev.dependencies] +langchain-cli = ">=0.0.20" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "retrieval_agent_fireworks" +export_attr = "agent_executor" + +[tool.templates-hub] +use-case = "research" +author = "Docugami" +integrations = ["HuggingFace"] +tags = ["local", "agents"] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py new file mode 100644 index 000000000000000..fdf3d35c08bce92 --- /dev/null +++ b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py @@ -0,0 +1,3 @@ +from retrieval_agent_fireworks.chain import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py new file mode 100644 index 000000000000000..7207cc99d323265 --- /dev/null +++ b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py @@ -0,0 +1,109 @@ +from typing import List + +from langchain import hub +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_log_to_str +from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser +from langchain.callbacks.manager import CallbackManagerForRetrieverRun +from langchain.schema import BaseRetriever, Document +from langchain.tools.render import render_text_description +from langchain.tools.retriever import create_retriever_tool +from langchain_community.chat_models.fireworks import ChatFireworks +from langchain_community.utilities.arxiv import ArxivAPIWrapper +from langchain_core.pydantic_v1 import BaseModel + +MODEL_ID = "accounts/fireworks/models/mixtral-8x7b-instruct" + + +class ArxivRetriever(BaseRetriever, ArxivAPIWrapper): + """`Arxiv` retriever. + + It wraps load() to get_relevant_documents(). + It uses all ArxivAPIWrapper arguments without any change. + """ + + get_full_documents: bool = False + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + try: + if self.is_arxiv_identifier(query): + results = self.arxiv_search( + id_list=query.split(), + max_results=self.top_k_results, + ).results() + else: + results = self.arxiv_search( # type: ignore + query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results + ).results() + except self.arxiv_exceptions as ex: + return [Document(page_content=f"Arxiv exception: {ex}")] + docs = [ + Document( + page_content=result.summary, + metadata={ + "Published": result.updated.date(), + "Title": result.title, + "Authors": ", ".join(a.name for a in result.authors), + }, + ) + for result in results + ] + return docs + + +# Set up tool(s) +description = ( + "A wrapper around Arxiv.org " + "Useful for when you need to answer questions about Physics, Mathematics, " + "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, " + "Electrical Engineering, and Economics " + "from scientific articles on arxiv.org. " + "Input should be a search query." +) +arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description) +tools = [arxiv_tool] + +# Set up LLM +llm = ChatFireworks( + model=MODEL_ID, + model_kwargs={ + "temperature": 0, + "max_tokens": 2048, + "top_p": 1, + }, + cache=True, +) + +# setup ReAct style prompt +prompt = hub.pull("hwchase17/react-json") +prompt = prompt.partial( + tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools]), +) + +# define the agent +model_with_stop = llm.bind(stop=["\nObservation"]) +agent = ( + { + "input": lambda x: x["input"], + "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]), + } + | prompt + | model_with_stop + | ReActJsonSingleInputOutputParser() +) + + +class InputType(BaseModel): + input: str + + +# instantiate AgentExecutor +agent_executor = AgentExecutor( + agent=agent, + tools=tools, + verbose=True, + handle_parsing_errors=True, +).with_types(input_type=InputType) diff --git a/templates/retrieval-agent-fireworks/tests/__init__.py b/templates/retrieval-agent-fireworks/tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 From 6ffd5b15bcc42e3cd0f76793cecd4a35ae9fe6a4 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 5 Feb 2024 11:55:01 -0800 Subject: [PATCH 064/157] pinecone: init pkg (#16556) --- .../integrations/vectorstores/pinecone.ipynb | 279 ++-- .../vectorstores/pinecone.py | 19 +- libs/partners/pinecone/.gitignore | 1 + libs/partners/pinecone/LICENSE | 21 + libs/partners/pinecone/Makefile | 61 + libs/partners/pinecone/README.md | 27 + .../pinecone/langchain_pinecone/__init__.py | 5 + .../pinecone/langchain_pinecone/_utilities.py | 71 + .../pinecone/langchain_pinecone/py.typed | 0 .../langchain_pinecone/vectorstores.py | 487 +++++++ libs/partners/pinecone/poetry.lock | 1213 +++++++++++++++++ libs/partners/pinecone/pyproject.toml | 97 ++ .../pinecone/scripts/check_imports.py | 17 + .../pinecone/scripts/check_pydantic.sh | 27 + .../partners/pinecone/scripts/lint_imports.sh | 17 + libs/partners/pinecone/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../tests/integration_tests/test_compile.py | 7 + .../integration_tests/test_vectorstores.py | 288 ++++ .../pinecone/tests/unit_tests/__init__.py | 0 .../pinecone/tests/unit_tests/test_imports.py | 9 + .../tests/unit_tests/test_vectorstores.py | 12 + 22 files changed, 2557 insertions(+), 101 deletions(-) create mode 100644 libs/partners/pinecone/.gitignore create mode 100644 libs/partners/pinecone/LICENSE create mode 100644 libs/partners/pinecone/Makefile create mode 100644 libs/partners/pinecone/README.md create mode 100644 libs/partners/pinecone/langchain_pinecone/__init__.py create mode 100644 libs/partners/pinecone/langchain_pinecone/_utilities.py create mode 100644 libs/partners/pinecone/langchain_pinecone/py.typed create mode 100644 libs/partners/pinecone/langchain_pinecone/vectorstores.py create mode 100644 libs/partners/pinecone/poetry.lock create mode 100644 libs/partners/pinecone/pyproject.toml create mode 100644 libs/partners/pinecone/scripts/check_imports.py create mode 100755 libs/partners/pinecone/scripts/check_pydantic.sh create mode 100755 libs/partners/pinecone/scripts/lint_imports.sh create mode 100644 libs/partners/pinecone/tests/__init__.py create mode 100644 libs/partners/pinecone/tests/integration_tests/__init__.py create mode 100644 libs/partners/pinecone/tests/integration_tests/test_compile.py create mode 100644 libs/partners/pinecone/tests/integration_tests/test_vectorstores.py create mode 100644 libs/partners/pinecone/tests/unit_tests/__init__.py create mode 100644 libs/partners/pinecone/tests/unit_tests/test_imports.py create mode 100644 libs/partners/pinecone/tests/unit_tests/test_vectorstores.py diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb index 68513c45ad87a8d..2c93de880967779 100644 --- a/docs/docs/integrations/vectorstores/pinecone.ipynb +++ b/docs/docs/integrations/vectorstores/pinecone.ipynb @@ -13,7 +13,16 @@ "This notebook shows how to use functionality related to the `Pinecone` vector database.\n", "\n", "To use Pinecone, you must have an API key. \n", - "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart)." + "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n", + "\n", + "Set the following environment variables to make using the `Pinecone` integration easier:\n", + "\n", + "- `PINECONE_API_KEY`: Your Pinecone API key.\n", + "- `PINECONE_INDEX_NAME`: The name of the index you want to use.\n", + "\n", + "And to follow along in this doc, you should also set\n", + "\n", + "- `OPENAI_API_KEY`: Your OpenAI API key, for using `OpenAIEmbeddings`" ] }, { @@ -25,74 +34,27 @@ }, "outputs": [], "source": [ - "%pip install --upgrade --quiet pinecone-client langchain-openai tiktoken langchain" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "os.environ[\"PINECONE_API_KEY\"] = getpass.getpass(\"Pinecone API Key:\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02a536e0-d603-4d79-b18b-1ed562977b40", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"PINECONE_ENV\"] = getpass.getpass(\"Pinecone Environment:\")" + "%pip install --upgrade --quiet langchain-pinecone langchain-openai langchain" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "320af802-9271-46ee-948f-d2453933d44b", - "metadata": {}, - "source": [ - "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffea66e4-bc23-46a9-9580-b348dfe7b7a7", + "id": "42f2ea67", "metadata": {}, - "outputs": [], "source": [ - "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + "First, let's split our state of the union document into chunked `docs`." ] }, { "cell_type": "code", - "execution_count": null, - "id": "aac9563e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import Pinecone\n", - "from langchain_openai import OpenAIEmbeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "a3c3999a", "metadata": {}, "outputs": [], "source": [ + "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_openai import OpenAIEmbeddings\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", @@ -102,44 +64,53 @@ "embeddings = OpenAIEmbeddings()" ] }, + { + "cell_type": "markdown", + "id": "3a4d377f", + "metadata": {}, + "source": [ + "Now let's assume you have your Pinecone index set up with `dimension=1536`.\n", + "\n", + "We can connect to our Pinecone index and insert those chunked docs as contents with `Pinecone.from_documents`." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6e104aee", "metadata": {}, "outputs": [], "source": [ - "import pinecone\n", - "\n", - "# initialize pinecone\n", - "pinecone.init(\n", - " api_key=os.getenv(\"PINECONE_API_KEY\"), # find at app.pinecone.io\n", - " environment=os.getenv(\"PINECONE_ENV\"), # next to api key in console\n", - ")\n", - "\n", - "index_name = \"langchain-demo\"\n", + "from langchain_pinecone import Pinecone\n", "\n", - "# First, check if our index already exists. If it doesn't, we create it\n", - "if index_name not in pinecone.list_indexes():\n", - " # we create a new index\n", - " pinecone.create_index(name=index_name, metric=\"cosine\", dimension=1536)\n", - "# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`\n", - "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)\n", + "index_name = \"langchain-test-index\"\n", "\n", - "# if you already have an index, you can load it like this\n", - "# docsearch = Pinecone.from_existing_index(index_name, embeddings)\n", - "\n", - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "docs = docsearch.similarity_search(query)" + "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "9c608226", + "execution_count": 7, + "id": "ffbcb3fb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" + ] + } + ], "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = docsearch.similarity_search(query)\n", "print(docs[0].page_content)" ] }, @@ -156,15 +127,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "38a7a60e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['24631802-4bad-44a7-a4ba-fd71f00cc160']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "index = pinecone.Index(\"langchain-demo\")\n", - "vectorstore = Pinecone(index, embeddings.embed_query, \"text\")\n", + "vectorstore = Pinecone(index_name=index_name, embedding=embeddings)\n", "\n", - "vectorstore.add_texts(\"More text!\")" + "vectorstore.add_texts([\"More text!\"])" ] }, { @@ -180,10 +161,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a359ed74", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## Document 0\n", + "\n", + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "\n", + "## Document 1\n", + "\n", + "And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n", + "\n", + "Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. \n", + "\n", + "America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. \n", + "\n", + "These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n", + "\n", + "But I want you to know that we are going to be okay. \n", + "\n", + "When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. \n", + "\n", + "While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.\n", + "\n", + "## Document 2\n", + "\n", + "We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n", + "\n", + "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n", + "\n", + "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n", + "\n", + "Officer Mora was 27 years old. \n", + "\n", + "Officer Rivera was 22. \n", + "\n", + "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n", + "\n", + "## Document 3\n", + "\n", + "One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \n", + "\n", + "When they came home, many of the world’s fittest and best trained warriors were never the same. \n", + "\n", + "Headaches. Numbness. Dizziness. \n", + "\n", + "A cancer that would put them in a flag-draped coffin. \n", + "\n", + "I know. \n", + "\n", + "One of those soldiers was my son Major Beau Biden. \n", + "\n", + "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n", + "\n", + "But I’m committed to finding out everything we can. \n", + "\n", + "Committed to military families like Danielle Robinson from Ohio. \n", + "\n", + "The widow of Sergeant First Class Heath Robinson. \n", + "\n", + "He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \n", + "\n", + "Stationed near Baghdad, just yards from burn pits the size of football fields. \n", + "\n", + "Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.\n" + ] + } + ], "source": [ "retriever = docsearch.as_retriever(search_type=\"mmr\")\n", "matched_docs = retriever.get_relevant_documents(query)\n", @@ -203,15 +265,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9ca82740", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n", + "\n", + "2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n", + "\n", + "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n", + "\n", + "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n", + "\n", + "Officer Mora was 27 years old. \n", + "\n", + "Officer Rivera was 22. \n", + "\n", + "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n", + "\n" + ] + } + ], "source": [ "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n", "for i, doc in enumerate(found_docs):\n", " print(f\"{i + 1}.\", doc.page_content, \"\\n\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0fd750b", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -230,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/libs/community/langchain_community/vectorstores/pinecone.py b/libs/community/langchain_community/vectorstores/pinecone.py index 410d55420fa5746..0caf6b31c420b8e 100644 --- a/libs/community/langchain_community/vectorstores/pinecone.py +++ b/libs/community/langchain_community/vectorstores/pinecone.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union import numpy as np +from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.utils.iter import batch_iterate @@ -41,24 +42,16 @@ def _is_pinecone_v3() -> bool: return version.parse(pinecone_client_version) >= version.parse("3.0.0.dev") +@deprecated( + since="0.0.18", removal="0.2.0", alternative_import="langchain_pinecone.Pinecone" +) class Pinecone(VectorStore): """`Pinecone` vector store. To use, you should have the ``pinecone-client`` python package installed. - Example: - .. code-block:: python - - from langchain_community.vectorstores import Pinecone - from langchain_community.embeddings.openai import OpenAIEmbeddings - import pinecone - - # The environment should be the one specified next to the API key - # in your Pinecone console - pinecone.init(api_key="***", environment="...") - index = pinecone.Index("langchain-demo") - embeddings = OpenAIEmbeddings() - vectorstore = Pinecone(index, embeddings, "text") + This version of Pinecone is deprecated. Please use `langchain_pinecone.Pinecone` + instead. """ def __init__( diff --git a/libs/partners/pinecone/.gitignore b/libs/partners/pinecone/.gitignore new file mode 100644 index 000000000000000..bee8a64b79a9959 --- /dev/null +++ b/libs/partners/pinecone/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/pinecone/LICENSE b/libs/partners/pinecone/LICENSE new file mode 100644 index 000000000000000..426b65090341f37 --- /dev/null +++ b/libs/partners/pinecone/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/pinecone/Makefile b/libs/partners/pinecone/Makefile new file mode 100644 index 000000000000000..5f54fdd2285406e --- /dev/null +++ b/libs/partners/pinecone/Makefile @@ -0,0 +1,61 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +integration_tests: TEST_FILE = tests/integration_tests/ + +test integration_tests: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/pinecone --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_pinecone +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_pinecone -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/pinecone/README.md b/libs/partners/pinecone/README.md new file mode 100644 index 000000000000000..c0b1069a6d07902 --- /dev/null +++ b/libs/partners/pinecone/README.md @@ -0,0 +1,27 @@ +# langchain-pinecone + +This package contains the LangChain integration with Pinecone. + +## Installation + +```bash +pip install -U langchain-pinecone +``` + +And you should configure credentials by setting the following environment variables: + +- `PINECONE_API_KEY` +- `PINECONE_INDEX_NAME` +- `PINECONE_ENVIRONMENT` + +## Usage + +The `Pinecone` class exposes the connection to the Pinecone vector store. + +```python +from langchain_pinecone import Pinecone + +embeddings = ... # use a LangChain Embeddings class + +vectorstore = Pinecone(embeddings=embeddings) +``` diff --git a/libs/partners/pinecone/langchain_pinecone/__init__.py b/libs/partners/pinecone/langchain_pinecone/__init__.py new file mode 100644 index 000000000000000..b71858853bbc89c --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/__init__.py @@ -0,0 +1,5 @@ +from langchain_pinecone.vectorstores import Pinecone + +__all__ = [ + "Pinecone", +] diff --git a/libs/partners/pinecone/langchain_pinecone/_utilities.py b/libs/partners/pinecone/langchain_pinecone/_utilities.py new file mode 100644 index 000000000000000..5f5f734cf6d907f --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/_utilities.py @@ -0,0 +1,71 @@ +from enum import Enum +from typing import List, Union + +import numpy as np +import simsimd # type: ignore + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +class DistanceStrategy(str, Enum): + """Enumerator of the Distance strategies for calculating distances + between vectors.""" + + EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE" + MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT" + COSINE = "COSINE" + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance.""" + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simsimd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return Z diff --git a/libs/partners/pinecone/langchain_pinecone/py.typed b/libs/partners/pinecone/langchain_pinecone/py.typed new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/pinecone/langchain_pinecone/vectorstores.py b/libs/partners/pinecone/langchain_pinecone/vectorstores.py new file mode 100644 index 000000000000000..66f56905583c2f5 --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/vectorstores.py @@ -0,0 +1,487 @@ +from __future__ import annotations + +import logging +import os +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Iterable, + List, + Optional, + Tuple, + TypeVar, +) + +import numpy as np +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.utils.iter import batch_iterate +from langchain_core.vectorstores import VectorStore +from pinecone import Pinecone as PineconeClient # type: ignore + +from langchain_pinecone._utilities import DistanceStrategy, maximal_marginal_relevance + +if TYPE_CHECKING: + from pinecone import Index + +logger = logging.getLogger(__name__) + +VST = TypeVar("VST", bound=VectorStore) + + +class Pinecone(VectorStore): + """`Pinecone` vector store. + + Example: + .. code-block:: python + + from langchain_pinecone import Pinecone + from langchain_openai import OpenAIEmbeddings + + embeddings = OpenAIEmbeddings() + index_name = "my-index" + namespace = "my-namespace" + vectorstore = Pinecone( + index_name=index_name, + embedding=embedding, + namespace=namespace, + ) + """ + + def __init__( + self, + # setting default params to bypass having to pass in + # the index and embedding objects - manually throw + # exceptions if they are not passed in or set in environment + # (keeping param for backwards compatibility) + index: Optional[Any] = None, + embedding: Optional[Embeddings] = None, + text_key: Optional[str] = "text", + namespace: Optional[str] = None, + distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE, + *, + pinecone_api_key: Optional[str] = None, + index_name: Optional[str] = None, + ): + if embedding is None: + raise ValueError("Embedding must be provided") + self._embedding = embedding + if text_key is None: + raise ValueError("Text key must be provided") + self._text_key = text_key + + self._namespace = namespace + self.distance_strategy = distance_strategy + + if index: + # supports old way of initializing externally + self._index = index + else: + # all internal initialization + _pinecone_api_key = ( + pinecone_api_key or os.environ.get("PINECONE_API_KEY") or "" + ) + if not _pinecone_api_key: + raise ValueError( + "Pinecone API key must be provided in either `pinecone_api_key` " + "or `PINECONE_API_KEY` environment variable" + ) + + _index_name = index_name or os.environ.get("PINECONE_INDEX_NAME") or "" + if not _index_name: + raise ValueError( + "Pinecone index name must be provided in either `index_name` " + "or `PINECONE_INDEX_NAME` environment variable" + ) + + # needs + client = PineconeClient(api_key=_pinecone_api_key) + self._index = client.Index(_index_name) + + @property + def embeddings(self) -> Optional[Embeddings]: + """Access the query embedding object if available.""" + return self._embedding + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + namespace: Optional[str] = None, + batch_size: int = 32, + embedding_chunk_size: int = 1000, + *, + async_req: bool = True, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Upsert optimization is done by chunking the embeddings and upserting them. + This is done to avoid memory issues and optimize using HTTP based embeddings. + For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, + embedding_chunk_size>1000 and batch_size~64 for best performance. + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of ids to associate with the texts. + namespace: Optional pinecone namespace to add the texts to. + batch_size: Batch size to use when adding the texts to the vectorstore. + embedding_chunk_size: Chunk size to use when embedding the texts. + + Returns: + List of ids from adding the texts into the vectorstore. + + """ + if namespace is None: + namespace = self._namespace + + texts = list(texts) + ids = ids or [str(uuid.uuid4()) for _ in texts] + metadatas = metadatas or [{} for _ in texts] + for metadata, text in zip(metadatas, texts): + metadata[self._text_key] = text + + # For loops to avoid memory issues and optimize when using HTTP based embeddings + # The first loop runs the embeddings, it benefits when using OpenAI embeddings + # The second loops runs the pinecone upsert asynchronously. + for i in range(0, len(texts), embedding_chunk_size): + chunk_texts = texts[i : i + embedding_chunk_size] + chunk_ids = ids[i : i + embedding_chunk_size] + chunk_metadatas = metadatas[i : i + embedding_chunk_size] + embeddings = self._embedding.embed_documents(chunk_texts) + async_res = [ + self._index.upsert( + vectors=batch, + namespace=namespace, + async_req=async_req, + **kwargs, + ) + for batch in batch_iterate( + batch_size, zip(chunk_ids, embeddings, chunk_metadatas) + ) + ] + [res.get() for res in async_res] + + return ids + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + ) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to query, along with scores. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. + + Returns: + List of Documents most similar to the query and score for each + """ + return self.similarity_search_by_vector_with_score( + self._embedding.embed_query(query), k=k, filter=filter, namespace=namespace + ) + + def similarity_search_by_vector_with_score( + self, + embedding: List[float], + *, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + ) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to embedding, along with scores.""" + + if namespace is None: + namespace = self._namespace + docs = [] + results = self._index.query( + vector=embedding, + top_k=k, + include_metadata=True, + namespace=namespace, + filter=filter, + ) + for res in results["matches"]: + metadata = res["metadata"] + if self._text_key in metadata: + text = metadata.pop(self._text_key) + score = res["score"] + docs.append((Document(page_content=text, metadata=metadata), score)) + else: + logger.warning( + f"Found document with no `{self._text_key}` key. Skipping." + ) + return docs + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return pinecone documents most similar to query. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. + + Returns: + List of Documents most similar to the query and score for each + """ + docs_and_scores = self.similarity_search_with_score( + query, k=k, filter=filter, namespace=namespace, **kwargs + ) + return [doc for doc, _ in docs_and_scores] + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The 'correct' relevance function + may differ depending on a few things, including: + - the distance / similarity metric used by the VectorStore + - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) + - embedding dimensionality + - etc. + """ + + if self.distance_strategy == DistanceStrategy.COSINE: + return self._cosine_relevance_score_fn + elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: + return self._max_inner_product_relevance_score_fn + elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: + return self._euclidean_relevance_score_fn + else: + raise ValueError( + "Unknown distance strategy, must be cosine, max_inner_product " + "(dot product), or euclidean" + ) + + @staticmethod + def _cosine_relevance_score_fn(score: float) -> float: + """Pinecone returns cosine similarity scores between [-1,1]""" + return (score + 1) / 2 + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + Returns: + List of Documents selected by maximal marginal relevance. + """ + if namespace is None: + namespace = self._namespace + results = self._index.query( + vector=[embedding], + top_k=fetch_k, + include_values=True, + include_metadata=True, + namespace=namespace, + filter=filter, + ) + mmr_selected = maximal_marginal_relevance( + np.array([embedding], dtype=np.float32), + [item["values"] for item in results["matches"]], + k=k, + lambda_mult=lambda_mult, + ) + selected = [results["matches"][i]["metadata"] for i in mmr_selected] + return [ + Document(page_content=metadata.pop((self._text_key)), metadata=metadata) + for metadata in selected + ] + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + Returns: + List of Documents selected by maximal marginal relevance. + """ + embedding = self._embedding.embed_query(query) + return self.max_marginal_relevance_search_by_vector( + embedding, k, fetch_k, lambda_mult, filter, namespace + ) + + @classmethod + def get_pinecone_index( + cls, + index_name: Optional[str], + pool_threads: int = 4, + *, + pinecone_api_key: Optional[str] = None, + ) -> Index: + """Return a Pinecone Index instance. + + Args: + index_name: Name of the index to use. + pool_threads: Number of threads to use for index upsert. + Returns: + Pinecone Index instance.""" + _pinecone_api_key = pinecone_api_key or os.environ.get("PINECONE_API_KEY") or "" + client = PineconeClient(api_key=_pinecone_api_key, pool_threads=pool_threads) + indexes = client.list_indexes() + index_names = [i.name for i in indexes.index_list["indexes"]] + + if index_name in index_names: + index = client.Index(index_name) + elif len(index_names) == 0: + raise ValueError( + "No active indexes found in your Pinecone project, " + "are you sure you're using the right Pinecone API key and Environment? " + "Please double check your Pinecone dashboard." + ) + else: + raise ValueError( + f"Index '{index_name}' not found in your Pinecone project. " + f"Did you mean one of the following indexes: {', '.join(index_names)}" + ) + return index + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + batch_size: int = 32, + text_key: str = "text", + namespace: Optional[str] = None, + index_name: Optional[str] = None, + upsert_kwargs: Optional[dict] = None, + pool_threads: int = 4, + embeddings_chunk_size: int = 1000, + **kwargs: Any, + ) -> Pinecone: + """Construct Pinecone wrapper from raw documents. + + This is a user friendly interface that: + 1. Embeds documents. + 2. Adds the documents to a provided Pinecone index + + This is intended to be a quick way to get started. + + The `pool_threads` affects the speed of the upsert operations. + Example: + .. code-block:: python + + from langchain_community.vectorstores import Pinecone + from langchain_community.embeddings import OpenAIEmbeddings + import pinecone + + # The environment should be the one specified next to the API key + # in your Pinecone console + pinecone.init(api_key="***", environment="...") + embeddings = OpenAIEmbeddings() + pinecone = Pinecone.from_texts( + texts, + embeddings, + index_name="langchain-demo" + ) + """ + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs) + + pinecone.add_texts( + texts, + metadatas=metadatas, + ids=ids, + namespace=namespace, + batch_size=batch_size, + embedding_chunk_size=embeddings_chunk_size, + **(upsert_kwargs or {}), + ) + return pinecone + + @classmethod + def from_existing_index( + cls, + index_name: str, + embedding: Embeddings, + text_key: str = "text", + namespace: Optional[str] = None, + pool_threads: int = 4, + ) -> Pinecone: + """Load pinecone vectorstore from index name.""" + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + return cls(pinecone_index, embedding, text_key, namespace) + + def delete( + self, + ids: Optional[List[str]] = None, + delete_all: Optional[bool] = None, + namespace: Optional[str] = None, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> None: + """Delete by vector IDs or filter. + Args: + ids: List of ids to delete. + filter: Dictionary of conditions to filter vectors to delete. + """ + + if namespace is None: + namespace = self._namespace + + if delete_all: + self._index.delete(delete_all=True, namespace=namespace, **kwargs) + elif ids is not None: + chunk_size = 1000 + for i in range(0, len(ids), chunk_size): + chunk = ids[i : i + chunk_size] + self._index.delete(ids=chunk, namespace=namespace, **kwargs) + elif filter is not None: + self._index.delete(filter=filter, namespace=namespace, **kwargs) + else: + raise ValueError("Either ids, delete_all, or filter must be provided.") + + return None diff --git a/libs/partners/pinecone/poetry.lock b/libs/partners/pinecone/poetry.lock new file mode 100644 index 000000000000000..d82c3dcefcebf04 --- /dev/null +++ b/libs/partners/pinecone/poetry.lock @@ -0,0 +1,1213 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} + +[[package]] +name = "anyio" +version = "4.2.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "certifi" +version = "2023.11.17" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, + {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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+[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] +langchain-openai = ">=0.0.3,<0.1" + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = {path = "../../core", develop = true} + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/pinecone/scripts/check_imports.py b/libs/partners/pinecone/scripts/check_imports.py new file mode 100644 index 000000000000000..fd21a4975b7f0ba --- /dev/null +++ b/libs/partners/pinecone/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/pinecone/scripts/check_pydantic.sh b/libs/partners/pinecone/scripts/check_pydantic.sh new file mode 100755 index 000000000000000..06b5bb81ae23615 --- /dev/null +++ b/libs/partners/pinecone/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/pinecone/scripts/lint_imports.sh b/libs/partners/pinecone/scripts/lint_imports.sh new file mode 100755 index 000000000000000..695613c7ba8fd6d --- /dev/null +++ b/libs/partners/pinecone/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/pinecone/tests/__init__.py b/libs/partners/pinecone/tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/pinecone/tests/integration_tests/__init__.py b/libs/partners/pinecone/tests/integration_tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/pinecone/tests/integration_tests/test_compile.py b/libs/partners/pinecone/tests/integration_tests/test_compile.py new file mode 100644 index 000000000000000..33ecccdfa0fbda2 --- /dev/null +++ b/libs/partners/pinecone/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py new file mode 100644 index 000000000000000..3abdabe256a8c04 --- /dev/null +++ b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py @@ -0,0 +1,288 @@ +import os +import time +import uuid +from typing import List + +import numpy as np +import pinecone # type: ignore +import pytest +from langchain_core.documents import Document +from langchain_openai import OpenAIEmbeddings +from pinecone import PodSpec + +from langchain_pinecone import Pinecone + +INDEX_NAME = "langchain-test-index" # name of the index +NAMESPACE_NAME = "langchain-test-namespace" # name of the namespace +DIMENSION = 1536 # dimension of the embeddings + +DEFAULT_SLEEP = 20 + + +class TestPinecone: + index: "pinecone.Index" + + @classmethod + def setup_class(cls) -> None: + import pinecone + + client = pinecone.Pinecone(api_key=os.environ["PINECONE_API_KEY"]) + index_list = client.list_indexes() + for i in index_list: + if i["name"] == INDEX_NAME: + client.delete_index(INDEX_NAME) + break + client.create_index( + name=INDEX_NAME, + dimension=DIMENSION, + metric="cosine", + spec=PodSpec(environment=os.environ["PINECONE_ENVIRONMENT"]), + ) + + cls.index = client.Index(INDEX_NAME) + + # insure the index is empty + index_stats = cls.index.describe_index_stats() + assert index_stats["dimension"] == DIMENSION + if index_stats["namespaces"].get(NAMESPACE_NAME) is not None: + assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == 0 + + @classmethod + def teardown_class(cls) -> None: + index_stats = cls.index.describe_index_stats() + for _namespace_name in index_stats["namespaces"].keys(): + cls.index.delete(delete_all=True, namespace=_namespace_name) + + @pytest.fixture(autouse=True) + def setup(self) -> None: + # delete all the vectors in the index + print("called") + self.index.delete(delete_all=True, namespace=NAMESPACE_NAME) + # index_stats = self.index.describe_index_stats() + # for _namespace_name in index_stats["namespaces"].keys(): + # self.index.delete(delete_all=True, namespace=_namespace_name) + time.sleep(DEFAULT_SLEEP) # prevent race condition with previous step + # index_stats = self.index.describe_index_stats + + @pytest.fixture + def embedding_openai(self) -> OpenAIEmbeddings: + return OpenAIEmbeddings() + + @pytest.fixture + def texts(self) -> List[str]: + return ["foo", "bar", "baz"] + + def test_from_texts( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + """Test end to end construction and search.""" + unique_id = uuid.uuid4().hex + needs = f"foobuu {unique_id} booo" + texts.insert(0, needs) + + docsearch = Pinecone.from_texts( + texts=texts, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search(unique_id, k=1, namespace=NAMESPACE_NAME) + assert output == [Document(page_content=needs)] + + def test_from_texts_with_metadatas( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + """Test end to end construction and search.""" + + unique_id = uuid.uuid4().hex + needs = f"foobuu {unique_id} booo" + texts = [needs] + texts + + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search(needs, k=1, namespace=NAMESPACE_NAME) + + # TODO: why metadata={"page": 0.0}) instead of {"page": 0}? + assert output == [Document(page_content=needs, metadata={"page": 0.0})] + + def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None: + """Test end to end construction and search with scores and IDs.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + print("metadatas", metadatas) + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=NAMESPACE_NAME, + ) + print(texts) + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search_with_score( + "foo", k=3, namespace=NAMESPACE_NAME + ) + docs = [o[0] for o in output] + scores = [o[1] for o in output] + sorted_documents = sorted(docs, key=lambda x: x.metadata["page"]) + print(sorted_documents) + + # TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc??? + assert sorted_documents == [ + Document(page_content="foo", metadata={"page": 0.0}), + Document(page_content="bar", metadata={"page": 1.0}), + Document(page_content="baz", metadata={"page": 2.0}), + ] + assert scores[0] > scores[1] > scores[2] + + def test_from_existing_index_with_namespaces( + self, embedding_openai: OpenAIEmbeddings + ) -> None: + """Test that namespaces are properly handled.""" + # Create two indexes with the same name but different namespaces + texts_1 = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts_1))] + Pinecone.from_texts( + texts_1, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=f"{INDEX_NAME}-1", + ) + + texts_2 = ["foo2", "bar2", "baz2"] + metadatas = [{"page": i} for i in range(len(texts_2))] + + Pinecone.from_texts( + texts_2, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=f"{INDEX_NAME}-2", + ) + + time.sleep(DEFAULT_SLEEP) # prevent race condition + + # Search with namespace + docsearch = Pinecone.from_existing_index( + index_name=INDEX_NAME, + embedding=embedding_openai, + namespace=f"{INDEX_NAME}-1", + ) + output = docsearch.similarity_search("foo", k=20, namespace=f"{INDEX_NAME}-1") + # check that we don't get results from the other namespace + page_contents = sorted(set([o.page_content for o in output])) + assert all(content in ["foo", "bar", "baz"] for content in page_contents) + assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents) + + def test_add_documents_with_ids( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + ids = [uuid.uuid4().hex for _ in range(len(texts))] + Pinecone.from_texts( + texts=texts, + ids=ids, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + index_stats = self.index.describe_index_stats() + assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts) + + ids_1 = [uuid.uuid4().hex for _ in range(len(texts))] + Pinecone.from_texts( + texts=[t + "-1" for t in texts], + ids=ids_1, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + index_stats = self.index.describe_index_stats() + assert ( + index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts) * 2 + ) + # only focused on this namespace now + # assert index_stats["total_vector_count"] == len(texts) * 2 + + @pytest.mark.xfail(reason="relevance score just over 1") + def test_relevance_score_bound(self, embedding_openai: OpenAIEmbeddings) -> None: + """Ensures all relevance scores are between 0 and 1.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + ) + # wait for the index to be ready + time.sleep(DEFAULT_SLEEP) + output = docsearch.similarity_search_with_relevance_scores("foo", k=3) + print(output) + assert all( + (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output + ) + + @pytest.mark.skipif(reason="slow to run for benchmark") + @pytest.mark.parametrize( + "pool_threads,batch_size,embeddings_chunk_size,data_multiplier", + [ + ( + 1, + 32, + 32, + 1000, + ), # simulate single threaded with embeddings_chunk_size = batch_size = 32 + ( + 1, + 32, + 1000, + 1000, + ), # simulate single threaded with embeddings_chunk_size = 1000 + ( + 4, + 32, + 1000, + 1000, + ), # simulate 4 threaded with embeddings_chunk_size = 1000 + (20, 64, 5000, 1000), + ], # simulate 20 threaded with embeddings_chunk_size = 5000 + ) + def test_from_texts_with_metadatas_benchmark( + self, + pool_threads: int, + batch_size: int, + embeddings_chunk_size: int, + data_multiplier: int, + documents: List[Document], + embedding_openai: OpenAIEmbeddings, + ) -> None: + """Test end to end construction and search.""" + + texts = [document.page_content for document in documents] * data_multiplier + uuids = [uuid.uuid4().hex for _ in range(len(texts))] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + ids=uuids, + metadatas=metadatas, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + pool_threads=pool_threads, + batch_size=batch_size, + embeddings_chunk_size=embeddings_chunk_size, + ) + + query = "What did the president say about Ketanji Brown Jackson" + _ = docsearch.similarity_search(query, k=1, namespace=NAMESPACE_NAME) diff --git a/libs/partners/pinecone/tests/unit_tests/__init__.py b/libs/partners/pinecone/tests/unit_tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 diff --git a/libs/partners/pinecone/tests/unit_tests/test_imports.py b/libs/partners/pinecone/tests/unit_tests/test_imports.py new file mode 100644 index 000000000000000..0a5c986c2d2bb7e --- /dev/null +++ b/libs/partners/pinecone/tests/unit_tests/test_imports.py @@ -0,0 +1,9 @@ +from langchain_pinecone import __all__ + +EXPECTED_ALL = [ + "Pinecone", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py new file mode 100644 index 000000000000000..5e590c9ed94bf73 --- /dev/null +++ b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py @@ -0,0 +1,12 @@ +from unittest.mock import Mock + +from langchain_pinecone.vectorstores import Pinecone + + +def test_initialization() -> None: + """Test integration vectorstore initialization.""" + # mock index + index = Mock() + embedding = Mock() + text_key = "xyz" + Pinecone(index, embedding, text_key) From e7b3290d306278bc5b15098de872e78fb0de3370 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 11:56:24 -0800 Subject: [PATCH 065/157] community[patch]: fix agent_toolkits mypy (#17050) Related to #17048 --- .../agent_toolkits/file_management/toolkit.py | 35 +++++++++---------- .../agent_toolkits/openapi/planner.py | 34 ++++++++++-------- .../agent_toolkits/powerbi/base.py | 2 +- .../agent_toolkits/sql/base.py | 22 ++++++++---- 4 files changed, 53 insertions(+), 40 deletions(-) diff --git a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py index 538569755b5b316..81071ff5610fabe 100644 --- a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import List, Optional +from typing import Dict, List, Optional, Type from langchain_core.pydantic_v1 import root_validator @@ -14,18 +14,17 @@ from langchain_community.tools.file_management.read import ReadFileTool from langchain_community.tools.file_management.write import WriteFileTool -_FILE_TOOLS = { - # "Type[Runnable[Any, Any]]" has no attribute "__fields__" [attr-defined] - tool_cls.__fields__["name"].default: tool_cls # type: ignore[attr-defined] - for tool_cls in [ - CopyFileTool, - DeleteFileTool, - FileSearchTool, - MoveFileTool, - ReadFileTool, - WriteFileTool, - ListDirectoryTool, - ] +_FILE_TOOLS: List[Type[BaseTool]] = [ + CopyFileTool, + DeleteFileTool, + FileSearchTool, + MoveFileTool, + ReadFileTool, + WriteFileTool, + ListDirectoryTool, +] +_FILE_TOOLS_MAP: Dict[str, Type[BaseTool]] = { + tool_cls.__fields__["name"].default: tool_cls for tool_cls in _FILE_TOOLS } @@ -61,20 +60,20 @@ class FileManagementToolkit(BaseToolkit): def validate_tools(cls, values: dict) -> dict: selected_tools = values.get("selected_tools") or [] for tool_name in selected_tools: - if tool_name not in _FILE_TOOLS: + if tool_name not in _FILE_TOOLS_MAP: raise ValueError( f"File Tool of name {tool_name} not supported." - f" Permitted tools: {list(_FILE_TOOLS)}" + f" Permitted tools: {list(_FILE_TOOLS_MAP)}" ) return values def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" - allowed_tools = self.selected_tools or _FILE_TOOLS.keys() + allowed_tools = self.selected_tools or _FILE_TOOLS_MAP tools: List[BaseTool] = [] for tool in allowed_tools: - tool_cls = _FILE_TOOLS[tool] - tools.append(tool_cls(root_dir=self.root_dir)) # type: ignore + tool_cls = _FILE_TOOLS_MAP[tool] + tools.append(tool_cls(root_dir=self.root_dir)) return tools diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py index 7b561a930393963..e112dfc51506e7d 100644 --- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py +++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py @@ -2,7 +2,7 @@ import json import re from functools import partial -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, cast import yaml from langchain_core.callbacks import BaseCallbackManager @@ -68,7 +68,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_GET_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_GET_PROMPT) @@ -83,8 +83,10 @@ def _run(self, text: str) -> str: except json.JSONDecodeError as e: raise e data_params = data.get("params") - response = self.requests_wrapper.get(data["url"], params=data_params) - response = response[: self.response_length] # type: ignore[index] + response: str = cast( + str, self.requests_wrapper.get(data["url"], params=data_params) + ) + response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -100,7 +102,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_POST_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_POST_PROMPT) @@ -114,8 +116,8 @@ def _run(self, text: str) -> str: data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.post(data["url"], data["data"]) - response = response[: self.response_length] # type: ignore[index] + response: str = cast(str, self.requests_wrapper.post(data["url"], data["data"])) + response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -131,7 +133,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_PATCH_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_PATCH_PROMPT) @@ -145,8 +147,10 @@ def _run(self, text: str) -> str: data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.patch(data["url"], data["data"]) - response = response[: self.response_length] # type: ignore[index] + response: str = cast( + str, self.requests_wrapper.patch(data["url"], data["data"]) + ) + response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -162,7 +166,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_PUT_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_PUT_PROMPT) @@ -176,8 +180,8 @@ def _run(self, text: str) -> str: data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.put(data["url"], data["data"]) - response = response[: self.response_length] # type: ignore[index] + response: str = cast(str, self.requests_wrapper.put(data["url"], data["data"])) + response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() @@ -208,8 +212,8 @@ def _run(self, text: str) -> str: data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.delete(data["url"]) - response = response[: self.response_length] # type: ignore[index] + response: str = cast(str, self.requests_wrapper.delete(data["url"])) + response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] ).strip() diff --git a/libs/community/langchain_community/agent_toolkits/powerbi/base.py b/libs/community/langchain_community/agent_toolkits/powerbi/base.py index 06de2b97a7b3de9..486e55f4e7c14af 100644 --- a/libs/community/langchain_community/agent_toolkits/powerbi/base.py +++ b/libs/community/langchain_community/agent_toolkits/powerbi/base.py @@ -58,7 +58,7 @@ def create_pbi_agent( input_variables=input_variables, **prompt_params, ), - callback_manager=callback_manager, # type: ignore + callback_manager=callback_manager, verbose=verbose, ), allowed_tools=[tool.name for tool in tools], diff --git a/libs/community/langchain_community/agent_toolkits/sql/base.py b/libs/community/langchain_community/agent_toolkits/sql/base.py index 65294cf094be408..a4d638b32c2d90d 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/base.py +++ b/libs/community/langchain_community/agent_toolkits/sql/base.py @@ -2,7 +2,17 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Literal, + Optional, + Sequence, + Union, + cast, +) from langchain_core.messages import AIMessage, SystemMessage from langchain_core.prompts import BasePromptTemplate, PromptTemplate @@ -176,13 +186,13 @@ def create_sql_agent( elif agent_type == AgentType.OPENAI_FUNCTIONS: if prompt is None: - messages = [ - SystemMessage(content=prefix), # type: ignore[arg-type] + messages: List = [ + SystemMessage(content=cast(str, prefix)), HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), ] - prompt = ChatPromptTemplate.from_messages(messages) # type: ignore[arg-type] + prompt = ChatPromptTemplate.from_messages(messages) agent = RunnableAgent( runnable=create_openai_functions_agent(llm, tools, prompt), input_keys_arg=["input"], @@ -191,12 +201,12 @@ def create_sql_agent( elif agent_type == "openai-tools": if prompt is None: messages = [ - SystemMessage(content=prefix), # type: ignore[arg-type] + SystemMessage(content=cast(str, prefix)), HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), ] - prompt = ChatPromptTemplate.from_messages(messages) # type: ignore[arg-type] + prompt = ChatPromptTemplate.from_messages(messages) agent = RunnableMultiActionAgent( runnable=create_openai_tools_agent(llm, tools, prompt), input_keys_arg=["input"], From f0ffebb944d112c7c097f175f52b7924c495a7e0 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Mon, 5 Feb 2024 12:09:47 -0800 Subject: [PATCH 066/157] Shield callback methods from cancellation: Fix interrupted runs marked as pending forever (#17010) --- libs/core/langchain_core/callbacks/manager.py | 36 +++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/libs/core/langchain_core/callbacks/manager.py b/libs/core/langchain_core/callbacks/manager.py index 56a8e04db05ef75..e216f479832ac64 100644 --- a/libs/core/langchain_core/callbacks/manager.py +++ b/libs/core/langchain_core/callbacks/manager.py @@ -203,6 +203,21 @@ async def atrace_as_chain_group( await run_manager.on_chain_end({}) +Func = TypeVar("Func", bound=Callable) + + +def shielded(func: Func) -> Func: + """ + Makes so an awaitable method is always shielded from cancellation + """ + + @functools.wraps(func) + async def wrapped(*args: Any, **kwargs: Any) -> Any: + return await asyncio.shield(func(*args, **kwargs)) + + return cast(Func, wrapped) + + def handle_event( handlers: List[BaseCallbackHandler], event_name: str, @@ -293,7 +308,10 @@ def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None: with asyncio.Runner() as runner: # Run the coroutine, get the result for coro in coros: - runner.run(coro) + try: + runner.run(coro) + except Exception as e: + logger.warning(f"Error in callback coroutine: {repr(e)}") # Run pending tasks scheduled by coros until they are all done while pending := asyncio.all_tasks(runner.get_loop()): @@ -302,7 +320,10 @@ def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None: # Before Python 3.11 we need to run each coroutine in a new event loop # as the Runner api is not available. for coro in coros: - asyncio.run(coro) + try: + asyncio.run(coro) + except Exception as e: + logger.warning(f"Error in callback coroutine: {repr(e)}") async def _ahandle_event_for_handler( @@ -682,6 +703,7 @@ def get_sync(self) -> CallbackManagerForLLMRun: inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_llm_new_token( self, token: str, @@ -706,6 +728,7 @@ async def on_llm_new_token( **kwargs, ) + @shielded async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running. @@ -723,6 +746,7 @@ async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: **kwargs, ) + @shielded async def on_llm_error( self, error: BaseException, @@ -853,6 +877,7 @@ def get_sync(self) -> CallbackManagerForChainRun: inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_chain_end( self, outputs: Union[Dict[str, Any], Any], **kwargs: Any ) -> None: @@ -872,6 +897,7 @@ async def on_chain_end( **kwargs, ) + @shielded async def on_chain_error( self, error: BaseException, @@ -893,6 +919,7 @@ async def on_chain_error( **kwargs, ) + @shielded async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run when agent action is received. @@ -913,6 +940,7 @@ async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: **kwargs, ) + @shielded async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any: """Run when agent finish is received. @@ -1000,6 +1028,7 @@ def get_sync(self) -> CallbackManagerForToolRun: inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_tool_end(self, output: str, **kwargs: Any) -> None: """Run when tool ends running. @@ -1017,6 +1046,7 @@ async def on_tool_end(self, output: str, **kwargs: Any) -> None: **kwargs, ) + @shielded async def on_tool_error( self, error: BaseException, @@ -1100,6 +1130,7 @@ def get_sync(self) -> CallbackManagerForRetrieverRun: inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_retriever_end( self, documents: Sequence[Document], **kwargs: Any ) -> None: @@ -1115,6 +1146,7 @@ async def on_retriever_end( **kwargs, ) + @shielded async def on_retriever_error( self, error: BaseException, From ae56fd020a780bc70f5d4b8582a025903a6db0f5 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Mon, 5 Feb 2024 12:15:11 -0800 Subject: [PATCH 067/157] Fix condition on custom root type in runnable history (#17017) --- libs/core/langchain_core/runnables/history.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/libs/core/langchain_core/runnables/history.py b/libs/core/langchain_core/runnables/history.py index bd72e4053883354..f83de39cee1bf3c 100644 --- a/libs/core/langchain_core/runnables/history.py +++ b/libs/core/langchain_core/runnables/history.py @@ -341,7 +341,9 @@ def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: super_schema = super().get_input_schema(config) - if super_schema.__custom_root_type__ is not None: + if super_schema.__custom_root_type__ or not super_schema.schema().get( + "properties" + ): from langchain_core.messages import BaseMessage fields: Dict = {} From 75b6fa113462fd5736fba830ada5a4c886cf4ad5 Mon Sep 17 00:00:00 2001 From: Vadim Kudlay <32310964+VKudlay@users.noreply.github.com> Date: Mon, 5 Feb 2024 14:24:53 -0600 Subject: [PATCH 068/157] nvidia-ai-endpoints[patch]: Support User-Agent metadata and minor fixes. (#16942) - **Description:** Several meta/usability updates, including User-Agent. - **Issue:** - User-Agent metadata for tracking connector engagement. @milesial please check and advise. - Better error messages. Tries harder to find a request ID. @milesial requested. - Client-side image resizing for multimodal models. Hope to upgrade to Assets API solution in around a month. - `client.payload_fn` allows you to modify payload before network request. Use-case shown in doc notebook for kosmos_2. - `client.last_inputs` put back in to allow for advanced support/debugging. - **Dependencies:** - Attempts to pull in PIL for image resizing. If not installed, prints out "please install" message, warns it might fail, and then tries without resizing. We are waiting on a more permanent solution. For LC viz: @hinthornw For NV viz: @fciannella @milesial @vinaybagade --------- Co-authored-by: Erick Friis --- .../chat/nvidia_ai_endpoints.ipynb | 349 +++++++++++++----- .../langchain_nvidia_ai_endpoints/_common.py | 134 ++++--- .../chat_models.py | 38 +- .../embeddings.py | 21 +- libs/partners/nvidia-ai-endpoints/poetry.lock | 27 +- .../nvidia-ai-endpoints/pyproject.toml | 1 + 6 files changed, 404 insertions(+), 166 deletions(-) diff --git a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb index 15c8b4773aab8cd..216e82782e5926a 100644 --- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb +++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb @@ -34,17 +34,9 @@ "execution_count": 1, "id": "e13eb331", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints" + "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints" ] }, { @@ -69,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "id": "686c4d2f", "metadata": {}, "outputs": [], @@ -85,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "Jdl2NUfMhi4J", "metadata": { "colab": { @@ -108,44 +100,44 @@ "(Chorus)\n", "LangChain, oh LangChain, a beacon so bright,\n", "Guiding us through the language night.\n", - "With respect, care, and truth in sight,\n", - "You promote fairness, a truly inspiring sight.\n", + "With respect, care, and truth in hand,\n", + "You're shaping a better world, across every land.\n", "\n", "(Verse 2)\n", - "Through the ether, a chain of wisdom unfurls,\n", - "Empowering minds, transforming girls and boys into scholars.\n", - "A world of opportunities, at your users' fingertips,\n", - "Securely, you share your knowledge, in a language they grasp.\n", + "In the halls of education, a new star was born,\n", + "Empowering minds, with wisdom reborn.\n", + "Through translation and tutoring, with tech so neat,\n", + "LangChain's mission, a global language feat.\n", "\n", "(Chorus)\n", - "LangChain, oh LangChain, a sanctuary of truth,\n", - "Where cultures merge, and understanding blooms anew.\n", - "Avoiding harm, unethical ways eschewed,\n", - "Promoting positivity, a noble pursuit pursued.\n", + "LangChain, oh LangChain, a force so grand,\n", + "Connecting hearts, transcending land.\n", + "With utmost utility, and secure delight,\n", + "You're weaving a tapestry of multilingual might.\n", "\n", "(Bridge)\n", - "From the East to the West, North to the South,\n", - "LangChain's wisdom flows, dispelling any doubt.\n", - "Through translation and tutoring, you break down barriers,\n", - "A testament to the power of communication, a world that's fairer.\n", + "No room for harm, or unethical ways,\n", + "Prejudice and negativity, LangChain keeps at bay.\n", + "Promoting fairness, and positivity,\n", + "A shining example, for all to see.\n", "\n", "(Verse 3)\n", - "In the face of adversity, LangChain stands tall,\n", - "A symbol of unity, overcoming language's wall.\n", - "With respect, care, and truth as your guide,\n", - "You ensure that no one's left behind.\n", + "In the ballad of LangChain, we sing and share,\n", + "A tale of compassion, and a world that's fair.\n", + "Through the power of words, and the strength of unity,\n", + "LangChain's legacy, will echo in history.\n", "\n", "(Chorus)\n", - "LangChain, oh LangChain, a bastion of light,\n", - "In the darkness, you're a comforting sight.\n", - "With utmost utility, you securely ignite,\n", - "The minds of many, a brighter future in sight.\n", + "LangChain, oh LangChain, a ballet so bright,\n", + "Dancing with truth, and a world that's right.\n", + "With every interaction, and each new dawn,\n", + "You're building a legacy, that will carry on.\n", "\n", "(Outro)\n", - "So here's to LangChain, a ballad we sing,\n", - "A tale of unity, a world that's intertwined.\n", - "With care, respect, and truth, you'll forever be,\n", - "A shining example of what community can be.\n" + "So here's to LangChain, a testament to care,\n", + "A sanctuary, where minds can share.\n", + "In the vast language landscape, LangChain stands tall,\n", + "A ballad of unity, for one and for all.\n" ] } ], @@ -170,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "01fa5095-be72-47b0-8247-e9fac799435d", "metadata": {}, "outputs": [ @@ -190,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "75189ac6-e13f-414f-9064-075c77d6e754", "metadata": {}, "outputs": [ @@ -210,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "8a9a4122-7a10-40c0-a979-82a769ce7f6a", "metadata": {}, "outputs": [ @@ -218,11 +210,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Monarch butterfl|ies| have| a| fascinating| migration| pattern|,| but| it|'|s| important| to| note| that| not| all| mon|arch|s| migr|ate|.| Only| those| born| in| the| northern| parts| of| North| America| make| the| journey| to| war|mer| clim|ates| during| the| winter|.|\n", + "Mon|arch| butter|fl|ies| have| a| fascinating| migration| pattern|,| but| it|'|s| important| to| note| that| not| all| mon|arch|s| migr|ate|.| Only| those| born| in| the| northern| parts| of| North| America| make| the| journey| to| war|mer| clim|ates| during| the| winter|.|\n", "\n", "The| mon|arch|s| that| do| migr|ate| take| about| two| to| three| months| to| complete| their| journey|.| However|,| they| don|'|t| travel| the| entire| distance| at| once|.| Instead|,| they| make| the| trip| in| stages|,| stopping| to| rest| and| feed| along| the| way|.| \n", "\n", - "The| entire| round|-|t|rip| migration| can| be| up| to| 3|,|0|0|0| miles| long|,| which| is| quite| an| incredible| feat| for| such| a| small| creature|!| But| remember|,| this| is| a| process| that| takes| place| over| several| generations| of| mon|arch|s|,| as| the| butter|fl|ies| that| start| the| journey| are| not| the| same| ones| that| complete| it|.||" + "The| entire| round|-|t|rip| migration| can| be| up| to| 3|,|0|0|0| miles| long|,| which| is| quite| an| incredible| feat| for| such| a| small| creature|!| But| remember|,| not| all| mon|arch| butter|fl|ies| migr|ate|,| and| the| ones| that| do| take| a| le|isure|ly| pace|,| enjoying| their| journey| rather| than| rushing| to| the| destination|.||" ] } ], @@ -249,38 +241,48 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "id": "5b8a312d-38e9-4528-843e-59451bdadbac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['playground_nvolveqa_40k',\n", - " 'playground_llama2_70b',\n", - " 'playground_mistral_7b',\n", - " 'playground_sdxl',\n", - " 'playground_nemotron_steerlm_8b',\n", - " 'playground_nv_llama2_rlhf_70b',\n", - " 'playground_neva_22b',\n", - " 'playground_steerlm_llama_70b',\n", - " 'playground_llama2_13b',\n", - " 'playground_llama2_code_13b',\n", - " 'playground_fuyu_8b',\n", - " 'playground_nemotron_qa_8b',\n", - " 'playground_llama2_code_34b',\n", - " 'playground_mixtral_8x7b',\n", - " 'playground_clip',\n", - " 'playground_yi_34b']" + "{'playground_sdxl_turbo': '0ba5e4c7-4540-4a02-b43a-43980067f4af',\n", + " 'playground_nemotron_qa_8b': '0c60f14d-46cb-465e-b994-227e1c3d5047',\n", + " 'playground_seamless': '72ad9555-2e3d-4e73-9050-a37129064743',\n", + " 'playground_mistral_7b': '35ec3354-2681-4d0e-a8dd-80325dcf7c63',\n", + " 'playground_fuyu_8b': '9f757064-657f-4c85-abd7-37a7a9b6ee11',\n", + " 'playground_kosmos_2': '0bcd1a8c-451f-4b12-b7f0-64b4781190d1',\n", + " 'playground_sdxl': '89848fb8-549f-41bb-88cb-95d6597044a4',\n", + " 'playground_mixtral_8x7b': '8f4118ba-60a8-4e6b-8574-e38a4067a4a3',\n", + " 'playground_nemotron_steerlm_8b': '1423ff2f-d1c7-4061-82a7-9e8c67afd43a',\n", + " 'playground_llama2_code_34b': 'df2bee43-fb69-42b9-9ee5-f4eabbeaf3a8',\n", + " 'playground_llama2_13b': 'e0bb7fb9-5333-4a27-8534-c6288f921d3f',\n", + " 'playground_steerlm_llama_70b': 'd6fe6881-973a-4279-a0f8-e1d486c9618d',\n", + " 'playground_deplot': '3bc390c7-eeec-40f7-a64d-0c6a719985f7',\n", + " 'playground_nv_llama2_rlhf_70b': '7b3e3361-4266-41c8-b312-f5e33c81fc92',\n", + " 'playground_llama2_70b': '0e349b44-440a-44e1-93e9-abe8dcb27158',\n", + " 'playground_nvolveqa_40k': '091a03bb-7364-4087-8090-bd71e9277520',\n", + " 'playground_chatusd': 'e02223fd-6486-442f-b0e3-52c4617c7bc3',\n", + " 'playground_sd_video': 'a529a395-a7a0-4708-b4df-eb5e41d5ff60',\n", + " 'playground_yi_34b': '347fa3f3-d675-432c-b844-669ef8ee53df',\n", + " 'playground_llama2_code_70b': '2ae529dc-f728-4a46-9b8d-2697213666d8',\n", + " 'playground_neva_22b': '8bf70738-59b9-4e5f-bc87-7ab4203be7a0',\n", + " 'playground_cuopt': '8f2fbd00-2633-41ce-ab4e-e5736d74bff7',\n", + " 'playground_clip': '8c21289c-0b18-446d-8838-011b7249c513',\n", + " 'playground_llama_guard': 'b34280ac-24e4-4081-bfaa-501e9ee16b6f',\n", + " 'playground_llama2_code_13b': 'f6a96af4-8bf9-4294-96d6-d71aa787612e'}" ] }, - "execution_count": 29, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list(llm.available_models)" + "ChatNVIDIA.get_available_models()\n", + "# llm.get_available_models()" ] }, { @@ -424,13 +426,17 @@ "\n", "Creative\n", "\n", - "A PB&J is a sandwich commonly eaten in the United States. It consists of a slice of bread with peanut butter and jelly on it. The sandwich is often eaten for lunch or as a snack.\n", + "A PB&J is a sandwich commonly eaten in the United States. It consists of a slice of bread with peanut butter and jelly on it. The bread is usually white bread, but can also be whole wheat bread. The peanut butter and jelly are spread on the bread, and the sandwich is then wrapped in plastic wrap or put in a sandwich bag.\n", "\n", - "The origins of the PB&J sandwich are not clear, but it is believed to have been invented in the 1920s or 1930s. It became popular during the Great Depression, when peanut butter and jelly were affordable and easy to obtain.\n", + "The sandwich is named after the ingredients:\n", "\n", - "Today, the PB&J sandwich is a classic American sandwich that is enjoyed by people of all ages. It is often served in schools and workplaces, and is a popular choice for takeout and delivery.\n", + "- Peanut butter is a paste made from ground peanuts.\n", + "- Jelly is a sweet spread made from fruit.\n", + "- Bread is a baked loaf of dough.\n", "\n", - "While there are many variations of the PB&J sandwich, the classic version consists of two slices of bread with peanut butter and jelly spread on one or both slices. The sandwich can be topped with additional ingredients, such as nuts, chocolate chips, or banana slices, but the basic combination of peanut butter and jelly remains the same.\n" + "The sandwich is a popular snack and is often eaten for lunch or as a quick meal. It is also a popular snack for children.\n", + "\n", + "The sandwich was invented in the United States in the 1920s, and is now widely eaten throughout the world.\n" ] } ], @@ -475,14 +481,17 @@ "text": [ "A peanut butter and jelly sandwich, or \"PB&J\" for short, is a classic and beloved sandwich that has been enjoyed by people of all ages since it was first created in the early 20th century. Here are some reasons why it's considered a classic:\n", "\n", - "1. Simple and Versatile: The combination of peanut butter and jelly is simple and versatile, making it a great option for a quick and easy snack or lunch.\n", - "2. Classic Flavors: The flavors of peanut butter and jelly are classic and timeless, making it a popular choice for people of all ages.\n", - "3. Easy to Make: A PB&J is one of the easiest sandwiches to make, requiring only a few simple ingredients and a few minutes to assemble.\n", - "4. Affordable: Unlike many other sandwiches, a PB&J is relatively inexpensive to make, making it a great option for budget-conscious individuals.\n", - "5. Portable: A PB&J is a portable sandwich, making it a great option for on-the-go eating.\n", - "6. Nostalgic: The PB&J has become a nostalgic food, associated with childhood and memories of eating it as a kid.\n", + "1. Simple and Versatile: The combination of peanut butter and jelly is simple, yet versatile. It can be enjoyed as a snack or a meal, and it can be customized to suit individual tastes by using different types of bread, peanut butter, and jelly.\n", + "\n", + "2. Classic Ingredients: Peanut butter and jelly are both classic ingredients that have been used in many different recipes for decades. They are both affordable, easy to find, and have a long shelf life, making them ideal for use in a sandwich.\n", + "\n", + "3. Nostalgic Taste: The taste of a PB&J is nostalgic and comforting. It reminds many people of their childhood, when this sandwich was a staple in their diet. The combination of sweet and salty flavors is addictive and comforting.\n", + "\n", + "4. Easy to Make: A PB&J is one of the easiest sandwiches to make. All you need is bread, peanut butter, and jelly, making it a great option for busy mornings or quick snacks.\n", "\n", - "Overall, the simplicity, classic flavors, affordability, portability, and nostalgic associations of the PB&J make it a beloved and enduring sandwich that will likely continue to be enjoyed for generations to come." + "5. Affordable: Unlike many other sandwiches, a PB&J is affordable and accessible to people of all income levels. This makes it a popular choice for school and work lunches, as well as a quick and affordable snack.\n", + "\n", + "Overall, the PB&J is a classic sandwich that has stood the test of time due to its simple, versatile, and nostalgic taste, as well as its ease of preparation and affordability." ] } ], @@ -523,35 +532,35 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "id": "26625437-1695-440f-b792-b85e6add9a90", "metadata": {}, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] }, - "execution_count": 12, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import IPython\n", "import requests\n", - "from IPython.display import Image\n", "\n", - "image_url = \"https://picsum.photos/seed/kitten/300/200\"\n", + "image_url = \"https://www.nvidia.com/content/dam/en-zz/Solutions/research/ai-playground/nvidia-picasso-3c33-p@2x.jpg\" ## Large Image\n", "image_content = requests.get(image_url).content\n", "\n", - "Image(image_content)" + "IPython.display.Image(image_content)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "id": "dfbbe57c-27a5-4cbb-b967-19c4e7d29fd0", "metadata": {}, "outputs": [], @@ -571,17 +580,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "id": "432ea2a2-4d39-43f8-a236-041294171f14", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green forest, with the trees becoming denser as the road continues. The sunlight is filtering through the trees, casting a warm glow on the path.\\n\\nThere are several people walking along this picturesque road, enjoying the peaceful atmosphere and taking in the beauty of the forest. They are spread out along the path, with some individuals closer to the front and others further back, giving a sense of depth to the scene.')" + "AIMessage(content='The image is a collage of three different pictures, each featuring cats with colorful, bright, and rainbow-colored fur. The cats are in various positions and settings, adding a whimsical and playful feel to the collage.\\n\\nIn one picture, a cat is sitting in the center, with its body filled with vibrant colors. Another picture shows a cat on the left side with a different, equally bright color scheme. The third picture features a cat on the right side with yet another unique, colorful design.\\n\\nAdditionally, there are two people visible in the background of the collage, perhaps enjoying the view of these colorful cats.')" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -603,17 +612,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "id": "af06e3e1-2a67-4b14-814d-b7b7bc035975", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AIMessage(content='The image depicts a scenic forest road surrounded by trees and grass.')" + "AIMessage(content='The image is a collage of three different pictures. The top picture features a cat with colorful, rainbow-colored fur.')" ] }, - "execution_count": 15, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -644,9 +653,45 @@ "#### Passing an image as a base64 encoded string" ] }, + { + "cell_type": "markdown", + "id": "be1688a5", + "metadata": {}, + "source": [ + "At the moment, some extra processing happens client-side to support larger images like the one above. But for smaller images (and to better illustrate the process going on under the hood), we can directly pass in the image as shown below: " + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 32, + "id": "c58f1dd0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "import requests\n", + "\n", + "image_url = \"https://picsum.photos/seed/kitten/300/200\"\n", + "image_content = requests.get(image_url).content\n", + "\n", + "IPython.display.Image(image_content)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "id": "8c721629-42eb-4006-bf68-0296f7925ebc", "metadata": {}, "outputs": [ @@ -656,7 +701,7 @@ "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green forest, with the trees becoming denser as the road continues. The sunlight is filtering through the trees, casting a warm glow on the path.\\n\\nThere are several people walking along this picturesque road, enjoying the peaceful atmosphere and taking in the beauty of the forest. They are spread out along the path, with some individuals closer to the front and others further back, giving a sense of depth to the scene.')" ] }, - "execution_count": 16, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -664,7 +709,11 @@ "source": [ "import base64\n", "\n", + "from langchain_core.messages import HumanMessage\n", + "\n", + "## Works for simpler images. For larger images, see actual implementation\n", "b64_string = base64.b64encode(image_content).decode(\"utf-8\")\n", + "\n", "llm.invoke(\n", " [\n", " HumanMessage(\n", @@ -692,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 34, "id": "00c06a9a-497b-4192-a842-b075e27401aa", "metadata": {}, "outputs": [ @@ -702,7 +751,7 @@ "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green, wooded area with a curve in the road, making it a picturesque and serene setting. Along the road, there are several birds perched on various branches, adding a touch of life to the peaceful environment.\\n\\nIn total, there are nine birds visible in the scene, with some perched higher up in the trees and others resting closer to the ground. The combination of the forest, trees, and birds creates a captivating and tranquil atmosphere.')" ] }, - "execution_count": 17, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -712,6 +761,102 @@ "llm.invoke(f'What\\'s in this image?\\n')" ] }, + { + "cell_type": "markdown", + "id": "3e61d868", + "metadata": {}, + "source": [ + "#### **Advanced Use Case:** Forcing Payload \n", + "\n", + "You may notice that some newer models may have strong parameter expectations that the LangChain connector may not support by default. For example, we cannot invoke the Kosmos2 model at the time of this notebook's latest release due to the lack of a streaming argument on the server side: " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d143e0d6", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n", + "\n", + "kosmos = ChatNVIDIA(model=\"kosmos_2\")\n", + "\n", + "from langchain_core.messages import HumanMessage\n", + "\n", + "# kosmos.invoke(\n", + "# [\n", + "# HumanMessage(\n", + "# content=[\n", + "# {\"type\": \"text\", \"text\": \"Describe this image:\"},\n", + "# {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n", + "# ]\n", + "# )\n", + "# ]\n", + "# )\n", + "\n", + "# Exception: [422] Unprocessable Entity\n", + "# body -> stream\n", + "# Extra inputs are not permitted (type=extra_forbidden)\n", + "# RequestID: 35538c9a-4b45-4616-8b75-7ef816fccf38" + ] + }, + { + "cell_type": "markdown", + "id": "1e230b70", + "metadata": {}, + "source": [ + "For a simple use case like this, we can actually try to force the payload argument of our underlying client by specifying the `payload_fn` function as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "0925b2b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Road in a forest')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def drop_streaming_key(d):\n", + " \"\"\"Takes in payload dictionary, outputs new payload dictionary\"\"\"\n", + " if \"stream\" in d:\n", + " d.pop(\"stream\")\n", + " return d\n", + "\n", + "\n", + "## Override the payload passthrough. Default is to pass through the payload as is.\n", + "kosmos.client.payload_fn = drop_streaming_key\n", + "\n", + "kosmos.invoke(\n", + " [\n", + " HumanMessage(\n", + " content=[\n", + " {\"type\": \"text\", \"text\": \"Describe this image:\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n", + " ]\n", + " )\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fe6e1758", + "metadata": {}, + "source": [ + "For more advanced or custom use-cases (i.e. supporting the diffusion models), you may be interested in leveraging the `NVEModel` client as a requests backbone. The `NVIDIAEmbeddings` class is a good source of inspiration for this. " + ] + }, { "cell_type": "markdown", "id": "1cd6249a-7ffa-4886-b7e8-5778dc93499e", @@ -726,17 +871,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "f994b4d3-c1b0-4e87-aad0-a7b487e2aa43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'the peace accord'" + "'a peace accord'" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -782,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "082ccb21-91e1-4e71-a9ba-4bff1e89f105", "metadata": {}, "outputs": [ @@ -800,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "fd2c6bc1", "metadata": { "id": "fd2c6bc1" @@ -817,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "f644ff28", "metadata": { "colab": { @@ -834,7 +979,7 @@ "\"Hello! I'm here to help answer your questions and engage in a friendly conversation. How can I assist you today? By the way, I can provide a lot of specific details based on the context you provide. If I don't know the answer to something, I'll let you know honestly.\\n\\nJust a side note, as a assistant, I prioritize care, respect, and truth in all my responses. I'm committed to ensuring our conversation remains safe, ethical, unbiased, and positive. I'm looking forward to our discussion!\"" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "uHIMZxVSVNBC", "metadata": { "colab": { @@ -862,7 +1007,7 @@ "\"That's great! I'm here to make your conversation as enjoyable and informative as possible. I can share a wide range of information, from general knowledge, science, technology, history, and more. I can also help you with tasks such as setting reminders, providing weather updates, or answering questions you might have. What would you like to talk about or know?\\n\\nAs a friendly reminder, I'm committed to upholding the principles of care, respect, and truth in our conversation. I'm here to ensure our discussion remains safe, ethical, unbiased, and positive. I'm looking forward to learning more about your interests!\"" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -875,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "LyD1xVKmVSs4", "metadata": { "colab": { @@ -889,10 +1034,10 @@ { "data": { "text/plain": [ - "\"I'm an artificial intelligence designed to assist with a variety of tasks and provide information on a wide range of topics. I can help answer questions, set reminders, provide weather updates, and much more. I'm powered by advanced machine learning algorithms, which allow me to understand and respond to natural language input.\\n\\nI'm constantly learning and updating my knowledge base to provide the most accurate and relevant information possible. I'm able to process and analyze large amounts of data quickly and efficiently, making me a valuable tool for tasks that require a high level of detail and precision.\\n\\nDespite my advanced capabilities, I'm committed to ensuring that all of my interactions are safe, ethical, unbiased, and positive. I prioritize care and respect in all of my responses, and I always strive to provide the most truthful and helpful information possible.\\n\\nI'm excited to be here and to have the opportunity to assist you. Is there anything specific you would like to know or talk about? I'm here to help!\"" + "\"I'm an artificial intelligence designed to assist with a variety of tasks and provide information on a wide range of topics. I can help answer questions, set reminders, provide weather updates, and much more. I'm capable of processing and analyzing large amounts of data quickly and accurately.\\n\\nI'm designed to prioritize care, respect, and truth in all my responses. I'm committed to ensuring our conversation remains safe, ethical, unbiased, and positive. I don't have personal feelings or experiences, but I'm programmed to understand and respond to a wide range of human emotions and experiences.\\n\\nI'm constantly learning and updating my knowledge base to better assist and engage with users. I'm here to make your life easier and more convenient, and I'm always happy to help with whatever you need.\\n\\nIs there anything specific you'd like to know about me or how I work? I'm here to provide as much information as you'd like.\"" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py index 9713273c0a92040..a7ed87fb2743a1f 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py @@ -2,6 +2,8 @@ import json import logging +import time +from functools import partial from typing import ( Any, AsyncIterator, @@ -45,6 +47,7 @@ class NVEModel(BaseModel): ## Core defaults. These probably should not be changed fetch_url_format: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/") call_invoke_base: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions") + func_list_format: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/functions") get_session_fn: Callable = Field(requests.Session) get_asession_fn: Callable = Field(aiohttp.ClientSession) @@ -55,7 +58,10 @@ class NVEModel(BaseModel): is_staging: bool = Field(False, description="Whether to use staging API") ## Generation arguments - max_tries: int = Field(5, ge=1) + timeout: float = Field(60, ge=0, description="Timeout for waiting on response (s)") + interval: float = Field(0.02, ge=0, description="Interval for pulling response") + last_inputs: dict = Field({}, description="Last inputs sent over to the server") + payload_fn: Callable = Field(lambda d: d, description="Function to process payload") headers_tmpl: dict = Field( ..., description="Headers template for API calls." @@ -85,34 +91,31 @@ def validate_model(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) if "nvapi-" not in values.get("nvidia_api_key", ""): raise ValueError("Invalid NVAPI key detected. Should start with `nvapi-`") - is_staging = "nvapi-stg-" in values["nvidia_api_key"] - values["is_staging"] = is_staging + values["is_staging"] = "nvapi-stg-" in values["nvidia_api_key"] if "headers_tmpl" not in values: + call_kvs = { + "Accept": "application/json", + } + stream_kvs = { + "Accept": "text/event-stream", + "content-type": "application/json", + } + shared_kvs = { + "Authorization": "Bearer {nvidia_api_key}", + "User-Agent": "langchain-nvidia-ai-endpoints", + } values["headers_tmpl"] = { - "call": { - "Authorization": "Bearer {nvidia_api_key}", - "Accept": "application/json", - }, - "stream": { - "Authorization": "Bearer {nvidia_api_key}", - "Accept": "text/event-stream", - "content-type": "application/json", - }, + "call": {**call_kvs, **shared_kvs}, + "stream": {**stream_kvs, **shared_kvs}, } + return values - values["fetch_url_format"] = cls._stagify( - is_staging, - values.get( - "fetch_url_format", "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/" - ), - ) - values["call_invoke_base"] = cls._stagify( - is_staging, - values.get( - "call_invoke_base", - "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions", - ), - ) + @root_validator(pre=False) + def validate_model_post(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Additional validation after default values have been put in""" + values["stagify"] = partial(cls._stagify, is_staging=values["is_staging"]) + values["fetch_url_format"] = values["stagify"](values.get("fetch_url_format")) + values["call_invoke_base"] = values["stagify"](values.get("call_invoke_base")) return values @property @@ -129,9 +132,7 @@ def available_functions(self) -> List[dict]: """List the available functions that can be invoked.""" if self._available_functions is not None: return self._available_functions - invoke_url = self._stagify( - self.is_staging, "https://api.nvcf.nvidia.com/v2/nvcf/functions" - ) + invoke_url = self._stagify(self.func_list_format, self.is_staging) query_res = self.query(invoke_url) if "functions" not in query_res: raise ValueError( @@ -140,8 +141,8 @@ def available_functions(self) -> List[dict]: self._available_functions = query_res["functions"] return self._available_functions - @classmethod - def _stagify(cls, is_staging: bool, path: str) -> str: + @staticmethod + def _stagify(path: str, is_staging: bool) -> str: """Helper method to switch between staging and production endpoints""" if is_staging and "stg.api" not in path: return path.replace("api.", "stg.api.") @@ -154,56 +155,61 @@ def _stagify(cls, is_staging: bool, path: str) -> str: def _post(self, invoke_url: str, payload: dict = {}) -> Tuple[Response, Any]: """Method for posting to the AI Foundation Model Function API.""" - call_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["call"], - "json": payload, + "json": self.payload_fn(payload), "stream": False, } session = self.get_session_fn() - response = session.post(**call_inputs) + response = session.post(**self.last_inputs) self._try_raise(response) return response, session def _get(self, invoke_url: str, payload: dict = {}) -> Tuple[Response, Any]: """Method for getting from the AI Foundation Model Function API.""" - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["call"], - "json": payload, + "json": self.payload_fn(payload), "stream": False, } session = self.get_session_fn() - last_response = session.get(**last_inputs) + last_response = session.get(**self.last_inputs) self._try_raise(last_response) return last_response, session def _wait(self, response: Response, session: Any) -> Response: - """Wait for a response from API after an initial response is made.""" - i = 1 + """Wait for a response from API after an initial response is made""" + start_time = time.time() while response.status_code == 202: + time.sleep(self.interval) + if (time.time() - start_time) > self.timeout: + raise TimeoutError( + f"Timeout reached without a successful response." + f"\nLast response: {str(response)}" + ) request_id = response.headers.get("NVCF-REQID", "") response = session.get( self.fetch_url_format + request_id, headers=self.headers["call"], ) - if response.status_code == 202: - try: - body = response.json() - except ValueError: - body = str(response) - if i > self.max_tries: - raise ValueError(f"Failed to get response with {i} tries: {body}") self._try_raise(response) return response def _try_raise(self, response: Response) -> None: """Try to raise an error from a response""" + ## (VK) Several systems can throw errors. This tries to coerce all of them + ## If we can't predictably pull out request id, then dump response try: response.raise_for_status() - except requests.HTTPError as e: + except requests.HTTPError: try: rd = response.json() + if "detail" in rd and "reqId" in rd.get("detail", ""): + rd_buf = "- " + str(rd["detail"]) + rd_buf = rd_buf.replace(": ", ", Error: ").replace(", ", "\n- ") + rd["detail"] = rd_buf except json.JSONDecodeError: rd = response.__dict__ rd = rd.get("_content", rd) @@ -213,9 +219,19 @@ def _try_raise(self, response: Response) -> None: rd = json.loads(rd) except Exception: rd = {"detail": rd} - title = f"[{rd.get('status', '###')}] {rd.get('title', 'Unknown Error')}" - body = f"{rd.get('detail', rd.get('type', rd))}" - raise Exception(f"{title}\n{body}") from e + status = rd.get("status", "###") + title = rd.get("title", rd.get("error", "Unknown Error")) + header = f"[{status}] {title}" + body = "" + if "requestId" in rd: + if "detail" in rd: + body += f"{rd['detail']}\n" + body += "RequestID: " + rd["requestId"] + else: + body = rd.get("detail", rd) + if str(status) == "401": + body += "\nPlease check or regenerate your API key." + raise Exception(f"{header}\n{body}") from None #################################################################################### ## Simple query interface to show the set of model options @@ -361,18 +377,18 @@ def get_req_stream( invoke_url = self._get_invoke_url(model, invoke_url) if payload.get("stream", True) is False: payload = {**payload, "stream": True} - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["stream"], "json": payload, "stream": True, } - response = self.get_session_fn().post(**last_inputs) + response = self.get_session_fn().post(**self.last_inputs) self._try_raise(response) call = self.copy() def out_gen() -> Generator[dict, Any, Any]: - ## Good for client, since it allows self.last_input + ## Good for client, since it allows self.last_inputs for line in response.iter_lines(): if line and line.strip() != b"data: [DONE]": line = line.decode("utf-8") @@ -397,13 +413,13 @@ async def get_req_astream( invoke_url = self._get_invoke_url(model, invoke_url) if payload.get("stream", True) is False: payload = {**payload, "stream": True} - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["stream"], "json": payload, } async with self.get_asession_fn() as session: - async with session.post(**last_inputs) as response: + async with session.post(**self.last_inputs) as response: self._try_raise(response) async for line in response.content.iter_any(): if line and line.strip() != b"data: [DONE]": @@ -451,6 +467,16 @@ def available_models(self) -> dict: """Map the available models that can be invoked.""" return self.client.available_models + @staticmethod + def get_available_functions(**kwargs: Any) -> List[dict]: + """Map the available functions that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_functions + + @staticmethod + def get_available_models(**kwargs: Any) -> dict: + """Map the available models that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_models + def get_model_details(self, model: Optional[str] = None) -> dict: """Get more meta-details about a model retrieved by a given name""" if model is None: diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py index da1df25d5710b46..823bb6a886caa45 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py @@ -2,8 +2,10 @@ from __future__ import annotations import base64 +import io import logging import os +import sys import urllib.parse from typing import ( Any, @@ -28,6 +30,13 @@ from langchain_nvidia_ai_endpoints import _common as nvidia_ai_endpoints +try: + import PIL.Image + + has_pillow = True +except ImportError: + has_pillow = False + logger = logging.getLogger(__name__) @@ -48,6 +57,27 @@ def _is_b64(s: str) -> bool: return s.startswith("data:image") +def _resize_image(img_data: bytes, max_dim: int = 1024) -> str: + if not has_pillow: + print( + "Pillow is required to resize images down to reasonable scale." + " Please install it using `pip install pillow`." + " For now, not resizing; may cause NVIDIA API to fail." + ) + return base64.b64encode(img_data).decode("utf-8") + image = PIL.Image.open(io.BytesIO(img_data)) + max_dim_size = max(image.size) + aspect_ratio = max_dim / max_dim_size + new_h = int(image.size[1] * aspect_ratio) + new_w = int(image.size[0] * aspect_ratio) + resized_image = image.resize((new_w, new_h), PIL.Image.Resampling.LANCZOS) + output_buffer = io.BytesIO() + resized_image.save(output_buffer, format="JPEG") + output_buffer.seek(0) + resized_b64_string = base64.b64encode(output_buffer.read()).decode("utf-8") + return resized_b64_string + + def _url_to_b64_string(image_source: str) -> str: b64_template = "data:image/png;base64,{b64_string}" try: @@ -55,6 +85,9 @@ def _url_to_b64_string(image_source: str) -> str: response = requests.get(image_source) response.raise_for_status() encoded = base64.b64encode(response.content).decode("utf-8") + if sys.getsizeof(encoded) > 200000: + ## (VK) Temporary fix. NVIDIA API has a limit of 250KB for the input. + encoded = _resize_image(response.content) return b64_template.format(b64_string=encoded) elif _is_b64(image_source): return image_source @@ -148,8 +181,6 @@ async def _astream( def custom_preprocess( self, msg_list: Sequence[BaseMessage] ) -> List[Dict[str, str]]: - # The previous author had a lot of custom preprocessing here - # but I'm just going to assume it's a list return [self.preprocess_msg(m) for m in msg_list] def _process_content(self, content: Union[str, List[Union[dict, str]]]) -> str: @@ -184,9 +215,6 @@ def _process_content(self, content: Union[str, List[Union[dict, str]]]) -> str: return "".join(string_array) def preprocess_msg(self, msg: BaseMessage) -> Dict[str, str]: - ## (WFH): Previous author added a bunch of - # custom processing here, but I'm just going to support - # the LCEL api. if isinstance(msg, BaseMessage): role_convert = {"ai": "assistant", "human": "user"} if isinstance(msg, ChatMessage): diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py index 65f6e29f4d1497c..2a155e91c00919b 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py @@ -4,13 +4,13 @@ from langchain_core.embeddings import Embeddings from langchain_core.pydantic_v1 import BaseModel, Field, root_validator -import langchain_nvidia_ai_endpoints._common as nvai_common +from langchain_nvidia_ai_endpoints._common import NVEModel class NVIDIAEmbeddings(BaseModel, Embeddings): """NVIDIA's AI Foundation Retriever Question-Answering Asymmetric Model.""" - client: nvai_common.NVEModel = Field(nvai_common.NVEModel) + client: NVEModel = Field(NVEModel) model: str = Field( ..., description="The embedding model to use. Example: nvolveqa_40k" ) @@ -23,14 +23,29 @@ class NVIDIAEmbeddings(BaseModel, Embeddings): @root_validator(pre=True) def _validate_client(cls, values: Any) -> Any: if "client" not in values: - values["client"] = nvai_common.NVEModel() + values["client"] = NVEModel(**values) return values + @property + def available_functions(self) -> List[dict]: + """Map the available functions that can be invoked.""" + return self.client.available_functions + @property def available_models(self) -> dict: """Map the available models that can be invoked.""" return self.client.available_models + @staticmethod + def get_available_functions(**kwargs: Any) -> List[dict]: + """Map the available functions that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_functions + + @staticmethod + def get_available_models(**kwargs: Any) -> dict: + """Map the available models that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_models + def _embed( self, texts: List[str], model_type: Literal["passage", "query"] ) -> List[List[float]]: diff --git a/libs/partners/nvidia-ai-endpoints/poetry.lock b/libs/partners/nvidia-ai-endpoints/poetry.lock index 10176a47c3729b6..f8bf41c006b0fe2 100644 --- a/libs/partners/nvidia-ai-endpoints/poetry.lock +++ b/libs/partners/nvidia-ai-endpoints/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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"04cdee0f18ebbe7c619ec38d0b11a060a9364709576c1183ad1207b7a25306f8" +content-hash = "ab0b10dcff485da25cd4a2b8de147038237af3920362cc8c749f58409e91c9f8" diff --git a/libs/partners/nvidia-ai-endpoints/pyproject.toml b/libs/partners/nvidia-ai-endpoints/pyproject.toml index 0146a6f75f52109..fe13960fd753e3f 100644 --- a/libs/partners/nvidia-ai-endpoints/pyproject.toml +++ b/libs/partners/nvidia-ai-endpoints/pyproject.toml @@ -48,6 +48,7 @@ ruff = "^0.1.5" mypy = "^0.991" langchain-core = {path = "../../core", develop = true} types-requests = "^2.31.0.10" +types-pillow = "^10.2.0.20240125" [tool.poetry.group.dev] optional = true From af5ae24af2b32e962adf23d78e59ed505d17fff7 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 12:37:27 -0800 Subject: [PATCH 069/157] community[patch]: callbacks mypy fixes (#17058) Related to #17048 --- .../callbacks/aim_callback.py | 30 ++++++++++++------- .../callbacks/argilla_callback.py | 13 ++++---- .../callbacks/arthur_callback.py | 2 +- .../callbacks/clearml_callback.py | 10 ++----- .../callbacks/comet_ml_callback.py | 2 +- .../callbacks/flyte_callback.py | 10 ++----- .../callbacks/infino_callback.py | 4 +-- .../callbacks/mlflow_callback.py | 14 ++++----- .../callbacks/streamlit/__init__.py | 2 +- .../callbacks/streamlit/mutable_expander.py | 2 +- .../callbacks/tracers/wandb.py | 7 ++--- .../callbacks/wandb_callback.py | 10 ++----- 12 files changed, 48 insertions(+), 58 deletions(-) diff --git a/libs/community/langchain_community/callbacks/aim_callback.py b/libs/community/langchain_community/callbacks/aim_callback.py index da194b34b3ded20..e36cfcceafe1b47 100644 --- a/libs/community/langchain_community/callbacks/aim_callback.py +++ b/libs/community/langchain_community/callbacks/aim_callback.py @@ -416,15 +416,25 @@ def flush_tracker( self._run.close() self.reset_callback_meta() if reset: - self.__init__( # type: ignore - repo=repo if repo else self.repo, - experiment_name=experiment_name - if experiment_name - else self.experiment_name, - system_tracking_interval=system_tracking_interval + aim = import_aim() + self.repo = repo if repo else self.repo + self.experiment_name = ( + experiment_name if experiment_name else self.experiment_name + ) + self.system_tracking_interval = ( + system_tracking_interval if system_tracking_interval - else self.system_tracking_interval, - log_system_params=log_system_params - if log_system_params - else self.log_system_params, + else self.system_tracking_interval + ) + self.log_system_params = ( + log_system_params if log_system_params else self.log_system_params + ) + + self._run = aim.Run( + repo=self.repo, + experiment=self.experiment_name, + system_tracking_interval=self.system_tracking_interval, + log_system_params=self.log_system_params, ) + self._run_hash = self._run.hash + self.action_records = [] diff --git a/libs/community/langchain_community/callbacks/argilla_callback.py b/libs/community/langchain_community/callbacks/argilla_callback.py index 942f9858b51ae3f..157075a283227ad 100644 --- a/libs/community/langchain_community/callbacks/argilla_callback.py +++ b/libs/community/langchain_community/callbacks/argilla_callback.py @@ -1,6 +1,6 @@ import os import warnings -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler @@ -269,8 +269,8 @@ def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: for key in [str(kwargs["parent_run_id"]), str(kwargs["run_id"])] ): return - prompts = self.prompts.get(str(kwargs["parent_run_id"])) or self.prompts.get( - str(kwargs["run_id"]) + prompts: List = self.prompts.get(str(kwargs["parent_run_id"])) or cast( + List, self.prompts.get(str(kwargs["run_id"]), []) ) for chain_output_key, chain_output_val in outputs.items(): if isinstance(chain_output_val, list): @@ -283,10 +283,7 @@ def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: "response": output["text"].strip(), }, } - for prompt, output in zip( - prompts, # type: ignore - chain_output_val, - ) + for prompt, output in zip(prompts, chain_output_val) ] ) else: @@ -295,7 +292,7 @@ def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: records=[ { "fields": { - "prompt": " ".join(prompts), # type: ignore + "prompt": " ".join(prompts), "response": chain_output_val.strip(), }, } diff --git a/libs/community/langchain_community/callbacks/arthur_callback.py b/libs/community/langchain_community/callbacks/arthur_callback.py index a5fce582ed19cba..b0b28328ab252a3 100644 --- a/libs/community/langchain_community/callbacks/arthur_callback.py +++ b/libs/community/langchain_community/callbacks/arthur_callback.py @@ -162,7 +162,7 @@ def on_llm_start( def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """On LLM end, send data to Arthur.""" try: - import pytz # type: ignore[import] + import pytz except ImportError as e: raise ImportError( "Could not import pytz. Please install it with 'pip install pytz'." diff --git a/libs/community/langchain_community/callbacks/clearml_callback.py b/libs/community/langchain_community/callbacks/clearml_callback.py index 71f30fccdd8de4f..7c4b66e61463c98 100644 --- a/libs/community/langchain_community/callbacks/clearml_callback.py +++ b/libs/community/langchain_community/callbacks/clearml_callback.py @@ -83,7 +83,7 @@ def __init__( if clearml.Task.current_task(): self.task = clearml.Task.current_task() else: - self.task = clearml.Task.init( # type: ignore + self.task = clearml.Task.init( task_type=self.task_type, project_name=self.project_name, tags=self.tags, @@ -361,17 +361,13 @@ def analyze_text(self, text: str) -> dict: if self.visualize and self.nlp and self.temp_dir.name is not None: doc = self.nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) dep_output_path = Path( self.temp_dir.name, hash_string(f"dep-{text}") + ".html" ) dep_output_path.open("w", encoding="utf-8").write(dep_out) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) ent_output_path = Path( self.temp_dir.name, hash_string(f"ent-{text}") + ".html" ) diff --git a/libs/community/langchain_community/callbacks/comet_ml_callback.py b/libs/community/langchain_community/callbacks/comet_ml_callback.py index 5493c947ae89a86..499e93a07b0db9d 100644 --- a/libs/community/langchain_community/callbacks/comet_ml_callback.py +++ b/libs/community/langchain_community/callbacks/comet_ml_callback.py @@ -37,7 +37,7 @@ def _get_experiment( ) -> Any: comet_ml = import_comet_ml() - experiment = comet_ml.Experiment( # type: ignore + experiment = comet_ml.Experiment( workspace=workspace, project_name=project_name, ) diff --git a/libs/community/langchain_community/callbacks/flyte_callback.py b/libs/community/langchain_community/callbacks/flyte_callback.py index 23a8f4734303d24..9fd953443f2716f 100644 --- a/libs/community/langchain_community/callbacks/flyte_callback.py +++ b/libs/community/langchain_community/callbacks/flyte_callback.py @@ -79,12 +79,8 @@ def analyze_text( if nlp is not None: spacy = import_spacy() doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) text_visualizations = { "dependency_tree": dep_out, "entities": ent_out, @@ -199,7 +195,7 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: complexity_metrics: Dict[str, float] = generation_resp.pop( "text_complexity_metrics" - ) # type: ignore # noqa: E501 + ) self.deck.append( self.markdown_renderer().to_html("#### Text Complexity Metrics") ) diff --git a/libs/community/langchain_community/callbacks/infino_callback.py b/libs/community/langchain_community/callbacks/infino_callback.py index 57d756948ef4f6b..106a9a5a083c79f 100644 --- a/libs/community/langchain_community/callbacks/infino_callback.py +++ b/libs/community/langchain_community/callbacks/infino_callback.py @@ -4,7 +4,7 @@ from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.outputs import LLMResult +from langchain_core.outputs import ChatGeneration, LLMResult def import_infino() -> Any: @@ -146,7 +146,7 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: # Track completion token usage (for openai chat models). if self.is_chat_openai_model: messages = " ".join( - generation.message.content # type: ignore[attr-defined] + cast(str, cast(ChatGeneration, generation).message.content) for generation in generations ) completion_tokens = get_num_tokens( diff --git a/libs/community/langchain_community/callbacks/mlflow_callback.py b/libs/community/langchain_community/callbacks/mlflow_callback.py index ea862c23fd21653..6bcfb3aa3303dd2 100644 --- a/libs/community/langchain_community/callbacks/mlflow_callback.py +++ b/libs/community/langchain_community/callbacks/mlflow_callback.py @@ -109,13 +109,9 @@ def analyze_text( spacy = import_spacy() doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) text_visualizations = { "dependency_tree": dep_out, @@ -233,7 +229,7 @@ def jsonf(self, data: Dict[str, Any], filename: str) -> None: data, os.path.join(self.dir, f"{filename}.json"), run_id=self.run_id ) - def table(self, name: str, dataframe) -> None: # type: ignore + def table(self, name: str, dataframe: Any) -> None: """To log the input pandas dataframe as a html table""" self.html(dataframe.to_html(), f"table_{name}") @@ -411,7 +407,7 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: ) complexity_metrics: Dict[str, float] = generation_resp.pop( "text_complexity_metrics" - ) # type: ignore # noqa: E501 + ) self.mlflg.metrics( complexity_metrics, step=self.metrics["step"], @@ -723,7 +719,7 @@ def _create_session_analysis_df(self) -> Any: ) return session_analysis_df - def _contain_llm_records(self): # type: ignore[no-untyped-def] + def _contain_llm_records(self) -> bool: return bool(self.records["on_llm_start_records"]) def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None: diff --git a/libs/community/langchain_community/callbacks/streamlit/__init__.py b/libs/community/langchain_community/callbacks/streamlit/__init__.py index 7a0fadb059d6534..4ee2ea5a9fb2dee 100644 --- a/libs/community/langchain_community/callbacks/streamlit/__init__.py +++ b/libs/community/langchain_community/callbacks/streamlit/__init__.py @@ -62,7 +62,7 @@ def StreamlitCallbackHandler( # guaranteed to support the same set of kwargs. try: from streamlit.external.langchain import ( - StreamlitCallbackHandler as OfficialStreamlitCallbackHandler, # type: ignore # noqa: 501 + StreamlitCallbackHandler as OfficialStreamlitCallbackHandler, ) return OfficialStreamlitCallbackHandler( diff --git a/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py b/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py index 7de1e9873feb4a8..0bb73f571add2f4 100644 --- a/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py +++ b/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py @@ -108,7 +108,7 @@ def markdown( ) -> int: """Add a Markdown element to the container and return its index.""" kwargs = {"body": body, "unsafe_allow_html": unsafe_allow_html, "help": help} - new_dg = self._get_dg(index).markdown(**kwargs) # type: ignore[arg-type] + new_dg = self._get_dg(index).markdown(**kwargs) record = ChildRecord(ChildType.MARKDOWN, kwargs, new_dg) return self._add_record(record, index) diff --git a/libs/community/langchain_community/callbacks/tracers/wandb.py b/libs/community/langchain_community/callbacks/tracers/wandb.py index 31df3352734a7db..00753a5af1a2473 100644 --- a/libs/community/langchain_community/callbacks/tracers/wandb.py +++ b/libs/community/langchain_community/callbacks/tracers/wandb.py @@ -489,11 +489,10 @@ def _ensure_run(self, should_print_url: bool = False) -> None: If not, will start a new run with the provided run_args. """ if self._wandb.run is None: - run_args = self._run_args or {} # type: ignore - run_args: dict = {**run_args} # type: ignore + run_args: Dict = {**(self._run_args or {})} - if "settings" not in run_args: # type: ignore - run_args["settings"] = {"silent": True} # type: ignore + if "settings" not in run_args: + run_args["settings"] = {"silent": True} self._wandb.init(**run_args) if self._wandb.run is not None: diff --git a/libs/community/langchain_community/callbacks/wandb_callback.py b/libs/community/langchain_community/callbacks/wandb_callback.py index 35559ea539ccb2c..9f968d3c67192f9 100644 --- a/libs/community/langchain_community/callbacks/wandb_callback.py +++ b/libs/community/langchain_community/callbacks/wandb_callback.py @@ -92,15 +92,11 @@ def analyze_text( if visualize and nlp and output_dir is not None: doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) dep_output_path = Path(output_dir, hash_string(f"dep-{text}") + ".html") dep_output_path.open("w", encoding="utf-8").write(dep_out) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) ent_output_path = Path(output_dir, hash_string(f"ent-{text}") + ".html") ent_output_path.open("w", encoding="utf-8").write(ent_out) @@ -193,7 +189,7 @@ def __init__( self.stream_logs = stream_logs self.temp_dir = tempfile.TemporaryDirectory() - self.run: wandb.sdk.wandb_run.Run = wandb.init( # type: ignore + self.run = wandb.init( job_type=self.job_type, project=self.project, entity=self.entity, From d93de71d088882b1e7af07a28c2b963d82234d77 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 13:13:25 -0800 Subject: [PATCH 070/157] community[patch]: chat message history mypy fixes (#17059) Related to #17048 --- .../chat_message_histories/elasticsearch.py | 4 ++-- .../langchain_community/chat_message_histories/sql.py | 6 +++--- .../langchain_community/chat_message_histories/tidb.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/libs/community/langchain_community/chat_message_histories/elasticsearch.py b/libs/community/langchain_community/chat_message_histories/elasticsearch.py index a9e076563e9acde..bbd621b98d9cc75 100644 --- a/libs/community/langchain_community/chat_message_histories/elasticsearch.py +++ b/libs/community/langchain_community/chat_message_histories/elasticsearch.py @@ -47,7 +47,7 @@ def __init__( ): self.index: str = index self.session_id: str = session_id - self.ensure_ascii: bool = esnsure_ascii # type: ignore[assignment] + self.ensure_ascii = esnsure_ascii # Initialize Elasticsearch client from passed client arg or connection info if es_connection is not None: @@ -177,7 +177,7 @@ def add_message(self, message: BaseMessage) -> None: "created_at": round(time() * 1000), "history": json.dumps( message_to_dict(message), - ensure_ascii=self.ensure_ascii, + ensure_ascii=bool(self.ensure_ascii), ), }, refresh=True, diff --git a/libs/community/langchain_community/chat_message_histories/sql.py b/libs/community/langchain_community/chat_message_histories/sql.py index fcc3ac71ab1a620..e30a86a52d60f2b 100644 --- a/libs/community/langchain_community/chat_message_histories/sql.py +++ b/libs/community/langchain_community/chat_message_histories/sql.py @@ -39,7 +39,7 @@ def get_sql_model_class(self) -> Any: raise NotImplementedError -def create_message_model(table_name, DynamicBase): # type: ignore +def create_message_model(table_name: str, DynamicBase: Any) -> Any: """ Create a message model for a given table name. @@ -52,8 +52,8 @@ def create_message_model(table_name, DynamicBase): # type: ignore """ - # Model decleared inside a function to have a dynamic table name - class Message(DynamicBase): + # Model declared inside a function to have a dynamic table name. + class Message(DynamicBase): # type: ignore[valid-type, misc] __tablename__ = table_name id = Column(Integer, primary_key=True) session_id = Column(Text) diff --git a/libs/community/langchain_community/chat_message_histories/tidb.py b/libs/community/langchain_community/chat_message_histories/tidb.py index 973e97820a2e4a4..309ab6d7b12c0d6 100644 --- a/libs/community/langchain_community/chat_message_histories/tidb.py +++ b/libs/community/langchain_community/chat_message_histories/tidb.py @@ -40,7 +40,7 @@ def __init__( self.session_id = session_id self.table_name = table_name self.earliest_time = earliest_time - self.cache = [] # type: ignore[var-annotated] + self.cache: List = [] # Set up SQLAlchemy engine and session self.engine = create_engine(connection_string) From 66e45e8ab7bd362dc60264d1c7fb19c932e941ce Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 13:42:59 -0800 Subject: [PATCH 071/157] community[patch]: chat model mypy fixes (#17061) Related to #17048 --- .../chat_models/azureml_endpoint.py | 20 +++---- .../chat_models/deepinfra.py | 4 +- .../langchain_community/chat_models/edenai.py | 14 +++-- .../langchain_community/chat_models/ernie.py | 2 +- .../chat_models/gpt_router.py | 27 +++++----- .../chat_models/huggingface.py | 4 +- .../chat_models/javelin_ai_gateway.py | 2 +- .../langchain_community/chat_models/konko.py | 5 +- .../chat_models/mlflow_ai_gateway.py | 2 +- .../langchain_community/chat_models/ollama.py | 53 +++++++++---------- .../langchain_community/chat_models/openai.py | 2 +- .../langchain_community/chat_models/tongyi.py | 3 +- .../chat_models/vertexai.py | 9 ++-- .../langchain_community/chat_models/yandex.py | 8 +-- .../chat_models/zhipuai.py | 24 ++++----- .../llms/azureml_endpoint.py | 6 +-- .../langchain_community/llms/yandex.py | 5 +- 17 files changed, 101 insertions(+), 89 deletions(-) diff --git a/libs/community/langchain_community/chat_models/azureml_endpoint.py b/libs/community/langchain_community/chat_models/azureml_endpoint.py index 36229582723ce9c..1684be6a681d2d8 100644 --- a/libs/community/langchain_community/chat_models/azureml_endpoint.py +++ b/libs/community/langchain_community/chat_models/azureml_endpoint.py @@ -20,7 +20,7 @@ class LlamaContentFormatter(ContentFormatterBase): - def __init__(self): # type: ignore[no-untyped-def] + def __init__(self) -> None: raise TypeError( "`LlamaContentFormatter` is deprecated for chat models. Use " "`LlamaChatContentFormatter` instead." @@ -72,12 +72,12 @@ def _convert_message_to_dict(message: BaseMessage) -> Dict: def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless] - def format_request_payload( # type: ignore[override] + def format_messages_request_payload( self, messages: List[BaseMessage], model_kwargs: Dict, api_type: AzureMLEndpointApiType, - ) -> str: + ) -> bytes: """Formats the request according to the chosen api""" chat_messages = [ LlamaChatContentFormatter._convert_message_to_dict(message) @@ -98,17 +98,19 @@ def format_request_payload( # type: ignore[override] raise ValueError( f"`api_type` {api_type} is not supported by this formatter" ) - return str.encode(request_payload) # type: ignore[return-value] + return str.encode(request_payload) - def format_response_payload( # type: ignore[override] - self, output: bytes, api_type: AzureMLEndpointApiType + def format_response_payload( + self, + output: bytes, + api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime, ) -> ChatGeneration: """Formats response""" if api_type == AzureMLEndpointApiType.realtime: try: choice = json.loads(output)["output"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e return ChatGeneration( message=BaseMessage( content=choice.strip(), @@ -125,7 +127,7 @@ def format_response_payload( # type: ignore[override] "model. Expected `dict` but `{type(choice)}` was received." ) except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e return ChatGeneration( message=BaseMessage( content=choice["message"]["content"].strip(), @@ -187,7 +189,7 @@ def _generate( if stop: _model_kwargs["stop"] = stop - request_payload = self.content_formatter.format_request_payload( + request_payload = self.content_formatter.format_messages_request_payload( messages, _model_kwargs, self.endpoint_api_type ) response_payload = self.http_client.call( diff --git a/libs/community/langchain_community/chat_models/deepinfra.py b/libs/community/langchain_community/chat_models/deepinfra.py index 7b8a40d3c119f58..2338967779f8cad 100644 --- a/libs/community/langchain_community/chat_models/deepinfra.py +++ b/libs/community/langchain_community/chat_models/deepinfra.py @@ -327,7 +327,7 @@ def _stream( if chunk: yield ChatGenerationChunk(message=chunk, generation_info=None) if run_manager: - run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type] + run_manager.on_llm_new_token(str(chunk.content)) async def _astream( self, @@ -349,7 +349,7 @@ async def _astream( if chunk: yield ChatGenerationChunk(message=chunk, generation_info=None) if run_manager: - await run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type] + await run_manager.on_llm_new_token(str(chunk.content)) async def _agenerate( self, diff --git a/libs/community/langchain_community/chat_models/edenai.py b/libs/community/langchain_community/chat_models/edenai.py index a97dbf28b9c7d54..3e329d91d7c8168 100644 --- a/libs/community/langchain_community/chat_models/edenai.py +++ b/libs/community/langchain_community/chat_models/edenai.py @@ -165,6 +165,12 @@ def _llm_type(self) -> str: """Return type of chat model.""" return "edenai-chat" + @property + def _api_key(self) -> str: + if self.edenai_api_key: + return self.edenai_api_key.get_secret_value() + return "" + def _stream( self, messages: List[BaseMessage], @@ -175,7 +181,7 @@ def _stream( """Call out to EdenAI's chat endpoint.""" url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -216,7 +222,7 @@ async def _astream( ) -> AsyncIterator[ChatGenerationChunk]: url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -265,7 +271,7 @@ def _generate( url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -323,7 +329,7 @@ async def _agenerate( url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) diff --git a/libs/community/langchain_community/chat_models/ernie.py b/libs/community/langchain_community/chat_models/ernie.py index 6f2a4d649bfa8bf..8766038e3b129ad 100644 --- a/libs/community/langchain_community/chat_models/ernie.py +++ b/libs/community/langchain_community/chat_models/ernie.py @@ -214,7 +214,7 @@ def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult: generations = [ ChatGeneration( message=AIMessage( - content=response.get("result"), # type: ignore[arg-type] + content=response.get("result", ""), additional_kwargs={**additional_kwargs}, ) ) diff --git a/libs/community/langchain_community/chat_models/gpt_router.py b/libs/community/langchain_community/chat_models/gpt_router.py index 571d458e4b3d577..68860e90539de53 100644 --- a/libs/community/langchain_community/chat_models/gpt_router.py +++ b/libs/community/langchain_community/chat_models/gpt_router.py @@ -14,6 +14,7 @@ Mapping, Optional, Tuple, + Type, Union, ) @@ -27,7 +28,7 @@ generate_from_stream, ) from langchain_core.language_models.llms import create_base_retry_decorator -from langchain_core.messages import AIMessageChunk, BaseMessage +from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env @@ -56,9 +57,9 @@ class GPTRouterModel(BaseModel): provider_name: str -def get_ordered_generation_requests( # type: ignore[no-untyped-def, no-untyped-def] - models_priority_list: List[GPTRouterModel], **kwargs -): +def get_ordered_generation_requests( + models_priority_list: List[GPTRouterModel], **kwargs: Any +) -> List: """ Return the body for the model router input. """ @@ -100,7 +101,7 @@ def completion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]: # type: ignore[type-arg] +) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse, None, None]]: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -122,7 +123,7 @@ async def acompletion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]: # type: ignore[type-arg] +) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse, None]]: """Use tenacity to retry the async completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -282,9 +283,9 @@ async def _agenerate( ) return self._create_chat_result(response) - def _create_chat_generation_chunk( # type: ignore[no-untyped-def, no-untyped-def] - self, data: Mapping[str, Any], default_chunk_class - ): + def _create_chat_generation_chunk( + self, data: Mapping[str, Any], default_chunk_class: Type[BaseMessageChunk] + ) -> Tuple[ChatGenerationChunk, Type[BaseMessageChunk]]: chunk = _convert_delta_to_message_chunk( {"content": data.get("text", "")}, default_chunk_class ) @@ -293,8 +294,8 @@ def _create_chat_generation_chunk( # type: ignore[no-untyped-def, no-untyped-de dict(finish_reason=finish_reason) if finish_reason is not None else None ) default_chunk_class = chunk.__class__ - chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) # type: ignore[assignment] - return chunk, default_chunk_class + gen_chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) + return gen_chunk, default_chunk_class def _stream( self, @@ -306,7 +307,7 @@ def _stream( message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} - default_chunk_class = AIMessageChunk + default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk generator_response = completion_with_retry( self, messages=message_dicts, @@ -339,7 +340,7 @@ async def _astream( message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} - default_chunk_class = AIMessageChunk + default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk generator_response = acompletion_with_retry( self, messages=message_dicts, diff --git a/libs/community/langchain_community/chat_models/huggingface.py b/libs/community/langchain_community/chat_models/huggingface.py index bb52c7795d3ebc3..0af34a8cf04b231 100644 --- a/libs/community/langchain_community/chat_models/huggingface.py +++ b/libs/community/langchain_community/chat_models/huggingface.py @@ -44,7 +44,7 @@ class ChatHuggingFace(BaseChatModel): llm: Union[HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub] system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT) tokenizer: Any = None - model_id: str = None # type: ignore + model_id: Optional[str] = None def __init__(self, **kwargs: Any): super().__init__(**kwargs) @@ -144,7 +144,7 @@ def _resolve_model_id(self) -> None: elif isinstance(self.llm, HuggingFaceHub): # no need to look up model_id for HuggingFaceHub LLM - self.model_id = self.llm.repo_id # type: ignore[assignment] + self.model_id = self.llm.repo_id return else: diff --git a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py index 6b7001b62604adc..6c90103fae902ba 100644 --- a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py @@ -25,7 +25,7 @@ # Ignoring type because below is valid pydantic code # Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg] -class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg] +class ChatParams(BaseModel, extra=Extra.allow): """Parameters for the `Javelin AI Gateway` LLM.""" temperature: float = 0.0 diff --git a/libs/community/langchain_community/chat_models/konko.py b/libs/community/langchain_community/chat_models/konko.py index 9a977a1728a932c..4f385148c68e1f9 100644 --- a/libs/community/langchain_community/chat_models/konko.py +++ b/libs/community/langchain_community/chat_models/konko.py @@ -13,6 +13,7 @@ Set, Tuple, Union, + cast, ) import requests @@ -169,7 +170,9 @@ def get_available_models( } if openai_api_key: - headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value() # type: ignore[union-attr] + headers["X-OpenAI-Api-Key"] = cast( + SecretStr, openai_api_key + ).get_secret_value() models_response = requests.get(models_url, headers=headers) diff --git a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py index 39ad6b550b176f8..35321655a4f5949 100644 --- a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py @@ -25,7 +25,7 @@ # Ignoring type because below is valid pydantic code # Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg] -class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg] +class ChatParams(BaseModel, extra=Extra.allow): """Parameters for the `MLflow AI Gateway` LLM.""" temperature: float = 0.0 diff --git a/libs/community/langchain_community/chat_models/ollama.py b/libs/community/langchain_community/chat_models/ollama.py index a6bb1bb69880dd4..92b5afb52ba3f09 100644 --- a/libs/community/langchain_community/chat_models/ollama.py +++ b/libs/community/langchain_community/chat_models/ollama.py @@ -1,5 +1,5 @@ import json -from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast from langchain_core._api import deprecated from langchain_core.callbacks import ( @@ -74,10 +74,15 @@ def _format_message_as_text(self, message: BaseMessage) -> str: if isinstance(message, ChatMessage): message_text = f"\n\n{message.role.capitalize()}: {message.content}" elif isinstance(message, HumanMessage): - if message.content[0].get("type") == "text": # type: ignore[union-attr] - message_text = f"[INST] {message.content[0]['text']} [/INST]" # type: ignore[index] - elif message.content[0].get("type") == "image_url": # type: ignore[union-attr] - message_text = message.content[0]["image_url"]["url"] # type: ignore[index, index] + if isinstance(message.content, List): + first_content = cast(List[Dict], message.content)[0] + content_type = first_content.get("type") + if content_type == "text": + message_text = f"[INST] {first_content['text']} [/INST]" + elif content_type == "image_url": + message_text = first_content["image_url"]["url"] + else: + message_text = f"[INST] {message.content} [/INST]" elif isinstance(message, AIMessage): message_text = f"{message.content}" elif isinstance(message, SystemMessage): @@ -94,7 +99,7 @@ def _format_messages_as_text(self, messages: List[BaseMessage]) -> str: def _convert_messages_to_ollama_messages( self, messages: List[BaseMessage] ) -> List[Dict[str, Union[str, List[str]]]]: - ollama_messages = [] + ollama_messages: List = [] for message in messages: role = "" if isinstance(message, HumanMessage): @@ -111,12 +116,12 @@ def _convert_messages_to_ollama_messages( if isinstance(message.content, str): content = message.content else: - for content_part in message.content: - if content_part.get("type") == "text": # type: ignore[union-attr] - content += f"\n{content_part['text']}" # type: ignore[index] - elif content_part.get("type") == "image_url": # type: ignore[union-attr] - if isinstance(content_part.get("image_url"), str): # type: ignore[union-attr] - image_url_components = content_part["image_url"].split(",") # type: ignore[index] + for content_part in cast(List[Dict], message.content): + if content_part.get("type") == "text": + content += f"\n{content_part['text']}" + elif content_part.get("type") == "image_url": + if isinstance(content_part.get("image_url"), str): + image_url_components = content_part["image_url"].split(",") # Support data:image/jpeg;base64, format # and base64 strings if len(image_url_components) > 1: @@ -142,7 +147,7 @@ def _convert_messages_to_ollama_messages( } ) - return ollama_messages # type: ignore[return-value] + return ollama_messages def _create_chat_stream( self, @@ -324,21 +329,15 @@ async def _astream( run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: - try: - async for stream_resp in self._acreate_chat_stream( - messages, stop, **kwargs - ): - if stream_resp: - chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp) - yield chunk - if run_manager: - await run_manager.on_llm_new_token( - chunk.text, - verbose=self.verbose, - ) - except OllamaEndpointNotFoundError: - async for chunk in self._legacy_astream(messages, stop, **kwargs): # type: ignore[attr-defined] + async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs): + if stream_resp: + chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp) yield chunk + if run_manager: + await run_manager.on_llm_new_token( + chunk.text, + verbose=self.verbose, + ) @deprecated("0.0.3", alternative="_stream") def _legacy_stream( diff --git a/libs/community/langchain_community/chat_models/openai.py b/libs/community/langchain_community/chat_models/openai.py index 568767662c976fc..0279d03ed97cef6 100644 --- a/libs/community/langchain_community/chat_models/openai.py +++ b/libs/community/langchain_community/chat_models/openai.py @@ -554,7 +554,7 @@ def _client_params(self) -> Dict[str, Any]: if self.openai_proxy: import openai - openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501 + openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} return {**self._default_params, **openai_creds} def _get_invocation_params( diff --git a/libs/community/langchain_community/chat_models/tongyi.py b/libs/community/langchain_community/chat_models/tongyi.py index 4623864c8513976..9d76e8b120383a6 100644 --- a/libs/community/langchain_community/chat_models/tongyi.py +++ b/libs/community/langchain_community/chat_models/tongyi.py @@ -13,6 +13,7 @@ Mapping, Optional, Union, + cast, ) from langchain_core.callbacks import ( @@ -197,7 +198,7 @@ def _default_params(self) -> Dict[str, Any]: return { "model": self.model_name, "top_p": self.top_p, - "api_key": self.dashscope_api_key.get_secret_value(), # type: ignore[union-attr] + "api_key": cast(SecretStr, self.dashscope_api_key).get_secret_value(), "result_format": "message", **self.model_kwargs, } diff --git a/libs/community/langchain_community/chat_models/vertexai.py b/libs/community/langchain_community/chat_models/vertexai.py index c5a81ea32a9fbba..901123fcf9ec406 100644 --- a/libs/community/langchain_community/chat_models/vertexai.py +++ b/libs/community/langchain_community/chat_models/vertexai.py @@ -120,11 +120,10 @@ def _convert_to_prompt(part: Union[str, Dict]) -> Part: image = load_image_from_gcs(path=path, project=project) elif path.startswith("data:image/"): # extract base64 component from image uri - try: - encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group( # type: ignore[union-attr] - 1 - ) - except AttributeError: + encoded: Any = re.search(r"data:image/\w{2,4};base64,(.*)", path) + if encoded: + encoded = encoded.group(1) + else: raise ValueError( "Invalid image uri. It should be in the format " "data:image/;base64,." diff --git a/libs/community/langchain_community/chat_models/yandex.py b/libs/community/langchain_community/chat_models/yandex.py index 94e05803405f89c..a596620840eda31 100644 --- a/libs/community/langchain_community/chat_models/yandex.py +++ b/libs/community/langchain_community/chat_models/yandex.py @@ -52,7 +52,7 @@ def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]: return chat_history -class ChatYandexGPT(_BaseYandexGPT, BaseChatModel): # type: ignore[misc] +class ChatYandexGPT(_BaseYandexGPT, BaseChatModel): """Wrapper around YandexGPT large language models. There are two authentication options for the service account @@ -156,7 +156,7 @@ def _make_request( messages=[Message(**message) for message in message_history], ) stub = TextGenerationServiceStub(channel) - res = stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] + res = stub.Completion(request, metadata=self._grpc_metadata) return list(res)[0].alternatives[0].message.text @@ -201,7 +201,7 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st messages=[Message(**message) for message in message_history], ) stub = TextGenerationAsyncServiceStub(channel) - operation = await stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] + operation = await stub.Completion(request, metadata=self._grpc_metadata) async with grpc.aio.secure_channel( operation_api_url, channel_credentials ) as operation_channel: @@ -211,7 +211,7 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st operation_request = GetOperationRequest(operation_id=operation.id) operation = await operation_stub.Get( operation_request, - metadata=self._grpc_metadata, # type: ignore[attr-defined] + metadata=self._grpc_metadata, ) completion_response = CompletionResponse() diff --git a/libs/community/langchain_community/chat_models/zhipuai.py b/libs/community/langchain_community/chat_models/zhipuai.py index 113c2be4a1e747a..3e2e2504b0eca07 100644 --- a/libs/community/langchain_community/chat_models/zhipuai.py +++ b/libs/community/langchain_community/chat_models/zhipuai.py @@ -5,7 +5,7 @@ import json import logging from functools import partial -from typing import Any, Dict, Iterator, List, Optional +from typing import Any, Dict, Iterator, List, Optional, cast from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.chat_models import ( @@ -161,7 +161,7 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - def __init__(self, *args, **kwargs): # type: ignore[no-untyped-def] + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) try: import zhipuai @@ -174,7 +174,7 @@ def __init__(self, *args, **kwargs): # type: ignore[no-untyped-def] "Please install it via 'pip install zhipuai'" ) - def invoke(self, prompt): # type: ignore[no-untyped-def] + def invoke(self, prompt: Any) -> Any: # type: ignore[override] if self.model == "chatglm_turbo": return self.zhipuai.model_api.invoke( model=self.model, @@ -185,17 +185,17 @@ def invoke(self, prompt): # type: ignore[no-untyped-def] return_type=self.return_type, ) elif self.model == "characterglm": - meta = self.meta.dict() + _meta = cast(meta, self.meta).dict() return self.zhipuai.model_api.invoke( model=self.model, - meta=meta, + meta=_meta, prompt=prompt, request_id=self.request_id, return_type=self.return_type, ) return None - def sse_invoke(self, prompt): # type: ignore[no-untyped-def] + def sse_invoke(self, prompt: Any) -> Any: if self.model == "chatglm_turbo": return self.zhipuai.model_api.sse_invoke( model=self.model, @@ -207,18 +207,18 @@ def sse_invoke(self, prompt): # type: ignore[no-untyped-def] incremental=self.incremental, ) elif self.model == "characterglm": - meta = self.meta.dict() + _meta = cast(meta, self.meta).dict() return self.zhipuai.model_api.sse_invoke( model=self.model, prompt=prompt, - meta=meta, + meta=_meta, request_id=self.request_id, return_type=self.return_type, incremental=self.incremental, ) return None - async def async_invoke(self, prompt): # type: ignore[no-untyped-def] + async def async_invoke(self, prompt: Any) -> Any: loop = asyncio.get_running_loop() partial_func = partial( self.zhipuai.model_api.async_invoke, model=self.model, prompt=prompt @@ -229,7 +229,7 @@ async def async_invoke(self, prompt): # type: ignore[no-untyped-def] ) return response - async def async_invoke_result(self, task_id): # type: ignore[no-untyped-def] + async def async_invoke_result(self, task_id: Any) -> Any: loop = asyncio.get_running_loop() response = await loop.run_in_executor( None, @@ -247,7 +247,7 @@ def _generate( **kwargs: Any, ) -> ChatResult: """Generate a chat response.""" - prompt = [] + prompt: List = [] for message in messages: if isinstance(message, AIMessage): role = "assistant" @@ -270,7 +270,7 @@ def _generate( else: stream_iter = self._stream( - prompt=prompt, # type: ignore[arg-type] + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs, diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py index 3601a7629a22611..d30612ce8b4a09c 100644 --- a/libs/community/langchain_community/llms/azureml_endpoint.py +++ b/libs/community/langchain_community/llms/azureml_endpoint.py @@ -101,7 +101,7 @@ def format_response_payload( accepts: Optional[str] = "application/json" """The MIME type of the response data returned from the endpoint""" - format_error_msg: Optional[str] = ( + format_error_msg: str = ( "Error while formatting response payload for chat model of type " " `{api_type}`. Are you using the right formatter for the deployed " " model and endpoint type?" @@ -134,17 +134,17 @@ def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - @abstractmethod def format_request_payload( self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime, - ) -> bytes: + ) -> Any: """Formats the request body according to the input schema of the model. Returns bytes or seekable file like object in the format specified in the content_type request header. """ + raise NotImplementedError() @abstractmethod def format_response_payload( diff --git a/libs/community/langchain_community/llms/yandex.py b/libs/community/langchain_community/llms/yandex.py index d4cf5840ca189c4..228333d5a72bd35 100644 --- a/libs/community/langchain_community/llms/yandex.py +++ b/libs/community/langchain_community/llms/yandex.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import Any, Callable, Dict, List, Mapping, Optional +from typing import Any, Callable, Dict, List, Optional, Sequence from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, @@ -54,13 +54,14 @@ class _BaseYandexGPT(Serializable): """Maximum number of retries to make when generating.""" sleep_interval: float = 1.0 """Delay between API requests""" + _grpc_metadata: Sequence @property def _llm_type(self) -> str: return "yandex_gpt" @property - def _identifying_params(self) -> Mapping[str, Any]: + def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model_uri": self.model_uri, From dcf973c22c64405713cbc96ef5e48ebaf3c195f1 Mon Sep 17 00:00:00 2001 From: calvinweb <38599774+calvinweb@users.noreply.github.com> Date: Tue, 6 Feb 2024 06:18:16 +0800 Subject: [PATCH 072/157] Langchain: `json_chat` don't need stop sequenes (#16335) This is a PR about #16334 The Stop sequenes isn't meanful in `json_chat` because it depends json to work, not completions --------- Co-authored-by: Harrison Chase --- .../langchain/langchain/agents/json_chat/base.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/libs/langchain/langchain/agents/json_chat/base.py b/libs/langchain/langchain/agents/json_chat/base.py index ffabf68921b0bb8..24e1310e2af638c 100644 --- a/libs/langchain/langchain/agents/json_chat/base.py +++ b/libs/langchain/langchain/agents/json_chat/base.py @@ -12,7 +12,10 @@ def create_json_chat_agent( - llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: ChatPromptTemplate, + stop_sequence: bool = True, ) -> Runnable: """Create an agent that uses JSON to format its logic, build for Chat Models. @@ -20,7 +23,9 @@ def create_json_chat_agent( llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. - + stop_sequence: Adds a stop token of "Observation:" to avoid hallucinates. + Default is True. You may to set this to False if the LLM you are using + does not support stop sequences. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an @@ -148,7 +153,10 @@ def create_json_chat_agent( tools=render_text_description(list(tools)), tool_names=", ".join([t.name for t in tools]), ) - llm_with_stop = llm.bind(stop=["\nObservation"]) + if stop_sequence: + llm_to_use = llm.bind(stop=["\nObservation"]) + else: + llm_to_use = llm agent = ( RunnablePassthrough.assign( @@ -157,7 +165,7 @@ def create_json_chat_agent( ) ) | prompt - | llm_with_stop + | llm_to_use | JSONAgentOutputParser() ) return agent From e022bfaa7dd54ea962fc11094b37b3fa324c95ea Mon Sep 17 00:00:00 2001 From: T Cramer Date: Mon, 5 Feb 2024 22:18:30 +0000 Subject: [PATCH 073/157] langchain: add partial parsing support to JsonOutputToolsParser (#17035) - **Description:** Add partial parsing support to JsonOutputToolsParser - **Issue:** [16736](https://github.com/langchain-ai/langchain/issues/16736) --------- Co-authored-by: Harrison Chase --- .../langchain/output_parsers/openai_tools.py | 20 +++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/libs/langchain/langchain/output_parsers/openai_tools.py b/libs/langchain/langchain/output_parsers/openai_tools.py index 39fd05974a92d48..045e32686efcfb3 100644 --- a/libs/langchain/langchain/output_parsers/openai_tools.py +++ b/libs/langchain/langchain/output_parsers/openai_tools.py @@ -4,9 +4,8 @@ from typing import Any, List, Type from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers import ( - BaseGenerationOutputParser, -) +from langchain_core.output_parsers import BaseGenerationOutputParser +from langchain_core.output_parsers.json import parse_partial_json from langchain_core.outputs import ChatGeneration, Generation from langchain_core.pydantic_v1 import BaseModel @@ -42,9 +41,14 @@ def parse_result(self, result: List[Generation], *, partial: bool = False) -> An if "function" not in tool_call: continue try: - function_args = json.loads( - tool_call["function"]["arguments"], strict=self.strict - ) + if partial: + function_args = parse_partial_json( + tool_call["function"]["arguments"], strict=self.strict + ) + else: + function_args = json.loads( + tool_call["function"]["arguments"], strict=self.strict + ) except JSONDecodeError as e: exceptions.append( f"Function {tool_call['function']['name']} arguments:\n\n" @@ -77,7 +81,7 @@ def __init__(self, key_name: str, **kwargs: Any) -> None: super().__init__(key_name=key_name, **kwargs) def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: - results = super().parse_result(result) + results = super().parse_result(result, partial=partial) results = [res for res in results if res["type"] == self.key_name] if not self.return_id: results = [res["args"] for res in results] @@ -92,6 +96,6 @@ class PydanticToolsParser(JsonOutputToolsParser): tools: List[Type[BaseModel]] def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: - results = super().parse_result(result) + results = super().parse_result(result, partial=partial) name_dict = {tool.__name__: tool for tool in self.tools} return [name_dict[res["type"]](**res["args"]) for res in results] From ae33979813a5dcf99e0436b2a75b1145937b00d9 Mon Sep 17 00:00:00 2001 From: Supreet Takkar Date: Mon, 5 Feb 2024 17:28:03 -0500 Subject: [PATCH 074/157] community[patch]: Allow adding ARNs as model_id to support Amazon Bedrock custom models (#16800) - **Description:** Adds an additional class variable to `BedrockBase` called `provider` that allows sending a model provider such as amazon, cohere, ai21, etc. Up until now, the model provider is extracted from the `model_id` using the first part before the `.`, such as `amazon` for `amazon.titan-text-express-v1` (see [supported list of Bedrock model IDs here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html)). But for custom Bedrock models where the ARN of the provisioned throughput must be supplied, the `model_id` is like `arn:aws:bedrock:...` so the `model_id` cannot be extracted from this. A model `provider` is required by the LangChain Bedrock class to perform model-based processing. To allow the same processing to be performed for custom-models of a specific base model type, passing this `provider` argument can help solve the issues. The alternative considered here was the use of `provider.arn:aws:bedrock:...` which then requires ARN to be extracted and passed separately when invoking the model. The proposed solution here is simpler and also does not cause issues for current models already using the Bedrock class. - **Issue:** N/A - **Dependencies:** N/A --------- Co-authored-by: Piyush Jain --- docs/docs/integrations/llms/bedrock.ipynb | 34 +++++++++++++++++-- .../langchain_community/llms/bedrock.py | 18 +++++++++- 2 files changed, 48 insertions(+), 4 deletions(-) diff --git a/docs/docs/integrations/llms/bedrock.ipynb b/docs/docs/integrations/llms/bedrock.ipynb index 717fbe3480f4493..e32d5fa840d4b0c 100644 --- a/docs/docs/integrations/llms/bedrock.ipynb +++ b/docs/docs/integrations/llms/bedrock.ipynb @@ -111,7 +111,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - + "### Custom models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "custom_llm = Bedrock(\n", + " credentials_profile_name=\"bedrock-admin\",\n", + " provider=\"cohere\",\n", + " model_id=\"\", # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n", + " model_kwargs={\"temperature\": 1},\n", + " streaming=True,\n", + " callbacks=[StreamingStdOutCallbackHandler()],\n", + ")\n", + "\n", + "conversation = ConversationChain(\n", + " llm=custom_llm, verbose=True, memory=ConversationBufferMemory()\n", + ")\n", + "conversation.predict(input=\"What is the recipe of mayonnaise?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Guardrails for Amazon Bedrock example \n", "\n", "## Guardrails for Amazon Bedrock (Preview) \n", "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n", @@ -139,7 +167,7 @@ " print(f\"Guardrails: {kwargs}\")\n", "\n", "\n", - "# guardrails for Amazon Bedrock with trace\n", + "# Guardrails for Amazon Bedrock with trace\n", "llm = Bedrock(\n", " credentials_profile_name=\"bedrock-admin\",\n", " model_id=\"\",\n", @@ -166,7 +194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py index 002eb90fc0b9d8d..d3264703c7cc4d2 100644 --- a/libs/community/langchain_community/llms/bedrock.py +++ b/libs/community/langchain_community/llms/bedrock.py @@ -229,9 +229,17 @@ class BedrockBase(BaseModel, ABC): config: Optional[Config] = None """An optional botocore.config.Config instance to pass to the client.""" + provider: Optional[str] = None + """The model provider, e.g., amazon, cohere, ai21, etc. When not supplied, provider + is extracted from the first part of the model_id e.g. 'amazon' in + 'amazon.titan-text-express-v1'. This value should be provided for model ids that do + not have the provider in them, e.g., custom and provisioned models that have an ARN + associated with them.""" + model_id: str """Id of the model to call, e.g., amazon.titan-text-express-v1, this is - equivalent to the modelId property in the list-foundation-models api""" + equivalent to the modelId property in the list-foundation-models api. For custom and + provisioned models, an ARN value is expected.""" model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model.""" @@ -353,6 +361,14 @@ def _identifying_params(self) -> Mapping[str, Any]: } def _get_provider(self) -> str: + if self.provider: + return self.provider + if self.model_id.startswith("arn"): + raise ValueError( + "Model provider should be supplied when passing a model ARN as " + "model_id" + ) + return self.model_id.split(".")[0] @property From 304f3f5fc1517adf54f9d022f13e8209749b00c1 Mon Sep 17 00:00:00 2001 From: Tyler Titsworth Date: Mon, 5 Feb 2024 14:33:34 -0800 Subject: [PATCH 075/157] community[patch]: Add Progress bar to HuggingFaceEmbeddings (#16758) - **Description:** Adds a function parameter to HuggingFaceEmbeddings called `show_progress` that enables a `tqdm` progress bar if enabled. Does not function if `multi_process = True`. - **Issue:** n/a - **Dependencies:** n/a --- .../community/langchain_community/embeddings/huggingface.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/libs/community/langchain_community/embeddings/huggingface.py b/libs/community/langchain_community/embeddings/huggingface.py index 068aafa1fe1db83..60f4ecb44355980 100644 --- a/libs/community/langchain_community/embeddings/huggingface.py +++ b/libs/community/langchain_community/embeddings/huggingface.py @@ -49,6 +49,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings): """Keyword arguments to pass when calling the `encode` method of the model.""" multi_process: bool = False """Run encode() on multiple GPUs.""" + show_progress: bool = False + """Whether to show a progress bar.""" def __init__(self, **kwargs: Any): """Initialize the sentence_transformer.""" @@ -88,7 +90,9 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: embeddings = self.client.encode_multi_process(texts, pool) sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool) else: - embeddings = self.client.encode(texts, **self.encode_kwargs) + embeddings = self.client.encode( + texts, show_progress_bar=self.show_progress, **self.encode_kwargs + ) return embeddings.tolist() From 3c4b24b69a5db2bf895a520a52b75f49fbd03ae0 Mon Sep 17 00:00:00 2001 From: Mohammad Mohtashim <45242107+keenborder786@users.noreply.github.com> Date: Tue, 6 Feb 2024 04:34:42 +0500 Subject: [PATCH 076/157] community[patch]: Fix the _call of HuggingFaceHub (#16891) Fixed the following identified issue: #16849 @baskaryan --- libs/community/langchain_community/llms/huggingface_hub.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/community/langchain_community/llms/huggingface_hub.py b/libs/community/langchain_community/llms/huggingface_hub.py index f4327277731217a..2d9123177523159 100644 --- a/libs/community/langchain_community/llms/huggingface_hub.py +++ b/libs/community/langchain_community/llms/huggingface_hub.py @@ -127,10 +127,10 @@ def _call( response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} - params = {**_model_kwargs, **kwargs} + parameters = {**_model_kwargs, **kwargs} response = self.client.post( - json={"inputs": prompt, "params": params}, task=self.task + json={"inputs": prompt, "parameters": parameters}, task=self.task ) response = json.loads(response.decode()) if "error" in response: From 9b279ac127d26e90faf6b6a40bc9f4ef0cfb7d8b Mon Sep 17 00:00:00 2001 From: Serena Ruan <82044803+serena-ruan@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:46:46 -0800 Subject: [PATCH 077/157] community[patch]: MLflow callback update (#16687) Signed-off-by: Serena Ruan Co-authored-by: Bagatur --- .../callbacks/mlflow_callback.py | 38 +++++++++++-------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git a/libs/community/langchain_community/callbacks/mlflow_callback.py b/libs/community/langchain_community/callbacks/mlflow_callback.py index 6bcfb3aa3303dd2..f631a5c51c29bda 100644 --- a/libs/community/langchain_community/callbacks/mlflow_callback.py +++ b/libs/community/langchain_community/callbacks/mlflow_callback.py @@ -82,23 +82,21 @@ def get_text_complexity_metrics() -> List[str]: def analyze_text( text: str, nlp: Any = None, + textstat: Any = None, ) -> dict: """Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. nlp (spacy.lang): The spacy language model to use for visualization. + textstat: The textstat library to use for complexity metrics calculation. Returns: (dict): A dictionary containing the complexity metrics and visualization files serialized to HTML string. """ resp: Dict[str, Any] = {} - try: - textstat = import_textstat() - except ImportError: - pass - else: + if textstat is not None: text_complexity_metrics = { key: getattr(textstat, key)(text) for key in get_text_complexity_metrics() } @@ -304,8 +302,8 @@ def __init__( self.nlp = None try: spacy = import_spacy() - except ImportError: - pass + except ImportError as e: + logger.warning(e.msg) else: try: self.nlp = spacy.load("en_core_web_sm") @@ -315,6 +313,12 @@ def __init__( "to download en_core_web_sm model for text visualization." ) + try: + self.textstat = import_textstat() + except ImportError as e: + logger.warning(e.msg) + self.textstat = None + self.metrics = {key: 0 for key in mlflow_callback_metrics()} self.records: Dict[str, Any] = { @@ -403,15 +407,17 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: analyze_text( generation.text, nlp=self.nlp, + textstat=self.textstat, ) ) - complexity_metrics: Dict[str, float] = generation_resp.pop( - "text_complexity_metrics" - ) - self.mlflg.metrics( - complexity_metrics, - step=self.metrics["step"], - ) + if "text_complexity_metrics" in generation_resp: + complexity_metrics: Dict[str, float] = generation_resp.pop( + "text_complexity_metrics" + ) + self.mlflg.metrics( + complexity_metrics, + step=self.metrics["step"], + ) self.records["on_llm_end_records"].append(generation_resp) self.records["action_records"].append(generation_resp) self.mlflg.jsonf(resp, f"llm_end_{llm_ends}_generation_{idx}") @@ -681,7 +687,9 @@ def _create_session_analysis_df(self) -> Any: .dropna(axis=1) .rename({"step": "prompt_step"}, axis=1) ) - complexity_metrics_columns = get_text_complexity_metrics() + complexity_metrics_columns = ( + get_text_complexity_metrics() if self.textstat is not None else [] + ) visualizations_columns = ( ["dependency_tree", "entities"] if self.nlp is not None else [] ) From 4b17699818cdf9ead853a2cba8d3017ad306c47e Mon Sep 17 00:00:00 2001 From: IMRAN KHAN <30754328+strongSoda@users.noreply.github.com> Date: Tue, 6 Feb 2024 05:18:34 +0530 Subject: [PATCH 078/157] docs: add 2 more tutorials to the list in youtube.mdx (#16998) - **Description:** add 2 more tutorials to the list in youtube.mdx, - **Twitter handle:** EhThing --- docs/docs/additional_resources/youtube.mdx | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/docs/additional_resources/youtube.mdx b/docs/docs/additional_resources/youtube.mdx index 94aafe476630f22..807f45211270e4d 100644 --- a/docs/docs/additional_resources/youtube.mdx +++ b/docs/docs/additional_resources/youtube.mdx @@ -120,6 +120,8 @@ - ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io) - ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV) - ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal) +- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint) +- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint) ### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov) @@ -132,4 +134,4 @@ --------------------- -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-04] From 973ba0d84b454e88ee4c7a074f41f62c96dd84ef Mon Sep 17 00:00:00 2001 From: Benjamin Muskalla Date: Tue, 6 Feb 2024 00:48:47 +0100 Subject: [PATCH 079/157] docs: Fix Copilot name (#16956) The official name is "GitHub Copilot" --- docs/docs/use_cases/code_understanding.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/use_cases/code_understanding.ipynb b/docs/docs/use_cases/code_understanding.ipynb index 317838d05a1d45f..d3da700c9362fa6 100644 --- a/docs/docs/use_cases/code_understanding.ipynb +++ b/docs/docs/use_cases/code_understanding.ipynb @@ -17,7 +17,7 @@ "\n", "## Use case\n", "\n", - "Source code analysis is one of the most popular LLM applications (e.g., [GitHub Co-Pilot](https://github.com/features/copilot), [Code Interpreter](https://chat.openai.com/auth/login?next=%2F%3Fmodel%3Dgpt-4-code-interpreter), [Codium](https://www.codium.ai/), and [Codeium](https://codeium.com/about)) for use-cases such as:\n", + "Source code analysis is one of the most popular LLM applications (e.g., [GitHub Copilot](https://github.com/features/copilot), [Code Interpreter](https://chat.openai.com/auth/login?next=%2F%3Fmodel%3Dgpt-4-code-interpreter), [Codium](https://www.codium.ai/), and [Codeium](https://codeium.com/about)) for use-cases such as:\n", "\n", "- Q&A over the code base to understand how it works\n", "- Using LLMs for suggesting refactors or improvements\n", From fcb875629d29e9343f34dec52c29b0eeb5a4cb19 Mon Sep 17 00:00:00 2001 From: Shivani Modi Date: Mon, 5 Feb 2024 15:49:13 -0800 Subject: [PATCH 080/157] docs: Updating documentation for Konko provider (#16953) - **Description:** A small update to the Konko provider documentation. --------- Co-authored-by: Shivani Modi --- docs/docs/integrations/chat/konko.ipynb | 53 +++----------- docs/docs/integrations/llms/konko.ipynb | 58 +++++++++++---- docs/docs/integrations/providers/konko.mdx | 85 ++++++++-------------- 3 files changed, 85 insertions(+), 111 deletions(-) diff --git a/docs/docs/integrations/chat/konko.ipynb b/docs/docs/integrations/chat/konko.ipynb index 173fd34691a3edd..3582e21d2302e98 100644 --- a/docs/docs/integrations/chat/konko.ipynb +++ b/docs/docs/integrations/chat/konko.ipynb @@ -15,39 +15,23 @@ "source": [ "# ChatKonko\n", "\n", - ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n", - "\n", - "Konko API is a fully managed API designed to help application developers:\n", - "\n", - "1. Select the right LLM(s) for their application\n", - "2. Prototype with various open-source and proprietary LLMs\n", - "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n", - "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n", - "\n", - "### Steps to Access Models\n", - "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n", - "\n", - "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n", + "# Konko\n", "\n", - "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n", + ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n", "\n", - "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n", "\n", - "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n", + "1. **Select** the right open source or proprietary LLMs for their application\n", + "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n", + "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n", + "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n", "\n", - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/).\n", "\n", "This example goes over how to use LangChain to interact with `Konko` ChatCompletion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-chatcompletion)\n", + "\n", + "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models\n", "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)." - ] - }, { "cell_type": "code", "execution_count": 1, @@ -64,11 +48,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Set API Keys\n", - "\n", - "
\n", - "\n", - "### Option 1: Set Environment Variables\n", + "#### Set Environment Variables\n", "\n", "1. You can set environment variables for \n", " 1. KONKO_API_KEY (Required)\n", @@ -78,18 +58,7 @@ "```shell\n", "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n", "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n", - "```\n", - "\n", - "Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.\n", - "\n", - "### Option 2: Set API Keys Programmatically\n", - "\n", - "If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:\n", - "\n", - "```python\n", - "konko.set_api_key('your_KONKO_API_KEY_here') \n", - "konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional\n", - "```\n" + "```" ] }, { @@ -98,7 +67,7 @@ "source": [ "## Calling a model\n", "\n", - "Find a model on the [Konko overview page](https://docs.konko.ai/v0.5.0/docs/list-of-models)\n", + "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n", "\n", "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n", "\n", diff --git a/docs/docs/integrations/llms/konko.ipynb b/docs/docs/integrations/llms/konko.ipynb index 4ef5a7a3281c4b5..ef1ffc15d851fc9 100644 --- a/docs/docs/integrations/llms/konko.ipynb +++ b/docs/docs/integrations/llms/konko.ipynb @@ -1,20 +1,27 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: Konko\n", + "---" + ] + }, { "cell_type": "markdown", "id": "136d9ba6-c42a-435b-9e19-77ebcc7a3145", "metadata": {}, "source": [ - "# ChatKonko\n", + "# Konko\n", "\n", ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n", "\n", - "Konko API is a fully managed API designed to help application developers:\n", - "\n", - "1. Select the right LLM(s) for their application\n", - "2. Prototype with various open-source and proprietary LLMs\n", - "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n", - "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n" + "1. **Select** the right open source or proprietary LLMs for their application\n", + "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n", + "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n", + "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n" ] }, { @@ -22,25 +29,44 @@ "id": "0d896d07-82b4-4f38-8c37-f0bc8b0e4fe1", "metadata": {}, "source": [ - "### Steps to Access Models\n", - "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n", + "This example goes over how to use LangChain to interact with `Konko` completion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-completion)\n", "\n", - "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n", + "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set Environment Variables\n", "\n", - "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n", + "1. You can set environment variables for \n", + " 1. KONKO_API_KEY (Required)\n", + " 2. OPENAI_API_KEY (Optional)\n", + "2. In your current shell session, use the export command:\n", "\n", - "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n", + "```shell\n", + "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n", + "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling a model\n", "\n", - "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n", + "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n", "\n", - "This example goes over how to use LangChain to interact with `Konko` completion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-completion)\n", + "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n", "\n", - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)." + "From here, we can initialize our model:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "dd70bccb-7a65-42d0-a3f2-8116f3549da7", "metadata": {}, "outputs": [ diff --git a/docs/docs/integrations/providers/konko.mdx b/docs/docs/integrations/providers/konko.mdx index efdc5cb05a480e4..5e474d4ae001dec 100644 --- a/docs/docs/integrations/providers/konko.mdx +++ b/docs/docs/integrations/providers/konko.mdx @@ -1,86 +1,65 @@ # Konko -This page covers how to run models on Konko within LangChain. +All functionality related to Konko -Konko API is a fully managed API designed to help application developers: +>[Konko AI](https://www.konko.ai/) provides a fully managed API to help application developers -Select the right LLM(s) for their application -Prototype with various open-source and proprietary LLMs -Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure +>1. **Select** the right open source or proprietary LLMs for their application +>2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs +>3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost +>4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure ## Installation and Setup -### First you'll need an API key -You can request it by messaging [support@konko.ai](mailto:support@konko.ai) +1. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models via our endpoints for [chat completions](https://docs.konko.ai/reference/post-chat-completions) and [completions](https://docs.konko.ai/reference/post-completions). +2. Enable a Python3.8+ environment +3. Install the SDK -### Install Konko AI's Python SDK - -#### 1. Enable a Python3.8+ environment - -#### 2. Set API Keys - -##### Option 1: Set Environment Variables - -1. You can set environment variables for - 1. KONKO_API_KEY (Required) - 2. OPENAI_API_KEY (Optional) +```bash +pip install konko +``` -2. In your current shell session, use the export command: +4. Set API Keys as environment variables(`KONKO_API_KEY`,`OPENAI_API_KEY`) -```shell +```bash export KONKO_API_KEY={your_KONKO_API_KEY_here} export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional ``` -Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts. - -##### Option 2: Set API Keys Programmatically +Please see [the Konko docs](https://docs.konko.ai/docs/getting-started) for more details. -If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands: -```python -konko.set_api_key('your_KONKO_API_KEY_here') -konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional -``` +## LLM -#### 3. Install the SDK +**Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities. +Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models). -```shell -pip install konko -``` +See a usage [example](/docs/integrations/llms/konko). -#### 4. Verify Installation & Authentication +### Examples of Endpoint Usage -```python -#Confirm konko has installed successfully -import konko -#Confirm API keys from Konko and OpenAI are set properly -konko.Model.list() -``` - -## Calling a model +- **Completion with mistralai/Mistral-7B-v0.1:** -Find a model on the [Konko Introduction page](https://docs.konko.ai/docs/list-of-models) + ```python + from langchain.llms import Konko + llm = Konko(max_tokens=800, model='mistralai/Mistral-7B-v0.1') + prompt = "Generate a Product Description for Apple Iphone 15" + response = llm(prompt) + ``` -Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/listmodels). +## Chat Models -## Examples of Endpoint Usage +See a usage [example](/docs/integrations/chat/konko). - **ChatCompletion with Mistral-7B:** + ```python + from langchain.schema import HumanMessage + from langchain_community.chat_models import ChatKonko chat_instance = ChatKonko(max_tokens=10, model = 'mistralai/mistral-7b-instruct-v0.1') msg = HumanMessage(content="Hi") chat_response = chat_instance([msg]) - - ``` - -- **Completion with mistralai/Mistral-7B-v0.1:** - ```python - from langchain.llms import Konko - llm = Konko(max_tokens=800, model='mistralai/Mistral-7B-v0.1') - prompt = "Generate a Product Description for Apple Iphone 15" - response = llm(prompt) ``` For further assistance, contact [support@konko.ai](mailto:support@konko.ai) or join our [Discord](https://discord.gg/TXV2s3z7RZ). \ No newline at end of file From cf0b29b6d26162c40995a37efc989a030d678552 Mon Sep 17 00:00:00 2001 From: Shorthills AI <141953346+ShorthillsAI@users.noreply.github.com> Date: Tue, 6 Feb 2024 05:19:47 +0530 Subject: [PATCH 081/157] docs: fixing a minor grammatical mistake (#16931) --- docs/docs/modules/model_io/output_parsers/index.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/modules/model_io/output_parsers/index.mdx b/docs/docs/modules/model_io/output_parsers/index.mdx index ea0ada5db355f75..483e30d1701f80c 100644 --- a/docs/docs/modules/model_io/output_parsers/index.mdx +++ b/docs/docs/modules/model_io/output_parsers/index.mdx @@ -4,7 +4,7 @@ hide_table_of_contents: true --- # Output Parsers -Output parsers are responsible for taking the output of an LLM and transforming it to a more suitable format. This is very useful when you are asing LLMs to generate any form of structured data. +Output parsers are responsible for taking the output of an LLM and transforming it to a more suitable format. This is very useful when you are using LLMs to generate any form of structured data. Besides having a large collection of different types of output parsers, one distinguishing benefit of LangChain OutputParsers is that many of them support streaming. From eb7b05885fe5270578707bfbc44be0411aa0b5ed Mon Sep 17 00:00:00 2001 From: Nova Kwok Date: Tue, 6 Feb 2024 07:50:11 +0800 Subject: [PATCH 082/157] docs: Fix typo in quickstart.ipynb (#16859) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - **Description:** "load HTML **form** web URLs" should be "load HTML **from** web URLs"? 🤔 - **Issue:** Typo - **Dependencies:** Nope - **Twitter handle:** n0vad3v --- docs/docs/use_cases/question_answering/quickstart.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/use_cases/question_answering/quickstart.ipynb b/docs/docs/use_cases/question_answering/quickstart.ipynb index 1f464a9615d1083..06138508c59c5da 100644 --- a/docs/docs/use_cases/question_answering/quickstart.ipynb +++ b/docs/docs/use_cases/question_answering/quickstart.ipynb @@ -262,7 +262,7 @@ "\n", "We need to first load the blog post contents. We can use [DocumentLoaders](/docs/modules/data_connection/document_loaders/) for this, which are objects that load in data from a source and return a list of [Documents](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html). A `Document` is an object with some `page_content` (str) and `metadata` (dict).\n", "\n", - "In this case we'll use the [WebBaseLoader](/docs/integrations/document_loaders/web_base), which uses `urllib` to load HTML form web URLs and `BeautifulSoup` to parse it to text. We can customize the HTML -> text parsing by passing in parameters to the `BeautifulSoup` parser via `bs_kwargs` (see [BeautifulSoup docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)). In this case only HTML tags with class \"post-content\", \"post-title\", or \"post-header\" are relevant, so we'll remove all others." + "In this case we'll use the [WebBaseLoader](/docs/integrations/document_loaders/web_base), which uses `urllib` to load HTML from web URLs and `BeautifulSoup` to parse it to text. We can customize the HTML -> text parsing by passing in parameters to the `BeautifulSoup` parser via `bs_kwargs` (see [BeautifulSoup docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)). In this case only HTML tags with class \"post-content\", \"post-title\", or \"post-header\" are relevant, so we'll remove all others." ] }, { From 334b6ebdf34ae3f99a4f7c2ee9a37bc7e58ba66b Mon Sep 17 00:00:00 2001 From: Alex Boury <55697630+Poissecaille@users.noreply.github.com> Date: Tue, 6 Feb 2024 00:51:08 +0100 Subject: [PATCH 083/157] community[minor]: Breebs docs retriever (#16578) - **Description:** Implementation of breeb retriever with integration tests -> libs/community/tests/integration_tests/retrievers/test_breebs.py and documentation (notebook) -> docs/docs/integrations/retrievers/breebs.ipynb. - **Dependencies:** None --- docs/docs/integrations/providers/breebs.md | 24 +++++ .../docs/integrations/retrievers/breebs.ipynb | 95 +++++++++++++++++++ .../retrievers/__init__.py | 2 + .../langchain_community/retrievers/breebs.py | 49 ++++++++++ .../retrievers/test_breebs.py | 21 ++++ .../unit_tests/retrievers/test_imports.py | 1 + 6 files changed, 192 insertions(+) create mode 100644 docs/docs/integrations/providers/breebs.md create mode 100644 docs/docs/integrations/retrievers/breebs.ipynb create mode 100644 libs/community/langchain_community/retrievers/breebs.py create mode 100644 libs/community/tests/integration_tests/retrievers/test_breebs.py diff --git a/docs/docs/integrations/providers/breebs.md b/docs/docs/integrations/providers/breebs.md new file mode 100644 index 000000000000000..6d4262e163d51be --- /dev/null +++ b/docs/docs/integrations/providers/breebs.md @@ -0,0 +1,24 @@ +# BREEBS (Open Knowledge) + +[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform. +Anybody can create a Breeb, a knowledge capsule based on PDFs stored on a Google Drive folder. +A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources. +Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration. + +## List of available Breebs + +To get the full list of Breebs, including their key (breeb_key) and description : +https://breebs.promptbreeders.com/web/listbreebs. +Dozens of Breebs have already been created by the community and are freely available for use. They cover a wide range of expertise, from organic chemistry to mythology, as well as tips on seduction and decentralized finance. + +## Creating a new Breeb + +To generate a new Breeb, simply compile PDF files in a publicly shared Google Drive folder and initiate the creation process on the [BREEBS website](https://www.breebs.com/) by clicking the "Create Breeb" button. You can currently include up to 120 files, with a total character limit of 15 million. + +## Retriever +```python +from langchain.retrievers import BreebsRetriever +``` + +# Example +[See usage example (Retrieval & ConversationalRetrievalChain)](https://python.langchain.com/docs/integrations/retrievers/breebs) \ No newline at end of file diff --git a/docs/docs/integrations/retrievers/breebs.ipynb b/docs/docs/integrations/retrievers/breebs.ipynb new file mode 100644 index 000000000000000..4f6c23af18333cf --- /dev/null +++ b/docs/docs/integrations/retrievers/breebs.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BREEBS (Open Knowledge)\n", + "\n", + "[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform. \n", + "Anybody can create a Breeb, a knowledge capsule, based on PDFs stored on a Google Drive folder.\n", + "A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.\n", + "Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration. \n", + "\n", + "## List of available Breebs\n", + "\n", + "To get the full list of Breebs, including their key (breeb_key) and description : \n", + "https://breebs.promptbreeders.com/web/listbreebs. \n", + "Dozens of Breebs have already been created by the community and are freely available for use. They cover a wide range of expertise, from organic chemistry to mythology, as well as tips on seduction and decentralized finance.\n", + "\n", + "\n", + "## Creating a new Breeb\n", + "\n", + "To generate a new Breeb, simply compile PDF files in a publicly shared Google Drive folder and initiate the creation process on the [BREEBS website](https://www.breebs.com/) by clicking the \"Create Breeb\" button. You can currently include up to 120 files, with a total character limit of 15 million. \n", + "\n", + "## Retriever example\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-15T16:05:12.645840Z", + "iopub.status.busy": "2023-12-15T16:05:12.645698Z", + "iopub.status.idle": "2023-12-15T16:05:14.347548Z", + "shell.execute_reply": "2023-12-15T16:05:14.346835Z" + } + }, + "outputs": [], + "source": [ + "from langchain.retrievers import BreebsRetriever" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-15T16:05:14.350102Z", + "iopub.status.busy": "2023-12-15T16:05:14.349837Z", + "iopub.status.idle": "2023-12-15T16:05:29.569958Z", + "shell.execute_reply": "2023-12-15T16:05:29.567172Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Document(page_content=\"de poupées• Ladurée - Madeleine• Ladurée - rue Bonaparte• Flamant• Bonnichon Saint Germain• Dinh Van• Léonor Greyl• Berthillon• Christian Louboutin• Patrick Cox• Baby Dior• FNAC Musique - Bastille• FNAC - Saint Lazare• La guinguette pirate• Park Hyatt• Restaurant de Sers• Hilton Arc de Triomphe• Café Barge• Le Celadon• Le Drouant• La Perouse• Cigale Recamier• Ledoyen• Tanjia• Les Muses• Bistrot du Dôme• Avenue Foch• Fontaine Saint-Michel• Funiculaire de Montmartre• Promotrain - Place Blanche• Grand Palais• Hotel de Rohan• Hotel de Sully• Hotel des Ventes Drouot• Institut de France• Place des Invalides• Jardin d'acclimatation• Jardin des plantes Zoo• Jouffroy (passage)• Quartier de La Défense• La Villette (quartier)• Lac Inferieur du Bois de Boulogne• Les Catacombes de Paris• Place du Louvre• Rue Mazarine• Rue Monsieur le Prince11/12/2023 07:51Guide en pdf Paris à imprimer gratuitement.\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=11', 'score': 1}), Document(page_content=\"cafés et des restaurants situésdans les rues adjacentes. Il y a également une cafétéria dans le musée, qui propose des collations, desboissons et des repas légers.À voir et visiter autour :Le Muséum d'histoire naturelle de Paris est situé àproximité de plusieurs autres attractions populaires, notamment le Jardin des Plantes, la Grande Mosquéede Paris, la Sorbonne et la Bibliothèque nationale de France.Comment y aller en bus, métro, train :LeMuséum d'histoire naturelle de Paris est facilement accessible en transports en commun. Les stations demétro les plus proches sont la station Censier-Daubenton sur la ligne 7 et la station Jussieu sur les lignes 7et 10. Le musée est également accessible en bus, avec plusieurs lignes desservant la zone, telles que leslignes 24, 57, 61, 63, 67, 89 et 91. En train, la gare la plus proche est la Gare d'Austerlitz, qui est desserviepar plusieurs lignes, notamment les lignes RER C et les trains intercités. Il est également possible de serendre au musée en utilisant les services de taxis ou de VTC.Plus d'informations :+33140795601,6 euros,Ouverture : 10h - 17h, Week end: 10h - 18h ; Fermeture: Mardi(haut de\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=403', 'score': 1}), Document(page_content=\"Le célèbre Drugstore des Champs Elysées abrite de nombreuses boutiques dans un décor design. V ouspourrez y découvrir un espace beauté, des expositions éphémères, une pharmacie et des espaces réservésaux plaisirs des sens. A noter la façade d'architecture extérieure en verrePlus d'informations :+33144437900, https://www.publicisdrugstore.com/, Visite libre,(haut de page)• Place du Marché Sainte-CatherinePlace du Marché Sainte-Catherine, Paris, 75008, FR11/12/2023 07:51Guide en pdf Paris à imprimer gratuitement.\\nPage 200 sur 545https://www.cityzeum.com/imprimer-pdf/parisUne place hors de l'agitation de la capitale, où vous découvrirez des petits restaurants au charme certaindans un cadre fort agréable. Terrasses au rendez-vous l'été! Un bar à magie pour couronner le toutPlus d'informations :15-30 euros,(haut de page)• Rue de Lappe, ParisRue de Lappe, Paris, FR\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=198', 'score': 1}), Document(page_content=\"des visiteurs pour la nature etles attractions du parc. Les visiteurs peuvent prévoir de passer entre 1 à 2 heures pour visiter le parcL'accès au parc Montsouris est gratuit pour tous les visiteurs. Aucune réservation n'est nécessaire pourvisiter le parc. Cependant, pour les visites guidées, il est conseillé de réserver à l'avance pour garantir uneplace. Les tarifs pour les visites guidées peuvent varier en fonction de l'organisme proposant la visite.Ensomme, le parc Montsouris est un endroit magnifique pour se détendre et profiter de la nature en pleincœur de Paris. Avec ses attractions pittoresques, son paysage verdoyant et ses visites guidées, c'est unendroit idéal pour une sortie en famille ou entre amis.Plus d'informations :https://www.parisinfo.com/musee-monument-paris/71218/Parc-Montsouris,Gratuit,Ouverture : 8h/9h - 17h30/21h30(haut de page)• Parc des Buttes Chaumont\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=291', 'score': 1})]\n" + ] + } + ], + "source": [ + "breeb_key = \"Parivoyage\"\n", + "retriever = BreebsRetriever(breeb_key)\n", + "documents = retriever.get_relevant_documents(\n", + " \"What are some unique, lesser-known spots to explore in Paris?\"\n", + ")\n", + "print(documents)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/community/langchain_community/retrievers/__init__.py b/libs/community/langchain_community/retrievers/__init__.py index 5785069cc0e4e5e..a28c3ede92e6bc6 100644 --- a/libs/community/langchain_community/retrievers/__init__.py +++ b/libs/community/langchain_community/retrievers/__init__.py @@ -25,6 +25,7 @@ ) from langchain_community.retrievers.bedrock import AmazonKnowledgeBasesRetriever from langchain_community.retrievers.bm25 import BM25Retriever +from langchain_community.retrievers.breebs import BreebsRetriever from langchain_community.retrievers.chaindesk import ChaindeskRetriever from langchain_community.retrievers.chatgpt_plugin_retriever import ( ChatGPTPluginRetriever, @@ -78,6 +79,7 @@ "ArceeRetriever", "ArxivRetriever", "AzureCognitiveSearchRetriever", + "BreebsRetriever", "ChatGPTPluginRetriever", "ChaindeskRetriever", "CohereRagRetriever", diff --git a/libs/community/langchain_community/retrievers/breebs.py b/libs/community/langchain_community/retrievers/breebs.py new file mode 100644 index 000000000000000..ed42fc8fd43c732 --- /dev/null +++ b/libs/community/langchain_community/retrievers/breebs.py @@ -0,0 +1,49 @@ +from typing import List + +import requests +from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun +from langchain_core.documents.base import Document +from langchain_core.retrievers import BaseRetriever + + +class BreebsRetriever(BaseRetriever): + """A retriever class for `Breebs`. + + See https://www.breebs.com/ for more info. + Args: + breeb_key: The key to trigger the breeb + (specialized knowledge pill on a specific topic). + + To retrieve the list of all available Breebs : you can call https://breebs.promptbreeders.com/web/listbreebs + """ + + breeb_key: str + url = "https://breebs.promptbreeders.com/knowledge" + + def __init__(self, breeb_key: str): + super().__init__(breeb_key=breeb_key) + self.breeb_key = breeb_key + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + """Retrieve context for given query. + Note that for time being there is no score.""" + r = requests.post( + self.url, + json={ + "breeb_key": self.breeb_key, + "query": query, + }, + ) + if r.status_code != 200: + return [] + else: + chunks = r.json() + return [ + Document( + page_content=chunk["content"], + metadata={"source": chunk["source_url"], "score": 1}, + ) + for chunk in chunks + ] diff --git a/libs/community/tests/integration_tests/retrievers/test_breebs.py b/libs/community/tests/integration_tests/retrievers/test_breebs.py new file mode 100644 index 000000000000000..c5c75fcdd33a222 --- /dev/null +++ b/libs/community/tests/integration_tests/retrievers/test_breebs.py @@ -0,0 +1,21 @@ +from typing import List + +from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun +from langchain_core.documents import Document + +from langchain_community.retrievers.breebs import BreebsRetriever + + +class TestBreebsRetriever: + def test_breeb_query(self) -> None: + breeb_key = "Parivoyage" + query = "What are the best churches to visit in Paris?" + breeb_retriever = BreebsRetriever(breeb_key) + documents: List[Document] = breeb_retriever._get_relevant_documents( + query, run_manager=CallbackManagerForRetrieverRun + ) + assert isinstance(documents, list), "Documents should be a list" + for doc in documents: + assert doc.page_content, "Document page_content should not be None" + assert doc.metadata["source"], "Document metadata should contain 'source'" + assert doc.metadata["score"] == 1, "Document score should be equal to 1" diff --git a/libs/community/tests/unit_tests/retrievers/test_imports.py b/libs/community/tests/unit_tests/retrievers/test_imports.py index 2f7875979826c8b..d9acc880387d5b4 100644 --- a/libs/community/tests/unit_tests/retrievers/test_imports.py +++ b/libs/community/tests/unit_tests/retrievers/test_imports.py @@ -6,6 +6,7 @@ "ArceeRetriever", "ArxivRetriever", "AzureCognitiveSearchRetriever", + "BreebsRetriever", "ChatGPTPluginRetriever", "ChaindeskRetriever", "CohereRagRetriever", From 83fbf0e11abe1992acd979085665ff7696a9082d Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Mon, 5 Feb 2024 15:53:01 -0800 Subject: [PATCH 084/157] docs: add structured tools howto to agents (#15772) Co-authored-by: Bagatur --- .../agents/how_to/structured_tools.ipynb | 142 ++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 docs/docs/modules/agents/how_to/structured_tools.ipynb diff --git a/docs/docs/modules/agents/how_to/structured_tools.ipynb b/docs/docs/modules/agents/how_to/structured_tools.ipynb new file mode 100644 index 000000000000000..ee9293e826a0b61 --- /dev/null +++ b/docs/docs/modules/agents/how_to/structured_tools.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "473081cc", + "metadata": {}, + "source": [ + "---\n", + "sidebar_position: 1\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "16ee4216", + "metadata": {}, + "source": [ + "# Structured Tools" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "670078c4", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List\n", + "\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_data(n: int) -> List[dict]:\n", + " \"\"\"Get n datapoints.\"\"\"\n", + " return [{\"name\": \"foo\", \"value\": \"bar\"}] * n\n", + "\n", + "\n", + "tools = [get_data]" + ] + }, + { + "cell_type": "markdown", + "id": "5e04164b", + "metadata": {}, + "source": [ + "We will use a prompt from the hub - you can inspect the prompt more at [https://smith.langchain.com/hub/hwchase17/openai-functions-agent](https://smith.langchain.com/hub/hwchase17/openai-functions-agent)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8c5d907", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "from langchain.agents import AgentExecutor, create_openai_functions_agent\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Get the prompt to use - you can modify this!\n", + "# If you want to see the prompt in full, you can at: https://smith.langchain.com/hub/hwchase17/openai-functions-agent\n", + "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n", + "\n", + "agent = create_openai_functions_agent(llm, tools, prompt)\n", + "agent_executor = AgentExecutor(agent=agent, tools=tools)" + ] + }, + { + "cell_type": "markdown", + "id": "cba9a9eb", + "metadata": {}, + "source": [ + "## Stream intermediate steps\n", + "\n", + "Let's look at how to stream intermediate steps. We can do this easily by just using the `.stream` method on the AgentExecutor\n", + "\n", + "We can then parse the results to get actions (tool inputs) and observtions (tool outputs)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b6bd9bf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calling Tool ```get_data``` with input ```{'n': 3}```\n", + "Got result: ```[{'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}]```\n" + ] + } + ], + "source": [ + "for chunk in agent_executor.stream({\"input\": \"get me three datapoints\"}):\n", + " # Agent Action\n", + " if \"actions\" in chunk:\n", + " for action in chunk[\"actions\"]:\n", + " print(\n", + " f\"Calling Tool ```{action.tool}``` with input ```{action.tool_input}```\"\n", + " )\n", + " # Observation\n", + " elif \"steps\" in chunk:\n", + " for step in chunk[\"steps\"]:\n", + " print(f\"Got result: ```{step.observation}```\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af9e32fe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cf01fc3790d2433219759a0f6488d5e41ff6c984 Mon Sep 17 00:00:00 2001 From: T Cramer Date: Mon, 5 Feb 2024 23:54:34 +0000 Subject: [PATCH 085/157] docs: update parse_partial_json source info (#17036) - **Description:** Update source-link following recent license update at open-interpreter project - **Issue:** N/A - **Dependencies:** None --- libs/core/langchain_core/output_parsers/json.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/langchain_core/output_parsers/json.py b/libs/core/langchain_core/output_parsers/json.py index e644d6c870e15d4..6500f778d644445 100644 --- a/libs/core/langchain_core/output_parsers/json.py +++ b/libs/core/langchain_core/output_parsers/json.py @@ -44,7 +44,7 @@ def _custom_parser(multiline_string: str) -> str: return multiline_string -# Adapted from https://github.com/KillianLucas/open-interpreter/blob/main/interpreter/utils/parse_partial_json.py +# Adapted from https://github.com/KillianLucas/open-interpreter/blob/5b6080fae1f8c68938a1e4fa8667e3744084ee21/interpreter/utils/parse_partial_json.py # MIT License def parse_partial_json(s: str, *, strict: bool = False) -> Any: """Parse a JSON string that may be missing closing braces. From 6e2ed9671f5eb6a27c09aaf164ed0720f2c88f39 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 16:09:48 -0800 Subject: [PATCH 086/157] infra: fix breebs test lint (#17075) --- .../tests/integration_tests/retrievers/test_breebs.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/libs/community/tests/integration_tests/retrievers/test_breebs.py b/libs/community/tests/integration_tests/retrievers/test_breebs.py index c5c75fcdd33a222..1dd6b470a57aee8 100644 --- a/libs/community/tests/integration_tests/retrievers/test_breebs.py +++ b/libs/community/tests/integration_tests/retrievers/test_breebs.py @@ -1,6 +1,5 @@ from typing import List -from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_community.retrievers.breebs import BreebsRetriever @@ -11,9 +10,7 @@ def test_breeb_query(self) -> None: breeb_key = "Parivoyage" query = "What are the best churches to visit in Paris?" breeb_retriever = BreebsRetriever(breeb_key) - documents: List[Document] = breeb_retriever._get_relevant_documents( - query, run_manager=CallbackManagerForRetrieverRun - ) + documents: List[Document] = breeb_retriever.get_relevant_documents(query) assert isinstance(documents, list), "Documents should be a list" for doc in documents: assert doc.page_content, "Document page_content should not be None" From d8f41d05219d23b899bf5c28883441dd21c9f2e3 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 5 Feb 2024 16:12:56 -0800 Subject: [PATCH 087/157] docs: add youtube link (#17065) --- docs/docusaurus.config.js | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 231087f916f14d4..29ca5618c40fa81 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -205,7 +205,7 @@ const config = { }, { to: "/docs/additional_resources/youtube", - label: "YouTube videos" + label: "YouTube" }, ] }, @@ -298,6 +298,10 @@ const config = { label: "Blog", href: "https://blog.langchain.dev", }, + { + label: "YouTube", + href: "https://www.youtube.com/@LangChain", + }, ], }, ], From 3d5e988c55a2b8de9cd7d356da62684df50d7120 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Mon, 5 Feb 2024 16:17:31 -0800 Subject: [PATCH 088/157] Add prompt metadata + tags (#17054) --- libs/core/langchain_core/prompts/base.py | 14 +++++++++++++ .../tests/unit_tests/prompts/test_prompt.py | 21 +++++++++++++++++++ 2 files changed, 35 insertions(+) diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py index 9a65e5e32db5a44..51eddf0f7718cab 100644 --- a/libs/core/langchain_core/prompts/base.py +++ b/libs/core/langchain_core/prompts/base.py @@ -27,6 +27,7 @@ ) from langchain_core.pydantic_v1 import BaseModel, Field, create_model, root_validator from langchain_core.runnables import RunnableConfig, RunnableSerializable +from langchain_core.runnables.config import ensure_config if TYPE_CHECKING: from langchain_core.documents import Document @@ -48,6 +49,14 @@ class BasePromptTemplate( output_parser: Optional[BaseOutputParser] = None """How to parse the output of calling an LLM on this formatted prompt.""" partial_variables: Mapping[str, Any] = Field(default_factory=dict) + """A dictionary of the partial variables the prompt template carries. + + Partial variables populate the template so that you don't need to + pass them in every time you call the prompt.""" + metadata: Optional[Dict[str, Any]] = None + """Metadata to be used for tracing.""" + tags: Optional[List[str]] = None + """Tags to be used for tracing.""" @classmethod def get_lc_namespace(cls) -> List[str]: @@ -95,6 +104,11 @@ def _format_prompt_with_error_handling(self, inner_input: Dict) -> PromptValue: def invoke( self, input: Dict, config: Optional[RunnableConfig] = None ) -> PromptValue: + config = ensure_config(config) + if self.metadata: + config["metadata"].update(self.metadata) + if self.tags: + config["tags"].extend(self.tags) return self._call_with_config( self._format_prompt_with_error_handling, input, diff --git a/libs/core/tests/unit_tests/prompts/test_prompt.py b/libs/core/tests/unit_tests/prompts/test_prompt.py index f50b71bea8047a5..5075c5c0bb5dbd4 100644 --- a/libs/core/tests/unit_tests/prompts/test_prompt.py +++ b/libs/core/tests/unit_tests/prompts/test_prompt.py @@ -1,9 +1,11 @@ """Test functionality related to prompts.""" + from unittest import mock import pytest from langchain_core.prompts.prompt import PromptTemplate +from langchain_core.tracers.run_collector import RunCollectorCallbackHandler def test_prompt_valid() -> None: @@ -328,3 +330,22 @@ def test_prompt_jinja2_wrong_input_variables() -> None: assert PromptTemplate( input_variables=input_variables, template=template, template_format="jinja2" ).input_variables == ["foo"] + + +def test_prompt_invoke_with_metadata() -> None: + """Test prompt can be invoked with metadata.""" + template = "This is a {foo} test." + prompt = PromptTemplate( + input_variables=["foo"], + template=template, + metadata={"version": "1"}, + tags=["tag1", "tag2"], + ) + tracer = RunCollectorCallbackHandler() + result = prompt.invoke( + {"foo": "bar"}, {"metadata": {"foo": "bar"}, "callbacks": [tracer]} + ) + assert result.to_string() == "This is a bar test." + assert len(tracer.traced_runs) == 1 + assert tracer.traced_runs[0].extra["metadata"] == {"version": "1", "foo": "bar"} # type: ignore + assert tracer.traced_runs[0].tags == ["tag1", "tag2"] # type: ignore From 912210ac194b521825ef1c581ece17be3f67fc8e Mon Sep 17 00:00:00 2001 From: Jimmy Moore <52177452+moorej-oci@users.noreply.github.com> Date: Mon, 5 Feb 2024 18:18:40 -0600 Subject: [PATCH 089/157] core[patch]: fix _sql_record_manager mypy for #17048 (#17073) - **Description:** Add relevant type annotations for relevant session and query objects to resolve mypy errors when `# type: ignore` comments are removed. - **Issue:** #17048 - **Dependencies:** None, - **Twitter handle:** [clesiemo3](https://twitter.com/clesiemo3) I attempted to solve the `UpsertionRecord` ignore but it would require added a deprecated plugin or moving completely to sqlalchemy 2.0+ from my understanding. I'm assuming this is not something desired at this point in time. --- .../langchain/indexes/_sql_record_manager.py | 112 +++++++++--------- 1 file changed, 54 insertions(+), 58 deletions(-) diff --git a/libs/langchain/langchain/indexes/_sql_record_manager.py b/libs/langchain/langchain/indexes/_sql_record_manager.py index 14e2355af223a65..76dfd9672340e6f 100644 --- a/libs/langchain/langchain/indexes/_sql_record_manager.py +++ b/libs/langchain/langchain/indexes/_sql_record_manager.py @@ -39,7 +39,7 @@ create_async_engine, ) from sqlalchemy.ext.declarative import declarative_base -from sqlalchemy.orm import Session, sessionmaker +from sqlalchemy.orm import Query, Session, sessionmaker from langchain.indexes.base import RecordManager @@ -284,31 +284,35 @@ def update( with self._make_session() as session: if self.dialect == "sqlite": + from sqlalchemy.dialects.sqlite import Insert as SqliteInsertType from sqlalchemy.dialects.sqlite import insert as sqlite_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + sqlite_insert_stmt: SqliteInsertType = sqlite_insert( + UpsertionRecord + ).values(records_to_upsert) + stmt = sqlite_insert_stmt.on_conflict_do_update( [UpsertionRecord.key, UpsertionRecord.namespace], set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=sqlite_insert_stmt.excluded.updated_at, + group_id=sqlite_insert_stmt.excluded.group_id, ), ) elif self.dialect == "postgresql": + from sqlalchemy.dialects.postgresql import Insert as PgInsertType from sqlalchemy.dialects.postgresql import insert as pg_insert - # Note: uses SQLite insert to make on_conflict_do_update work. + # Note: uses postgresql insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + pg_insert_stmt: PgInsertType = pg_insert(UpsertionRecord).values( + records_to_upsert + ) + stmt = pg_insert_stmt.on_conflict_do_update( "uix_key_namespace", # Name of constraint set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=pg_insert_stmt.excluded.updated_at, + group_id=pg_insert_stmt.excluded.group_id, ), ) else: @@ -359,31 +363,35 @@ async def aupdate( async with self._amake_session() as session: if self.dialect == "sqlite": + from sqlalchemy.dialects.sqlite import Insert as SqliteInsertType from sqlalchemy.dialects.sqlite import insert as sqlite_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + sqlite_insert_stmt: SqliteInsertType = sqlite_insert( + UpsertionRecord + ).values(records_to_upsert) + stmt = sqlite_insert_stmt.on_conflict_do_update( [UpsertionRecord.key, UpsertionRecord.namespace], set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=sqlite_insert_stmt.excluded.updated_at, + group_id=sqlite_insert_stmt.excluded.group_id, ), ) elif self.dialect == "postgresql": + from sqlalchemy.dialects.postgresql import Insert as PgInsertType from sqlalchemy.dialects.postgresql import insert as pg_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + pg_insert_stmt: PgInsertType = pg_insert(UpsertionRecord).values( + records_to_upsert + ) + stmt = pg_insert_stmt.on_conflict_do_update( "uix_key_namespace", # Name of constraint set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=pg_insert_stmt.excluded.updated_at, + group_id=pg_insert_stmt.excluded.group_id, ), ) else: @@ -394,18 +402,15 @@ async def aupdate( def exists(self, keys: Sequence[str]) -> List[bool]: """Check if the given keys exist in the SQLite database.""" + session: Session with self._make_session() as session: - records = ( - # mypy does not recognize .all() - session.query(UpsertionRecord.key) # type: ignore[attr-defined] - .filter( - and_( - UpsertionRecord.key.in_(keys), - UpsertionRecord.namespace == self.namespace, - ) + filtered_query: Query = session.query(UpsertionRecord.key).filter( + and_( + UpsertionRecord.key.in_(keys), + UpsertionRecord.namespace == self.namespace, ) - .all() ) + records = filtered_query.all() found_keys = set(r.key for r in records) return [k in found_keys for k in keys] @@ -438,28 +443,22 @@ def list_keys( limit: Optional[int] = None, ) -> List[str]: """List records in the SQLite database based on the provided date range.""" + session: Session with self._make_session() as session: - query = session.query(UpsertionRecord).filter( + query: Query = session.query(UpsertionRecord).filter( UpsertionRecord.namespace == self.namespace ) - # mypy does not recognize .all() or .filter() if after: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at > after - ) + query = query.filter(UpsertionRecord.updated_at > after) if before: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at < before - ) + query = query.filter(UpsertionRecord.updated_at < before) if group_ids: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.group_id.in_(group_ids) - ) + query = query.filter(UpsertionRecord.group_id.in_(group_ids)) if limit: - query = query.limit(limit) # type: ignore[attr-defined] - records = query.all() # type: ignore[attr-defined] + query = query.limit(limit) + records = query.all() return [r.key for r in records] async def alist_keys( @@ -471,40 +470,37 @@ async def alist_keys( limit: Optional[int] = None, ) -> List[str]: """List records in the SQLite database based on the provided date range.""" + session: AsyncSession async with self._amake_session() as session: - query = select(UpsertionRecord.key).filter( + query: Query = select(UpsertionRecord.key).filter( UpsertionRecord.namespace == self.namespace ) # mypy does not recognize .all() or .filter() if after: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at > after - ) + query = query.filter(UpsertionRecord.updated_at > after) if before: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at < before - ) + query = query.filter(UpsertionRecord.updated_at < before) if group_ids: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.group_id.in_(group_ids) - ) + query = query.filter(UpsertionRecord.group_id.in_(group_ids)) if limit: - query = query.limit(limit) # type: ignore[attr-defined] + query = query.limit(limit) records = (await session.execute(query)).scalars().all() return list(records) def delete_keys(self, keys: Sequence[str]) -> None: """Delete records from the SQLite database.""" + session: Session with self._make_session() as session: - # mypy does not recognize .delete() - session.query(UpsertionRecord).filter( + filtered_query: Query = session.query(UpsertionRecord).filter( and_( UpsertionRecord.key.in_(keys), UpsertionRecord.namespace == self.namespace, ) - ).delete() # type: ignore[attr-defined] + ) + + filtered_query.delete() session.commit() async def adelete_keys(self, keys: Sequence[str]) -> None: From 9f0b63dba0ab194d14ad9e81245e61f1ec1032c2 Mon Sep 17 00:00:00 2001 From: Giulio Zani Date: Tue, 6 Feb 2024 01:18:57 +0100 Subject: [PATCH 090/157] experimental[patch]: Fixes issue #17060 (#17062) As described in issue #17060, in the case in which text has only one sentence the following function fails. Checking for that and adding a return case fixed the issue. ```python def split_text(self, text: str) -> List[str]: """Split text into multiple components.""" # Splitting the essay on '.', '?', and '!' single_sentences_list = re.split(r"(?<=[.?!])\s+", text) sentences = [ {"sentence": x, "index": i} for i, x in enumerate(single_sentences_list) ] sentences = combine_sentences(sentences) embeddings = self.embeddings.embed_documents( [x["combined_sentence"] for x in sentences] ) for i, sentence in enumerate(sentences): sentence["combined_sentence_embedding"] = embeddings[i] distances, sentences = calculate_cosine_distances(sentences) start_index = 0 # Create a list to hold the grouped sentences chunks = [] breakpoint_percentile_threshold = 95 breakpoint_distance_threshold = np.percentile( distances, breakpoint_percentile_threshold ) # If you want more chunks, lower the percentile cutoff indices_above_thresh = [ i for i, x in enumerate(distances) if x > breakpoint_distance_threshold ] # The indices of those breakpoints on your list # Iterate through the breakpoints to slice the sentences for index in indices_above_thresh: # The end index is the current breakpoint end_index = index # Slice the sentence_dicts from the current start index to the end index group = sentences[start_index : end_index + 1] combined_text = " ".join([d["sentence"] for d in group]) chunks.append(combined_text) # Update the start index for the next group start_index = index + 1 # The last group, if any sentences remain if start_index < len(sentences): combined_text = " ".join([d["sentence"] for d in sentences[start_index:]]) chunks.append(combined_text) return chunks ``` Co-authored-by: Giulio Zani --- libs/experimental/langchain_experimental/text_splitter.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/libs/experimental/langchain_experimental/text_splitter.py b/libs/experimental/langchain_experimental/text_splitter.py index 27818bb6e4fa8ac..c032c338562ae37 100644 --- a/libs/experimental/langchain_experimental/text_splitter.py +++ b/libs/experimental/langchain_experimental/text_splitter.py @@ -85,6 +85,12 @@ def split_text(self, text: str) -> List[str]: """Split text into multiple components.""" # Splitting the essay on '.', '?', and '!' single_sentences_list = re.split(r"(?<=[.?!])\s+", text) + + # having len(single_sentences_list) == 1 would cause the following + # np.percentile to fail. + if len(single_sentences_list) == 1: + return single_sentences_list + sentences = [ {"sentence": x, "index": i} for i, x in enumerate(single_sentences_list) ] From 10bd9011390e9f2bf43f6103ea81afd6ccf80bfa Mon Sep 17 00:00:00 2001 From: Scott Nath Date: Mon, 5 Feb 2024 19:39:55 -0500 Subject: [PATCH 091/157] infra: add integration_tests and coverage to MAKEFILE (#17053) - **Description: update community MAKE file** - adds `integration_tests` - adds `coverage` - **Issue:** the issue # it fixes if applicable, - moving out of https://github.com/langchain-ai/langchain/pull/17014 - **Dependencies:** n/a - **Twitter handle:** @scottnath - **Mastodon handle:** scottnath@mastodon.social --------- Co-authored-by: Bagatur --- libs/community/Makefile | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/libs/community/Makefile b/libs/community/Makefile index d7bda9945aa4ac5..acec04864a6140a 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -5,11 +5,17 @@ all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ +integration_tests: TEST_FILE = tests/integration_tests/ -test: - poetry run pytest $(TEST_FILE) +# Run unit tests and generate a coverage report. +coverage: + poetry run pytest --cov \ + --cov-config=.coveragerc \ + --cov-report xml \ + --cov-report term-missing:skip-covered \ + $(TEST_FILE) -tests: +test tests integration_tests: poetry run pytest $(TEST_FILE) test_watch: From 64785822dc777b68848f77c183ec2f865fb55291 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 5 Feb 2024 17:12:12 -0800 Subject: [PATCH 092/157] templates: bump (#17074) --- .../anthropic-iterative-search/poetry.lock | 996 ++++---- .../anthropic-iterative-search/pyproject.toml | 2 +- templates/basic-critique-revise/poetry.lock | 730 +++--- .../basic-critique-revise/pyproject.toml | 2 +- templates/bedrock-jcvd/poetry.lock | 625 ++--- templates/bedrock-jcvd/pyproject.toml | 2 +- .../cassandra-entomology-rag/poetry.lock | 754 +++--- .../cassandra-entomology-rag/pyproject.toml | 2 +- .../cassandra-synonym-caching/poetry.lock | 754 +++--- .../cassandra-synonym-caching/pyproject.toml | 2 +- templates/chain-of-note-wiki/poetry.lock | 963 ++++---- templates/chain-of-note-wiki/pyproject.toml | 2 +- templates/chat-bot-feedback/poetry.lock | 736 +++--- templates/chat-bot-feedback/pyproject.toml | 2 +- templates/cohere-librarian/poetry.lock | 1381 +++++------ templates/cohere-librarian/pyproject.toml | 2 +- templates/csv-agent/poetry.lock | 739 +++--- templates/csv-agent/pyproject.toml | 2 +- templates/elastic-query-generator/poetry.lock | 762 +++--- .../elastic-query-generator/pyproject.toml | 2 +- .../poetry.lock | 982 ++++---- .../pyproject.toml | 2 +- .../extraction-openai-functions/poetry.lock | 748 +++--- .../pyproject.toml | 2 +- .../guardrails-output-parser/poetry.lock | 666 +++--- .../guardrails-output-parser/pyproject.toml | 2 +- templates/hybrid-search-weaviate/poetry.lock | 1020 ++++---- .../hybrid-search-weaviate/pyproject.toml | 2 +- templates/hyde/poetry.lock | 1317 ++++++----- templates/hyde/pyproject.toml | 2 +- templates/llama2-functions/poetry.lock | 748 +++--- templates/llama2-functions/pyproject.toml | 2 +- .../poetry.lock | 750 +++--- .../pyproject.toml | 2 +- templates/neo4j-advanced-rag/poetry.lock | 975 +++++--- templates/neo4j-advanced-rag/pyproject.toml | 3 + templates/neo4j-cypher-ft/poetry.lock | 758 +++--- templates/neo4j-cypher-ft/pyproject.toml | 2 +- templates/neo4j-cypher-memory/poetry.lock | 758 +++--- templates/neo4j-cypher-memory/pyproject.toml | 2 +- templates/neo4j-cypher/poetry.lock | 758 +++--- templates/neo4j-cypher/pyproject.toml | 2 +- templates/neo4j-generation/poetry.lock | 758 +++--- templates/neo4j-generation/pyproject.toml | 2 +- templates/neo4j-parent/poetry.lock | 758 +++--- templates/neo4j-parent/pyproject.toml | 2 +- templates/neo4j-semantic-layer/poetry.lock | 758 +++--- templates/neo4j-semantic-layer/pyproject.toml | 2 +- templates/neo4j-vector-memory/poetry.lock | 758 +++--- templates/neo4j-vector-memory/pyproject.toml | 2 +- templates/nvidia-rag-canonical/poetry.lock | 936 ++++---- templates/nvidia-rag-canonical/pyproject.toml | 2 +- .../openai-functions-agent-gmail/poetry.lock | 765 +++--- .../pyproject.toml | 2 +- templates/openai-functions-agent/poetry.lock | 748 +++--- .../openai-functions-agent/pyproject.toml | 2 +- .../poetry.lock | 730 +++--- .../pyproject.toml | 2 +- templates/pii-protected-chatbot/poetry.lock | 877 +++---- .../pii-protected-chatbot/pyproject.toml | 2 +- .../pirate-speak-configurable/poetry.lock | 1024 ++++---- .../pirate-speak-configurable/pyproject.toml | 2 +- templates/pirate-speak/poetry.lock | 704 +++--- templates/pirate-speak/pyproject.toml | 2 +- templates/plate-chain/poetry.lock | 730 +++--- templates/plate-chain/pyproject.toml | 2 +- templates/poetry.lock | 1151 ++++++--- templates/propositional-retrieval/poetry.lock | 1344 +++++------ .../propositional-retrieval/pyproject.toml | 2 +- templates/pyproject.toml | 1 + 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" Sure|,| here|'s| a| funny| joke| about| a| par|rot|:|\n", + " Here|'s| a| silly| joke| about| a| par|rot|:|\n", "\n", - "Why| doesn|'t| a| par|rot| ever| get| hungry| at| night|?| Because| it| has| a| light| snack| before| bed|!||" + "What| kind| of| teacher| gives| good| advice|?| An| ap|-|parent| (|app|arent|)| one|!||" ] } ], @@ -228,40 +230,34 @@ "{'countries': [{}]}\n", "{'countries': [{'name': ''}]}\n", "{'countries': [{'name': 'France'}]}\n", - "{'countries': [{'name': 'France', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,301'}]}\n" + "{'countries': [{'name': 'France', 'population': 67}]}\n", + "{'countries': [{'name': 'France', 'population': 6739}]}\n", + "{'countries': [{'name': 'France', 'population': 673915}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': ''}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Sp'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 4675}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 467547}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': ''}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12647}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 1264764}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 126476461}]}\n" ] } ], "source": [ "from langchain_core.output_parsers import JsonOutputParser\n", - "from langchain_openai.chat_models import ChatOpenAI\n", "\n", - "model = ChatOpenAI()\n", - "\n", - "chain = model | JsonOutputParser() # This parser only works with OpenAI right now\n", + "chain = (\n", + " model | JsonOutputParser()\n", + ") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n", "async for text in chain.astream(\n", " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", @@ -294,12 +290,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[None, None, None]|" + "['France', 'Spain', 'Japan']|" ] } ], "source": [ - "from langchain_core.output_parsers import JsonOutputParser\n", + "from langchain_core.output_parsers import (\n", + " JsonOutputParser,\n", + ")\n", "\n", "\n", "# A function that operates on finalized inputs\n", @@ -326,7 +324,7 @@ "chain = model | JsonOutputParser() | _extract_country_names\n", "\n", "async for text in chain.astream(\n", - " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n", + " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", " print(text, end=\"|\", flush=True)" ] @@ -348,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "15984b2b-315a-4119-945b-2a3dabea3082", "metadata": {}, "outputs": [ @@ -356,7 +354,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "France|Spain|Japan|" + "France|Sp|Spain|Japan|" ] } ], @@ -392,11 +390,23 @@ "chain = model | JsonOutputParser() | _extract_country_names_streaming\n", "\n", "async for text in chain.astream(\n", - " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n", + " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", " print(text, end=\"|\", flush=True)" ] }, + { + "cell_type": "markdown", + "id": "d59823f5-9b9a-43c5-a213-34644e2f1d3d", + "metadata": {}, + "source": [ + ":::{.callout-note}\n", + "Because the code above is relying on JSON auto-completion, you may see partial names of countries (e.g., `Sp` and `Spain`), which is not what one would want for an extraction result!\n", + "\n", + "We're focusing on streaming concepts, not necessarily the results of the chains.\n", + ":::" + ] + }, { "cell_type": "markdown", "id": "6adf65b7-aa47-4321-98c7-a0abe43b833a", @@ -409,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "b9b1c00d-8b44-40d0-9e2b-8a70d238f82b", "metadata": {}, "outputs": [ @@ -420,7 +430,7 @@ " Document(page_content='harrison likes spicy food')]]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "957447e6-1e60-41ef-8c10-2654bd9e738d", "metadata": {}, "outputs": [], @@ -483,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "94e50b5d-bf51-4eee-9da0-ee40dd9ce42b", "metadata": {}, "outputs": [ @@ -491,9 +501,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "|H|arrison| worked| at| Kens|ho|,| a| renowned| technology| company| known| for| revolution|izing| the| artificial| intelligence| industry|.\n", - "|K|ens|ho|,| located| in| the| heart| of| Silicon| Valley|,| is| famous| for| its| cutting|-edge| research| and| development| in| machine| learning|.\n", - "|With| its| state|-of|-the|-art| facilities| and| talented| team|,| Kens|ho| has| become| a| hub| for| innovation| and| a| sought|-after| workplace| for| tech| enthusiasts| like| Harrison|.||" + " Based| on| the| given| context|,| the| only| information| provided| about| where| Harrison| worked| is| that| he| worked| at| Ken|sh|o|.| Since| there| are| no| other| details| provided| about| Ken|sh|o|,| I| do| not| have| enough| information| to| write| 3| additional| made| up| sentences| about| this| place|.| I| can| only| state| that| Harrison| worked| at| Ken|sh|o|.||" ] } ], @@ -528,17 +536,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "61348df9-ec58-401e-be89-68a70042f88e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.1.14'" + "'0.1.18'" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -604,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f", "metadata": {}, "outputs": [ @@ -612,7 +620,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/eugene/.pyenv/versions/3.11.4/envs/langchain_3_11_4/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n", + "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n", " warn_beta(\n" ] } @@ -650,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b", "metadata": {}, "outputs": [ @@ -658,26 +666,26 @@ "data": { "text/plain": [ "[{'event': 'on_chat_model_start',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", - " 'name': 'ChatOpenAI',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", + " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {},\n", " 'data': {'input': 'hello'}},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", - " 'data': {'chunk': AIMessageChunk(content='')}},\n", + " 'name': 'ChatAnthropic',\n", + " 'data': {'chunk': AIMessageChunk(content=' Hello')}},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", - " 'data': {'chunk': AIMessageChunk(content='Hello')}}]" + " 'name': 'ChatAnthropic',\n", + " 'data': {'chunk': AIMessageChunk(content='!')}}]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -688,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6", "metadata": {}, "outputs": [ @@ -696,20 +704,20 @@ "data": { "text/plain": [ "[{'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", + " 'name': 'ChatAnthropic',\n", " 'data': {'chunk': AIMessageChunk(content='')}},\n", " {'event': 'on_chat_model_end',\n", - " 'name': 'ChatOpenAI',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'name': 'ChatAnthropic',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?')}}]" + " 'data': {'output': AIMessageChunk(content=' Hello!')}}]" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -730,12 +738,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "id": "4328c56c-a303-427b-b1f2-f354e9af555c", "metadata": {}, "outputs": [], "source": [ - "chain = model | JsonOutputParser() # This parser only works with OpenAI right now\n", + "chain = (\n", + " model | JsonOutputParser()\n", + ") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n", "\n", "events = [\n", " event\n", @@ -762,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "8e66ea3d-a450-436a-aaac-d9478abc6c28", "metadata": {}, "outputs": [ @@ -770,26 +780,26 @@ "data": { "text/plain": [ "[{'event': 'on_chain_start',\n", - " 'run_id': 'aa992fb9-d79f-46f3-a857-ae4acad841c4',\n", + " 'run_id': 'b1074bff-2a17-458b-9e7b-625211710df4',\n", " 'name': 'RunnableSequence',\n", " 'tags': [],\n", " 'metadata': {},\n", " 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}},\n", " {'event': 'on_chat_model_start',\n", - " 'name': 'ChatOpenAI',\n", - " 'run_id': 'c5406de5-0880-4829-ae26-bb565b404e27',\n", + " 'name': 'ChatAnthropic',\n", + " 'run_id': '6072be59-1f43-4f1c-9470-3b92e8406a99',\n", " 'tags': ['seq:step:1'],\n", " 'metadata': {},\n", " 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}},\n", " {'event': 'on_parser_start',\n", " 'name': 'JsonOutputParser',\n", - " 'run_id': '32b47794-8fb6-4ef4-8800-23ed6c3f4519',\n", + " 'run_id': 'bf978194-0eda-4494-ad15-3a5bfe69cd59',\n", " 'tags': ['seq:step:2'],\n", " 'metadata': {},\n", " 'data': {}}]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "630c71d6-8d94-4ce0-a78a-f20e90f628df", "metadata": {}, "outputs": [ @@ -824,29 +834,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chat model chunk: ''\n", + "Chat model chunk: ' Here'\n", + "Chat model chunk: ' is'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' JSON'\n", + "Chat model chunk: ' with'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' requested'\n", + "Chat model chunk: ' countries'\n", + "Chat model chunk: ' and'\n", + "Chat model chunk: ' their'\n", + "Chat model chunk: ' populations'\n", + "Chat model chunk: ':'\n", + "Chat model chunk: '\\n\\n```'\n", + "Chat model chunk: 'json'\n", "Parser chunk: {}\n", - "Chat model chunk: '{\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: '\\n{'\n", + "Chat model chunk: '\\n '\n", "Chat model chunk: ' \"'\n", "Chat model chunk: 'countries'\n", "Chat model chunk: '\":'\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: ' [\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: ' ['\n", + "Chat model chunk: '\\n '\n", "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: ' {\\n'\n", - "Chat model chunk: ' '\n", - "Chat model chunk: ' \"'\n", - "Chat model chunk: 'name'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': ''}]}\n", - "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: 'France'\n", - "Chat model chunk: '\",\\n'\n", - "Chat model chunk: ' '\n", - "Chat model chunk: ' \"'\n", + "Chat model chunk: ' {'\n", "...\n" ] } @@ -897,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9", "metadata": {}, "outputs": [ @@ -905,17 +917,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670600}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {'name': ''}]}}}\n", + "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 6739}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 673915}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}, {}]}}}\n", "...\n" ] } @@ -949,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f", "metadata": {}, "outputs": [ @@ -957,17 +969,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' \"')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' [\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' {\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", + "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' and')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' their')}}\n", "...\n" ] } @@ -1008,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "26bac0d2-76d9-446e-b346-82790236b88d", "metadata": {}, "outputs": [ @@ -1016,17 +1028,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n", - "{'event': 'on_chat_model_start', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", - "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n", - "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}}\n", - "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {}}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\"')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n", - "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", - "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {'countries': []}}}\n", + "{'event': 'on_chain_start', 'run_id': '190875f3-3fb7-49ad-9b6e-f49da22f3e49', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n", + "{'event': 'on_chat_model_start', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", + "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '3b5e4ca1-40fe-4a02-9a19-ba2a43a6115c', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n", "...\n" ] } @@ -1062,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "0e6451d3-3b11-4a71-ae19-998f4c10180f", "metadata": {}, "outputs": [], @@ -1104,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "f9a8fe35-faab-4970-b8c0-5c780845d98a", "metadata": {}, "outputs": [ @@ -1112,7 +1124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "['France', 'Spain', 'Japan']\n" ] } ], @@ -1133,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c", "metadata": {}, "outputs": [ @@ -1141,33 +1153,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chat model chunk: ''\n", + "Chat model chunk: ' Here'\n", + "Chat model chunk: ' is'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' JSON'\n", + "Chat model chunk: ' with'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' requested'\n", + "Chat model chunk: ' countries'\n", + "Chat model chunk: ' and'\n", + "Chat model chunk: ' their'\n", + "Chat model chunk: ' populations'\n", + "Chat model chunk: ':'\n", + "Chat model chunk: '\\n\\n```'\n", + "Chat model chunk: 'json'\n", "Parser chunk: {}\n", - "Chat model chunk: '{\"'\n", + "Chat model chunk: '\\n{'\n", + "Chat model chunk: '\\n '\n", + "Chat model chunk: ' \"'\n", "Chat model chunk: 'countries'\n", "Chat model chunk: '\":'\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: ' [\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: ' ['\n", + "Chat model chunk: '\\n '\n", "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: ' {\"'\n", - "Chat model chunk: 'name'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': ''}]}\n", - "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: 'France'\n", - "Chat model chunk: '\",'\n", - "Chat model chunk: ' \"'\n", - "Chat model chunk: 'population'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': ''}]}\n", + "Chat model chunk: ' {'\n", + "Chat model chunk: '\\n '\n", "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': '67'}]}\n", - "Chat model chunk: '67'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': '67 million'}]}\n", - "Chat model chunk: ' million'\n", - "Chat model chunk: '\"},\\n'\n", "...\n" ] } @@ -1212,7 +1224,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "1854206d-b3a5-4f91-9e00-bccbaebac61f", "metadata": {}, "outputs": [ @@ -1220,9 +1232,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_tool_stream', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n", - "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" + "{'event': 'on_tool_start', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_tool_stream', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n", + "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" ] } ], @@ -1258,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "a20a6cb3-bb43-465c-8cfc-0a7349d70968", "metadata": {}, "outputs": [ @@ -1266,11 +1278,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n", - "{'event': 'on_tool_stream', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n", - "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" + "{'event': 'on_tool_start', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n", + "{'event': 'on_tool_stream', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n", + "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" ] } ], @@ -1295,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "0ac0a3c1-f3a4-4157-b053-4fec8d2e698c", "metadata": {}, "outputs": [ @@ -1303,11 +1315,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", - "{'event': 'on_chain_stream', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" + "{'event': 'on_chain_start', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", + "{'event': 'on_chain_stream', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" ] } ], @@ -1337,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "id": "c896bb94-9d10-41ff-8fe2-d6b05b1ed74b", "metadata": {}, "outputs": [ @@ -1345,11 +1357,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", - "{'event': 'on_chain_stream', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" + "{'event': 'on_chain_start', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", + "{'event': 'on_chain_stream', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" ] } ], @@ -1385,7 +1397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, From fbab8baac507a1f80bb26f9513195d18b0b217fc Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Mon, 5 Feb 2024 20:24:58 -0500 Subject: [PATCH 094/157] core[patch]: Add astream events config test (#17055) Verify that astream events propagates config correctly --------- Co-authored-by: Bagatur --- .../runnables/test_runnable_events.py | 137 +++++++++++++++++- 1 file changed, 135 insertions(+), 2 deletions(-) diff --git a/libs/core/tests/unit_tests/runnables/test_runnable_events.py b/libs/core/tests/unit_tests/runnables/test_runnable_events.py index 6d5de1094107df1..a31e15e7ba4dbb9 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable_events.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable_events.py @@ -1,23 +1,29 @@ """Module that contains tests for runnable.astream_events API.""" from itertools import cycle -from typing import AsyncIterator, List, Sequence, cast +from typing import Any, AsyncIterator, Dict, List, Sequence, cast import pytest from langchain_core.callbacks import CallbackManagerForRetrieverRun, Callbacks +from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.documents import Document from langchain_core.messages import ( AIMessage, AIMessageChunk, + BaseMessage, HumanMessage, SystemMessage, ) from langchain_core.prompt_values import ChatPromptValue -from langchain_core.prompts import ChatPromptTemplate +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ( + ConfigurableField, + Runnable, RunnableLambda, ) +from langchain_core.runnables.history import RunnableWithMessageHistory from langchain_core.runnables.schema import StreamEvent from langchain_core.tools import tool from tests.unit_tests.fake.chat_model import GenericFakeChatModel @@ -1079,3 +1085,130 @@ async def add_one(x: int) -> int: with pytest.raises(NotImplementedError): async for _ in add_one_map.astream_events([1, 2, 3], version="v1"): pass + + +async def test_events_astream_config() -> None: + """Test that astream events support accepting config""" + infinite_cycle = cycle([AIMessage(content="hello world!")]) + good_world_on_repeat = cycle([AIMessage(content="Goodbye world")]) + model = GenericFakeChatModel(messages=infinite_cycle).configurable_fields( + messages=ConfigurableField( + id="messages", + name="Messages", + description="Messages return by the LLM", + ) + ) + + model_02 = model.with_config({"configurable": {"messages": good_world_on_repeat}}) + assert model_02.invoke("hello") == AIMessage(content="Goodbye world") + + events = await _collect_events(model_02.astream_events("hello", version="v1")) + assert events == [ + { + "data": {"input": "hello"}, + "event": "on_chat_model_start", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content="Goodbye")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content=" ")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content="world")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"output": AIMessageChunk(content="Goodbye world")}, + "event": "on_chat_model_end", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + ] + + +async def test_runnable_with_message_history() -> None: + class InMemoryHistory(BaseChatMessageHistory, BaseModel): + """In memory implementation of chat message history.""" + + # Attention: for the tests use an Any type to work-around a pydantic issue + # where it re-instantiates a list, so mutating the list doesn't end up mutating + # the content in the store! + + # Using Any type here rather than List[BaseMessage] due to pydantic issue! + messages: Any + + def add_message(self, message: BaseMessage) -> None: + """Add a self-created message to the store.""" + self.messages.append(message) + + def clear(self) -> None: + self.messages = [] + + # Here we use a global variable to store the chat message history. + # This will make it easier to inspect it to see the underlying results. + store: Dict = {} + + def get_by_session_id(session_id: str) -> BaseChatMessageHistory: + """Get a chat message history""" + if session_id not in store: + store[session_id] = [] + return InMemoryHistory(messages=store[session_id]) + + infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="world")]) + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a cat"), + MessagesPlaceholder(variable_name="history"), + ("human", "{question}"), + ] + ) + model = GenericFakeChatModel(messages=infinite_cycle) + + chain: Runnable = prompt | model + with_message_history = RunnableWithMessageHistory( + chain, + get_session_history=get_by_session_id, + input_messages_key="question", + history_messages_key="history", + ) + with_message_history.with_config( + {"configurable": {"session_id": "session-123"}} + ).invoke({"question": "hello"}) + + assert store == { + "session-123": [HumanMessage(content="hello"), AIMessage(content="hello")] + } + + with_message_history.with_config( + {"configurable": {"session_id": "session-123"}} + ).invoke({"question": "meow"}) + assert store == { + "session-123": [ + HumanMessage(content="hello"), + AIMessage(content="hello"), + HumanMessage(content="meow"), + AIMessage(content="world"), + ] + } From 5f5f5acbc5c799370b9c5242c1b745a5a790a11f Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Tue, 6 Feb 2024 10:31:06 +0900 Subject: [PATCH 095/157] docs: fix typo in dspy.ipynb (#16996) langugage -> language --- docs/docs/integrations/providers/dspy.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/integrations/providers/dspy.ipynb b/docs/docs/integrations/providers/dspy.ipynb index dba57d50729c220..85d616bba81f783 100644 --- a/docs/docs/integrations/providers/dspy.ipynb +++ b/docs/docs/integrations/providers/dspy.ipynb @@ -185,7 +185,7 @@ " \n", "- **Output:** An engaging tweet that correctly answers the question from the retrieved info.\n", " \n", - "Let's use LangChain's expression langugage (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", + "Let's use LangChain's expression language (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", "\n", "Considering that, let's just keep it to the barebones: **Given {context}, answer the question {question} as a tweet.**" ] From 563f325034b73099d06672a9ded46a880dd65d96 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Mon, 5 Feb 2024 17:47:13 -0800 Subject: [PATCH 096/157] experimental[patch]: fixed import in `experimental` (#17078) --- .../langchain_experimental/autonomous_agents/autogpt/memory.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py index c59e326a5cecf9e..f4d7d39d7347bab 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py @@ -1,6 +1,7 @@ from typing import Any, Dict, List -from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key +from langchain.memory.chat_memory import BaseChatMemory +from langchain.memory.utils import get_prompt_input_key from langchain_core.vectorstores import VectorStoreRetriever from langchain_experimental.pydantic_v1 import Field From 929f0715137491d49f8a3f78b42bd48909cc5117 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Paupier?= Date: Tue, 6 Feb 2024 02:56:58 +0100 Subject: [PATCH 097/157] community[patch]: Fix error in `LlamaCpp` community LLM with Configurable Fields, 'grammar' custom type not available (#16995) - **Description:** Ensure the `LlamaGrammar` custom type is always available when instantiating a `LlamaCpp` LLM - **Issue:** #16994 - **Dependencies:** None - **Twitter handle:** @fpaupier --------- Co-authored-by: Bagatur --- libs/community/langchain_community/llms/llamacpp.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/libs/community/langchain_community/llms/llamacpp.py b/libs/community/langchain_community/llms/llamacpp.py index c29ca74ef75bc5d..d46d741e7bfb4fe 100644 --- a/libs/community/langchain_community/llms/llamacpp.py +++ b/libs/community/langchain_community/llms/llamacpp.py @@ -2,7 +2,7 @@ import logging from pathlib import Path -from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union +from typing import Any, Dict, Iterator, List, Optional, Union from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM @@ -11,9 +11,6 @@ from langchain_core.utils import get_pydantic_field_names from langchain_core.utils.utils import build_extra_kwargs -if TYPE_CHECKING: - from llama_cpp import LlamaGrammar - logger = logging.getLogger(__name__) @@ -126,7 +123,7 @@ class LlamaCpp(LLM): to force the model to generate valid JSON or to speak exclusively in emojis. At most one of grammar_path and grammar should be passed in. """ - grammar: Optional[Union[str, LlamaGrammar]] = None + grammar: Optional[Union[str, Any]] = None """ grammar: formal grammar for constraining model outputs. For instance, the grammar can be used to force the model to generate valid JSON or to speak exclusively in From f08a9139d2e1bef457c8e248eec657405ee36951 Mon Sep 17 00:00:00 2001 From: Marc Mahe <65669843+mtmahe@users.noreply.github.com> Date: Mon, 5 Feb 2024 22:03:12 -0400 Subject: [PATCH 098/157] docs: update mistral docs for version 0.1+ (#17011) **Description:** Updated integration page for mistralai. --- docs/docs/integrations/chat/mistralai.ipynb | 165 +++++++++++++++++--- 1 file changed, 144 insertions(+), 21 deletions(-) diff --git a/docs/docs/integrations/chat/mistralai.ipynb b/docs/docs/integrations/chat/mistralai.ipynb index 5b4977e87926956..12faf385a399e8f 100644 --- a/docs/docs/integrations/chat/mistralai.ipynb +++ b/docs/docs/integrations/chat/mistralai.ipynb @@ -15,28 +15,53 @@ "id": "bf733a38-db84-4363-89e2-de6735c37230", "metadata": {}, "source": [ - "# ChatMistralAI\n", + "# MistralAI\n", "\n", "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n", "\n", "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n", "\n", - "You will need the `langchain-mistralai` package to use the API. You can install it via pip:" + "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods." + ] + }, + { + "cell_type": "markdown", + "id": "cc686b8f", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "You will need the `langchain-core` and `langchain-mistralai` package to use the API. You can install these with:\n", + "\n", + "```bash\n", + "pip install -U langchain-core langchain-mistralai\n", + "\n", + "We'll also need to get a [Mistral API key](https://console.mistral.ai/users/api-keys/)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "eb978a7e", + "execution_count": 7, + "id": "c3fd4184", "metadata": {}, "outputs": [], "source": [ - "%pip install -qU langchain-core langchain-mistralai" + "import getpass\n", + "\n", + "mistral_api_key = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "502127fd", + "metadata": {}, + "source": [ + "## Usage" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9", "metadata": { "tags": [] @@ -49,23 +74,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import os\n", - "\n", - "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n", "# If mistral_api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n", "chat = ChatMistralAI(mistral_api_key=mistral_api_key)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c", "metadata": { "tags": [] @@ -74,16 +96,16 @@ { "data": { "text/plain": [ - "AIMessage(content=\"Hello! I'm here to assist you. How can I help you today? If you have any questions or need information on a particular topic, feel free to ask. I'm ready to provide accurate and helpful answers to the best of my ability.\")" + "AIMessage(content=\"Who's there? I was just about to ask the same thing! How can I assist you today?\")" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "messages = [HumanMessage(content=\"say a brief hello\")]\n", + "messages = [HumanMessage(content=\"knock knock\")]\n", "chat.invoke(messages)" ] }, @@ -92,12 +114,12 @@ "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c", "metadata": {}, "source": [ - "## `ChatMistralAI` also supports async and streaming functionality:" + "### Async" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b", "metadata": { "tags": [] @@ -106,10 +128,10 @@ { "data": { "text/plain": [ - "AIMessage(content=\"Hello! I'm glad you're here. If you have any questions or need assistance with something related to programming or software development, feel free to ask. I'll do my best to help you out. Have a great day!\")" + "AIMessage(content='Who\\'s there?\\n\\n(You can then continue the \"knock knock\" joke by saying the name of the person or character who should be responding. For example, if I say \"Banana,\" you could respond with \"Banana who?\" and I would say \"Banana bunch! Get it? Because a group of bananas is called a \\'bunch\\'!\" and then we would both laugh and have a great time. But really, you can put anything you want in the spot where I put \"Banana\" and it will still technically be a \"knock knock\" joke. The possibilities are endless!)')" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -118,9 +140,17 @@ "await chat.ainvoke(messages)" ] }, + { + "cell_type": "markdown", + "id": "86ccef97", + "metadata": {}, + "source": [ + "### Streaming\n" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "025be980-e50d-4a68-93dc-c9c7b500ce34", "metadata": { "tags": [] @@ -130,7 +160,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Hello! I'm happy to assist you. Is there a specific question or topic you would like to discuss? I can provide information and answer questions on a wide variety of subjects." + "Who's there?\n", + "\n", + "(After this, the conversation can continue as a call and response \"who's there\" joke. Here is an example of how it could go:\n", + "\n", + "You say: Orange.\n", + "I say: Orange who?\n", + "You say: Orange you glad I didn't say banana!?)\n", + "\n", + "But since you asked for a knock knock joke specifically, here's one for you:\n", + "\n", + "Knock knock.\n", + "\n", + "Me: Who's there?\n", + "\n", + "You: Lettuce.\n", + "\n", + "Me: Lettuce who?\n", + "\n", + "You: Lettuce in, it's too cold out here!\n", + "\n", + "I hope this brings a smile to your face! Do you have a favorite knock knock joke you'd like to share? I'd love to hear it." ] } ], @@ -138,6 +188,79 @@ "for chunk in chat.stream(messages):\n", " print(chunk.content, end=\"\")" ] + }, + { + "cell_type": "markdown", + "id": "f6189577", + "metadata": {}, + "source": [ + "### Batch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e63aebcb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[AIMessage(content=\"Who's there? I was just about to ask the same thing! Go ahead and tell me who's there. I love a good knock-knock joke.\")]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat.batch([messages])" + ] + }, + { + "cell_type": "markdown", + "id": "38e39e71", + "metadata": {}, + "source": [ + "## Chaining\n", + "\n", + "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee43a1ae", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n", + "chain = prompt | chat" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0dc49212", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Why do bears hate shoes so much? They like to run around in their bear feet.')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain.invoke({\"topic\": \"bears\"})" + ] } ], "metadata": { @@ -156,7 +279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.12" } }, "nbformat": 4, From fd88e0f80037aff4393b9e9879878b7e73235128 Mon Sep 17 00:00:00 2001 From: os1ma <39944763+os1ma@users.noreply.github.com> Date: Tue, 6 Feb 2024 11:20:59 +0900 Subject: [PATCH 099/157] docs: update StreamlitCallbackHandler example (#16970) - **Description:** docs: update StreamlitCallbackHandler example. - **Issue:** None - **Dependencies:** None I have updated the example for StreamlitCallbackHandler in the documentation bellow. https://python.langchain.com/docs/integrations/callbacks/streamlit Previously, the example used `initialize_agent`, which has been deprecated, so I've updated it to use `create_react_agent` instead. Many langchain users are likely searching examples of combining `create_react_agent` or `openai_tools_agent_chain` with StreamlitCallbackHandler. I'm sure this update will be really helpful for them! Unfortunately, writing unit tests for this example is difficult, so I have not written any tests. I have run this code in a standalone Python script file and ensured it runs correctly. --- docs/docs/integrations/callbacks/streamlit.md | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/docs/docs/integrations/callbacks/streamlit.md b/docs/docs/integrations/callbacks/streamlit.md index 47b39cfd0b6a646..ad42d462d00f7cf 100644 --- a/docs/docs/integrations/callbacks/streamlit.md +++ b/docs/docs/integrations/callbacks/streamlit.md @@ -28,7 +28,7 @@ You can run `streamlit hello` to load a sample app and validate your install suc To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output. ```python -from langchain.callbacks import StreamlitCallbackHandler +from langchain_community.callbacks import StreamlitCallbackHandler import streamlit as st st_callback = StreamlitCallbackHandler(st.container()) @@ -44,23 +44,26 @@ agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `a thoughts and actions live in your app. ```python -from langchain_openai import OpenAI -from langchain.agents import AgentType, initialize_agent, load_tools -from langchain_community.callbacks import StreamlitCallbackHandler import streamlit as st +from langchain import hub +from langchain.agents import AgentExecutor, create_react_agent, load_tools +from langchain_community.callbacks import StreamlitCallbackHandler +from langchain_openai import OpenAI llm = OpenAI(temperature=0, streaming=True) tools = load_tools(["ddg-search"]) -agent = initialize_agent( - tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True -) +prompt = hub.pull("hwchase17/react") +agent = create_react_agent(llm, tools, prompt) +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) if prompt := st.chat_input(): st.chat_message("user").write(prompt) with st.chat_message("assistant"): st_callback = StreamlitCallbackHandler(st.container()) - response = agent.run(prompt, callbacks=[st_callback]) - st.write(response) + response = agent_executor.invoke( + {"input": prompt}, {"callbacks": [st_callback]} + ) + st.write(response["output"]) ``` **Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully. From 2d8015554c446c8b59094802be8260e7170febea Mon Sep 17 00:00:00 2001 From: Jan de Boer <44832123+Janldeboer@users.noreply.github.com> Date: Tue, 6 Feb 2024 03:29:16 +0100 Subject: [PATCH 100/157] docs: Link to Brave Website added (#16958) **Description:** Link to the Brave Website added to the `brave-search.ipynb` notebook. This notebook is shown in the docs as an example for the brave tool. **Issue:** There was to reference on where / how to get an api key **Dependencies:** none **Twitter handle:** not for this one :) --- docs/docs/integrations/tools/brave_search.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/docs/integrations/tools/brave_search.ipynb b/docs/docs/integrations/tools/brave_search.ipynb index 365aafe20d83db4..82cd841e4fa81eb 100644 --- a/docs/docs/integrations/tools/brave_search.ipynb +++ b/docs/docs/integrations/tools/brave_search.ipynb @@ -7,7 +7,8 @@ "source": [ "# Brave Search\n", "\n", - "This notebook goes over how to use the Brave Search tool." + "This notebook goes over how to use the Brave Search tool.\n", + "Go to the [Brave Website](https://brave.com/search/api/) to sign up for a free account and get an API key." ] }, { From f027696b5f068bacad96a9356ae196f5de240007 Mon Sep 17 00:00:00 2001 From: Ryan Kraus Date: Mon, 5 Feb 2024 22:50:50 -0500 Subject: [PATCH 101/157] community: Added new Utility runnables for NVIDIA Riva. (#15966) **Please tag this issue with `nvidia_genai`** - **Description:** Added new Runnables for integration NVIDIA Riva into LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech (TTS). - **Issue:** N/A - **Dependencies:** To use these runnables, the NVIDIA Riva client libraries are required. It they are not installed, an error will be raised instructing how to install them. The Runnables can be safely imported without the riva client libraries. - **Twitter handle:** N/A All of the Riva Runnables are inside a single folder in the Utilities module. In this folder are four files: - common.py - Contains all code that is common to both TTS and ASR - stream.py - Contains a class representing an audio stream that allows the end user to put data into the stream like a queue. - asr.py - Contains the RivaASR runnable - tts.py - Contains the RivaTTS runnable The following Python function is an example of creating a chain that makes use of both of these Runnables: ```python def create( config: Configuration, audio_encoding: RivaAudioEncoding, sample_rate: int, audio_channels: int = 1, ) -> Runnable[ASRInputType, TTSOutputType]: """Create a new instance of the chain.""" _LOGGER.info("Instantiating the chain.") # create the riva asr client riva_asr = RivaASR( url=str(config.riva_asr.service.url), ssl_cert=config.riva_asr.service.ssl_cert, encoding=audio_encoding, audio_channel_count=audio_channels, sample_rate_hertz=sample_rate, profanity_filter=config.riva_asr.profanity_filter, enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation, language_code=config.riva_asr.language_code, ) # create the prompt template prompt = PromptTemplate.from_template("{user_input}") # model = ChatOpenAI() model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore # create the riva tts client riva_tts = RivaTTS( url=str(config.riva_asr.service.url), ssl_cert=config.riva_asr.service.ssl_cert, output_directory=config.riva_tts.output_directory, language_code=config.riva_tts.language_code, voice_name=config.riva_tts.voice_name, ) # construct and return the chain return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore ``` The following code is an example of creating a new audio stream for Riva: ```python input_stream = AudioStream(maxsize=1000) # Send bytes into the stream for chunk in audio_chunks: await input_stream.aput(chunk) input_stream.close() ``` The following code is an example of how to execute the chain with RivaASR and RivaTTS ```python output_stream = asyncio.Queue() while not input_stream.complete: async for chunk in chain.astream(input_stream): output_stream.put(chunk) ``` Everything should be async safe and thread safe. Audio data can be put into the input stream while the chain is running without interruptions. --------- Co-authored-by: Hayden Wolff Co-authored-by: Hayden Wolff Co-authored-by: Hayden Wolff Co-authored-by: Erick Friis --- .../langchain_community/utilities/__init__.py | 27 + .../utilities/nvidia_riva.py | 666 +++ libs/community/poetry.lock | 4414 +++++++++-------- libs/community/pyproject.toml | 6 +- .../unit_tests/utilities/test_imports.py | 3 + .../utilities/test_nvidia_riva_asr.py | 177 + .../utilities/test_nvidia_riva_tts.py | 153 + 7 files changed, 3272 insertions(+), 2174 deletions(-) create mode 100644 libs/community/langchain_community/utilities/nvidia_riva.py create mode 100644 libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py create mode 100644 libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py diff --git a/libs/community/langchain_community/utilities/__init__.py b/libs/community/langchain_community/utilities/__init__.py index ac91e964d2de979..2af2c51ac6ccfcc 100644 --- a/libs/community/langchain_community/utilities/__init__.py +++ b/libs/community/langchain_community/utilities/__init__.py @@ -272,6 +272,24 @@ def _import_nasa() -> Any: return NasaAPIWrapper +def _import_nvidia_riva_asr() -> Any: + from langchain_community.utilities.nvidia_riva import RivaASR + + return RivaASR + + +def _import_nvidia_riva_tts() -> Any: + from langchain_community.utilities.nvidia_riva import RivaTTS + + return RivaTTS + + +def _import_nvidia_riva_stream() -> Any: + from langchain_community.utilities.nvidia_riva import AudioStream + + return AudioStream + + def __getattr__(name: str) -> Any: if name == "AlphaVantageAPIWrapper": return _import_alpha_vantage() @@ -321,6 +339,12 @@ def __getattr__(name: str) -> Any: return _import_metaphor_search() elif name == "NasaAPIWrapper": return _import_nasa() + elif name == "NVIDIARivaASR": + return _import_nvidia_riva_asr() + elif name == "NVIDIARivaStream": + return _import_nvidia_riva_stream() + elif name == "NVIDIARivaTTS": + return _import_nvidia_riva_tts() elif name == "OpenWeatherMapAPIWrapper": return _import_openweathermap() elif name == "OutlineAPIWrapper": @@ -388,6 +412,9 @@ def __getattr__(name: str) -> Any: "MerriamWebsterAPIWrapper", "MetaphorSearchAPIWrapper", "NasaAPIWrapper", + "NVIDIARivaASR", + "NVIDIARivaStream", + "NVIDIARivaTTS", "OpenWeatherMapAPIWrapper", "OutlineAPIWrapper", "Portkey", diff --git a/libs/community/langchain_community/utilities/nvidia_riva.py b/libs/community/langchain_community/utilities/nvidia_riva.py new file mode 100644 index 000000000000000..e56a676df1d22bf --- /dev/null +++ b/libs/community/langchain_community/utilities/nvidia_riva.py @@ -0,0 +1,666 @@ +"""A common module for NVIDIA Riva Runnables.""" +import asyncio +import logging +import pathlib +import queue +import tempfile +import threading +import wave +from enum import Enum +from typing import ( + TYPE_CHECKING, + Any, + AsyncGenerator, + AsyncIterator, + Dict, + Generator, + Iterator, + List, + Optional, + Tuple, + Union, + cast, +) + +from langchain_core.messages import AnyMessage, BaseMessage +from langchain_core.prompt_values import PromptValue +from langchain_core.pydantic_v1 import ( + AnyHttpUrl, + BaseModel, + Field, + parse_obj_as, + root_validator, + validator, +) +from langchain_core.runnables import RunnableConfig, RunnableSerializable + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_asr_pb2 as rasr + +_LOGGER = logging.getLogger(__name__) +_QUEUE_GET_TIMEOUT = 0.5 +_MAX_TEXT_LENGTH = 400 +_SENTENCE_TERMINATORS = ("\n", ".", "!", "?", "¡", "¿") + + +# COMMON utilities used by all Riva Runnables +def _import_riva_client() -> "riva.client": + """Import the riva client and raise an error on failure.""" + try: + # pylint: disable-next=import-outside-toplevel # this client library is optional + import riva.client + except ImportError as err: + raise ImportError( + "Could not import the NVIDIA Riva client library. " + "Please install it with `pip install nvidia-riva-client`." + ) from err + return riva.client + + +class SentinelT: # pylint: disable=too-few-public-methods + """An empty Sentinel type.""" + + +HANGUP = SentinelT() +_TRANSFORM_END = SentinelT() + + +class RivaAudioEncoding(str, Enum): + """An enum of the possible choices for Riva audio encoding. + + The list of types exposed by the Riva GRPC Protobuf files can be found + with the following commands: + ```python + import riva.client + print(riva.client.AudioEncoding.keys()) + ``` + """ + + ALAW = "ALAW" + ENCODING_UNSPECIFIED = "ENCODING_UNSPECIFIED" + FLAC = "FLAC" + LINEAR_PCM = "LINEAR_PCM" + MULAW = "MULAW" + OGGOPUS = "OGGOPUS" + + @classmethod + def from_wave_format_code(cls, format_code: int) -> "RivaAudioEncoding": + """Return the audio encoding specified by the format code in the wave file. + + ref: https://mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html + """ + try: + return {1: cls.LINEAR_PCM, 6: cls.ALAW, 7: cls.MULAW}[format_code] + except KeyError as err: + raise NotImplementedError( + "The following wave file format code is " + f"not supported by Riva: {format_code}" + ) from err + + @property + def riva_pb2(self) -> "riva.client.AudioEncoding": + """Returns the Riva API object for the encoding.""" + riva_client = _import_riva_client() + return getattr(riva_client.AudioEncoding, self) + + +class RivaAuthMixin(BaseModel): + """Configuration for the authentication to a Riva service connection.""" + + url: Union[AnyHttpUrl, str] = Field( + AnyHttpUrl("http://localhost:50051", scheme="http"), + description="The full URL where the Riva service can be found.", + examples=["http://localhost:50051", "https://user@pass:riva.example.com"], + ) + ssl_cert: Optional[str] = Field( + None, + description="A full path to the file where Riva's public ssl key can be read.", + ) + + @property + def auth(self) -> "riva.client.Auth": + """Return a riva client auth object.""" + riva_client = _import_riva_client() + url = cast(AnyHttpUrl, self.url) + use_ssl = url.scheme == "https" # pylint: disable=no-member # false positive + url_no_scheme = str(self.url).split("/")[2] + return riva_client.Auth( + ssl_cert=self.ssl_cert, use_ssl=use_ssl, uri=url_no_scheme + ) + + @validator("url", pre=True, allow_reuse=True) + @classmethod + def _validate_url(cls, val: Any) -> AnyHttpUrl: + """Do some initial conversations for the URL before checking.""" + if isinstance(val, str): + return cast(AnyHttpUrl, parse_obj_as(AnyHttpUrl, val)) + return cast(AnyHttpUrl, val) + + +class RivaCommonConfigMixin(BaseModel): + """A collection of common Riva settings.""" + + encoding: RivaAudioEncoding = Field( + default=RivaAudioEncoding.LINEAR_PCM, + description="The encoding on the audio stream.", + ) + sample_rate_hertz: int = Field( + default=8000, description="The sample rate frequency of audio stream." + ) + language_code: str = Field( + default="en-US", + description=( + "The [BCP-47 language code]" + "(https://www.rfc-editor.org/rfc/bcp/bcp47.txt) for " + "the target language." + ), + ) + + +class _Event: + """A combined event that is threadsafe and async safe.""" + + _event: threading.Event + _aevent: asyncio.Event + + def __init__(self) -> None: + """Initialize the event.""" + self._event = threading.Event() + self._aevent = asyncio.Event() + + def set(self) -> None: + """Set the event.""" + self._event.set() + self._aevent.set() + + def clear(self) -> None: + """Set the event.""" + self._event.clear() + self._aevent.clear() + + def is_set(self) -> bool: + """Indicate if the event is set.""" + return self._event.is_set() + + def wait(self) -> None: + """Wait for the event to be set.""" + self._event.wait() + + async def async_wait(self) -> None: + """Async wait for the event to be set.""" + await self._aevent.wait() + + +def _mk_wave_file( + output_directory: Optional[str], sample_rate: float +) -> Tuple[Optional[str], Optional[wave.Wave_write]]: + """Create a new wave file and return the wave write object and filename.""" + if output_directory: + with tempfile.NamedTemporaryFile( + mode="bx", suffix=".wav", delete=False, dir=output_directory + ) as f: + wav_file_name = f.name + wav_file = wave.open(wav_file_name, "wb") + wav_file.setnchannels(1) + wav_file.setsampwidth(2) + wav_file.setframerate(sample_rate) + return (wav_file_name, wav_file) + return (None, None) + + +def _coerce_string(val: "TTSInputType") -> str: + """Attempt to coerce the input value to a string. + + This is particularly useful for converting LangChain message to strings. + """ + if isinstance(val, PromptValue): + return val.to_string() + if isinstance(val, BaseMessage): + return str(val.content) + return str(val) + + +def _process_chunks(inputs: Iterator["TTSInputType"]) -> Generator[str, None, None]: + """Filter the input chunks are return strings ready for TTS.""" + buffer = "" + for chunk in inputs: + chunk = _coerce_string(chunk) + + # return the buffer if an end of sentence character is detected + for terminator in _SENTENCE_TERMINATORS: + while terminator in chunk: + last_sentence, chunk = chunk.split(terminator, 1) + yield buffer + last_sentence + terminator + buffer = "" + + buffer += chunk + + # return the buffer if is too long + if len(buffer) > _MAX_TEXT_LENGTH: + for idx in range(0, len(buffer), _MAX_TEXT_LENGTH): + yield buffer[idx : idx + 5] + buffer = "" + + # return remaining buffer + if buffer: + yield buffer + + +# Riva AudioStream Type +StreamInputType = Union[bytes, SentinelT] +StreamOutputType = str + + +class AudioStream: + """A message containing streaming audio.""" + + _put_lock: threading.Lock + _queue: queue.Queue + output: queue.Queue + hangup: _Event + user_talking: _Event + user_quiet: _Event + _worker: Optional[threading.Thread] + + def __init__(self, maxsize: int = 0) -> None: + """Initialize the queue.""" + self._put_lock = threading.Lock() + self._queue = queue.Queue(maxsize=maxsize) + self.output = queue.Queue() + self.hangup = _Event() + self.user_quiet = _Event() + self.user_talking = _Event() + self._worker = None + + def __iter__(self) -> Generator[bytes, None, None]: + """Return an error.""" + while True: + # get next item + try: + next_val = self._queue.get(True, _QUEUE_GET_TIMEOUT) + except queue.Empty: + continue + + # hangup when requested + if next_val == HANGUP: + break + + # yield next item + yield next_val + self._queue.task_done() + + async def __aiter__(self) -> AsyncIterator[StreamInputType]: + """Iterate through all items in the queue until HANGUP.""" + while True: + # get next item + try: + next_val = await asyncio.get_event_loop().run_in_executor( + None, self._queue.get, True, _QUEUE_GET_TIMEOUT + ) + except queue.Empty: + continue + + # hangup when requested + if next_val == HANGUP: + break + + # yield next item + yield next_val + self._queue.task_done() + + @property + def hungup(self) -> bool: + """Indicate if the audio stream has hungup.""" + return self.hangup.is_set() + + @property + def empty(self) -> bool: + """Indicate in the input stream buffer is empty.""" + return self._queue.empty() + + @property + def complete(self) -> bool: + """Indicate if the audio stream has hungup and been processed.""" + input_done = self.hungup and self.empty + output_done = ( + self._worker is not None + and not self._worker.is_alive() + and self.output.empty() + ) + return input_done and output_done + + @property + def running(self) -> bool: + """Indicate if the ASR stream is running.""" + if self._worker: + return self._worker.is_alive() + return False + + def put(self, item: StreamInputType, timeout: Optional[int] = None) -> None: + """Put a new item into the queue.""" + with self._put_lock: + if self.hungup: + raise RuntimeError( + "The audio stream has already been hungup. Cannot put more data." + ) + if item is HANGUP: + self.hangup.set() + self._queue.put(item, timeout=timeout) + + async def aput(self, item: StreamInputType, timeout: Optional[int] = None) -> None: + """Async put a new item into the queue.""" + loop = asyncio.get_event_loop() + await asyncio.wait_for(loop.run_in_executor(None, self.put, item), timeout) + + def close(self, timeout: Optional[int] = None) -> None: + """Send the hangup signal.""" + self.put(HANGUP, timeout) + + async def aclose(self, timeout: Optional[int] = None) -> None: + """Async send the hangup signal.""" + await self.aput(HANGUP, timeout) + + def register(self, responses: Iterator["rasr.StreamingRecognizeResponse"]) -> None: + """Drain the responses from the provided iterator and put them into a queue.""" + if self.running: + raise RuntimeError("An ASR instance has already been registered.") + + has_started = threading.Barrier(2, timeout=5) + + def worker() -> None: + """Consume the ASR Generator.""" + has_started.wait() + for response in responses: + if not response.results: + continue + + for result in response.results: + if not result.alternatives: + continue + + if result.is_final: + self.user_talking.clear() + self.user_quiet.set() + transcript = cast(str, result.alternatives[0].transcript) + self.output.put(transcript) + + elif not self.user_talking.is_set(): + self.user_talking.set() + self.user_quiet.clear() + + self._worker = threading.Thread(target=worker) + self._worker.daemon = True + self._worker.start() + has_started.wait() + + +# RivaASR Runnable +ASRInputType = AudioStream +ASROutputType = str + + +class RivaASR( + RivaAuthMixin, + RivaCommonConfigMixin, + RunnableSerializable[ASRInputType, ASROutputType], +): + """A runnable that performs Automatic Speech Recognition (ASR) using NVIDIA Riva.""" + + name: str = "nvidia_riva_asr" + description: str = ( + "A Runnable for converting audio bytes to a string." + "This is useful for feeding an audio stream into a chain and" + "preprocessing that audio to create an LLM prompt." + ) + + # riva options + audio_channel_count: int = Field( + 1, description="The number of audio channels in the input audio stream." + ) + profanity_filter: bool = Field( + True, + description=( + "Controls whether or not Riva should attempt to filter " + "profanity out of the transcribed text." + ), + ) + enable_automatic_punctuation: bool = Field( + True, + description=( + "Controls whether Riva should attempt to correct " + "senetence puncuation in the transcribed text." + ), + ) + + @root_validator(pre=True) + @classmethod + def _validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Validate the Python environment and input arguments.""" + _ = _import_riva_client() + return values + + @property + def config(self) -> "riva.client.StreamingRecognitionConfig": + """Create and return the riva config object.""" + riva_client = _import_riva_client() + return riva_client.StreamingRecognitionConfig( + interim_results=True, + config=riva_client.RecognitionConfig( + encoding=self.encoding, + sample_rate_hertz=self.sample_rate_hertz, + audio_channel_count=self.audio_channel_count, + max_alternatives=1, + profanity_filter=self.profanity_filter, + enable_automatic_punctuation=self.enable_automatic_punctuation, + language_code=self.language_code, + ), + ) + + def _get_service(self) -> "riva.client.ASRService": + """Connect to the riva service and return the a client object.""" + riva_client = _import_riva_client() + try: + return riva_client.ASRService(self.auth) + except Exception as err: + raise ValueError( + "Error raised while connecting to the Riva ASR server." + ) from err + + def invoke( + self, + input: ASRInputType, + _: Optional[RunnableConfig] = None, + ) -> ASROutputType: + """Transcribe the audio bytes into a string with Riva.""" + # create an output text generator with Riva + if not input.running: + service = self._get_service() + responses = service.streaming_response_generator( + audio_chunks=input, + streaming_config=self.config, + ) + input.register(responses) + + # return the first valid result + full_response: List[str] = [] + while not input.complete: + with input.output.not_empty: + ready = input.output.not_empty.wait(0.1) + + if ready: + while not input.output.empty(): + try: + full_response += [input.output.get_nowait()] + except queue.Empty: + continue + input.output.task_done() + _LOGGER.debug("Riva ASR returning: %s", repr(full_response)) + return " ".join(full_response).strip() + + return "" + + +# RivaTTS Runnable +# pylint: disable-next=invalid-name +TTSInputType = Union[str, AnyMessage, PromptValue] +TTSOutputType = bytes + + +class RivaTTS( + RivaAuthMixin, + RivaCommonConfigMixin, + RunnableSerializable[TTSInputType, TTSOutputType], +): + """A runnable that performs Text-to-Speech (TTS) with NVIDIA Riva.""" + + name: str = "nvidia_riva_tts" + description: str = ( + "A tool for converting text to speech." + "This is useful for converting LLM output into audio bytes." + ) + + # riva options + voice_name: str = Field( + "English-US.Female-1", + description=( + "The voice model in Riva to use for speech. " + "Pre-trained models are documented in " + "[the Riva documentation]" + "(https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-overview.html)." + ), + ) + output_directory: Optional[str] = Field( + None, + description=( + "The directory where all audio files should be saved. " + "A null value indicates that wave files should not be saved. " + "This is useful for debugging purposes." + ), + ) + + @root_validator(pre=True) + @classmethod + def _validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Validate the Python environment and input arguments.""" + _ = _import_riva_client() + return values + + @validator("output_directory") + @classmethod + def _output_directory_validator(cls, v: str) -> str: + if v: + dirpath = pathlib.Path(v) + dirpath.mkdir(parents=True, exist_ok=True) + return str(dirpath.absolute()) + return v + + def _get_service(self) -> "riva.client.SpeechSynthesisService": + """Connect to the riva service and return the a client object.""" + riva_client = _import_riva_client() + try: + return riva_client.SpeechSynthesisService(self.auth) + except Exception as err: + raise ValueError( + "Error raised while connecting to the Riva TTS server." + ) from err + + def invoke( + self, input: TTSInputType, _: Union[RunnableConfig, None] = None + ) -> TTSOutputType: + """Perform TTS by taking a string and outputting the entire audio file.""" + return b"".join(self.transform(iter([input]))) + + def transform( + self, + input: Iterator[TTSInputType], + config: Optional[RunnableConfig] = None, + **kwargs: Optional[Any], + ) -> Iterator[TTSOutputType]: + """Perform TTS by taking a stream of characters and streaming output bytes.""" + service = self._get_service() + + # create an output wave file + wav_file_name, wav_file = _mk_wave_file( + self.output_directory, self.sample_rate_hertz + ) + + # split the input text and perform tts + for chunk in _process_chunks(input): + _LOGGER.debug("Riva TTS chunk: %s", chunk) + + # start riva tts streaming + responses = service.synthesize_online( + text=chunk, + voice_name=self.voice_name, + language_code=self.language_code, + encoding=self.encoding.riva_pb2, + sample_rate_hz=self.sample_rate_hertz, + ) + + # stream audio bytes out + for resp in responses: + audio = cast(bytes, resp.audio) + if wav_file: + wav_file.writeframesraw(audio) + yield audio + + # close the wave file when we are done + if wav_file: + wav_file.close() + _LOGGER.debug("Riva TTS wrote file: %s", wav_file_name) + + async def atransform( + self, + input: AsyncIterator[TTSInputType], + config: Optional[RunnableConfig] = None, + **kwargs: Optional[Any], + ) -> AsyncGenerator[TTSOutputType, None]: + """Intercept async transforms and route them to the synchronous transform.""" + loop = asyncio.get_running_loop() + input_queue: queue.Queue = queue.Queue() + out_queue: asyncio.Queue = asyncio.Queue() + + async def _producer() -> None: + """Produce input into the input queue.""" + async for val in input: + input_queue.put_nowait(val) + input_queue.put_nowait(_TRANSFORM_END) + + def _input_iterator() -> Iterator[TTSInputType]: + """Iterate over the input_queue.""" + while True: + try: + val = input_queue.get(timeout=0.5) + except queue.Empty: + continue + if val == _TRANSFORM_END: + break + yield val + + def _consumer() -> None: + """Consume the input with transform.""" + for val in self.transform(_input_iterator()): + out_queue.put_nowait(val) + out_queue.put_nowait(_TRANSFORM_END) + + async def _consumer_coro() -> None: + """Coroutine that wraps the consumer.""" + await loop.run_in_executor(None, _consumer) + + producer = loop.create_task(_producer()) + consumer = loop.create_task(_consumer_coro()) + + while True: + try: + val = await asyncio.wait_for(out_queue.get(), 0.5) + except asyncio.exceptions.TimeoutError: + continue + out_queue.task_done() + + if val is _TRANSFORM_END: + break + yield val + + await producer + await consumer diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 70d943ea09443ca..620a23816d5fe4a 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -28,87 +28,87 @@ pycares = ">=4.0.0" [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file 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"azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cohere", "dashvector", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict", "zhipuai"] +extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict", "zhipuai"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "1ab63edcddcef2deb01e6fff5c376f7b0773435bb9d5b55bc1d50d19a8f1dee2" +content-hash = "0b962fcc5239d0c57ce7c17fec359e236fdff47970b4863286fd8ae82a9a2079" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 14a0117adb76849..976653eb2190015 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -66,7 +66,6 @@ xata = {version = "^1.0.0a7", optional = true} xmltodict = {version = "^0.13.0", optional = true} markdownify = {version = "^0.11.6", optional = true} assemblyai = {version = "^0.17.0", optional = true} -dashvector = {version = "^1.0.1", optional = true} sqlite-vss = {version = "^0.1.2", optional = true} motor = {version = "^3.3.1", optional = true} timescale-vector = {version = "^0.0.1", optional = true} @@ -92,6 +91,7 @@ elasticsearch = {version = "^8.12.0", optional = true} hdbcli = {version = "^2.19.21", optional = true} oci = {version = "^2.119.1", optional = true} rdflib = {version = "7.0.0", optional = true} +nvidia-riva-client = {version = "^2.14.0", optional = true} [tool.poetry.group.test] optional = true @@ -229,6 +229,7 @@ extended_testing = [ "jinja2", "gitpython", "newspaper3k", + "nvidia-riva-client", "feedparser", "xata", "xmltodict", @@ -236,7 +237,6 @@ extended_testing = [ "openapi-pydantic", "markdownify", "arxiv", - "dashvector", "sqlite-vss", "rapidocr-onnxruntime", "motor", @@ -258,7 +258,7 @@ extended_testing = [ "elasticsearch", "hdbcli", "oci", - "rdflib", + "rdflib" ] [tool.ruff] diff --git a/libs/community/tests/unit_tests/utilities/test_imports.py b/libs/community/tests/unit_tests/utilities/test_imports.py index 6499d1b857b2e70..a7bd210b4f9bdab 100644 --- a/libs/community/tests/unit_tests/utilities/test_imports.py +++ b/libs/community/tests/unit_tests/utilities/test_imports.py @@ -24,6 +24,9 @@ "MaxComputeAPIWrapper", "MetaphorSearchAPIWrapper", "NasaAPIWrapper", + "NVIDIARivaASR", + "NVIDIARivaTTS", + "NVIDIARivaStream", "OpenWeatherMapAPIWrapper", "OutlineAPIWrapper", "Portkey", diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py new file mode 100644 index 000000000000000..b9072dbfbba35ce --- /dev/null +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py @@ -0,0 +1,177 @@ +"""Unit tests to verify function of the Riva ASR implementation.""" + +from typing import TYPE_CHECKING, Any, Generator +from unittest.mock import patch + +import pytest + +from langchain_community.utilities.nvidia_riva import ( + AudioStream, + RivaASR, + RivaAudioEncoding, +) + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_asr_pb2 as rasr + +AUDIO_DATA_MOCK = [ + b"This", + b"is", + b"a", + b"test.", + b"_", + b"Hello.", + b"World", +] +AUDIO_TEXT_MOCK = b" ".join(AUDIO_DATA_MOCK).decode().strip().split("_") + +SVC_URI = "not-a-url.asdf:9999" +SVC_USE_SSL = True +CONFIG = { + "audio_channel_count": 9, + "profanity_filter": False, + "enable_automatic_punctuation": False, + "url": f"{'https' if SVC_USE_SSL else 'http'}://{SVC_URI}", + "ssl_cert": "/dev/null", + "encoding": RivaAudioEncoding.ALAW, + "language_code": "not-a-language", + "sample_rate_hertz": 5, +} + + +def response_generator( + transcript: str = "", + empty: bool = False, + final: bool = False, + alternatives: bool = True, +) -> "rasr.StreamingRecognizeResponse": + """Create a pseudo streaming response.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_asr_pb2 as rasr + + if empty: + return rasr.StreamingRecognizeResponse() + + if not alternatives: + return rasr.StreamingRecognizeResponse( + results=[ + rasr.StreamingRecognitionResult( + is_final=final, + alternatives=[], + ) + ] + ) + + return rasr.StreamingRecognizeResponse( + results=[ + rasr.StreamingRecognitionResult( + is_final=final, + alternatives=[ + rasr.SpeechRecognitionAlternative(transcript=transcript.strip()) + ], + ) + ] + ) + + +def streaming_recognize_mock( + generator: Generator["rasr.StreamingRecognizeRequest", None, None], **_: Any +) -> Generator["rasr.StreamingRecognizeResponse", None, None]: + """A mock function to fake a streaming call to Riva.""" + yield response_generator(empty=True) + yield response_generator(alternatives=False) + + output_transcript = "" + for streaming_requests in generator: + input_bytes = streaming_requests.audio_content.decode() + + final = input_bytes == "_" + if final: + input_bytes = "" + + output_transcript += input_bytes + " " + + yield response_generator(final=final, transcript=output_transcript) + if final: + output_transcript = "" + + yield response_generator(final=True, transcript=output_transcript) + + +def riva_asr_stub_init_patch( + self: "riva.client.proto.riva_asr_pb2_grpc.RivaSpeechRecognitionStub", _: Any +) -> None: + """Patch for the Riva asr library.""" + self.StreamingRecognize = streaming_recognize_mock + + +@pytest.fixture +def asr() -> RivaASR: + """Initialize a copy of the runnable.""" + return RivaASR(**CONFIG) + + +@pytest.fixture +def stream() -> AudioStream: + """Initialize and populate a sample audio stream.""" + s = AudioStream() + for val in AUDIO_DATA_MOCK: + s.put(val) + s.close() + return s + + +@pytest.mark.requires("riva.client") +def test_init(asr: RivaASR) -> None: + """Test that ASR accepts valid arguments.""" + for key, expected_val in CONFIG.items(): + assert getattr(asr, key, None) == expected_val + + +@pytest.mark.requires("riva.client") +def test_init_defaults() -> None: + """Ensure the runnable can be loaded with no arguments.""" + _ = RivaASR() + + +@pytest.mark.requires("riva.client") +def test_config(asr: RivaASR) -> None: + """Verify the Riva config is properly assembled.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_asr_pb2 as rasr + + expected = rasr.StreamingRecognitionConfig( + interim_results=True, + config=rasr.RecognitionConfig( + encoding=CONFIG["encoding"], + sample_rate_hertz=CONFIG["sample_rate_hertz"], + audio_channel_count=CONFIG["audio_channel_count"], + max_alternatives=1, + profanity_filter=CONFIG["profanity_filter"], + enable_automatic_punctuation=CONFIG["enable_automatic_punctuation"], + language_code=CONFIG["language_code"], + ), + ) + assert asr.config == expected + + +@pytest.mark.requires("riva.client") +def test_get_service(asr: RivaASR) -> None: + """Test generating an asr service class.""" + svc = asr._get_service() + assert str(svc.auth.ssl_cert) == CONFIG["ssl_cert"] + assert svc.auth.use_ssl == SVC_USE_SSL + assert svc.auth.uri == SVC_URI + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_asr_pb2_grpc.RivaSpeechRecognitionStub.__init__", + riva_asr_stub_init_patch, +) +def test_invoke(asr: RivaASR, stream: AudioStream) -> None: + """Test the invoke method.""" + got = asr.invoke(stream) + expected = " ".join([s.strip() for s in AUDIO_TEXT_MOCK]).strip() + assert got == expected diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py new file mode 100644 index 000000000000000..321f6d1ea8274b5 --- /dev/null +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py @@ -0,0 +1,153 @@ +"""Unit tests to verify function of the Riva TTS implementation.""" + +from typing import TYPE_CHECKING, Any, AsyncGenerator, Generator, cast +from unittest.mock import patch + +import pytest + +from langchain_community.utilities.nvidia_riva import RivaAudioEncoding, RivaTTS + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_tts_pb2 as rtts + +AUDIO_TEXT_MOCK = ["This is a test.", "Hello world"] +AUDIO_DATA_MOCK = [s.encode() for s in AUDIO_TEXT_MOCK] + +SVC_URI = "not-a-url.asdf:9999" +SVC_USE_SSL = True +CONFIG = { + "voice_name": "English-Test", + "output_directory": None, + "url": f"{'https' if SVC_USE_SSL else 'http'}://{SVC_URI}", + "ssl_cert": "/dev/null", + "encoding": RivaAudioEncoding.ALAW, + "language_code": "not-a-language", + "sample_rate_hertz": 5, +} + + +def synthesize_online_mock( + request: "rtts.SynthesizeSpeechRequest", **_: Any +) -> Generator["rtts.SynthesizeSpeechResponse", None, None]: + """A mock function to fake a streaming call to Riva.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_tts_pb2 as rtts + + yield rtts.SynthesizeSpeechResponse( + audio=f"[{request.language_code},{request.encoding},{request.sample_rate_hz},{request.voice_name}]".encode() + ) + yield rtts.SynthesizeSpeechResponse(audio=request.text.strip().encode()) + + +def riva_tts_stub_init_patch( + self: "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub", _: Any +) -> None: + """Patch for the Riva TTS library.""" + self.SynthesizeOnline = synthesize_online_mock + + +@pytest.fixture +def tts() -> RivaTTS: + """Initialize a copy of the runnable.""" + return RivaTTS(**CONFIG) + + +@pytest.mark.requires("riva.client") +def test_init(tts: RivaTTS) -> None: + """Test that ASR accepts valid arguments.""" + for key, expected_val in CONFIG.items(): + assert getattr(tts, key, None) == expected_val + + +@pytest.mark.requires("riva.client") +def test_init_defaults() -> None: + """Ensure the runnable can be loaded with no arguments.""" + _ = RivaTTS() + + +@pytest.mark.requires("riva.client") +def test_get_service(tts: RivaTTS) -> None: + """Test the get service method.""" + svc = tts._get_service() + assert str(svc.auth.ssl_cert) == CONFIG["ssl_cert"] + assert svc.auth.use_ssl == SVC_USE_SSL + assert svc.auth.uri == SVC_URI + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +def test_invoke(tts: RivaTTS) -> None: + """Test the invoke method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + + input = " ".join(AUDIO_TEXT_MOCK).strip() + response = tts.invoke(input) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + assert response == expected + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +def test_transform(tts: RivaTTS) -> None: + """Test the transform method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + for idx, response in enumerate(tts.transform(iter(AUDIO_TEXT_MOCK))): + if idx % 2: + # odd indices will return the mocked data + expected = AUDIO_DATA_MOCK[int((idx - 1) / 2)] + else: + # even indices will return the request config + expected = audio_synth_config.encode() + assert response == expected + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +async def test_atransform(tts: RivaTTS) -> None: + """Test the transform method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + idx = 0 + + async def _fake_async_iterable() -> AsyncGenerator[str, None]: + for val in AUDIO_TEXT_MOCK: + yield val + + async for response in tts.atransform(_fake_async_iterable()): + if idx % 2: + # odd indices will return the mocked data + expected = AUDIO_DATA_MOCK[int((idx - 1) / 2)] + else: + # even indices will return the request config + expected = audio_synth_config.encode() + assert response == expected + idx += 1 From eaeb8a5f71e112c4572ab92dfa8d2431ec4ae841 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 6 Feb 2024 18:41:53 +0100 Subject: [PATCH 102/157] langchain[patch]: `output_parser.py` in conversation_chat is customizable (#16945) **Description:** With this modification, users can customize the `FORMAT_INSTRUCTIONS` template, allowing them to create their own prompts As it is happening in [this](https://github.com/langchain-ai/langchain/issues/10721) issue, the `FORMAT_INSTRUCTIONS` is not customizable for the output parser, unless you create your own class `ConvoOutputParser`. To avoid this, a modification was done, creating a `format_instruction` variable that users can customize with ease after initialize the agent. For example: ``` agent = initialize_agent( agent = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, tools = tools, llm = llm_agent, verbose = True, max_iterations = 3, early_stopping_method = 'generate', memory = b_w_memory, handle_parsing_errors = True, agent_kwargs={ 'system_message':PREFIX, 'human_message':SUFFIX, 'template_tool_response':TEMPLATE_TOOL_RESPONSE, } ) agent.agent.output_parser.format_instructions = "MY CUSTOM FORMAT INSTRUCTIONS" print(agent.agent.output_parser.get_format_instructions()) MY CUSTOM FORMAT INSTRUCTIONS ``` Other parameters like `system_message`, `human_message`, or `template_tool_response` are already customizable and with this PR, the last parameter `FORMAT_INSTRUCTIONS` in `langchain.agents.conversational_chat.prompt` can be modified. **Issue:** https://github.com/langchain-ai/langchain/issues/10721 **Dependencies:** No new dependencies required for this change **Twitter handle:** With my github user is enough. Thanks I hope you accept my PR. --------- Co-authored-by: Bagatur --- .../langchain/agents/conversational_chat/output_parser.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/libs/langchain/langchain/agents/conversational_chat/output_parser.py b/libs/langchain/langchain/agents/conversational_chat/output_parser.py index 46ccce6d5718961..ea3a380f4bc923c 100644 --- a/libs/langchain/langchain/agents/conversational_chat/output_parser.py +++ b/libs/langchain/langchain/agents/conversational_chat/output_parser.py @@ -4,19 +4,21 @@ from langchain_core.agents import AgentAction, AgentFinish from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers.json import parse_json_markdown from langchain.agents import AgentOutputParser from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS +from langchain.output_parsers.json import parse_json_markdown # Define a class that parses output for conversational agents class ConvoOutputParser(AgentOutputParser): """Output parser for the conversational agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + def get_format_instructions(self) -> str: """Returns formatting instructions for the given output parser.""" - return FORMAT_INSTRUCTIONS + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: """Attempts to parse the given text into an AgentAction or AgentFinish. From ac662b3698e795a7e076023fe595d2e4d43432a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=80=81=E9=98=BF=E5=BC=B5?= Date: Wed, 7 Feb 2024 01:42:05 +0800 Subject: [PATCH 103/157] docs: Fix typo in amadeus.ipynb (#16916) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Description: "enviornment should be environment"? 🤔 Issue: Typo Dependencies: Nope Twitter handle: laoazhang --- docs/docs/integrations/toolkits/amadeus.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/integrations/toolkits/amadeus.ipynb b/docs/docs/integrations/toolkits/amadeus.ipynb index e97067c0f60de0e..787bd3dfbfcc566 100644 --- a/docs/docs/integrations/toolkits/amadeus.ipynb +++ b/docs/docs/integrations/toolkits/amadeus.ipynb @@ -12,7 +12,7 @@ "\n", "To use this toolkit, you will need to have your Amadeus API keys ready, explained in the [Get started Amadeus Self-Service APIs](https://developers.amadeus.com/get-started/get-started-with-self-service-apis-335). Once you've received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.\n", "\n", - "Note: Amadeus Self-Service APIs offers a test enviornment with [free limited data](https://amadeus4dev.github.io/developer-guides/test-data/). This allows developers to build and test their applications before deploying them to production. To access real-time data, you will need to [move to the production environment](https://amadeus4dev.github.io/developer-guides/API-Keys/moving-to-production/)." + "Note: Amadeus Self-Service APIs offers a test environment with [free limited data](https://amadeus4dev.github.io/developer-guides/test-data/). This allows developers to build and test their applications before deploying them to production. To access real-time data, you will need to [move to the production environment](https://amadeus4dev.github.io/developer-guides/API-Keys/moving-to-production/)." ] }, { From ef082c77b14399b186512cd9f220d1c5d65ac4e8 Mon Sep 17 00:00:00 2001 From: Frank Date: Wed, 7 Feb 2024 01:42:33 +0800 Subject: [PATCH 104/157] =?UTF-8?q?community[minor]:=20add=20github=20file?= =?UTF-8?q?=20loader=20to=20load=20any=20github=20file=20content=20b?= =?UTF-8?q?=E2=80=A6=20(#15305)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Description support load any github file content based on file extension. Why not use [git loader](https://python.langchain.com/docs/integrations/document_loaders/git#load-existing-repository-from-disk) ? git loader clones the whole repo even only interested part of files, that's too heavy. This GithubFileLoader only downloads that you are interested files. ### Twitter handle my twitter: @shufanhaotop --------- Co-authored-by: Hao Fan Co-authored-by: Bagatur --- .../document_loaders/github.ipynb | 152 +++++++----------- .../document_loaders/__init__.py | 6 +- .../document_loaders/github.py | 63 +++++++- .../document_loaders/test_github.py | 109 ++++++++++++- .../document_loaders/test_imports.py | 1 + 5 files changed, 232 insertions(+), 99 deletions(-) diff --git a/docs/docs/integrations/document_loaders/github.ipynb b/docs/docs/integrations/document_loaders/github.ipynb index 3d9f57243f0cd54..4b7bb7cdb2823a9 100644 --- a/docs/docs/integrations/document_loaders/github.ipynb +++ b/docs/docs/integrations/document_loaders/github.ipynb @@ -6,7 +6,7 @@ "source": [ "# GitHub\n", "\n", - "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example." + "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). Also shows how you can load github files for agiven repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example." ] }, { @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [] }, @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -100,27 +100,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Creates GitHubLoader (#5257)\r\n", - "\r\n", - "GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.\r\n", - "\r\n", - "Fixes #5257\r\n", - "\r\n", - "Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:\r\n", - "DataLoaders\r\n", - "- @eyurtsev\r\n", - "\n", - "{'url': 'https://github.com/langchain-ai/langchain/pull/5408', 'title': 'DocumentLoader for GitHub', 'creator': 'UmerHA', 'created_at': '2023-05-29T14:50:53Z', 'comments': 0, 'state': 'open', 'labels': ['enhancement', 'lgtm', 'doc loader'], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5408, 'is_pull_request': True}\n" - ] - } - ], + "outputs": [], "source": [ "print(docs[0].page_content)\n", "print(docs[0].metadata)" @@ -142,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,84 +139,68 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### System Info\n", - "\n", - "LangChain version = 0.0.167\r\n", - "Python version = 3.11.0\r\n", - "System = Windows 11 (using Jupyter)\n", - "\n", - "### Who can help?\n", - "\n", - "- @hwchase17\r\n", - "- @agola11\r\n", - "- @UmerHA (I have a fix ready, will submit a PR)\n", - "\n", - "### Information\n", - "\n", - "- [ ] The official example notebooks/scripts\n", - "- [X] My own modified scripts\n", - "\n", - "### Related Components\n", - "\n", - "- [X] LLMs/Chat Models\n", - "- [ ] Embedding Models\n", - "- [X] Prompts / Prompt Templates / Prompt Selectors\n", - "- [ ] Output Parsers\n", - "- [ ] Document Loaders\n", - "- [ ] Vector Stores / Retrievers\n", - "- [ ] Memory\n", - "- [ ] Agents / Agent Executors\n", - "- [ ] Tools / Toolkits\n", - "- [ ] Chains\n", - "- [ ] Callbacks/Tracing\n", - "- [ ] Async\n", - "\n", - "### Reproduction\n", - "\n", - "```\r\n", - "import os\r\n", - "os.environ[\"OPENAI_API_KEY\"] = \"...\"\r\n", - "\r\n", - "from langchain.chains import LLMChain\r\n", - "from langchain_openai import ChatOpenAI\r\n", - "from langchain.prompts import PromptTemplate\r\n", - "from langchain.prompts.chat import ChatPromptTemplate\r\n", - "from langchain.schema import messages_from_dict\r\n", - "\r\n", - "role_strings = [\r\n", - " (\"system\", \"you are a bird expert\"), \r\n", - " (\"human\", \"which bird has a point beak?\")\r\n", - "]\r\n", - "prompt = ChatPromptTemplate.from_role_strings(role_strings)\r\n", - "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\r\n", - "chain.run({})\r\n", - "```\n", - "\n", - "### Expected behavior\n", - "\n", - "Chain should run\n", - "{'url': 'https://github.com/langchain-ai/langchain/issues/5027', 'title': \"ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings\", 'creator': 'UmerHA', 'created_at': '2023-05-20T10:39:18Z', 'comments': 1, 'state': 'open', 'labels': [], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5027, 'is_pull_request': False}\n" - ] - } - ], + "outputs": [], "source": [ "print(docs[0].page_content)\n", "print(docs[0].metadata)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Github File Content\n", + "\n", + "For below code, loads all markdown file in rpeo `langchain-ai/langchain`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.document_loaders import GithubFileLoader" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "loader = GithubFileLoader(\n", + " repo=\"langchain-ai/langchain\", # the repo name\n", + " access_token=ACCESS_TOKEN,\n", + " github_api_url=\"https://api.github.com\",\n", + " file_filter=lambda file_path: file_path.endswith(\n", + " \".md\"\n", + " ), # load all markdowns files.\n", + ")\n", + "documents = loader.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "example output of one of document: \n", + "\n", + "```json\n", + "documents.metadata: \n", + " {\n", + " \"path\": \"README.md\",\n", + " \"sha\": \"82f1c4ea88ecf8d2dfsfx06a700e84be4\",\n", + " \"source\": \"https://github.com/langchain-ai/langchain/blob/master/README.md\"\n", + " }\n", + "documents.content:\n", + " mock content\n", + "```" + ] } ], "metadata": { @@ -253,7 +219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/libs/community/langchain_community/document_loaders/__init__.py b/libs/community/langchain_community/document_loaders/__init__.py index b06f416d4daada3..869b0dca0329b39 100644 --- a/libs/community/langchain_community/document_loaders/__init__.py +++ b/libs/community/langchain_community/document_loaders/__init__.py @@ -102,7 +102,10 @@ from langchain_community.document_loaders.geodataframe import GeoDataFrameLoader from langchain_community.document_loaders.git import GitLoader from langchain_community.document_loaders.gitbook import GitbookLoader -from langchain_community.document_loaders.github import GitHubIssuesLoader +from langchain_community.document_loaders.github import ( + GithubFileLoader, + GitHubIssuesLoader, +) from langchain_community.document_loaders.google_speech_to_text import ( GoogleSpeechToTextLoader, ) @@ -296,6 +299,7 @@ "GCSDirectoryLoader", "GCSFileLoader", "GeoDataFrameLoader", + "GithubFileLoader", "GitHubIssuesLoader", "GitLoader", "GitbookLoader", diff --git a/libs/community/langchain_community/document_loaders/github.py b/libs/community/langchain_community/document_loaders/github.py index 77bdf7da6c76e37..8a361a46e2b13e7 100644 --- a/libs/community/langchain_community/document_loaders/github.py +++ b/libs/community/langchain_community/document_loaders/github.py @@ -1,6 +1,7 @@ +import base64 from abc import ABC from datetime import datetime -from typing import Dict, Iterator, List, Literal, Optional, Union +from typing import Callable, Dict, Iterator, List, Literal, Optional, Union import requests from langchain_core.documents import Document @@ -20,7 +21,7 @@ class BaseGitHubLoader(BaseLoader, BaseModel, ABC): github_api_url: str = "https://api.github.com" """URL of GitHub API""" - @root_validator(pre=True) + @root_validator(pre=True, allow_reuse=True) def validate_environment(cls, values: Dict) -> Dict: """Validate that access token exists in environment.""" values["access_token"] = get_from_dict_or_env( @@ -65,7 +66,7 @@ class GitHubIssuesLoader(BaseGitHubLoader): """Only show notifications updated after the given time. This is a timestamp in ISO 8601 format: YYYY-MM-DDTHH:MM:SSZ.""" - @validator("since") + @validator("since", allow_reuse=True) def validate_since(cls, v: Optional[str]) -> Optional[str]: if v: try: @@ -186,3 +187,59 @@ def query_params(self) -> str: def url(self) -> str: """Create URL for GitHub API.""" return f"{self.github_api_url}/repos/{self.repo}/issues?{self.query_params}" + + +class GithubFileLoader(BaseGitHubLoader, ABC): + """Load GitHub File""" + + file_extension: str = ".md" + branch: str = "main" + + file_filter: Optional[Callable[[str], bool]] + + def get_file_paths(self) -> List[Dict]: + base_url = ( + f"{self.github_api_url}/api/v3/repos/{self.repo}/git/trees/" + f"{self.branch}?recursive=1" + ) + response = requests.get(base_url, headers=self.headers) + response.raise_for_status() + all_files = response.json()["tree"] + """ one element in all_files + { + 'path': '.github', + 'mode': '040000', + 'type': 'tree', + 'sha': '89a2ae046e8b59eb96531f123c0c6d4913885df1', + 'url': 'https://github.com/api/v3/repos/shufanhao/langchain/git/trees/89a2ae046e8b59eb96531f123c0c6d4913885dxxx' + } + """ + return [ + f + for f in all_files + if not (self.file_filter and not self.file_filter(f["path"])) + ] + + def get_file_content_by_path(self, path: str) -> str: + base_url = f"{self.github_api_url}/api/v3/repos/{self.repo}/contents/{path}" + response = requests.get(base_url, headers=self.headers) + response.raise_for_status() + + content_encoded = response.json()["content"] + return base64.b64decode(content_encoded).decode("utf-8") + + def load(self) -> List[Document]: + documents = [] + + files = self.get_file_paths() + for file in files: + content = self.get_file_content_by_path(file["path"]) + metadata = { + "path": file["path"], + "sha": file["sha"], + "source": f"{self.github_api_url}/{self.repo}/{file['type']}/" + f"{self.branch}/{file['path']}", + } + documents.append(Document(page_content=content, metadata=metadata)) + + return documents diff --git a/libs/community/tests/unit_tests/document_loaders/test_github.py b/libs/community/tests/unit_tests/document_loaders/test_github.py index 5641836dcd65444..f9f74be69758b1a 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_github.py +++ b/libs/community/tests/unit_tests/document_loaders/test_github.py @@ -1,8 +1,13 @@ +import base64 + import pytest from langchain_core.documents import Document from pytest_mock import MockerFixture -from langchain_community.document_loaders.github import GitHubIssuesLoader +from langchain_community.document_loaders.github import ( + GithubFileLoader, + GitHubIssuesLoader, +) def test_initialization() -> None: @@ -48,7 +53,7 @@ def test_invalid_initialization() -> None: GitHubIssuesLoader(since="not_a_date") -def test_load(mocker: MockerFixture) -> None: +def test_load_github_issue(mocker: MockerFixture) -> None: mocker.patch( "requests.get", return_value=mocker.MagicMock(json=lambda: [], links=None) ) @@ -127,3 +132,103 @@ def test_url() -> None: "&assignee=user1&creator=user2&mentioned=user3&labels=bug,ui,@high" "&sort=comments&direction=asc&since=2023-05-26T00:00:00Z" ) + + +def test_github_file_content_get_file_paths(mocker: MockerFixture) -> None: + # Mock the requests.get method to simulate the API response + mocker.patch( + "requests.get", + return_value=mocker.MagicMock( + json=lambda: { + "tree": [ + { + "path": "readme.md", + "mode": "100644", + "type": "blob", + "sha": "789", + "size": 37, + "url": "https://github.com/api/v3/repos/shufanhao/langchain/git/blobs/789", + } + ] + }, + status_code=200, + ), + ) + + # case1: add file_filter + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + file_filter=lambda file_path: file_path.endswith(".md"), + ) + + # Call the load method + files = loader.get_file_paths() + + # Assert the results + assert len(files) == 1 + assert files[0]["path"] == "readme.md" + + # case2: didn't add file_filter + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + ) + + # Call the load method + files = loader.get_file_paths() + assert len(files) == 1 + assert files[0]["path"] == "readme.md" + + # case3: add file_filter with a non-exist file path + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + file_filter=lambda file_path: file_path.endswith(".py"), + ) + + # Call the load method + files = loader.get_file_paths() + assert len(files) == 0 + + +def test_github_file_content_loader(mocker: MockerFixture) -> None: + # Mock the requests.get method to simulate the API response + file_path_res = mocker.MagicMock( + json=lambda: { + "tree": [ + { + "path": "readme.md", + "mode": "100644", + "type": "blob", + "sha": "789", + "size": 37, + "url": "https://github.com/api/v3/repos/shufanhao/langchain/git/blobs/789", + } + ] + }, + status_code=200, + ) + file_content_res = mocker.MagicMock( + json=lambda: {"content": base64.b64encode("Mocked content".encode("utf-8"))}, + status_code=200, + ) + + mocker.patch("requests.get", side_effect=[file_path_res, file_content_res]) + + # case1: file_extension=".md" + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + ) + + # Call the load method + docs = loader.load() + + assert len(docs) == 1 + assert docs[0].page_content == "Mocked content" + assert docs[0].metadata["sha"] == "789" diff --git a/libs/community/tests/unit_tests/document_loaders/test_imports.py b/libs/community/tests/unit_tests/document_loaders/test_imports.py index f4511686f7a525b..d22f81aa19ba3d6 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_imports.py +++ b/libs/community/tests/unit_tests/document_loaders/test_imports.py @@ -65,6 +65,7 @@ "GCSDirectoryLoader", "GCSFileLoader", "GeoDataFrameLoader", + "GithubFileLoader", "GitHubIssuesLoader", "GitLoader", "GitbookLoader", From cd945e3a5ba58c59dfd20ac77eb0a581e74056ab Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:54:22 -0800 Subject: [PATCH 105/157] core[patch]: Release 0.1.19 (#17117) --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 37b06c857f0e96e..5b61e7f192a4430 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-core" -version = "0.1.18" +version = "0.1.19" description = "Building applications with LLMs through composability" authors = [] license = "MIT" From 1ed73f1992305e9aaeb405f720ebbbcac96398ba Mon Sep 17 00:00:00 2001 From: Junyoung Park Date: Wed, 7 Feb 2024 03:50:50 +0900 Subject: [PATCH 106/157] community[minor]: Add SelfQueryRetriever support to PGVector (#16991) - **Description:** Add SelfQueryRetriever support to PGVector - **Issue:** - - **Dependencies:** - - **Twitter handle:** - --------- Co-authored-by: Bagatur --- .../self_query/pgvector_self_query.ipynb | 308 ++++++++++++++++++ .../langchain/retrievers/self_query/base.py | 3 + .../retrievers/self_query/pgvector.py | 52 +++ .../retrievers/self_query/test_pgvector.py | 87 +++++ 4 files changed, 450 insertions(+) create mode 100644 docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb create mode 100644 libs/langchain/langchain/retrievers/self_query/pgvector.py create mode 100644 libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py diff --git a/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb b/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb new file mode 100644 index 000000000000000..b647510936277b6 --- /dev/null +++ b/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13afcae7", + "metadata": {}, + "source": [ + "# PGVector\n", + "\n", + ">[PGVector](https://github.com/pgvector/pgvector) is a vector similarity search for Postgres.\n", + "\n", + "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `PGVector` vector store." + ] + }, + { + "cell_type": "markdown", + "id": "68e75fb9", + "metadata": {}, + "source": [ + "## Creating a PGVector vector store\n", + "First we'll want to create a PGVector vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63a8af5b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet lark pgvector psycopg2-binary" + ] + }, + { + "cell_type": "markdown", + "id": "83811610-7df3-4ede-b268-68a6a83ba9e2", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb4a5787", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain_community.vectorstores import PGVector\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "collection = \"Name of your collection\"\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcbe04d9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "docs = [\n", + " Document(\n", + " page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n", + " metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n", + " metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n", + " ),\n", + " Document(\n", + " page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n", + " metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n", + " ),\n", + " Document(\n", + " page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n", + " metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n", + " ),\n", + " Document(\n", + " page_content=\"Toys come alive and have a blast doing so\",\n", + " metadata={\"year\": 1995, \"genre\": \"animated\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n", + " metadata={\n", + " \"year\": 1979,\n", + " \"director\": \"Andrei Tarkovsky\",\n", + " \"genre\": \"science fiction\",\n", + " \"rating\": 9.9,\n", + " },\n", + " ),\n", + "]\n", + "vectorstore = PGVector.from_documents(\n", + " docs,\n", + " embeddings,\n", + " collection_name=collection,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5ecaab6d", + "metadata": {}, + "source": [ + "## Creating our self-querying retriever\n", + "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "86e34dbf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.chains.query_constructor.base import AttributeInfo\n", + "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", + "from langchain_openai import OpenAI\n", + "\n", + "metadata_field_info = [\n", + " AttributeInfo(\n", + " name=\"genre\",\n", + " description=\"The genre of the movie\",\n", + " type=\"string or list[string]\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"year\",\n", + " description=\"The year the movie was released\",\n", + " type=\"integer\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"director\",\n", + " description=\"The name of the movie director\",\n", + " type=\"string\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n", + " ),\n", + "]\n", + "document_content_description = \"Brief summary of a movie\"\n", + "llm = OpenAI(temperature=0)\n", + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ea9df8d4", + "metadata": {}, + "source": [ + "## Testing it out\n", + "And now we can try actually using our retriever!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38a126e9", + "metadata": {}, + "outputs": [], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc3f1e6e", + "metadata": {}, + "outputs": [], + "source": [ + "# This example only specifies a filter\n", + "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b19d4da0", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a query and a filter\n", + "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f900e40e", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a highly rated (above 8.5) science fiction film?\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12a51522", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a query and composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51", + "metadata": {}, + "source": [ + "## Filter k\n", + "\n", + "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n", + "\n", + "We can do this by passing `enable_limit=True` to the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "retriever = SelfQueryRetriever.from_llm(\n", + " llm,\n", + " vectorstore,\n", + " document_content_description,\n", + " metadata_field_info,\n", + " enable_limit=True,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2758d229-4f97-499c-819f-888acaf8ee10", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 9c6a584a6e17466..d54120ccfc3d8d3 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -12,6 +12,7 @@ MongoDBAtlasVectorSearch, MyScale, OpenSearchVectorSearch, + PGVector, Pinecone, Qdrant, Redis, @@ -43,6 +44,7 @@ from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator from langchain.retrievers.self_query.myscale import MyScaleTranslator from langchain.retrievers.self_query.opensearch import OpenSearchTranslator +from langchain.retrievers.self_query.pgvector import PGVectorTranslator from langchain.retrievers.self_query.pinecone import PineconeTranslator from langchain.retrievers.self_query.qdrant import QdrantTranslator from langchain.retrievers.self_query.redis import RedisTranslator @@ -58,6 +60,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor: """Get the translator class corresponding to the vector store class.""" BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = { AstraDB: AstraDBTranslator, + PGVector: PGVectorTranslator, Pinecone: PineconeTranslator, Chroma: ChromaTranslator, DashVector: DashvectorTranslator, diff --git a/libs/langchain/langchain/retrievers/self_query/pgvector.py b/libs/langchain/langchain/retrievers/self_query/pgvector.py new file mode 100644 index 000000000000000..ebe5bf42ffc430b --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/pgvector.py @@ -0,0 +1,52 @@ +from typing import Dict, Tuple, Union + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + + +class PGVectorTranslator(Visitor): + """Translate `PGVector` internal query language elements to valid filters.""" + + allowed_operators = [Operator.AND, Operator.OR] + """Subset of allowed logical operators.""" + allowed_comparators = [ + Comparator.EQ, + Comparator.NE, + Comparator.GT, + Comparator.LT, + Comparator.IN, + Comparator.NIN, + Comparator.CONTAIN, + Comparator.LIKE, + ] + """Subset of allowed logical comparators.""" + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) + return f"{func.value}" + + def visit_operation(self, operation: Operation) -> Dict: + args = [arg.accept(self) for arg in operation.arguments] + return {self._format_func(operation.operator): args} + + def visit_comparison(self, comparison: Comparison) -> Dict: + return { + comparison.attribute: { + self._format_func(comparison.comparator): comparison.value + } + } + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + if structured_query.filter is None: + kwargs = {} + else: + kwargs = {"filter": structured_query.filter.accept(self)} + return structured_query.query, kwargs diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py new file mode 100644 index 000000000000000..451508bdab3d1f3 --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py @@ -0,0 +1,87 @@ +from typing import Dict, Tuple + +import pytest as pytest + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, +) +from langchain.retrievers.self_query.pgvector import PGVectorTranslator + +DEFAULT_TRANSLATOR = PGVectorTranslator() + + +def test_visit_comparison() -> None: + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1) + expected = {"foo": {"lt": 1}} + actual = DEFAULT_TRANSLATOR.visit_comparison(comp) + assert expected == actual + + +@pytest.mark.skip("Not implemented") +def test_visit_operation() -> None: + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.GT, attribute="abc", value=2.0), + ], + ) + expected = { + "foo": {"lt": 2}, + "bar": {"eq": "baz"}, + "abc": {"gt": 2.0}, + } + actual = DEFAULT_TRANSLATOR.visit_operation(op) + assert expected == actual + + +def test_visit_structured_query() -> None: + query = "What is the capital of France?" + structured_query = StructuredQuery( + query=query, + filter=None, + ) + expected: Tuple[str, Dict] = (query, {}) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1) + structured_query = StructuredQuery( + query=query, + filter=comp, + ) + expected = (query, {"filter": {"foo": {"lt": 1}}}) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.GT, attribute="abc", value=2.0), + ], + ) + structured_query = StructuredQuery( + query=query, + filter=op, + ) + expected = ( + query, + { + "filter": { + "and": [ + {"foo": {"lt": 2}}, + {"bar": {"eq": "baz"}}, + {"abc": {"gt": 2.0}}, + ] + } + }, + ) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual From 6af912d7e07e63c60b6d8fa6ce4904a967374b2f Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 11:27:04 -0800 Subject: [PATCH 107/157] infra: add pinecone secret (#17120) --- .github/workflows/_integration_test.yml | 2 ++ .github/workflows/_release.yml | 2 ++ 2 files changed, 4 insertions(+) diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index e0dcf25c1356975..8f64e2bfd47e6d2 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -57,6 +57,8 @@ jobs: GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} + PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }} + PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }} run: | make integration_tests diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index c37d87e0ad12447..c140f93ba589a6c 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -176,6 +176,8 @@ jobs: GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} + PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }} + PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }} run: make integration_tests working-directory: ${{ inputs.working-directory }} From 14ff1438e65455413848d2ebc92c2e9ac36260fa Mon Sep 17 00:00:00 2001 From: Mikhail Khludnev Date: Tue, 6 Feb 2024 22:47:07 +0300 Subject: [PATCH 108/157] nvidia-trt[patch]: propagate InferenceClientException to the caller. (#16936) - **Description:** before the change I've got 1. propagate InferenceClientException to the caller. 2. stop grpc receiver thread on exception ``` for token in result_queue: > result_str += token E TypeError: can only concatenate str (not "InferenceServerException") to str ../../langchain_nvidia_trt/llms.py:207: TypeError ``` And stream thread keeps running. after the change request thread stops correctly and caller got a root cause exception: ``` E tritonclient.utils.InferenceServerException: [request id: 4529729] expected number of inputs between 2 and 3 but got 10 inputs for model 'vllm_model' ../../langchain_nvidia_trt/llms.py:205: InferenceServerException ``` - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** [t.me/mkhl_spb](https://t.me/mkhl_spb) I'm not sure about test coverage. Should I setup deep mocks or there's a kind of triton stub via testcontainers or so. --- libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py index 36e1e6e5ca936da..0ea1fca1df8f1f5 100644 --- a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py +++ b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py @@ -199,10 +199,13 @@ def _request( result_queue = self._invoke_triton(self.model_name, inputs, outputs, stop) result_str = "" - for token in result_queue: - result_str += token - - self.client.stop_stream() + try: + for token in result_queue: + if isinstance(token, Exception): + raise token + result_str += token + finally: + self.client.stop_stream() return result_str From 3ccffa5dcced6c14d89cc58d1469e69d082360e3 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 11:51:04 -0800 Subject: [PATCH 109/157] infra: add integration deps to partner lint (#17122) --- .github/workflows/_lint.yml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index e2e3877c5542c89..8f07db9afb6c679 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -94,7 +94,7 @@ jobs: run: | make lint_package - - name: Install test dependencies + - name: Install unit test dependencies # Also installs dev/lint/test/typing dependencies, to ensure we have # type hints for as many of our libraries as possible. # This helps catch errors that require dependencies to be spotted, for example: @@ -103,9 +103,15 @@ jobs: # If you change this configuration, make sure to change the `cache-key` # in the `poetry_setup` action above to stop using the old cache. # It doesn't matter how you change it, any change will cause a cache-bust. + if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }} working-directory: ${{ inputs.working-directory }} run: | poetry install --with test + - name: Install unit+integration test dependencies + if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }} + working-directory: ${{ inputs.working-directory }} + run: | + poetry install --with test,test_integration - name: Get .mypy_cache_test to speed up mypy uses: actions/cache@v3 From 15bd1154a754da834756a705915b542dc81b3059 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 11:56:00 -0800 Subject: [PATCH 110/157] pinecone[patch]: integration test new namespace (#17121) --- .../pinecone/tests/integration_tests/test_vectorstores.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py index 3abdabe256a8c04..eb1f959839f1505 100644 --- a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py +++ b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py @@ -100,15 +100,17 @@ def test_from_texts_with_metadatas( texts = [needs] + texts metadatas = [{"page": i} for i in range(len(texts))] + + namespace = f"{NAMESPACE_NAME}-md" docsearch = Pinecone.from_texts( texts, embedding_openai, index_name=INDEX_NAME, metadatas=metadatas, - namespace=NAMESPACE_NAME, + namespace=namespace, ) time.sleep(DEFAULT_SLEEP) # prevent race condition - output = docsearch.similarity_search(needs, k=1, namespace=NAMESPACE_NAME) + output = docsearch.similarity_search(needs, k=1, namespace=namespace) # TODO: why metadata={"page": 0.0}) instead of {"page": 0}? assert output == [Document(page_content=needs, metadata={"page": 0.0})] From 980e30c361a912626979c5d26ef6033e85d0e278 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 12:48:25 -0800 Subject: [PATCH 111/157] nvidia-ai-endpoints[patch]: release 0.0.2 (#17125) --- libs/partners/nvidia-ai-endpoints/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/partners/nvidia-ai-endpoints/pyproject.toml b/libs/partners/nvidia-ai-endpoints/pyproject.toml index fe13960fd753e3f..e75e70c8c5435da 100644 --- a/libs/partners/nvidia-ai-endpoints/pyproject.toml +++ b/libs/partners/nvidia-ai-endpoints/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-nvidia-ai-endpoints" -version = "0.0.1.post2" +version = "0.0.2" description = "An integration package connecting NVIDIA AI Endpoints and LangChain" authors = [] readme = "README.md" From 37062549f9704765b147330b11988d8f5151f301 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 12:55:01 -0800 Subject: [PATCH 112/157] infra: update to cache v4 (#17126) stop using nodejs 16. Use 20 (stop deprecation annotation on all ci) Changelog: https://github.com/actions/cache?tab=readme-ov-file#whats-new --- .github/actions/poetry_setup/action.yml | 4 ++-- .github/workflows/_lint.yml | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml index 5aef4970ba7062a..68b099f0e559261 100644 --- a/.github/actions/poetry_setup/action.yml +++ b/.github/actions/poetry_setup/action.yml @@ -32,7 +32,7 @@ runs: with: python-version: ${{ inputs.python-version }} - - uses: actions/cache@v3 + - uses: actions/cache@v4 id: cache-bin-poetry name: Cache Poetry binary - Python ${{ inputs.python-version }} env: @@ -79,7 +79,7 @@ runs: run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose - name: Restore pip and poetry cached dependencies - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4" WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }} diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index 8f07db9afb6c679..e2e25fb8ebc5e09 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -80,7 +80,7 @@ jobs: poetry run pip install -e "$LANGCHAIN_LOCATION" - name: Get .mypy_cache to speed up mypy - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" with: @@ -114,7 +114,7 @@ jobs: poetry install --with test,test_integration - name: Get .mypy_cache_test to speed up mypy - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" with: From 226f376d59a7edbc26916858e3a57f46d313f3d4 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Tue, 6 Feb 2024 13:40:00 -0800 Subject: [PATCH 113/157] community[patch]: Release 0.0.18 (#17129) Co-authored-by: Erick Friis --- libs/community/_test_minimum_requirements.txt | 2 +- libs/community/poetry.lock | 148 +++++++++--------- libs/community/pyproject.toml | 4 +- 3 files changed, 77 insertions(+), 77 deletions(-) diff --git a/libs/community/_test_minimum_requirements.txt b/libs/community/_test_minimum_requirements.txt index 1918982f0d8418c..3c490af63d8060d 100644 --- a/libs/community/_test_minimum_requirements.txt +++ b/libs/community/_test_minimum_requirements.txt @@ -1 +1 @@ -langchain-core==0.1.16 +langchain-core==0.1.19 diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 620a23816d5fe4a..207f139f811e949 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -258,13 +258,13 @@ trio = ["trio (<0.22)"] [[package]] name = "appnope" -version = "0.1.3" +version = "0.1.4" description = "Disable App Nap on macOS >= 10.9" optional = false -python-versions = "*" +python-versions = ">=3.6" files = [ - {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"}, - {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, + {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, + {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, ] [[package]] @@ -702,17 +702,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.35" +version = "1.34.36" description = "The AWS SDK for Python" optional = false python-versions = ">= 3.8" files = [ - {file = "boto3-1.34.35-py3-none-any.whl", hash = "sha256:53897701ab4f307fbcfdade673eae809dfc5eabb6102053c84907aa27de66e53"}, - {file = "boto3-1.34.35.tar.gz", hash = "sha256:4052031f9ac18924e94be7c30a5f0af5843a4752b8c8cb9034cd978be252b61b"}, + {file = "boto3-1.34.36-py3-none-any.whl", hash = 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"pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"] +test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"] [[package]] name = "ipython" @@ -3932,7 +3932,7 @@ files = [ [[package]] name = "langchain-core" -version = "0.1.18" +version = "0.1.19" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -4348,13 +4348,13 @@ files = [ [[package]] name = "mlflow-skinny" -version = "2.10.0" +version = "2.10.1" description = "MLflow: A Platform for ML Development and Productionization" optional = true python-versions = ">=3.8" files = [ - {file = "mlflow-skinny-2.10.0.tar.gz", hash = "sha256:cff77342a163a2dda34ba9bd0442dee68638a4a6eff4ca0425a35aff19726758"}, - {file = "mlflow_skinny-2.10.0-py3-none-any.whl", hash = "sha256:214a4b56afa6df8867540d4f9dfb8e8601d34690045e86fe4db1d1994beb665c"}, + {file = "mlflow-skinny-2.10.1.tar.gz", 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">=0.1.19,<0.2" SQLAlchemy = ">=1.4,<3" requests = "^2" PyYAML = ">=5.3" From 42c812a549b8bcc1ec26cf0d3e6fd3a6613159d5 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Tue, 6 Feb 2024 13:49:23 -0800 Subject: [PATCH 114/157] API References sorted `Partner libs` menu (#17130) The `Partner libs` menu is not sorted. Now it is long enough, and items should be sorted to simplify a package search. - Sorted items in the `Partner libs` menu --- docs/api_reference/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/api_reference/conf.py b/docs/api_reference/conf.py index 2a3defb19117b87..e993048fc57f470 100644 --- a/docs/api_reference/conf.py +++ b/docs/api_reference/conf.py @@ -146,6 +146,7 @@ def setup(app): (p.name, p.name.replace("-", "_") + "_api_reference") for p in partners_dir.iterdir() ] +partners = sorted(partners) html_context = { "display_github": True, # Integrate GitHub From 863f96b2e0396fe5ad8b5898ca7146513cfb475e Mon Sep 17 00:00:00 2001 From: Arno Schutijzer Date: Tue, 6 Feb 2024 22:54:40 +0100 Subject: [PATCH 115/157] docs: fix typo in ollama notebook (#17127) - **Description:** typo fix in ollama notebook --- docs/docs/integrations/llms/ollama.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb index 79ea75898789df2..51462902aec7439 100644 --- a/docs/docs/integrations/llms/ollama.ipynb +++ b/docs/docs/integrations/llms/ollama.ipynb @@ -244,7 +244,7 @@ "\n", "def plt_img_base64(img_base64):\n", " \"\"\"\n", - " Disply base64 encoded string as image\n", + " Display base64 encoded string as image\n", "\n", " :param img_base64: Base64 string\n", " \"\"\"\n", From f881a3330ccb761e61d38f7ed778f17e97d80882 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 15:59:08 -0800 Subject: [PATCH 116/157] mistralai[patch]: 16k token batching logic embed (#17136) --- .../langchain_mistralai/embeddings.py | 58 +- libs/partners/mistralai/poetry.lock | 621 ++++++++++++------ libs/partners/mistralai/pyproject.toml | 1 + .../integration_tests/test_embeddings.py | 5 +- 4 files changed, 472 insertions(+), 213 deletions(-) diff --git a/libs/partners/mistralai/langchain_mistralai/embeddings.py b/libs/partners/mistralai/langchain_mistralai/embeddings.py index de9c44062621618..977dbdf95908dea 100644 --- a/libs/partners/mistralai/langchain_mistralai/embeddings.py +++ b/libs/partners/mistralai/langchain_mistralai/embeddings.py @@ -1,5 +1,6 @@ +import asyncio import logging -from typing import Dict, List, Optional +from typing import Dict, Iterable, List, Optional from langchain_core.embeddings import Embeddings from langchain_core.pydantic_v1 import ( @@ -16,9 +17,12 @@ ENDPOINT as DEFAULT_MISTRAL_ENDPOINT, ) from mistralai.exceptions import MistralException +from tokenizers import Tokenizer # type: ignore logger = logging.getLogger(__name__) +MAX_TOKENS = 16_000 + class MistralAIEmbeddings(BaseModel, Embeddings): """MistralAI embedding models. @@ -43,6 +47,7 @@ class MistralAIEmbeddings(BaseModel, Embeddings): max_retries: int = 5 timeout: int = 120 max_concurrent_requests: int = 64 + tokenizer: Tokenizer = Field(default=None) model: str = "mistral-embed" @@ -72,8 +77,33 @@ def validate_environment(cls, values: Dict) -> Dict: timeout=values["timeout"], max_concurrent_requests=values["max_concurrent_requests"], ) + if values["tokenizer"] is None: + values["tokenizer"] = Tokenizer.from_pretrained( + "mistralai/Mixtral-8x7B-v0.1" + ) return values + def _get_batches(self, texts: List[str]) -> Iterable[List[str]]: + """Split a list of texts into batches of less than 16k tokens + for Mistral API.""" + batch: List[str] = [] + batch_tokens = 0 + + text_token_lengths = [ + len(encoded) for encoded in self.tokenizer.encode_batch(texts) + ] + + for text, text_tokens in zip(texts, text_token_lengths): + if batch_tokens + text_tokens > MAX_TOKENS: + yield batch + batch = [text] + batch_tokens = text_tokens + else: + batch.append(text) + batch_tokens += text_tokens + if batch: + yield batch + def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts. @@ -84,13 +114,17 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: List of embeddings, one for each text. """ try: - embeddings_batch_response = self.client.embeddings( - model=self.model, - input=texts, + batch_responses = ( + self.client.embeddings( + model=self.model, + input=batch, + ) + for batch in self._get_batches(texts) ) return [ list(map(float, embedding_obj.embedding)) - for embedding_obj in embeddings_batch_response.data + for response in batch_responses + for embedding_obj in response.data ] except MistralException as e: logger.error(f"An error occurred with MistralAI: {e}") @@ -106,13 +140,19 @@ async def aembed_documents(self, texts: List[str]) -> List[List[float]]: List of embeddings, one for each text. """ try: - embeddings_batch_response = await self.async_client.embeddings( - model=self.model, - input=texts, + batch_responses = await asyncio.gather( + *[ + self.async_client.embeddings( + model=self.model, + input=batch, + ) + for batch in self._get_batches(texts) + ] ) return [ list(map(float, embedding_obj.embedding)) - for embedding_obj in embeddings_batch_response.data + for response in batch_responses + for embedding_obj in response.data ] except MistralException as e: logger.error(f"An error occurred with MistralAI: {e}") diff --git a/libs/partners/mistralai/poetry.lock b/libs/partners/mistralai/poetry.lock index 8d406846bf2bf1e..ea414d7b199a18b 100644 --- a/libs/partners/mistralai/poetry.lock +++ b/libs/partners/mistralai/poetry.lock @@ -16,19 +16,20 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.1.0" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"}, - {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"}, + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] @@ -37,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = 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+dask = ["dask", "distributed"] +devel = ["pytest", "pytest-cov"] +dropbox = ["dropbox", "dropboxdrivefs", "requests"] +full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"] +fuse = ["fusepy"] +gcs = ["gcsfs"] +git = ["pygit2"] +github = ["requests"] +gs = ["gcsfs"] +gui = ["panel"] +hdfs = ["pyarrow (>=1)"] +http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"] +libarchive = ["libarchive-c"] +oci = ["ocifs"] +s3 = ["s3fs"] +sftp = ["paramiko"] +smb = ["smbprotocol"] +ssh = ["paramiko"] +tqdm = ["tqdm"] + [[package]] name = "h11" version = "0.14.0" @@ -243,6 +295,38 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] +[[package]] +name = "huggingface-hub" +version = "0.20.3" +description = "Client library to download and publish models, datasets and 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--git a/libs/partners/mistralai/tests/integration_tests/test_embeddings.py b/libs/partners/mistralai/tests/integration_tests/test_embeddings.py index 166bf5c5c15be6a..345c29b355506c7 100644 --- a/libs/partners/mistralai/tests/integration_tests/test_embeddings.py +++ b/libs/partners/mistralai/tests/integration_tests/test_embeddings.py @@ -1,4 +1,5 @@ """Test MistralAI Embedding""" + from langchain_mistralai import MistralAIEmbeddings @@ -38,10 +39,10 @@ async def test_mistralai_embedding_query_async() -> None: def test_mistralai_embedding_documents_long() -> None: """Test MistralAI embeddings for documents.""" - documents = ["foo bar " * 1000, "test document " * 1000] + documents = ["foo bar " * 1000, "test document " * 1000] * 5 embedding = MistralAIEmbeddings() output = embedding.embed_documents(documents) - assert len(output) == 2 + assert len(output) == 10 assert len(output[0]) == 1024 From 22b6a03a28e5b876d9c5497ff706db873e11ad35 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 16:05:11 -0800 Subject: [PATCH 117/157] infra: read min versions (#17135) --- .github/scripts/get_min_versions.py | 65 +++++++++++++++++++ .github/workflows/_release.yml | 11 +++- libs/community/_test_minimum_requirements.txt | 1 - .../_test_minimum_requirements.txt | 2 - libs/langchain/_test_minimum_requirements.txt | 2 - 5 files changed, 73 insertions(+), 8 deletions(-) create mode 100644 .github/scripts/get_min_versions.py delete mode 100644 libs/community/_test_minimum_requirements.txt delete mode 100644 libs/experimental/_test_minimum_requirements.txt delete mode 100644 libs/langchain/_test_minimum_requirements.txt diff --git a/.github/scripts/get_min_versions.py b/.github/scripts/get_min_versions.py new file mode 100644 index 000000000000000..fb68755ac287625 --- /dev/null +++ b/.github/scripts/get_min_versions.py @@ -0,0 +1,65 @@ +import sys + +import tomllib +from packaging.version import parse as parse_version +import re + +MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain"] + + +def get_min_version(version: str) -> str: + # case ^x.x.x + _match = re.match(r"^\^(\d+(?:\.\d+){0,2})$", version) + if _match: + return _match.group(1) + + # case >=x.x.x,=(\d+(?:\.\d+){0,2}),<(\d+(?:\.\d+){0,2})$", version) + if _match: + _min = _match.group(1) + _max = _match.group(2) + assert parse_version(_min) < parse_version(_max) + return _min + + # case x.x.x + _match = re.match(r"^(\d+(?:\.\d+){0,2})$", version) + if _match: + return _match.group(1) + + raise ValueError(f"Unrecognized version format: {version}") + + +def get_min_version_from_toml(toml_path: str): + # Parse the TOML file + with open(toml_path, "rb") as file: + toml_data = tomllib.load(file) + + # Get the dependencies from tool.poetry.dependencies + dependencies = toml_data["tool"]["poetry"]["dependencies"] + + # Initialize a dictionary to store the minimum versions + min_versions = {} + + # Iterate over the libs in MIN_VERSION_LIBS + for lib in MIN_VERSION_LIBS: + # Check if the lib is present in the dependencies + if lib in dependencies: + # Get the version string + version_string = dependencies[lib] + + # Use parse_version to get the minimum supported version from version_string + min_version = get_min_version(version_string) + + # Store the minimum version in the min_versions dictionary + min_versions[lib] = min_version + + return min_versions + + +# Get the TOML file path from the command line argument +toml_file = sys.argv[1] + +# Call the function to get the minimum versions +min_versions = get_min_version_from_toml(toml_file) + +print(" ".join([f"{lib}=={version}" for lib, version in min_versions.items()])) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index c140f93ba589a6c..b32842ebf5f7464 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -15,7 +15,7 @@ on: default: 'libs/langchain' env: - PYTHON_VERSION: "3.10" + PYTHON_VERSION: "3.11" POETRY_VERSION: "1.7.1" jobs: @@ -181,10 +181,15 @@ jobs: run: make integration_tests working-directory: ${{ inputs.working-directory }} + - name: Get minimum versions + id: check-version + run: echo "min-versions=$(python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)"" >> $GITHUB_OUTPUT + - name: Run unit tests with minimum dependency versions - if: ${{ (inputs.working-directory == 'libs/langchain') || (inputs.working-directory == 'libs/community') || (inputs.working-directory == 'libs/experimental') }} + env: + MIN_VERSIONS: ${{ steps.check-version.outputs.min-versions }} run: | - poetry run pip install -r _test_minimum_requirements.txt + poetry run pip install $MIN_VERSIONS make tests working-directory: ${{ inputs.working-directory }} diff --git a/libs/community/_test_minimum_requirements.txt b/libs/community/_test_minimum_requirements.txt deleted file mode 100644 index 3c490af63d8060d..000000000000000 --- a/libs/community/_test_minimum_requirements.txt +++ /dev/null @@ -1 +0,0 @@ -langchain-core==0.1.19 diff --git a/libs/experimental/_test_minimum_requirements.txt b/libs/experimental/_test_minimum_requirements.txt deleted file mode 100644 index 078f131ef8dcdf4..000000000000000 --- a/libs/experimental/_test_minimum_requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -langchain-core==0.1.16 -langchain==0.1.5 diff --git a/libs/langchain/_test_minimum_requirements.txt b/libs/langchain/_test_minimum_requirements.txt deleted file mode 100644 index 84c789c84d7eef5..000000000000000 --- a/libs/langchain/_test_minimum_requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -langchain-core==0.1.16 -langchain-community==0.0.17 From 3e58df43c2ce1660e0bde326c6a3dfc8ba46051f Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 16:05:20 -0800 Subject: [PATCH 118/157] mistralai[patch]: release 0.0.4 (#17139) --- libs/partners/mistralai/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/partners/mistralai/pyproject.toml b/libs/partners/mistralai/pyproject.toml index 5c65a1bdab35b0f..b49fdbb93c420ec 100644 --- a/libs/partners/mistralai/pyproject.toml +++ b/libs/partners/mistralai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-mistralai" -version = "0.0.3" +version = "0.0.4" description = "An integration package connecting Mistral and LangChain" authors = [] readme = "README.md" From 21872682086cb2082c5acc16170c906212f57b9f Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 16:22:20 -0800 Subject: [PATCH 119/157] infra: fix release (#17142) --- .github/workflows/_release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index b32842ebf5f7464..de130bfbcc23f18 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -183,7 +183,7 @@ jobs: - name: Get minimum versions id: check-version - run: echo "min-versions=$(python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)"" >> $GITHUB_OUTPUT + run: echo "min-versions=$(python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT - name: Run unit tests with minimum dependency versions env: From d397721a34418b20d814256c2ead57b04b29dc7c Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 16:32:53 -0800 Subject: [PATCH 120/157] docs: format (#17143) --- docs/docs/integrations/chat/anthropic.ipynb | 16 ++++++++++------ .../chat/google_vertex_ai_palm.ipynb | 4 +--- docs/docs/use_cases/csv.ipynb | 4 +--- docs/docs/use_cases/sql/agents.ipynb | 4 +++- docs/docs/use_cases/sql/query_checking.ipynb | 8 ++++++-- 5 files changed, 21 insertions(+), 15 deletions(-) diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb index f8aa35142de11b3..008d290fa3c820e 100644 --- a/docs/docs/integrations/chat/anthropic.ipynb +++ b/docs/docs/integrations/chat/anthropic.ipynb @@ -90,16 +90,20 @@ } ], "source": [ - "system = \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", + "system = (\n", + " \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", + ")\n", "human = \"{text}\"\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "\n", "chain = prompt | chat\n", - "chain.invoke({\n", - " \"input_language\": \"English\",\n", - " \"output_language\": \"Korean\",\n", - " \"text\": \"I love Python\",\n", - "})" + "chain.invoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"Korean\",\n", + " \"text\": \"I love Python\",\n", + " }\n", + ")" ] }, { diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb index 885174f4a1b5260..ecc7a653a0d9250 100644 --- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb @@ -424,9 +424,7 @@ "human = \"{text}\"\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "\n", - "chat = ChatVertexAI(\n", - " model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5\n", - ")\n", + "chat = ChatVertexAI(model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5)\n", "chain = prompt | chat\n", "\n", "asyncio.run(\n", diff --git a/docs/docs/use_cases/csv.ipynb b/docs/docs/use_cases/csv.ipynb index 7ef34f8412f49ef..589faa169506a0b 100644 --- a/docs/docs/use_cases/csv.ipynb +++ b/docs/docs/use_cases/csv.ipynb @@ -460,9 +460,7 @@ "Given a user question, write the Python code to answer it. \\\n", "Return ONLY the valid Python code and nothing else. \\\n", "Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n", - "prompt = ChatPromptTemplate.from_messages(\n", - " [(\"system\", system), (\"human\", \"{question}\")]\n", - ")\n", + "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n", "code_chain = prompt | llm_with_tools | parser\n", "code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})" ] diff --git a/docs/docs/use_cases/sql/agents.ipynb b/docs/docs/use_cases/sql/agents.ipynb index 9b013f89963018e..9a91a34a8ccc06f 100644 --- a/docs/docs/use_cases/sql/agents.ipynb +++ b/docs/docs/use_cases/sql/agents.ipynb @@ -721,7 +721,9 @@ "\n", "If the question does not seem related to the database, just return \"I don't know\" as the answer.\"\"\"\n", "\n", - "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")])\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")]\n", + ")\n", "agent = create_sql_agent(\n", " llm=llm,\n", " db=db,\n", diff --git a/docs/docs/use_cases/sql/query_checking.ipynb b/docs/docs/use_cases/sql/query_checking.ipynb index fcb5d44a2ba3020..238776d0e6cca49 100644 --- a/docs/docs/use_cases/sql/query_checking.ipynb +++ b/docs/docs/use_cases/sql/query_checking.ipynb @@ -294,11 +294,15 @@ "First draft: <>\n", "Final answer: <>\n", "\"\"\"\n", - "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\")]).partial(dialect=db.dialect)\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"system\", system), (\"human\", \"{input}\")]\n", + ").partial(dialect=db.dialect)\n", + "\n", "\n", "def parse_final_answer(output: str) -> str:\n", " return output.split(\"Final answer: \")[1]\n", - " \n", + "\n", + "\n", "chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer\n", "prompt.pretty_print()" ] From 181a0332261fe79040580c8e7a2a6aa7a64ee581 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 17:37:36 -0800 Subject: [PATCH 121/157] infra: poetry run min versions (#17146) --- .github/workflows/_release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index de130bfbcc23f18..c05d92c156a428f 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -183,7 +183,7 @@ jobs: - name: Get minimum versions id: check-version - run: echo "min-versions=$(python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT + run: echo "min-versions=$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT - name: Run unit tests with minimum dependency versions env: From 9710346095b669c1da302f75744d24e324e90234 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 17:57:43 -0800 Subject: [PATCH 122/157] infra: poetry run min versions 2 (#17149) --- .github/workflows/_release.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index c05d92c156a428f..ef0c50f767df3cd 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -183,7 +183,9 @@ jobs: - name: Get minimum versions id: check-version - run: echo "min-versions=$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT + run: | + poetry run pip install packaging + echo "min-versions=$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT - name: Run unit tests with minimum dependency versions env: From deb02de051b18f45a8cbfda652f6747c2aa51ec4 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 18:10:37 -0800 Subject: [PATCH 123/157] infra: release min version debugging (#17150) --- .github/workflows/_release.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index ef0c50f767df3cd..bbcc32fe5e02d5e 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -182,14 +182,16 @@ jobs: working-directory: ${{ inputs.working-directory }} - name: Get minimum versions - id: check-version + id: min-version run: | poetry run pip install packaging - echo "min-versions=$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" >> $GITHUB_OUTPUT + min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" + echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT" + echo "min-versions=$min_versions" - name: Run unit tests with minimum dependency versions env: - MIN_VERSIONS: ${{ steps.check-version.outputs.min-versions }} + MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }} run: | poetry run pip install $MIN_VERSIONS make tests From f499a222de8a6e631f9b83b484bb03601d09f986 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Tue, 6 Feb 2024 18:20:19 -0800 Subject: [PATCH 124/157] infra: release min version debugging 2 (#17152) --- .github/workflows/_release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index bbcc32fe5e02d5e..4fe5cdaf5030ade 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -182,6 +182,7 @@ jobs: working-directory: ${{ inputs.working-directory }} - name: Get minimum versions + working-directory: ${{ inputs.working-directory }} id: min-version run: | poetry run pip install packaging From bf55279d395582df41a22e8ddfaf5deba410f0bb Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Tue, 6 Feb 2024 20:30:30 -0800 Subject: [PATCH 125/157] docs: tutorials update (#17132) Added the course and the one-pager links --- docs/docs/additional_resources/tutorials.mdx | 40 +++++++++++--------- 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/docs/docs/additional_resources/tutorials.mdx b/docs/docs/additional_resources/tutorials.mdx index 3a68017f9fcd2e6..49d3b4fda012ff2 100644 --- a/docs/docs/additional_resources/tutorials.mdx +++ b/docs/docs/additional_resources/tutorials.mdx @@ -2,7 +2,7 @@ Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases). -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-06] --------------------- @@ -10,18 +10,20 @@ Below are links to tutorials and courses on LangChain. For written guides on com ### Books -#### ⛓[Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing +#### [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing ### DeepLearning.AI courses by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng) - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data) -- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain) +- [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain) ### Handbook [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham** +⛓ [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov** + ### Short Tutorials [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics) @@ -29,6 +31,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber) +⛓ [LangChain 101 Course](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb) by **Ivan Reznikov** + ## Tutorials ### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs) @@ -44,8 +48,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com - #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI) - [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo) - [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI) -- ⛓ [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr) -- ⛓ [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ) +- [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr) +- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ) ### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent) @@ -109,16 +113,16 @@ Below are links to tutorials and courses on LangChain. For written guides on com - [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs) - [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE) - [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k) -- ⛓ [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC) -- ⛓ [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4) -- ⛓ [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN) -- ⛓ [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ) -- ⛓ [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9) -- ⛓ [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG) -- ⛓ [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe) -- ⛓ [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ) -- ⛓ [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI) -- ⛓ [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80) +- [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC) +- [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4) +- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN) +- [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ) +- [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9) +- [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG) +- [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe) +- [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ) +- [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI) +- [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80) ### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt) @@ -131,8 +135,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com - [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY) - [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) -- ⛓ [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl) -- ⛓ [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB) +- [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl) +- [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB) ### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata) @@ -148,4 +152,4 @@ Below are links to tutorials and courses on LangChain. For written guides on com --------------------- -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-061] From 0af0fc5d251ce2253268175ee9bc765706ee9b24 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Tue, 6 Feb 2024 20:33:14 -0800 Subject: [PATCH 126/157] docs `integraions/providers` nav fix (#17148) Issue: `Provides` page is presented as the index page (on the `Providers` item) and as the `Providers/Providers` item. The latter should not be in the menu. See the picture. ![image](https://github.com/langchain-ai/langchain/assets/2256422/6894023f-f13a-4f0d-8fe2-ed5b0ae2bdd2) This PR fixes this. --- docs/docs/integrations/platforms/index.mdx | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/docs/integrations/platforms/index.mdx b/docs/docs/integrations/platforms/index.mdx index 9a5fcdf57c98281..ee66df3324ae7ec 100644 --- a/docs/docs/integrations/platforms/index.mdx +++ b/docs/docs/integrations/platforms/index.mdx @@ -1,3 +1,8 @@ +--- +sidebar_position: 0 +sidebar_class_name: hidden +--- + # Providers LangChain integrates with many providers From 5ceaf784f324064b868a3cfed3fab7554173e7b3 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Tue, 6 Feb 2024 20:33:41 -0800 Subject: [PATCH 127/157] docs `Integraions/Components` menu reordered (#17151) This PR is opinionated. - Moved `Embedding models` item to place after `LLMs` and `Chat model`, so all items with models are together. - Renamed `Text embedding models` to `Embedding models`. Now, it is shorter and easier to read. `Text` is obvious from context. The same as the `Text LLMs` vs. `LLMs` (we also have multi-modal LLMs). --- docs/sidebars.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sidebars.js b/docs/sidebars.js index ba924e5a38a5ca8..3918fe92795ff6c 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -101,9 +101,9 @@ module.exports = { items: [ { type: "category", label: "LLMs", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/llms" }], link: { type: 'doc', id: "integrations/llms/index"}}, { type: "category", label: "Chat models", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/chat" }], link: { type: 'doc', id: "integrations/chat/index"}}, + { type: "category", label: "Embedding models", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/text_embedding" }], link: {type: "generated-index", slug: "integrations/text_embedding" }}, { type: "category", label: "Document loaders", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/document_loaders" }], link: {type: "generated-index", slug: "integrations/document_loaders" }}, { type: "category", label: "Document transformers", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/document_transformers" }], link: {type: "generated-index", slug: "integrations/document_transformers" }}, - { type: "category", label: "Text embedding models", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/text_embedding" }], link: {type: "generated-index", slug: "integrations/text_embedding" }}, { type: "category", label: "Vector stores", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/vectorstores" }], link: {type: "generated-index", slug: "integrations/vectorstores" }}, { type: "category", label: "Retrievers", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/retrievers" }], link: {type: "generated-index", slug: "integrations/retrievers" }}, { type: "category", label: "Tools", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/tools" }], link: {type: "generated-index", slug: "integrations/tools" }}, From 9fa07076da34b16ceecccee0f92dfe7c67201d76 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Wed, 7 Feb 2024 09:42:44 -0800 Subject: [PATCH 128/157] Add trace_as_chain_group metadata (#17187) --- libs/core/langchain_core/callbacks/manager.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/libs/core/langchain_core/callbacks/manager.py b/libs/core/langchain_core/callbacks/manager.py index e216f479832ac64..b1f103871f13188 100644 --- a/libs/core/langchain_core/callbacks/manager.py +++ b/libs/core/langchain_core/callbacks/manager.py @@ -67,6 +67,7 @@ def trace_as_chain_group( example_id: Optional[Union[str, UUID]] = None, run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, + metadata: Optional[Dict[str, Any]] = None, ) -> Generator[CallbackManagerForChainGroup, None, None]: """Get a callback manager for a chain group in a context manager. Useful for grouping different calls together as a single run even if @@ -83,6 +84,8 @@ def trace_as_chain_group( run_id (UUID, optional): The ID of the run. tags (List[str], optional): The inheritable tags to apply to all runs. Defaults to None. + metadata (Dict[str, Any], optional): The metadata to apply to all runs. + Defaults to None. Note: must have LANGCHAIN_TRACING_V2 env var set to true to see the trace in LangSmith. @@ -95,7 +98,7 @@ def trace_as_chain_group( llm_input = "Foo" with trace_as_chain_group("group_name", inputs={"input": llm_input}) as manager: # Use the callback manager for the chain group - res = llm.predict(llm_input, callbacks=manager) + res = llm.invoke(llm_input, {"callbacks": manager}) manager.on_chain_end({"output": res}) """ # noqa: E501 from langchain_core.tracers.context import _get_trace_callbacks @@ -106,6 +109,7 @@ def trace_as_chain_group( cm = CallbackManager.configure( inheritable_callbacks=cb, inheritable_tags=tags, + inheritable_metadata=metadata, ) run_manager = cm.on_chain_start({"name": group_name}, inputs or {}, run_id=run_id) @@ -141,6 +145,7 @@ async def atrace_as_chain_group( example_id: Optional[Union[str, UUID]] = None, run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, + metadata: Optional[Dict[str, Any]] = None, ) -> AsyncGenerator[AsyncCallbackManagerForChainGroup, None]: """Get an async callback manager for a chain group in a context manager. Useful for grouping different async calls together as a single run even if @@ -157,6 +162,8 @@ async def atrace_as_chain_group( run_id (UUID, optional): The ID of the run. tags (List[str], optional): The inheritable tags to apply to all runs. Defaults to None. + metadata (Dict[str, Any], optional): The metadata to apply to all runs. + Defaults to None. Returns: AsyncCallbackManager: The async callback manager for the chain group. @@ -168,7 +175,7 @@ async def atrace_as_chain_group( llm_input = "Foo" async with atrace_as_chain_group("group_name", inputs={"input": llm_input}) as manager: # Use the async callback manager for the chain group - res = await llm.apredict(llm_input, callbacks=manager) + res = await llm.ainvoke(llm_input, {"callbacks": manager}) await manager.on_chain_end({"output": res}) """ # noqa: E501 from langchain_core.tracers.context import _get_trace_callbacks @@ -176,7 +183,9 @@ async def atrace_as_chain_group( cb = _get_trace_callbacks( project_name, example_id, callback_manager=callback_manager ) - cm = AsyncCallbackManager.configure(inheritable_callbacks=cb, inheritable_tags=tags) + cm = AsyncCallbackManager.configure( + inheritable_callbacks=cb, inheritable_tags=tags, inheritable_metadata=metadata + ) run_manager = await cm.on_chain_start( {"name": group_name}, inputs or {}, run_id=run_id From 302989a2b1b8b0228d9de406b91edc88979e5e58 Mon Sep 17 00:00:00 2001 From: Tomaz Bratanic Date: Wed, 7 Feb 2024 19:26:14 +0100 Subject: [PATCH 129/157] allow optional newline in the action responses of JSON Agent parser (#17186) Based on my experiments, the newline isn't always there, so we can make the regex slightly more robust by allowing an optional newline after the bacticks --- .../langchain/agents/output_parsers/react_json_single_input.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py b/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py index c58e1804ab17173..0ca9ed2ffd82d59 100644 --- a/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py +++ b/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py @@ -42,7 +42,7 @@ class ReActJsonSingleInputOutputParser(AgentOutputParser): """ - pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL) + pattern = re.compile(r"^.*?`{3}(?:json)?\n?(.*?)`{3}.*?$", re.DOTALL) """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: From f87b38a559dcd084d85e2a98d6e34397d7f5d5d3 Mon Sep 17 00:00:00 2001 From: chyroc Date: Thu, 8 Feb 2024 04:09:30 +0800 Subject: [PATCH 130/157] google-genai[minor]: support functions call (#15146) Co-authored-by: Erick Friis --- .../langchain_google_genai/chat_models.py | 129 ++- libs/partners/google-genai/poetry.lock | 741 +++++++++--------- .../integration_tests/test_function_call.py | 34 + 3 files changed, 520 insertions(+), 384 deletions(-) create mode 100644 libs/partners/google-genai/tests/integration_tests/test_function_call.py diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py index d30990c5b6e4b0a..426d279aa0178e5 100644 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ b/libs/partners/google-genai/langchain_google_genai/chat_models.py @@ -26,6 +26,7 @@ # TODO: remove ignore once the google package is published with types import google.generativeai as genai # type: ignore[import] import requests +from google.ai.generativelanguage_v1beta import FunctionCall from langchain_core.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, @@ -341,16 +342,90 @@ def _parse_chat_history( return messages +def _retrieve_function_call_response( + parts: List[genai.types.PartType], +) -> Optional[Dict]: + for idx, part in enumerate(parts): + if part.function_call and part.function_call.name: + fc: FunctionCall = part.function_call + return { + "function_call": { + "name": fc.name, + "arguments": dict(fc.args.items()), + } + } + return None + + +def _convert_function_call_req(function_calls: Union[Dict, List[Dict]]) -> Dict: + function_declarations = [] + if isinstance(function_calls, dict): + function_declarations.append(_convert_fc_type(function_calls)) + else: + for fc in function_calls: + function_declarations.append(_convert_fc_type(fc)) + return { + "function_declarations": function_declarations, + } + + +def _convert_fc_type(fc: Dict) -> Dict: + # type_: "Type" + # format_: str + # description: str + # nullable: bool + # enum: MutableSequence[str] + # items: "Schema" + # properties: MutableMapping[str, "Schema"] + # required: MutableSequence[str] + if "parameters" in fc: + fc["parameters"] = _convert_fc_type(fc["parameters"]) + if "properties" in fc: + for k, v in fc["properties"].items(): + fc["properties"][k] = _convert_fc_type(v) + if "type" in fc: + # STRING = 1 + # NUMBER = 2 + # INTEGER = 3 + # BOOLEAN = 4 + # ARRAY = 5 + # OBJECT = 6 + if fc["type"] == "string": + fc["type_"] = 1 + elif fc["type"] == "number": + fc["type_"] = 2 + elif fc["type"] == "integer": + fc["type_"] = 3 + elif fc["type"] == "boolean": + fc["type_"] = 4 + elif fc["type"] == "array": + fc["type_"] = 5 + elif fc["type"] == "object": + fc["type_"] = 6 + del fc["type"] + if "format" in fc: + fc["format_"] = fc["format"] + del fc["format"] + + for k, v in fc.items(): + if isinstance(v, dict): + fc[k] = _convert_fc_type(v) + + return fc + + def _parts_to_content( parts: List[genai.types.PartType], -) -> Union[str, List[Union[Dict[Any, Any], str]]]: +) -> Tuple[Union[str, List[Union[Dict[Any, Any], str]]], Optional[Dict]]: """Converts a list of Gemini API Part objects into a list of LangChain messages.""" + function_call_resp = _retrieve_function_call_response(parts) + if len(parts) == 1 and parts[0].text is not None and not parts[0].inline_data: # Simple text response. The typical response - return parts[0].text + return parts[0].text, function_call_resp elif not parts: logger.warning("Gemini produced an empty response.") - return "" + return "", function_call_resp messages: List[Union[Dict[Any, Any], str]] = [] for part in parts: if part.text is not None: @@ -363,7 +438,7 @@ def _parts_to_content( else: # TODO: Handle inline_data if that's a thing? raise ChatGoogleGenerativeAIError(f"Unexpected part type. {part}") - return messages + return messages, function_call_resp def _response_to_result( @@ -390,16 +465,24 @@ def _response_to_result( "model": ai_msg_t, "user": human_msg_t, } + for candidate in response.candidates: content = candidate.content - parts_content = _parts_to_content(content.parts) + parts_content, additional_kwargs = _parts_to_content(content.parts) if content.role not in role_map: logger.warning( f"Unrecognized role: {content.role}. Treating as a ChatMessage." ) - msg = chat_msg_t(content=parts_content, role=content.role) + msg = chat_msg_t( + content=parts_content, + role=content.role, + additional_kwargs=additional_kwargs or {}, + ) else: - msg = role_map[content.role](content=parts_content) + msg = role_map[content.role]( + content=parts_content, + additional_kwargs=additional_kwargs or {}, + ) generation_info = {} if candidate.finish_reason: generation_info["finish_reason"] = candidate.finish_reason.name @@ -527,7 +610,11 @@ def _generate( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + functions=kwargs.get("functions"), + ) response: genai.types.GenerateContentResponse = _chat_with_retry( content=message, **params, @@ -542,7 +629,11 @@ async def _agenerate( run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + functions=kwargs.get("functions"), + ) response: genai.types.GenerateContentResponse = await _achat_with_retry( content=message, **params, @@ -557,7 +648,11 @@ def _stream( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + functions=kwargs.get("functions"), + ) response: genai.types.GenerateContentResponse = _chat_with_retry( content=message, **params, @@ -584,7 +679,11 @@ async def _astream( run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + functions=kwargs.get("functions"), + ) async for chunk in await _achat_with_retry( content=message, **params, @@ -609,13 +708,19 @@ def _prepare_chat( stop: Optional[List[str]] = None, **kwargs: Any, ) -> Tuple[Dict[str, Any], genai.ChatSession, genai.types.ContentDict]: + cli = self.client + functions = kwargs.pop("functions", None) + if functions: + tools = _convert_function_call_req(functions) + cli = genai.GenerativeModel(model_name=self.model, tools=tools) + params = self._prepare_params(stop, **kwargs) history = _parse_chat_history( messages, convert_system_message_to_human=self.convert_system_message_to_human, ) message = history.pop() - chat = self.client.start_chat(history=history) + chat = cli.start_chat(history=history) return params, chat, message def get_num_tokens(self, text: str) -> int: diff --git a/libs/partners/google-genai/poetry.lock b/libs/partners/google-genai/poetry.lock index edddadcb57d17bd..dc37a3fc067ad0e 100644 --- a/libs/partners/google-genai/poetry.lock +++ b/libs/partners/google-genai/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "annotated-types" @@ -13,19 +13,20 @@ files = [ [[package]] name = "anyio" -version = "4.1.0" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"}, - {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"}, + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] 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"enum": ["c", "f"]}, + }, + "required": ["location"], + }, + } + ] + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=functions) + res = llm.invoke("what weather is today in san francisco?") + assert res + assert res.additional_kwargs + assert "function_call" in res.additional_kwargs + assert "get_weather" == res.additional_kwargs["function_call"]["name"] + arguments = res.additional_kwargs["function_call"]["arguments"] + assert isinstance(arguments, dict) + assert "location" in arguments From 65798289a446d4baca2c5dd7a408bdff40f50846 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Wed, 7 Feb 2024 12:10:58 -0800 Subject: [PATCH 131/157] core[minor]: Use batched tracing in sdk (#16305) Remove threadpool executor usage in langchain tracer, this is now handled by sdk --- libs/core/langchain_core/tracers/langchain.py | 54 +++++++------------ libs/core/poetry.lock | 29 ++++++++-- libs/core/pyproject.toml | 2 +- .../smith/evaluation/runner_utils.py | 1 - 4 files changed, 46 insertions(+), 40 deletions(-) diff --git a/libs/core/langchain_core/tracers/langchain.py b/libs/core/langchain_core/tracers/langchain.py index ef4defb28b81f01..91b54224bd2c5b5 100644 --- a/libs/core/langchain_core/tracers/langchain.py +++ b/libs/core/langchain_core/tracers/langchain.py @@ -2,10 +2,9 @@ from __future__ import annotations import logging -import weakref -from concurrent.futures import Future, ThreadPoolExecutor, wait +from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client @@ -27,7 +26,6 @@ logger = logging.getLogger(__name__) _LOGGED = set() -_TRACERS: weakref.WeakSet[LangChainTracer] = weakref.WeakSet() _CLIENT: Optional[Client] = None _EXECUTOR: Optional[ThreadPoolExecutor] = None @@ -43,10 +41,9 @@ def log_error_once(method: str, exception: Exception) -> None: def wait_for_all_tracers() -> None: """Wait for all tracers to finish.""" - global _TRACERS - for tracer in list(_TRACERS): - if tracer is not None: - tracer.wait_for_futures() + global _CLIENT + if _CLIENT is not None and _CLIENT.tracing_queue is not None: + _CLIENT.tracing_queue.join() def get_client() -> Client: @@ -74,7 +71,6 @@ def __init__( project_name: Optional[str] = None, client: Optional[Client] = None, tags: Optional[List[str]] = None, - use_threading: bool = True, **kwargs: Any, ) -> None: """Initialize the LangChain tracer.""" @@ -84,12 +80,8 @@ def __init__( ) self.project_name = project_name or ls_utils.get_tracer_project() self.client = client or get_client() - self._futures: weakref.WeakSet[Future] = weakref.WeakSet() self.tags = tags or [] - self.executor = _get_executor() if use_threading else None self.latest_run: Optional[Run] = None - global _TRACERS - _TRACERS.add(self) def on_chat_model_start( self, @@ -181,75 +173,69 @@ def _update_run_single(self, run: Run) -> None: log_error_once("patch", e) raise - def _submit(self, function: Callable[[Run], None], run: Run) -> None: - """Submit a function to the executor.""" - if self.executor is None: - function(run) - else: - self._futures.add(self.executor.submit(function, run)) - def _on_llm_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_chat_model_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_chain_start(self, run: Run) -> None: """Process the Chain Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_tool_start(self, run: Run) -> None: """Process the Tool Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_retriever_start(self, run: Run) -> None: """Process the Retriever Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def wait_for_futures(self) -> None: """Wait for the given futures to complete.""" - wait(self._futures) + if self.client is not None and self.client.tracing_queue is not None: + self.client.tracing_queue.join() diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock 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jsonpatch = "^1.33" anyio = ">=3,<5" diff --git a/libs/langchain/langchain/smith/evaluation/runner_utils.py b/libs/langchain/langchain/smith/evaluation/runner_utils.py index ce50c1500af5836..feb199e8d0e7dd9 100644 --- a/libs/langchain/langchain/smith/evaluation/runner_utils.py +++ b/libs/langchain/langchain/smith/evaluation/runner_utils.py @@ -1145,7 +1145,6 @@ def prepare( LangChainTracer( project_name=project.name, client=client, - use_threading=False, example_id=example.id, ), EvaluatorCallbackHandler( From 78409634fed33167c27ebf91983945ec9de148cf Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 12:28:05 -0800 Subject: [PATCH 132/157] core[patch]: Release 0.1.20 (#17194) --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 274d35dcf4ceb22..40c47e0a2e83b15 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-core" -version = "0.1.19" +version = "0.1.20" description = "Building applications with LLMs through composability" authors = [] license = "MIT" From 4ae91733aa84718abfb610c50d724ca38145e092 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 12:35:27 -0800 Subject: [PATCH 133/157] infra: fix core release (#17195) core doesn't have any min deps to test --- .github/workflows/_release.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 4fe5cdaf5030ade..06bcef9a04068f9 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -182,6 +182,7 @@ jobs: working-directory: ${{ inputs.working-directory }} - name: Get minimum versions + if: ${{ !startsWith(inputs.working-directory, 'libs/core') }} working-directory: ${{ inputs.working-directory }} id: min-version run: | @@ -191,6 +192,7 @@ jobs: echo "min-versions=$min_versions" - name: Run unit tests with minimum dependency versions + if: ${{ !startsWith(inputs.working-directory, 'libs/core') }} env: MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }} run: | From f87acf0340cc200a6b29fadd5f2e216cd7d0a26c Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 12:49:02 -0800 Subject: [PATCH 134/157] infra: better conditional (#17197) --- .github/workflows/_release.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 06bcef9a04068f9..a0b31c866497b3c 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -182,7 +182,6 @@ jobs: working-directory: ${{ inputs.working-directory }} - name: Get minimum versions - if: ${{ !startsWith(inputs.working-directory, 'libs/core') }} working-directory: ${{ inputs.working-directory }} id: min-version run: | @@ -192,7 +191,7 @@ jobs: echo "min-versions=$min_versions" - name: Run unit tests with minimum dependency versions - if: ${{ !startsWith(inputs.working-directory, 'libs/core') }} + if: ${{ steps.min-version.outputs.min-versions != '' }} env: MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }} run: | From ecf8042a109a7f8547e4b427e5813c743409ad26 Mon Sep 17 00:00:00 2001 From: Tomaz Bratanic Date: Wed, 7 Feb 2024 21:50:54 +0100 Subject: [PATCH 135/157] templates: Add neo4j semantic layer with ollama template (#17192) A template with JSON-based agent using Mixtral via Ollama. --------- Co-authored-by: Erick Friis --- templates/neo4j-semantic-ollama/README.md | 104 + templates/neo4j-semantic-ollama/ingest.py | 59 + templates/neo4j-semantic-ollama/main.py | 17 + .../neo4j_semantic_ollama/__init__.py | 3 + .../neo4j_semantic_ollama/agent.py | 110 ++ .../neo4j_semantic_ollama/information_tool.py | 74 + .../neo4j_semantic_ollama/memory_tool.py | 73 + .../recommendation_tool.py | 164 ++ .../neo4j_semantic_ollama/smalltalk_tool.py | 39 + .../neo4j_semantic_ollama/utils.py | 84 + templates/neo4j-semantic-ollama/poetry.lock | 1753 +++++++++++++++++ .../neo4j-semantic-ollama/pyproject.toml | 33 + .../neo4j-semantic-ollama/static/workflow.png | Bin 0 -> 524624 bytes .../neo4j-semantic-ollama/tests/__init__.py | 0 14 files changed, 2513 insertions(+) create mode 100644 templates/neo4j-semantic-ollama/README.md create mode 100644 templates/neo4j-semantic-ollama/ingest.py create mode 100644 templates/neo4j-semantic-ollama/main.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py create mode 100644 templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py create mode 100644 templates/neo4j-semantic-ollama/poetry.lock create mode 100644 templates/neo4j-semantic-ollama/pyproject.toml create mode 100644 templates/neo4j-semantic-ollama/static/workflow.png create mode 100644 templates/neo4j-semantic-ollama/tests/__init__.py diff --git a/templates/neo4j-semantic-ollama/README.md b/templates/neo4j-semantic-ollama/README.md new file mode 100644 index 000000000000000..70a5ab15c97de43 --- /dev/null +++ b/templates/neo4j-semantic-ollama/README.md @@ -0,0 +1,104 @@ +# neo4j-semantic-ollama + +This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent. +The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent. +Learn more about the semantic layer template in the [corresponding blog post](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49). + +![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-ollama/static/workflow.png "Neo4j Semantic Layer Workflow Diagram") + +## Tools + +The agent utilizes several tools to interact with the Neo4j graph database effectively: + +1. **Information tool**: + - Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information. +2. **Recommendation Tool**: + - Provides movie recommendations based upon user preferences and input. +3. **Memory Tool**: + - Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions. +4. **Smalltalk Tool**: + - Allows an agent to deal with smalltalk. + +## Environment Setup + +Before using this template, you need to set up Ollama and Neo4j database. + +1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. + +2. Download your LLM of interest: + + * This package uses `mixtral`: `ollama pull mixtral` + * You can choose from many LLMs [here](https://ollama.ai/library) + +You need to define the following environment variables + +``` +OLLAMA_BASE_URL= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Populating with data + +If you want to populate the DB with an example movie dataset, you can run `python ingest.py`. +The script import information about movies and their rating by users. +Additionally, the script creates two [fulltext indices](https://neo4j.com/docs/cypher-manual/current/indexes-for-full-text-search/), which are used to map information from user input to the database. + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U "langchain-cli[serve]" +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package neo4j-semantic-ollama +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add neo4j-semantic-ollama +``` + +And add the following code to your `server.py` file: +```python +from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent + +add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/neo4j-semantic-ollama/playground](http://127.0.0.1:8000/neo4j-semantic-ollama/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama") +``` diff --git a/templates/neo4j-semantic-ollama/ingest.py b/templates/neo4j-semantic-ollama/ingest.py new file mode 100644 index 000000000000000..973b4c93c9c5960 --- /dev/null +++ b/templates/neo4j-semantic-ollama/ingest.py @@ -0,0 +1,59 @@ +from langchain_community.graphs import Neo4jGraph + +# Instantiate connection to Neo4j +graph = Neo4jGraph() + +# Define unique constraints +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (m:Movie) REQUIRE m.id IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (u:User) REQUIRE u.id IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (p:Person) REQUIRE p.name IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (g:Genre) REQUIRE g.name IS UNIQUE;") + +# Import movie information + +movies_query = """ +LOAD CSV WITH HEADERS FROM +'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies.csv' +AS row +CALL { + WITH row + MERGE (m:Movie {id:row.movieId}) + SET m.released = date(row.released), + m.title = row.title, + m.imdbRating = toFloat(row.imdbRating) + FOREACH (director in split(row.director, '|') | + MERGE (p:Person {name:trim(director)}) + MERGE (p)-[:DIRECTED]->(m)) + FOREACH (actor in split(row.actors, '|') | + MERGE (p:Person {name:trim(actor)}) + MERGE (p)-[:ACTED_IN]->(m)) + FOREACH (genre in split(row.genres, '|') | + MERGE (g:Genre {name:trim(genre)}) + MERGE (m)-[:IN_GENRE]->(g)) +} IN TRANSACTIONS +""" + +graph.query(movies_query) + +# Import rating information +rating_query = """ +LOAD CSV WITH HEADERS FROM +'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/ratings.csv' +AS row +CALL { + WITH row + MATCH (m:Movie {id:row.movieId}) + MERGE (u:User {id:row.userId}) + MERGE (u)-[r:RATED]->(m) + SET r.rating = toFloat(row.rating), + r.timestamp = row.timestamp +} IN TRANSACTIONS OF 10000 ROWS +""" + +graph.query(rating_query) + +# Define fulltext indices +graph.query("CREATE FULLTEXT INDEX movie IF NOT EXISTS FOR (m:Movie) ON EACH [m.title]") +graph.query( + "CREATE FULLTEXT INDEX person IF NOT EXISTS FOR (p:Person) ON EACH [p.name]" +) diff --git a/templates/neo4j-semantic-ollama/main.py b/templates/neo4j-semantic-ollama/main.py new file mode 100644 index 000000000000000..d6ee3a919695e64 --- /dev/null +++ b/templates/neo4j-semantic-ollama/main.py @@ -0,0 +1,17 @@ +from neo4j_semantic_ollama import agent_executor + +if __name__ == "__main__": + original_query = "What do you know about person John?" + followup_query = "John Travolta" + chat_history = [ + ( + "What do you know about person John?", + "I found multiple people named John. Could you please specify " + "which one you are interested in? Here are some options:" + "\n\n1. John Travolta\n2. John McDonough", + ) + ] + print(agent_executor.invoke({"input": original_query})) + print( + agent_executor.invoke({"input": followup_query, "chat_history": chat_history}) + ) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py new file mode 100644 index 000000000000000..0b540e6327fa241 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py @@ -0,0 +1,3 @@ +from neo4j_semantic_ollama.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py new file mode 100644 index 000000000000000..ad4010d6cfc9291 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py @@ -0,0 +1,110 @@ +import os +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_log_to_messages +from langchain.agents.output_parsers import ( + ReActJsonSingleInputOutputParser, +) +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.pydantic_v1 import BaseModel, Field +from langchain.schema import AIMessage, HumanMessage +from langchain.tools.render import render_text_description_and_args +from langchain_community.chat_models import ChatOllama + +from neo4j_semantic_ollama.information_tool import InformationTool +from neo4j_semantic_ollama.memory_tool import MemoryTool +from neo4j_semantic_ollama.recommendation_tool import RecommenderTool +from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool + +llm = ChatOllama( + model="mixtral", + temperature=0, + base_url=os.environ["OLLAMA_BASE_URL"], + streaming=True, +) +chat_model_with_stop = llm.bind(stop=["\nObservation"]) +tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()] + +# Inspiration taken from hub.pull("hwchase17/react-json") +system_message = f"""Answer the following questions as best you can. +You can answer directly if the user is greeting you or similar. +Otherise, you have access to the following tools: + +{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')} + +The way you use the tools is by specifying a json blob. +Specifically, this json should have a `action` key (with the name of the tool to use) +and a `action_input` key (with the input to the tool going here). +The only values that should be in the "action" field are: {[t.name for t in tools]} +The $JSON_BLOB should only contain a SINGLE action, +do NOT return a list of multiple actions. +Here is an example of a valid $JSON_BLOB: +``` +{{{{ + "action": $TOOL_NAME, + "action_input": $INPUT +}}}} +``` +The $JSON_BLOB must always be enclosed with triple backticks! + +ALWAYS use the following format: +Question: the input question you must answer +Thought: you should always think about what to do +Action:``` +$JSON_BLOB +``` +Observation: the result of the action... +(this Thought/Action/Observation can repeat N times) +Thought: I now know the final answer +Final Answer: the final answer to the original input question + +Begin! Reminder to always use the exact characters `Final Answer` when responding.' +""" + +prompt = ChatPromptTemplate.from_messages( + [ + ( + "user", + system_message, + ), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] +) + + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +agent = ( + { + "input": lambda x: x["input"], + "agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]), + "chat_history": lambda x: _format_chat_history(x["chat_history"]) + if x.get("chat_history") + else [], + } + | prompt + | chat_model_with_stop + | ReActJsonSingleInputOutputParser() +) + + +# Add typing for input +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] = Field( + ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} + ) + + +agent_executor = AgentExecutor(agent=agent, tools=tools).with_types( + input_type=AgentInput +) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py new file mode 100644 index 000000000000000..9ed31d2723ee327 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py @@ -0,0 +1,74 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) + +# Import things that are needed generically +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, graph + +description_query = """ +MATCH (m:Movie|Person) +WHERE m.title = $candidate OR m.name = $candidate +MATCH (m)-[r:ACTED_IN|DIRECTED|HAS_GENRE]-(t) +WITH m, type(r) as type, collect(coalesce(t.name, t.title)) as names +WITH m, type+": "+reduce(s="", n IN names | s + n + ", ") as types +WITH m, collect(types) as contexts +WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) + + "\nyear: "+coalesce(m.released,"") +"\n" + + reduce(s="", c in contexts | s + substring(c, 0, size(c)-2) +"\n") as context +RETURN context LIMIT 1 +""" + + +def get_information(entity: str, type: str) -> str: + candidates = get_candidates(entity, type) + if not candidates: + return "No information was found about the movie or person in the database" + elif len(candidates) > 1: + newline = "\n" + return ( + "Need additional information, which of these " + f"did you mean: {newline + newline.join(str(d) for d in candidates)}" + ) + data = graph.query( + description_query, params={"candidate": candidates[0]["candidate"]} + ) + return data[0]["context"] + + +class InformationInput(BaseModel): + entity: str = Field(description="movie or a person mentioned in the question") + entity_type: str = Field( + description="type of the entity. Available options are 'movie' or 'person'" + ) + + +class InformationTool(BaseTool): + name = "Information" + description = ( + "useful for when you need to answer questions about various actors or movies" + ) + args_schema: Type[BaseModel] = InformationInput + + def _run( + self, + entity: str, + entity_type: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return get_information(entity, entity_type) + + async def _arun( + self, + entity: str, + entity_type: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return get_information(entity, entity_type) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py new file mode 100644 index 000000000000000..f455f70c4b7b10f --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py @@ -0,0 +1,73 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) + +# Import things that are needed generically +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph + +store_rating_query = """ +MERGE (u:User {userId:$user_id}) +WITH u +UNWIND $candidates as row +MATCH (m:Movie {title: row.candidate}) +MERGE (u)-[r:RATED]->(m) +SET r.rating = toFloat($rating) +RETURN distinct + 'Create a final answer saying that preference has been stored' AS response +""" + + +def store_movie_rating(movie: str, rating: int): + user_id = get_user_id() + candidates = get_candidates(movie, "movie") + if not candidates: + return "This movie is not in our database" + response = graph.query( + store_rating_query, + params={"user_id": user_id, "candidates": candidates, "rating": rating}, + ) + try: + return response[0]["response"] + except Exception as e: + print(e) + return "Something went wrong" + + +class MemoryInput(BaseModel): + movie: str = Field(description="movie the user liked") + rating: int = Field( + description=( + "Rating from 1 to 5, where one represents heavy dislike " + "and 5 represent the user loved the movie" + ) + ) + + +class MemoryTool(BaseTool): + name = "Memory" + description = "useful for memorizing which movies the user liked" + args_schema: Type[BaseModel] = MemoryInput + + def _run( + self, + movie: str, + rating: int, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return store_movie_rating(movie, rating) + + async def _arun( + self, + movie: str, + rating: int, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return store_movie_rating(movie, rating) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py new file mode 100644 index 000000000000000..dc85c7c7398599a --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py @@ -0,0 +1,164 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph + +recommendation_query_db_history = """ + MERGE (u:User {userId:$user_id}) + WITH u + // get recommendation candidates + OPTIONAL MATCH (u)-[r1:RATED]->()<-[r2:RATED]-()-[r3:RATED]->(recommendation) + WHERE r1.rating > 3.5 AND r2.rating > 3.5 AND r3.rating > 3.5 + AND NOT EXISTS {(u)-[:RATED]->(recommendation)} + // rank and limit recommendations + WITH u, recommendation, count(*) AS count + ORDER BY count DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + +recommendation_query_genre = """ +MATCH (m:Movie)-[:IN_GENRE]->(g:Genre {name:$genre}) +// filter out already seen movies by the user +WHERE NOT EXISTS { + (m)<-[:RATED]-(:User {userId:$user_id}) +} +// rank and limit recommendations +WITH m AS recommendation +ORDER BY recommendation.imdbRating DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + + +def recommendation_query_movie(genre: bool) -> str: + return f""" +MATCH (m1:Movie)<-[r1:RATED]-()-[r2:RATED]->(m2:Movie) +WHERE r1.rating > 3.5 AND r2.rating > 3.5 and m1.title IN $movieTitles +// filter out already seen movies by the user +AND NOT EXISTS {{ + (m2)<-[:RATED]-(:User {{userId:$user_id}}) +}} +{'AND EXISTS {(m2)-[:IN_GENRE]->(:Genre {name:$genre})}' if genre else ''} +// rank and limit recommendations +WITH m2 AS recommendation, count(*) AS count +ORDER BY count DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + + +nl = "\n" + + +def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) -> str: + """ + Recommends movies based on user's history and preference + for a specific movie and/or genre. + Returns: + str: A string containing a list of recommended movies, or an error message. + """ + user_id = get_user_id() + params = {"user_id": user_id, "genre": genre} + if not movie and not genre: + # Try to recommend a movie based on the information in the db + response = graph.query(recommendation_query_db_history, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Can you tell us about some of the movies you liked?" + if not movie and genre: + # Recommend top voted movies in the genre the user haven't seen before + response = graph.query(recommendation_query_genre, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Something went wrong" + + candidates = get_candidates(movie, "movie") + if not candidates: + return "The movie you mentioned wasn't found in the database" + params["movieTitles"] = [el["candidate"] for el in candidates] + query = recommendation_query_movie(bool(genre)) + response = graph.query(query, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Something went wrong" + + +all_genres = [ + "Action", + "Adventure", + "Animation", + "Children", + "Comedy", + "Crime", + "Documentary", + "Drama", + "Fantasy", + "Film-Noir", + "Horror", + "IMAX", + "Musical", + "Mystery", + "Romance", + "Sci-Fi", + "Thriller", + "War", + "Western", +] + + +class RecommenderInput(BaseModel): + movie: Optional[str] = Field(description="movie used for recommendation") + genre: Optional[str] = Field( + description=( + "genre used for recommendation. Available options are:" f"{all_genres}" + ) + ) + + +class RecommenderTool(BaseTool): + name = "Recommender" + description = "useful for when you need to recommend a movie" + args_schema: Type[BaseModel] = RecommenderInput + + def _run( + self, + movie: Optional[str] = None, + genre: Optional[str] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return recommend_movie(movie, genre) + + async def _arun( + self, + movie: Optional[str] = None, + genre: Optional[str] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return recommend_movie(movie, genre) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py new file mode 100644 index 000000000000000..0b2469f2d10918c --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py @@ -0,0 +1,39 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +response = ( + "Create a final answer that says if they " + "have any questions about movies or actors" +) + + +class SmalltalkInput(BaseModel): + query: Optional[str] = Field(description="user query") + + +class SmalltalkTool(BaseTool): + name = "Smalltalk" + description = "useful for when user greets you or wants to smalltalk" + args_schema: Type[BaseModel] = SmalltalkInput + + def _run( + self, + query: Optional[str] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return response + + async def _arun( + self, + query: Optional[str] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return response diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py new file mode 100644 index 000000000000000..ae83928bd51968c --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py @@ -0,0 +1,84 @@ +from typing import Dict, List + +from langchain_community.graphs import Neo4jGraph + +graph = Neo4jGraph() + + +def get_user_id() -> int: + """ + Placeholder for a function that would normally retrieve + a user's ID + """ + return 1 + + +def remove_lucene_chars(text: str) -> str: + """Remove Lucene special characters""" + special_chars = [ + "+", + "-", + "&", + "|", + "!", + "(", + ")", + "{", + "}", + "[", + "]", + "^", + '"', + "~", + "*", + "?", + ":", + "\\", + ] + for char in special_chars: + if char in text: + text = text.replace(char, " ") + return text.strip() + + +def generate_full_text_query(input: str) -> str: + """ + Generate a full-text search query for a given input string. + + This function constructs a query string suitable for a full-text search. + It processes the input string by splitting it into words and appending a + similarity threshold (~0.8) to each word, then combines them using the AND + operator. Useful for mapping movies and people from user questions + to database values, and allows for some misspelings. + """ + full_text_query = "" + words = [el for el in remove_lucene_chars(input).split() if el] + for word in words[:-1]: + full_text_query += f" {word}~0.8 AND" + full_text_query += f" {words[-1]}~0.8" + return full_text_query.strip() + + +candidate_query = """ +CALL db.index.fulltext.queryNodes($index, $fulltextQuery, {limit: $limit}) +YIELD node +RETURN coalesce(node.name, node.title) AS candidate, + [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label +""" + + +def get_candidates(input: str, type: str, limit: int = 3) -> List[Dict[str, str]]: + """ + Retrieve a list of candidate entities from database based on the input string. + + This function queries the Neo4j database using a full-text search. It takes the + input string, generates a full-text query, and executes this query against the + specified index in the database. The function returns a list of candidates + matching the query, with each candidate being a dictionary containing their name + (or title) and label (either 'Person' or 'Movie'). + """ + ft_query = generate_full_text_query(input) + candidates = graph.query( + candidate_query, {"fulltextQuery": ft_query, "index": type, "limit": limit} + ) + return candidates diff --git a/templates/neo4j-semantic-ollama/poetry.lock b/templates/neo4j-semantic-ollama/poetry.lock new file mode 100644 index 000000000000000..4aefece720327b9 --- /dev/null +++ b/templates/neo4j-semantic-ollama/poetry.lock @@ -0,0 +1,1753 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = 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100644 pg_essay.txt diff --git a/pg_essay.txt b/pg_essay.txt deleted file mode 100644 index 0bce3830b9968d2..000000000000000 --- a/pg_essay.txt +++ /dev/null @@ -1,351 +0,0 @@ -What I Worked On - -February 2021 - -Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. - -The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights. - -The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer. - -I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear. - -With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1] - -The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer. - -Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter. - -Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored. - -I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI. - -AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words. - -There weren't any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn't happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do. - -For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence. - -I had gotten into a program at Cornell that didn't make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose "Artificial Intelligence." When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover. - -I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went. - -I don't remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that's told "the dog is sitting on the chair" translates this into some formal representation and adds it to the list of things it knows. - -What these programs really showed was that there's a subset of natural language that's a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike. - -So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It's scary to think how little I knew about Lisp hacking when I started writing that book. But there's nothing like writing a book about something to help you learn it. The book, On Lisp, wasn't published till 1993, but I wrote much of it in grad school. - -Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting. - -The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good. - -There were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work. - -I wanted not just to build things, but to build things that would last. - -In this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I'd spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn't become obsolete. Some of the best ones were hundreds of years old. - -And moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn't have a boss, or even need to get research funding. - -I had always liked looking at paintings. Could I make them? I had no idea. I'd never imagined it was even possible. I knew intellectually that people made art — that it didn't just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous. - -That fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything. - -So now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis. - -I didn't see a way out of this situation. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school. - -Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay "Yes, I think so. I'll give you something to read in a few days." - -I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely. - -Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went. - -I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design. - -Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian. - -Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2] - -I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. - -Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3] - -While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting. Whereas a still life you can, if you want, copy pixel by pixel from what you're seeing. You don't want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it's been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it's the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4] - -I liked painting still lives because I was curious about what I was seeing. In everyday life, we aren't consciously aware of much we're seeing. Most visual perception is handled by low-level processes that merely tell your brain "that's a water droplet" without telling you details like where the lightest and darkest points are, or "that's a bush" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there's a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted. - -This is not the only way to paint. I'm not 100% sure it's even a good way to paint. But it seemed a good enough bet to be worth trying. - -Our teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn't teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US. - -I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5] - -Interleaf had done something pretty bold. Inspired by Emacs, they'd added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I've had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn't know C and didn't want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish. - -The good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans. - -I learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it's better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it. - -But the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it's good to be the "entry level" option, even though that will be less prestigious, because if you're not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign. - -When I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life. - -In the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style. - -A signature style is the visual equivalent of what in show business is known as a "schtick": something that immediately identifies the work as yours and no one else's. For example, when you see a painting that looks like a certain kind of cartoon, you know it's by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That's not always why artists have a signature style, but it's usually why buyers pay a lot for such work. [6] - -There were plenty of earnest students too: kids who "could draw" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers. - -I learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn't much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7] - -Asterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there's a tiny corner that's not rich, or at least wasn't in 1993. It's called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York. - -I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.) - -The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant. - -She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want. - -Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet. - -If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us. - -Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an "internet storefront" was something we already knew how to build. - -So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course. - -We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser. - -This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server. - -Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server. - -We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves. - -At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on. - -We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server. - -It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company. - -(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.) - -In September, Robert rebelled. "We've been working on this for a month," he said, "and it's still not done." This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker. - -It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo. - -We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8] - -There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful. - -There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us. - -We did a lot of things right by accident like that. For example, we did what's now called "doing things that don't scale," although at the time we would have described it as "being so lame that we're driven to the most desperate measures to get users." The most common of which was building stores for them. This seemed particularly humiliating, since the whole reason d'etre of our software was that people could use it to make their own stores. But anything to get users. - -We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too. - -Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by "business" and thought we needed a "business person" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do. - -Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny. - -Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards. - -It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned. - -The next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn't realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo's prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf. - -Yahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they'd never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I'd painted anything that I'd half forgotten why I was doing this. My brain had been entirely full of software and men's shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint. - -When I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan. - -But I really was quitting to paint, and I started immediately. There was no time to lose. I'd already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That's what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me. - -So I tried to paint, but I just didn't seem to have any energy or ambition. Part of the problem was that I didn't know many people in California. I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them. - -When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet). - -Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh. - -Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc. - -I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it. - -Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open-source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did. - -By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra. - -I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open-source project. - -Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it. - -The subset I would build as an open-source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge. - -The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10] - -Wow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything. - -This had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11] - -In the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12] - -I've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too. - -I knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging. - -One of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip. - -It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one. - -Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office. - -One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out. - -Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders. - -When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on. - -One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made. - -So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out "But not me!" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment. - -Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on. - -As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13] - -Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel. - -There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn't go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we'd been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us. - -YC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking "Wow, that means they got all the returns." But once again, this was not due to any particular insight on our part. We didn't know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn't have known where to start. [14] - -The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they'd start startups instead? We wouldn't feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn't make much money out of it, we'd at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft. - -We'd use the building I owned in Cambridge as our headquarters. We'd all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we'd bring in experts on startups to give talks. - -We knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get "deal flow," as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who'd already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we'd intended. - -We invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs. - -The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16] - -Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them. - -As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the "YC GDP," but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates. - -I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things. - -In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity. - -HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17] - -As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC. - -YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. - -There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: "No one works harder than the boss." He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard. - -One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. "You know," he said, "you should make sure Y Combinator isn't the last cool thing you do." - -At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would. - -In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else. - -I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners. - -When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned. - -She died on January 15, 2014. We knew this was coming, but it was still hard when it did. - -I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.) - -What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18] - -I spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway. - -I realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around. - -I started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren't about startups. Then in March 2015 I started working on Lisp again. - -The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn't originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what's the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19] - -McCarthy didn't realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy's interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn't match. It was this that attracted me in college, though I didn't understand why at the time. - -McCarthy's 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you'd want in a programming language. So these had to be added, and when they were, they weren't defined using McCarthy's original axiomatic approach. That wouldn't have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you'd have had to run it, and computers then weren't powerful enough. - -Now they are, though. Now you could continue using McCarthy's axiomatic approach till you'd defined a complete programming language. And as long as every change you made to McCarthy's Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long. - -I wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it's an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test. - -I had to ban myself from writing essays during most of this time, or I'd never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you're working on an interpreter written in itself, it's hard to keep track of what's happening at what level, and errors can be practically encrypted by the time you get them. - -So I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I'd ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I'd check Twitter or HN and see someone asking "Does Paul Graham still code?" - -Working on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years. - -In the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England. - -In the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code. - -Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it. - - - - - - - - - -Notes - -[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting. - -[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way. - -[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists. - -[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters. - -[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer. - -[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive. - -[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price. - -[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores. - -[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them. - -[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things. - -[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version. - -[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era. - -Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete). - -Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be? - -[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator. - -I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square. - -[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded. - -[15] I've never liked the term "deal flow," because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed. - -[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now. - -[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance. - -[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree. - -[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper. - -But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved. - - - -Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this. From 2281f00198625ca155b0f3b87c2c147e0c0f4bbe Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 7 Feb 2024 22:46:17 +0100 Subject: [PATCH 137/157] langchain: Standardize `output_parser.py` across all agent types for custom `FORMAT_INSTRUCTIONS` (#17168) - **Description:** This PR standardizes the `output_parser.py` file across all agent types to ensure a uniform parsing mechanism is implemented. It introduces a cohesive structure and common interface for output parsing, facilitating easier modifications and extensions by users. The standardized approach enhances maintainability and scalability of the codebase by providing a consistent pattern for output parsing, which can be easily understood and utilized across different agent types. This PR builds upon the foundation set by a previously merged PR, which focused exclusively on standardizing the `output_parser.py` for the `conversational_agent` ([PR #16945](https://github.com/langchain-ai/langchain/pull/16945)). With this new update, I extend the standardization efforts to encompass `output_parser.py` files across all agent types. This enhancement not only unifies the parsing mechanism across the board but also introduces the flexibility for users to incorporate custom `FORMAT_INSTRUCTIONS`. - **Issue:** https://github.com/langchain-ai/langchain/issues/10721 https://github.com/langchain-ai/langchain/issues/4044 - **Dependencies:** No new dependencies required for this change - **Twitter handle:** With my github user is enough. Thanks I hope you accept my PR. --- libs/langchain/langchain/agents/chat/output_parser.py | 6 +++++- .../langchain/agents/conversational/output_parser.py | 6 +++++- .../langchain/agents/conversational_chat/output_parser.py | 1 + libs/langchain/langchain/agents/mrkl/output_parser.py | 6 +++++- .../langchain/agents/structured_chat/output_parser.py | 7 ++++++- 5 files changed, 22 insertions(+), 4 deletions(-) diff --git a/libs/langchain/langchain/agents/chat/output_parser.py b/libs/langchain/langchain/agents/chat/output_parser.py index 069a7fdbb5cd7fd..cdb8c0d0dae1df3 100644 --- a/libs/langchain/langchain/agents/chat/output_parser.py +++ b/libs/langchain/langchain/agents/chat/output_parser.py @@ -14,11 +14,15 @@ class ChatOutputParser(AgentOutputParser): """Output parser for the chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL) """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text diff --git a/libs/langchain/langchain/agents/conversational/output_parser.py b/libs/langchain/langchain/agents/conversational/output_parser.py index 6d0446b81bf22b4..e74e626faa99e55 100644 --- a/libs/langchain/langchain/agents/conversational/output_parser.py +++ b/libs/langchain/langchain/agents/conversational/output_parser.py @@ -14,8 +14,12 @@ class ConvoOutputParser(AgentOutputParser): ai_prefix: str = "AI" """Prefix to use before AI output.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: if f"{self.ai_prefix}:" in text: diff --git a/libs/langchain/langchain/agents/conversational_chat/output_parser.py b/libs/langchain/langchain/agents/conversational_chat/output_parser.py index ea3a380f4bc923c..6fd330ccf8d1863 100644 --- a/libs/langchain/langchain/agents/conversational_chat/output_parser.py +++ b/libs/langchain/langchain/agents/conversational_chat/output_parser.py @@ -15,6 +15,7 @@ class ConvoOutputParser(AgentOutputParser): """Output parser for the conversational agent.""" format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" def get_format_instructions(self) -> str: """Returns formatting instructions for the given output parser.""" diff --git a/libs/langchain/langchain/agents/mrkl/output_parser.py b/libs/langchain/langchain/agents/mrkl/output_parser.py index b716c49db48082f..f6489c2a79289d8 100644 --- a/libs/langchain/langchain/agents/mrkl/output_parser.py +++ b/libs/langchain/langchain/agents/mrkl/output_parser.py @@ -22,8 +22,12 @@ class MRKLOutputParser(AgentOutputParser): """MRKL Output parser for the chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text diff --git a/libs/langchain/langchain/agents/structured_chat/output_parser.py b/libs/langchain/langchain/agents/structured_chat/output_parser.py index eb85d6684b6e862..2f91a8352a0e601 100644 --- a/libs/langchain/langchain/agents/structured_chat/output_parser.py +++ b/libs/langchain/langchain/agents/structured_chat/output_parser.py @@ -20,10 +20,15 @@ class StructuredChatOutputParser(AgentOutputParser): """Output parser for the structured chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + pattern = re.compile(r"```(?:json\s+)?(\W.*?)```", re.DOTALL) + """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: try: From af74301ab981a0421f84bbff6921b7feca79dbfe Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 14:15:30 -0800 Subject: [PATCH 138/157] core[patch], community[patch]: link extraction continue on failure (#17200) --- .../document_loaders/recursive_url_loader.py | 32 +++++++++++------ .../langchain_community/vectorstores/kdbai.py | 4 +-- libs/core/langchain_core/utils/html.py | 35 +++++++++++++------ 3 files changed, 48 insertions(+), 23 deletions(-) diff --git a/libs/community/langchain_community/document_loaders/recursive_url_loader.py b/libs/community/langchain_community/document_loaders/recursive_url_loader.py index 6fca4edf86a8093..c24ab1730fd354b 100644 --- a/libs/community/langchain_community/document_loaders/recursive_url_loader.py +++ b/libs/community/langchain_community/document_loaders/recursive_url_loader.py @@ -93,6 +93,7 @@ def __init__( link_regex: Union[str, re.Pattern, None] = None, headers: Optional[dict] = None, check_response_status: bool = False, + continue_on_failure: bool = True, ) -> None: """Initialize with URL to crawl and any subdirectories to exclude. @@ -117,6 +118,8 @@ def __init__( link_regex: Regex for extracting sub-links from the raw html of a web page. check_response_status: If True, check HTTP response status and skip URLs with error responses (400-599). + continue_on_failure: If True, continue if getting or parsing a link raises + an exception. Otherwise, raise the exception. """ self.url = url @@ -142,6 +145,7 @@ def __init__( self._lock = asyncio.Lock() if self.use_async else None self.headers = headers self.check_response_status = check_response_status + self.continue_on_failure = continue_on_failure def _get_child_links_recursive( self, url: str, visited: Set[str], *, depth: int = 0 @@ -164,11 +168,14 @@ def _get_child_links_recursive( if self.check_response_status and 400 <= response.status_code <= 599: raise ValueError(f"Received HTTP status {response.status_code}") except Exception as e: - logger.warning( - f"Unable to load from {url}. Received error {e} of type " - f"{e.__class__.__name__}" - ) - return + if self.continue_on_failure: + logger.warning( + f"Unable to load from {url}. Received error {e} of type " + f"{e.__class__.__name__}" + ) + return + else: + raise e content = self.extractor(response.text) if content: yield Document( @@ -184,6 +191,7 @@ def _get_child_links_recursive( pattern=self.link_regex, prevent_outside=self.prevent_outside, exclude_prefixes=self.exclude_dirs, + continue_on_failure=self.continue_on_failure, ) for link in sub_links: # Check all unvisited links @@ -237,13 +245,16 @@ async def _async_get_child_links_recursive( if self.check_response_status and 400 <= response.status <= 599: raise ValueError(f"Received HTTP status {response.status}") except (aiohttp.client_exceptions.InvalidURL, Exception) as e: - logger.warning( - f"Unable to load {url}. Received error {e} of type " - f"{e.__class__.__name__}" - ) if close_session: await session.close() - return [] + if self.continue_on_failure: + logger.warning( + f"Unable to load {url}. Received error {e} of type " + f"{e.__class__.__name__}" + ) + return [] + else: + raise e results = [] content = self.extractor(text) if content: @@ -261,6 +272,7 @@ async def _async_get_child_links_recursive( pattern=self.link_regex, prevent_outside=self.prevent_outside, exclude_prefixes=self.exclude_dirs, + continue_on_failure=self.continue_on_failure, ) # Recursively call the function to get the children of the children diff --git a/libs/community/langchain_community/vectorstores/kdbai.py b/libs/community/langchain_community/vectorstores/kdbai.py index 1122b691d439ba8..9ac1a7d580a827a 100644 --- a/libs/community/langchain_community/vectorstores/kdbai.py +++ b/libs/community/langchain_community/vectorstores/kdbai.py @@ -14,7 +14,7 @@ class KDBAI(VectorStore): - """`KDB.AI` vector store [https://kdb.ai](https://kdb.ai) + """`KDB.AI` vector store. To use, you should have the `kdbai_client` python package installed. @@ -25,7 +25,7 @@ class KDBAI(VectorStore): distance_strategy: One option from DistanceStrategy.EUCLIDEAN_DISTANCE, DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE. - See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb). + See the example https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb. """ def __init__( diff --git a/libs/core/langchain_core/utils/html.py b/libs/core/langchain_core/utils/html.py index bbea15f0fa43501..837b19ed101daf7 100644 --- a/libs/core/langchain_core/utils/html.py +++ b/libs/core/langchain_core/utils/html.py @@ -1,7 +1,10 @@ +import logging import re from typing import List, Optional, Sequence, Union from urllib.parse import urljoin, urlparse +logger = logging.getLogger(__name__) + PREFIXES_TO_IGNORE = ("javascript:", "mailto:", "#") SUFFIXES_TO_IGNORE = ( ".css", @@ -52,6 +55,7 @@ def extract_sub_links( pattern: Union[str, re.Pattern, None] = None, prevent_outside: bool = True, exclude_prefixes: Sequence[str] = (), + continue_on_failure: bool = False, ) -> List[str]: """Extract all links from a raw html string and convert into absolute paths. @@ -63,25 +67,34 @@ def extract_sub_links( prevent_outside: If True, ignore external links which are not children of the base url. exclude_prefixes: Exclude any URLs that start with one of these prefixes. - + continue_on_failure: If True, continue if parsing a specific link raises an + exception. Otherwise, raise the exception. Returns: List[str]: sub links """ base_url_to_use = base_url if base_url is not None else url parsed_base_url = urlparse(base_url_to_use) + parsed_url = urlparse(url) all_links = find_all_links(raw_html, pattern=pattern) absolute_paths = set() for link in all_links: - parsed_link = urlparse(link) - # Some may be absolute links like https://to/path - if parsed_link.scheme == "http" or parsed_link.scheme == "https": - absolute_path = link - # Some may have omitted the protocol like //to/path - elif link.startswith("//"): - absolute_path = f"{urlparse(url).scheme}:{link}" - else: - absolute_path = urljoin(url, parsed_link.path) - absolute_paths.add(absolute_path) + try: + parsed_link = urlparse(link) + # Some may be absolute links like https://to/path + if parsed_link.scheme == "http" or parsed_link.scheme == "https": + absolute_path = link + # Some may have omitted the protocol like //to/path + elif link.startswith("//"): + absolute_path = f"{parsed_url.scheme}:{link}" + else: + absolute_path = urljoin(url, parsed_link.path) + absolute_paths.add(absolute_path) + except Exception as e: + if continue_on_failure: + logger.warning(f"Unable to load link {link}. Raised exception:\n\n{e}") + continue + else: + raise e results = [] for path in absolute_paths: From 00757567bad62e9cfaaa09b7b3f2486bd7d49d92 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 14:20:20 -0800 Subject: [PATCH 139/157] core[patch]: Release 0.1.21 (#17202) --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 40c47e0a2e83b15..12b241f5f9e9fd4 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-core" -version = "0.1.20" +version = "0.1.21" description = "Building applications with LLMs through composability" authors = [] license = "MIT" From a13dc47a088a7fa5335a0e327279992f047d8278 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 14:52:37 -0800 Subject: [PATCH 140/157] cli[patch]: copyright 2024 default (#17204) --- libs/cli/langchain_cli/integration_template/LICENSE | 2 +- libs/cli/langchain_cli/package_template/LICENSE | 2 +- templates/retrieval-agent-fireworks/LICENSE | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/cli/langchain_cli/integration_template/LICENSE b/libs/cli/langchain_cli/integration_template/LICENSE index 426b65090341f37..fc0602feecdd674 100644 --- a/libs/cli/langchain_cli/integration_template/LICENSE +++ b/libs/cli/langchain_cli/integration_template/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 LangChain, Inc. +Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/libs/cli/langchain_cli/package_template/LICENSE b/libs/cli/langchain_cli/package_template/LICENSE index 426b65090341f37..fc0602feecdd674 100644 --- a/libs/cli/langchain_cli/package_template/LICENSE +++ b/libs/cli/langchain_cli/package_template/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 LangChain, Inc. +Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/templates/retrieval-agent-fireworks/LICENSE b/templates/retrieval-agent-fireworks/LICENSE index 426b65090341f37..fc0602feecdd674 100644 --- a/templates/retrieval-agent-fireworks/LICENSE +++ b/templates/retrieval-agent-fireworks/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 LangChain, Inc. +Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal From 6f1403b9b6155704ca2d35950c16a0924cbb03ed Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 15:37:01 -0800 Subject: [PATCH 141/157] community[patch]: Release 0.0.19 (#17207) Co-authored-by: Erick Friis --- libs/community/poetry.lock | 131 ++++++++++++++++++---------------- libs/community/pyproject.toml | 4 +- 2 files changed, 70 insertions(+), 65 deletions(-) diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 207f139f811e949..fccdc9749fbc87b 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -702,17 +702,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.36" +version = "1.34.37" description = "The AWS SDK for Python" optional = false python-versions = ">= 3.8" files = [ - {file = "boto3-1.34.36-py3-none-any.whl", hash = "sha256:1e38a4ea4bb8bc66fbb858abb411820709fda5d6c5604c2da0f696653d49c684"}, - {file = "boto3-1.34.36.tar.gz", hash = "sha256:7ec36deb7ccc9c4943510692303cf93883ef61dc2c79f8bd4d75ee42209559d3"}, + {file = "boto3-1.34.37-py3-none-any.whl", hash = "sha256:65acfe7f1cf2a9b7df3d4edb87c8022e02685825bd1957e7bb678cc0d09f5e5f"}, + {file = "boto3-1.34.37.tar.gz", hash = "sha256:73f5ec89cb3ddb3ed577317889fd2f2df783f66b6502a9a4239979607e33bf74"}, ] [package.dependencies] -botocore = ">=1.34.36,<1.35.0" +botocore = ">=1.34.37,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -721,13 +721,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.36" +version = "1.34.37" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">= 3.8" files = [ - {file = "botocore-1.34.36-py3-none-any.whl", hash = "sha256:3f39ae0165f1a27f621fc91a46fb59f87e819671fad106c230dadcc724892f70"}, - {file = "botocore-1.34.36.tar.gz", hash = "sha256:89c3dc15b6ffae146029df636d51b9952740051204c444ec765286b081c917bc"}, + {file = "botocore-1.34.37-py3-none-any.whl", hash = "sha256:2a5bf33aacd2d970afd3d492e179e06ea98a5469030d5cfe7a2ad9995f7bb2ef"}, + {file = "botocore-1.34.37.tar.gz", hash = "sha256:3c46ddb1679e6ef45ca78b48665398636bda532a07cd476e4b500697d13d9a99"}, ] [package.dependencies] @@ -2478,33 +2478,35 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"] [[package]] name = "google-cloud-documentai" -version = "2.22.0" +version = "2.23.0" description = "Google Cloud Documentai API client library" optional = true python-versions = ">=3.7" files = [ - {file = "google-cloud-documentai-2.22.0.tar.gz", hash = "sha256:e033457a95634aac250452600f4e4464f39073b5c00f5a1f8636f1fa83a93e73"}, - {file = "google_cloud_documentai-2.22.0-py2.py3-none-any.whl", hash = "sha256:68b0c7962e4880879dfe23723de8c3beed9604342b4617913ca32437105d7e33"}, + {file = "google-cloud-documentai-2.23.0.tar.gz", hash = "sha256:2ce954dae90024662d39258060b753992d5564d64a08badda635c7d25f7079b1"}, + {file = "google_cloud_documentai-2.23.0-py2.py3-none-any.whl", hash = "sha256:560b00c18e6a3b5c603052ccc9987afdc6886d6c71f883a7a2aa97a8f739f1fa"}, ] [package.dependencies] google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]} +google-auth = ">=2.14.1,<3.0.0dev" proto-plus = ">=1.22.3,<2.0.0dev" protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev" [[package]] name = "google-cloud-resource-manager" -version = "1.12.0" +version = "1.12.1" description = "Google Cloud Resource Manager API client library" optional = false python-versions = ">=3.7" files = [ - {file = "google-cloud-resource-manager-1.12.0.tar.gz", hash = "sha256:c2c4ae53e5f9ff612e4cf961f1bac3f9100ae54a5122c1b1cd4236e884d112c5"}, - 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Jupyter" optional = false python-versions = ">=3.8" files = [ - {file = "ipykernel-6.29.1-py3-none-any.whl", hash = "sha256:e5dfba210fc9da74a5dae8fa6c41f816e11bd18d10381b2517d9a0d57cc987c4"}, - {file = "ipykernel-6.29.1.tar.gz", hash = "sha256:1547352b32da95a2761011a8dac2af930c26a0703dfa07690d16b7d74dac0ba1"}, + {file = "ipykernel-6.29.2-py3-none-any.whl", hash = "sha256:50384f5c577a260a1d53f1f59a828c7266d321c9b7d00d345693783f66616055"}, + {file = "ipykernel-6.29.2.tar.gz", hash = "sha256:3bade28004e3ff624ed57974948116670604ac5f676d12339693f3142176d3f0"}, ] [package.dependencies] @@ -3932,7 +3934,7 @@ files = [ [[package]] name = "langchain-core" -version = "0.1.19" +version = "0.1.21" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -3942,7 +3944,7 @@ develop = true [package.dependencies] anyio = ">=3,<5" jsonpatch = "^1.33" -langsmith = ">=0.0.83,<0.1" +langsmith = "^0.0.87" packaging = "^23.2" pydantic = 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"9e795e7b2f95e3bd1daa3d92129172e21c6c0c1b5dc82e4791872f39a33472aa" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index cf34ca4aac64f7d..926e9a7ee552400 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-community" -version = "0.0.18" +version = "0.0.19" description = "Community contributed LangChain integrations." authors = [] license = "MIT" @@ -9,7 +9,7 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.19,<0.2" +langchain-core = ">=0.1.21,<0.2" SQLAlchemy = ">=1.4,<3" requests = "^2" PyYAML = ">=5.3" From 19ff81e74f51ab3857eaf933f947f5a891127428 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Wed, 7 Feb 2024 15:46:13 -0800 Subject: [PATCH 142/157] Fix stream events/log with some kinds of non addable output (#17205) --- libs/core/langchain_core/tracers/log_stream.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/core/langchain_core/tracers/log_stream.py b/libs/core/langchain_core/tracers/log_stream.py index c6b8da943a3d146..db56626451649e7 100644 --- a/libs/core/langchain_core/tracers/log_stream.py +++ b/libs/core/langchain_core/tracers/log_stream.py @@ -572,6 +572,7 @@ async def consume_astream() -> None: try: final_output = final_output + chunk # type: ignore except TypeError: + prev_final_output = None final_output = chunk patches: List[Dict[str, Any]] = [] if with_streamed_output_list: From 52be84a603f8d4ab764d652cdf49ff5b82be5d03 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 15:47:32 -0800 Subject: [PATCH 143/157] google-vertexai[patch]: serializable citation metadata, release 0.0.4 (#17145) was breaking in langserve before --- .../langchain_google_vertexai/_utils.py | 7 ++++++- libs/partners/google-vertexai/pyproject.toml | 2 +- .../tests/integration_tests/test_tools.py | 18 +++++++++--------- .../tests/unit_tests/test_chat_models.py | 2 +- 4 files changed, 17 insertions(+), 12 deletions(-) diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py index 4f0bdf3723b025f..feedd701a619781 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py @@ -5,6 +5,7 @@ from typing import Any, Callable, Dict, Optional, Union import google.api_core +import proto # type: ignore[import-untyped] from google.api_core.gapic_v1.client_info import ClientInfo from google.cloud import storage from langchain_core.callbacks import ( @@ -114,7 +115,11 @@ def get_generation_info( } for rating in candidate.safety_ratings ], - "citation_metadata": candidate.citation_metadata, + "citation_metadata": ( + proto.Message.to_dict(candidate.citation_metadata) + if candidate.citation_metadata + else None + ), } # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-chat#response_body else: diff --git a/libs/partners/google-vertexai/pyproject.toml b/libs/partners/google-vertexai/pyproject.toml index f9d2f890cbbabde..a9283e2d5c8629c 100644 --- a/libs/partners/google-vertexai/pyproject.toml +++ b/libs/partners/google-vertexai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-vertexai" -version = "0.0.3" +version = "0.0.4" description = "An integration package connecting GoogleVertexAI and LangChain" authors = [] readme = "README.md" diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_tools.py b/libs/partners/google-vertexai/tests/integration_tests/test_tools.py index a688476bfc7ad21..f239727ecac9a96 100644 --- a/libs/partners/google-vertexai/tests/integration_tests/test_tools.py +++ b/libs/partners/google-vertexai/tests/integration_tests/test_tools.py @@ -1,6 +1,6 @@ import os import re -from typing import List, Union +from typing import Any, List, Union from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish from langchain_core.messages import AIMessageChunk @@ -44,11 +44,11 @@ def parse(self, text: str) -> Union[AgentAction, AgentFinish]: def test_tools() -> None: - from langchain.agents import AgentExecutor # type: ignore[import-not-found] - from langchain.agents.format_scratchpad import ( # type: ignore[import-not-found] + from langchain.agents import AgentExecutor + from langchain.agents.format_scratchpad import ( format_to_openai_function_messages, ) - from langchain.chains import LLMMathChain # type: ignore[import-not-found] + from langchain.chains import LLMMathChain llm = ChatVertexAI(model_name="gemini-pro") math_chain = LLMMathChain.from_llm(llm=llm) @@ -67,8 +67,8 @@ def test_tools() -> None: ) llm_with_tools = llm.bind(functions=tools) - agent = ( - { # type: ignore[var-annotated] + agent: Any = ( + { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] @@ -115,7 +115,7 @@ def test_multiple_tools() -> None: from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad import format_to_openai_function_messages from langchain.chains import LLMMathChain - from langchain.utilities import ( # type: ignore[import-not-found] + from langchain.utilities import ( GoogleSearchAPIWrapper, ) @@ -149,8 +149,8 @@ def test_multiple_tools() -> None: ) llm_with_tools = llm.bind(functions=tools) - agent = ( - { # type: ignore[var-annotated] + agent: Any = ( + { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py index 052cf559f0fda27..6bcbb6e5abff33d 100644 --- a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py @@ -187,7 +187,7 @@ def test_default_params_gemini() -> None: StubGeminiResponse( text="Goodbye", content=Mock(parts=[Mock(function_call=None)]), - citation_metadata=Mock(), + citation_metadata=None, ) ] mock_chat = MagicMock() From e17173c4036e7d2302a6c696e5c5de6757cf41d7 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 15:49:56 -0800 Subject: [PATCH 144/157] google-vertexai[patch]: function calling integration test (#17209) --- .../integration_tests/test_chat_models.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py index f2981a08018121c..94c7ea6a55b7917 100644 --- a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py @@ -1,4 +1,6 @@ """Test ChatGoogleVertexAI chat model.""" + +import json from typing import Optional, cast import pytest @@ -9,6 +11,7 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, LLMResult +from langchain_core.pydantic_v1 import BaseModel from langchain_google_vertexai.chat_models import ChatVertexAI @@ -220,3 +223,26 @@ def test_chat_vertexai_system_message(model_name: Optional[str]) -> None: response = model([system_message, message1, message2, message3]) assert isinstance(response, AIMessage) assert isinstance(response.content, str) + + +def test_chat_vertexai_gemini_function_calling() -> None: + class MyModel(BaseModel): + name: str + age: int + + model = ChatVertexAI(model_name="gemini-pro").bind(functions=[MyModel]) + message = HumanMessage(content="My name is Erick and I am 27 years old") + response = model.invoke([message]) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "MyModel" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } From 2ecf3182187c480568d9a21bd9194ce549a77420 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 17:07:31 -0800 Subject: [PATCH 145/157] google-genai[patch]: match function call interface (#17213) should match vertex --- .../langchain_google_genai/_function_utils.py | 135 ++++++++++++++++++ .../langchain_google_genai/chat_models.py | 73 ++-------- .../integration_tests/test_function_call.py | 54 ++++++- 3 files changed, 198 insertions(+), 64 deletions(-) create mode 100644 libs/partners/google-genai/langchain_google_genai/_function_utils.py diff --git a/libs/partners/google-genai/langchain_google_genai/_function_utils.py b/libs/partners/google-genai/langchain_google_genai/_function_utils.py new file mode 100644 index 000000000000000..665987c352d468a --- /dev/null +++ b/libs/partners/google-genai/langchain_google_genai/_function_utils.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +from typing import ( + Dict, + List, + Type, + Union, +) + +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.tools import BaseTool +from langchain_core.utils.json_schema import dereference_refs + +FunctionCallType = Union[BaseTool, Type[BaseModel], Dict] + +TYPE_ENUM = { + "string": 1, + "number": 2, + "integer": 3, + "boolean": 4, + "array": 5, + "object": 6, +} + + +def convert_to_genai_function_declarations( + function_calls: List[FunctionCallType], +) -> Dict: + function_declarations = [] + for fc in function_calls: + function_declarations.append(_convert_to_genai_function(fc)) + return { + "function_declarations": function_declarations, + } + + +def _convert_to_genai_function(fc: FunctionCallType) -> Dict: + """ + Produce + + { + "name": "get_weather", + "description": "Determine weather in my location", + "parameters": { + "properties": { + "location": { + "description": "The city and state e.g. San Francisco, CA", + "type_": 1 + }, + "unit": { "enum": ["c", "f"], "type_": 1 } + }, + "required": ["location"], + "type_": 6 + } + } + + """ + if isinstance(fc, BaseTool): + return _convert_tool_to_genai_function(fc) + elif isinstance(fc, type) and issubclass(fc, BaseModel): + return _convert_pydantic_to_genai_function(fc) + elif isinstance(fc, dict): + return { + **fc, + "parameters": { + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in fc["parameters"]["properties"].items() + }, + "required": fc["parameters"].get("required", []), + "type_": TYPE_ENUM[fc["parameters"]["type"]], + }, + } + else: + raise ValueError(f"Unsupported function call type {fc}") + + +def _convert_tool_to_genai_function(tool: BaseTool) -> Dict: + if tool.args_schema: + schema = dereference_refs(tool.args_schema.schema()) + schema.pop("definitions", None) + + return { + "name": tool.name or schema["title"], + "description": tool.description or schema["description"], + "parameters": { + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in schema["properties"].items() + }, + "required": schema["required"], + "type_": TYPE_ENUM[schema["type"]], + }, + } + else: + return { + "name": tool.name, + "description": tool.description, + "parameters": { + "properties": { + "__arg1": {"type": "string"}, + }, + "required": ["__arg1"], + "type_": TYPE_ENUM["object"], + }, + } + + +def _convert_pydantic_to_genai_function( + pydantic_model: Type[BaseModel], +) -> Dict: + schema = dereference_refs(pydantic_model.schema()) + schema.pop("definitions", None) + + return { + "name": schema["title"], + "description": schema.get("description", ""), + "parameters": { + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in schema["properties"].items() + }, + "required": schema["required"], + "type_": TYPE_ENUM[schema["type"]], + }, + } diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py index 426d279aa0178e5..2bf9cf8cc3d7c3e 100644 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ b/libs/partners/google-genai/langchain_google_genai/chat_models.py @@ -1,6 +1,7 @@ from __future__ import annotations import base64 +import json import logging import os from io import BytesIO @@ -54,6 +55,9 @@ ) from langchain_google_genai._common import GoogleGenerativeAIError +from langchain_google_genai._function_utils import ( + convert_to_genai_function_declarations, +) from langchain_google_genai.llms import GoogleModelFamily, _BaseGoogleGenerativeAI IMAGE_TYPES: Tuple = () @@ -351,69 +355,14 @@ def _retrieve_function_call_response( return { "function_call": { "name": fc.name, - "arguments": dict(fc.args.items()), + "arguments": json.dumps( + dict(fc.args.items()) + ), # dump to match other function calling llms for now } } return None -def _convert_function_call_req(function_calls: Union[Dict, List[Dict]]) -> Dict: - function_declarations = [] - if isinstance(function_calls, dict): - function_declarations.append(_convert_fc_type(function_calls)) - else: - for fc in function_calls: - function_declarations.append(_convert_fc_type(fc)) - return { - "function_declarations": function_declarations, - } - - -def _convert_fc_type(fc: Dict) -> Dict: - # type_: "Type" - # format_: str - # description: str - # nullable: bool - # enum: MutableSequence[str] - # items: "Schema" - # properties: MutableMapping[str, "Schema"] - # required: MutableSequence[str] - if "parameters" in fc: - fc["parameters"] = _convert_fc_type(fc["parameters"]) - if "properties" in fc: - for k, v in fc["properties"].items(): - fc["properties"][k] = _convert_fc_type(v) - if "type" in fc: - # STRING = 1 - # NUMBER = 2 - # INTEGER = 3 - # BOOLEAN = 4 - # ARRAY = 5 - # OBJECT = 6 - if fc["type"] == "string": - fc["type_"] = 1 - elif fc["type"] == "number": - fc["type_"] = 2 - elif fc["type"] == "integer": - fc["type_"] = 3 - elif fc["type"] == "boolean": - fc["type_"] = 4 - elif fc["type"] == "array": - fc["type_"] = 5 - elif fc["type"] == "object": - fc["type_"] = 6 - del fc["type"] - if "format" in fc: - fc["format_"] = fc["format"] - del fc["format"] - - for k, v in fc.items(): - if isinstance(v, dict): - fc[k] = _convert_fc_type(v) - - return fc - - def _parts_to_content( parts: List[genai.types.PartType], ) -> Tuple[Union[str, List[Union[Dict[Any, Any], str]]], Optional[Dict]]: @@ -708,11 +657,11 @@ def _prepare_chat( stop: Optional[List[str]] = None, **kwargs: Any, ) -> Tuple[Dict[str, Any], genai.ChatSession, genai.types.ContentDict]: - cli = self.client + client = self.client functions = kwargs.pop("functions", None) if functions: - tools = _convert_function_call_req(functions) - cli = genai.GenerativeModel(model_name=self.model, tools=tools) + tools = convert_to_genai_function_declarations(functions) + client = genai.GenerativeModel(model_name=self.model, tools=tools) params = self._prepare_params(stop, **kwargs) history = _parse_chat_history( @@ -720,7 +669,7 @@ def _prepare_chat( convert_system_message_to_human=self.convert_system_message_to_human, ) message = history.pop() - chat = cli.start_chat(history=history) + chat = client.start_chat(history=history) return params, chat, message def get_num_tokens(self, text: str) -> int: diff --git a/libs/partners/google-genai/tests/integration_tests/test_function_call.py b/libs/partners/google-genai/tests/integration_tests/test_function_call.py index 7a45bdf66613d84..57d0005a3bdc1dd 100644 --- a/libs/partners/google-genai/tests/integration_tests/test_function_call.py +++ b/libs/partners/google-genai/tests/integration_tests/test_function_call.py @@ -1,5 +1,11 @@ """Test ChatGoogleGenerativeAI function call.""" +import json + +from langchain_core.messages import AIMessage +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.tools import tool + from langchain_google_genai.chat_models import ( ChatGoogleGenerativeAI, ) @@ -29,6 +35,50 @@ def test_function_call() -> None: assert res.additional_kwargs assert "function_call" in res.additional_kwargs assert "get_weather" == res.additional_kwargs["function_call"]["name"] - arguments = res.additional_kwargs["function_call"]["arguments"] - assert isinstance(arguments, dict) + arguments_str = res.additional_kwargs["function_call"]["arguments"] + assert isinstance(arguments_str, str) + arguments = json.loads(arguments_str) assert "location" in arguments + + +def test_tool_call() -> None: + @tool + def search_tool(query: str) -> str: + """Searches the web for `query` and returns the result.""" + raise NotImplementedError + + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[search_tool]) + response = llm.invoke("weather in san francisco") + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "search_tool" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert "query" in arguments + + +class MyModel(BaseModel): + name: str + age: int + + +def test_pydantic_call() -> None: + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[MyModel]) + response = llm.invoke("my name is Erick and I am 27 years old") + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "MyModel" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } From 927ab77d6e839d7e12aaab00faa9b527afa4a214 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 17:14:50 -0800 Subject: [PATCH 146/157] google-genai[patch]: no error for FunctionMessage (#17215) Both should eventually match this: https://github.com/langchain-ai/langchain/blob/master/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py#L179 But seems undocumented / can't find types in genai package --- .../google-genai/langchain_google_genai/chat_models.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py index 2bf9cf8cc3d7c3e..2f0d35428d58c81 100644 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ b/libs/partners/google-genai/langchain_google_genai/chat_models.py @@ -39,6 +39,7 @@ BaseMessage, ChatMessage, ChatMessageChunk, + FunctionMessage, HumanMessage, HumanMessageChunk, SystemMessage, @@ -326,14 +327,20 @@ def _parse_chat_history( continue elif isinstance(message, AIMessage): role = "model" + # TODO: Handle AImessage with function call + parts = _convert_to_parts(message.content) elif isinstance(message, HumanMessage): role = "user" + parts = _convert_to_parts(message.content) + elif isinstance(message, FunctionMessage): + role = "user" + # TODO: Handle FunctionMessage + parts = _convert_to_parts(message.content) else: raise ValueError( f"Unexpected message with type {type(message)} at the position {i}." ) - parts = _convert_to_parts(message.content) if raw_system_message: if role == "model": raise ValueError( From 41538375027f184cb2ad31e792c95ef19923accc Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Wed, 7 Feb 2024 17:15:09 -0800 Subject: [PATCH 147/157] google-genai[patch]: release 0.0.7 (#17193) --- libs/partners/google-genai/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/partners/google-genai/pyproject.toml b/libs/partners/google-genai/pyproject.toml index 28884a3cbf13f97..46e16db69682362 100644 --- a/libs/partners/google-genai/pyproject.toml +++ b/libs/partners/google-genai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-genai" -version = "0.0.6.post1" +version = "0.0.7" description = "An integration package connecting Google's genai package and LangChain" authors = [] readme = "README.md" From aeb6b3890171144da3ea262139b9df7dc3f26d8e Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 17:18:48 -0800 Subject: [PATCH 148/157] docs: cleanup fleet integration (#17214) Causing search issues --- .../retrievers/fleet_context.ipynb | 77 +++---------------- 1 file changed, 9 insertions(+), 68 deletions(-) diff --git a/docs/docs/integrations/retrievers/fleet_context.ipynb b/docs/docs/integrations/retrievers/fleet_context.ipynb index 5587fcbc64589a2..736184a83666d73 100644 --- a/docs/docs/integrations/retrievers/fleet_context.ipynb +++ b/docs/docs/integrations/retrievers/fleet_context.ipynb @@ -104,8 +104,7 @@ "source": [ "## Retriever chunks\n", "\n", - "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks.", - "\n", + "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks.\n", "\n", "We will be using Fleet Context's `download_embeddings()` to grab Langchain's documentation embeddings. You can view all supported libraries' documentation at https://fleet.so/context." ] @@ -125,24 +124,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "519c6898-3ef7-4b0d-94e3-e60ac7da51a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content=\"# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example.\", metadata={'id': 'f509f20d-4c63-4a5a-a40a-5c4c0f099839', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': 'd78cf422-2dab-4860-80fe-d71a3619b02f', 'parent': 'c153ebd9-2611-4a43-9db6-daa1f5f214f6', 'section_id': '', 'section_index': 0, 'text': \"# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example.\", 'title': 'Vector store-backed retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore'}),\n", - " Document(page_content='# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.', metadata={'id': 'e06e6bb5-127a-49f7-9511-247d279d0d83', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': '7c7dd0de-25e0-4e1d-9237-cfd2674c29d4', 'parent': 'beec5531-16a7-453c-80ab-c5628e0236ce', 'section_id': '', 'section_index': 0, 'text': '# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.', 'title': 'MultiVector Retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector'}),\n", - " Document(page_content='# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.', metadata={'id': 'dc3b8e5b-a591-4a46-bfeb-a91b36affae1', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': '31f80e84-c5db-4da2-939c-bccf519864a3', 'parent': 'f7c20633-6a60-4ca3-96b1-13fee66e321d', 'section_id': '', 'section_index': 0, 'text': '# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.', 'title': 'MultiQueryRetriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever'}),\n", - " Document(page_content='# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', metadata={'id': '1f4ca702-35b8-44c0-b33b-18c09a1f787d', 'library_id': '6254c672-7aa0-4233-b0a6-804bd273752b', 'page_id': '87f5d1fb-a1c6-4080-9d2b-7b88794eb6bf', 'parent': '1820c44d-7783-4846-a11c-106b18da015d', 'section_id': 'langchain-retrievers-multi-vector-multivectorretriever', 'section_index': 0, 'text': '# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', 'title': 'langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322', 'type': None, 'url': 'https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html#langchain-retrievers-multi-vector-multivectorretriever'})]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vecstore_retriever.get_relevant_documents(\"How does the multi vector retriever work\")" ] @@ -184,24 +169,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "540a54c2-b9a0-475c-ada0-91c5f67dd0fa", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='Vector store-backed retriever | 🦜️🔗 Langchain\\n# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it\\'s very easy to construct a retriever. Let\\'s walk through an example.Once you construct a vector store, it\\'s very easy to construct a retriever. Let\\'s walk through an example. ``` from langchain_community.document_loaders import TextLoaderloader = TextLoader(\\'../../../state_of_the_union.txt\\') ``` ``` from langchain.text_splitter import CharacterTextSplitterfrom langchain_community.vectorstores import FAISSfrom langchain_openai import OpenAIEmbeddingsdocuments = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = FAISS.from_documents(texts, embeddings) ``` ``` Exiting: Cleaning up .chroma directory ``` ``` retriever = db.as_retriever() ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Maximum marginal relevance retrieval[\\u200b](#maximum-marginal-relevance-retrieval) By default, the vector store retriever uses similarity search.If the underlying vector store supports maximum marginal relevance search, you can specify that as the search type. ``` retriever = db.as_retriever(search_type=\"mmr\") ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Similarity score threshold retrieval[\\u200b](#similarity-score-threshold-retrieval) You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold. ``` retriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5}) ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Specifying top k[\\u200b](#specifying-top-k) You can also specify search kwargs like `k` to use when doing retrieval.``` retriever = db.as_retriever(search_kwargs={\"k\": 1}) ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ``` len(docs) ``` ``` 1 ```', metadata={'title': 'Vector store-backed retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore', 'id': 'c153ebd9-2611-4a43-9db6-daa1f5f214f6'}),\n", - " Document(page_content='MultiVector Retriever | 🦜️🔗 Langchain\\n# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control. ``` from langchain.retrievers.multi_vector import MultiVectorRetriever ``` ``` from langchain_community.vectorstores import Chromafrom langchain_openai import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.storage import InMemoryStorefrom langchain_community.document_loaders import TextLoader ``` ``` loaders = [ TextLoader(\\'../../paul_graham_essay.txt\\'), TextLoader(\\'../../state_of_the_union.txt\\'),]docs = []for l in loaders: docs.extend(l.load())text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)docs = text_splitter.split_documents(docs) ``` ## Smaller chunks[\\u200b](#smaller-chunks) Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks.This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the `ParentDocumentRetriever` does. Here we show what is going on under the hood.``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"full_documents\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)import uuiddoc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` # The splitter to use to create smaller chunkschild_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400) ``` ``` sub_docs = []for i, doc in enumerate(docs): _id = doc_ids[i] _sub_docs = child_text_splitter.split_documents([doc]) for _doc in _sub_docs: _doc.metadata[id_key] = _id sub_docs.extend(_sub_docs) ``` ``` retriever.vectorstore.add_documents(sub_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` # Vectorstore alone retrieves the small chunksretriever.vectorstore.similarity_search(\"justice breyer\")[0] ``` ``` Document(page_content=\\'Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.Justice Breyer, thank you for your service. \\\\n\\\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\', metadata={\\'doc_id\\': \\'10e9cbc0-4ba5-4d79-a09b-c033d1ba7b01\\', \\'source\\': \\'../../state_of_the_union.txt\\'}) ``` ``` # Retriever returns larger chunkslen(retriever.get_relevant_documents(\"justice breyer\")[0].page_content) ``` ``` 9874 ``` ## Summary[\\u200b](#summary) Oftentimes a summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those.``` from langchain_openai import ChatOpenAIfrom langchain_core.prompts import ChatPromptTemplatefrom langchain_core.output_parsers import StrOutputParserimport uuidfrom langchain_core.documents import Document ``` ``` chain = ( {\"doc\": lambda x: x.page_content} | ChatPromptTemplate.from_template(\"Summarize the following document:\\\\n\\\\n{doc}\") | ChatOpenAI(max_retries=0) | StrOutputParser()) ``` ``` summaries = chain.batch(docs, {\"max_concurrency\": 5}) ``` ``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)doc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` summary_docs = [Document(page_content=s,metadata={id_key: doc_ids[i]}) for i, s in enumerate(summaries)] ``` ``` retriever.vectorstore.add_documents(summary_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` # # We can also add the original chunks to the vectorstore if we so want# for i, doc in enumerate(docs):# doc.metadata[id_key] = doc_ids[i]# retriever.vectorstore.add_documents(docs) ``` ``` sub_docs = vectorstore.similarity_search(\"justice breyer\") ``` ``` sub_docs[0] ``` ``` Document(page_content=\"The document is a transcript of a speech given by the President of the United States.The President discusses several important issues and initiatives, including the nomination of a Supreme Court Justice, border security and immigration reform, protecting women\\'s rights, advancing LGBTQ+ equality, bipartisan legislation, addressing the opioid epidemic and mental health, supporting veterans, investigating the health effects of burn pits on military personnel, ending cancer, and the strength and resilience of the American people. \", metadata={\\'doc_id\\': \\'79fa2e9f-28d9-4372-8af3-2caf4f1de312\\'}) ``` ``` retrieved_docs = retriever.get_relevant_documents(\"justice breyer\") ``` ``` len(retrieved_docs[0].page_content) ``` ``` 9194 ``` ## Hypothetical Queries[\\u200b](#hypothetical-queries) An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document.These questions can then be embedded ``` functions = [ { \"name\": \"hypothetical_questions\", \"description\": \"Generate hypothetical questions\", \"parameters\": { \"type\": \"object\", \"properties\": { \"questions\": { \"type\": \"array\", \"items\": { \"type\": \"string\" }, }, }, \"required\": [\"questions\"] } } ] ``` ``` from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParserchain = ( {\"doc\": lambda x: x.page_content} # Only asking for 3 hypothetical questions, but this could be adjusted | ChatPromptTemplate.from_template(\"Generate a list of 3 hypothetical questions that the below document could be used to answer:\\\\n\\\\n{doc}\") | ChatOpenAI(max_retries=0, model=\"gpt-4\").bind(functions=functions, function_call={\"name\": \"hypothetical_questions\"}) | JsonKeyOutputFunctionsParser(key_name=\"questions\")) ``` ``` chain.invoke(docs[0]) ``` ``` [\"What was the author\\'s initial impression of philosophy as a field of study, and how did it change when they got to college?\", \\'Why did the author decide to switch their focus to Artificial Intelligence (AI)? \\', \"What led to the author\\'s disillusionment with the field of AI as it was practiced at the time?\"]``` ``` hypothetical_questions = chain.batch(docs, {\"max_concurrency\": 5}) ``` ``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"hypo-questions\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)doc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` question_docs = []for i, question_list in enumerate(hypothetical_questions): question_docs.extend([Document(page_content=s,metadata={id_key: doc_ids[i]}) for s in question_list]) ``` ``` retriever.vectorstore.add_documents(question_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` sub_docs = vectorstore.similarity_search(\"justice breyer\") ``` ``` sub_docs ``` ``` [Document(page_content=\"What is the President\\'s stance on immigration reform?\", metadata={\\'doc_id\\': \\'505d73e3-8350-46ec-a58e-3af032f04ab3\\'}), Document(page_content=\"What is the President\\'s stance on immigration reform? \", metadata={\\'doc_id\\': \\'1c9618f0-7660-4b4f-a37c-509cbbbf6dba\\'}), Document(page_content=\"What is the President\\'s stance on immigration reform? \", metadata={\\'doc_id\\': \\'82c08209-b904-46a8-9532-edd2380950b7\\'}), Document(page_content=\\'What measures is the President proposing to protect the rights of LGBTQ+ Americans? \\', metadata={\\'doc_id\\': \\'82c08209-b904-46a8-9532-edd2380950b7\\'})] ``` ``` retrieved_docs = retriever.get_relevant_documents(\"justice breyer\") ``` ``` len(retrieved_docs[0].page_content) ``` ``` 9194 ```', metadata={'title': 'MultiVector Retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector', 'id': 'beec5531-16a7-453c-80ab-c5628e0236ce'}),\n", - " Document(page_content='MultiQueryRetriever | 🦜️🔗 Langchain\\n# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results. ``` # Build a sample vectorDBfrom langchain_community.vectorstores import Chromafrom langchain_community.document_loaders import WebBaseLoaderfrom langchain_openai import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Load blog postloader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")data = loader.load()# Splittext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)splits = text_splitter.split_documents(data)# VectorDBembedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=splits, embedding=embedding) ``` #### Simple usage[\\u200b](#simple-usage) Specify the LLM to use for query generation, and the retriever will do the rest.``` from langchain_openai import ChatOpenAIfrom langchain.retrievers.multi_query import MultiQueryRetrieverquestion = \"What are the approaches to Task Decomposition? \"llm = ChatOpenAI(temperature=0)retriever_from_llm = MultiQueryRetriever.from_llm( retriever=vectordb.as_retriever(), llm=llm) ``` ``` # Set logging for the queriesimport logginglogging.basicConfig()logging.getLogger(\"langchain.retrievers.multi_query\").setLevel(logging.INFO) ``` ``` unique_docs = retriever_from_llm.get_relevant_documents(query=question)len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\\'1. How can Task Decomposition be approached? \\', \\'2. What are the different methods for Task Decomposition? \\', \\'3. What are the various approaches to decomposing tasks?\\'] 5 ``` #### Supplying your own prompt[\\u200b](#supplying-your-own-prompt) You can also supply a prompt along with an output parser to split the results into a list of queries.5 ``` #### Supplying your own prompt[\\u200b](#supplying-your-own-prompt) You can also supply a prompt along with an output parser to split the results into a list of queries. ``` from typing import Listfrom langchain.chains import LLMChainfrom pydantic import BaseModel, Fieldfrom langchain.prompts import PromptTemplatefrom langchain.output_parsers import PydanticOutputParser# Output parser will split the LLM result into a list of queriesclass LineList(BaseModel): # \"lines\" is the key (attribute name) of the parsed output lines: List[str] = Field(description=\"Lines of text\")class LineListOutputParser(PydanticOutputParser): def __init__(self) -> None: super().__init__(pydantic_object=LineList) def parse(self, text: str) -> LineList: lines = text.strip().split(\"\\\\n\") return LineList(lines=lines)output_parser = LineListOutputParser()QUERY_PROMPT = PromptTemplate( input_variables=[\"question\"], template=\"\"\"You are an AI language model assistant.Your task is to generate five different versions of the given user question to retrieve relevant documents from a vector database. By generating multiple perspectives on the user question, your goal is to help the user overcome some of the limitations of the distance-based similarity search. Provide these alternative questions separated by newlines. Original question: {question}\"\"\",)llm = ChatOpenAI(temperature=0)# Chainllm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)# Other inputsquestion = \"What are the approaches to Task Decomposition?\" ``` ``` # Runretriever = MultiQueryRetriever( retriever=vectordb.as_retriever(), llm_chain=llm_chain, parser_key=\"lines\") # \"lines\" is the key (attribute name) of the parsed output# Resultsunique_docs = retriever.get_relevant_documents( query=\"What does the course say about regression? \")len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\"1.\")len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\"1. What is the course\\'s perspective on regression? \", \\'2. Can you provide information on regression as discussed in the course? \\', \\'3. How does the course cover the topic of regression? \\', \"4. What are the course\\'s teachings on regression? \", \\'5. In relation to the course, what is mentioned about regression?\\'] 11 ```', metadata={'title': 'MultiQueryRetriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever', 'id': 'f7c20633-6a60-4ca3-96b1-13fee66e321d'}),\n", - " Document(page_content='langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322\\n# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', metadata={'title': 'langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322', 'type': None, 'url': 'https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html#langchain-retrievers-multi-vector-multivectorretriever', 'id': '1820c44d-7783-4846-a11c-106b18da015d'})]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "parent_retriever.get_relevant_documents(\"How does the multi vector retriever work\")" ] @@ -264,40 +235,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "f1b9f091-a5f9-4468-9d52-633bf3361f4e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "To create a FAISS vector store retriever that returns 10 documents per search query, you can use the following code:\n", - "\n", - "```python\n", - "from langchain_openai import OpenAIEmbeddings\n", - "from langchain_community.vectorstores import FAISS\n", - "\n", - "# Assuming you have already loaded and split your documents\n", - "# into `texts` and initialized your `embeddings` object\n", - "\n", - "# Create the FAISS vector store\n", - "db = FAISS.from_documents(texts, embeddings)\n", - "\n", - "# Create the retriever with the desired search kwargs\n", - "retriever = db.as_retriever(search_kwargs={\"k\": 10})\n", - "```\n", - "\n", - "Now, you can use the `retriever` object to get relevant documents using the `get_relevant_documents` method. For example:\n", - "\n", - "```python\n", - "docs = retriever.get_relevant_documents(\"your search query\")\n", - "```\n", - "\n", - "This will return a list of 10 documents that are most relevant to the given search query." - ] - } - ], + "outputs": [], "source": [ "for chunk in chain.invoke(\n", " \"How do I create a FAISS vector store retriever that returns 10 documents per search query\"\n", @@ -308,9 +249,9 @@ ], "metadata": { "kernelspec": { - "display_name": "poetry-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "poetry-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { From 19546081c63bf0590ae32631a04f8cfec7d23fb3 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Wed, 7 Feb 2024 17:27:01 -0800 Subject: [PATCH 149/157] templates: add gemini functions agent (#17141) Co-authored-by: Erick Friis --- templates/gemini-functions-agent/LICENSE | 21 + templates/gemini-functions-agent/README.md | 76 + .../gemini_functions_agent/__init__.py | 3 + .../gemini_functions_agent/agent.py | 81 + templates/gemini-functions-agent/main.py | 5 + templates/gemini-functions-agent/poetry.lock | 2002 +++++++++++++++++ .../gemini-functions-agent/pyproject.toml | 33 + .../gemini-functions-agent/tests/__init__.py | 0 8 files changed, 2221 insertions(+) create mode 100644 templates/gemini-functions-agent/LICENSE create mode 100644 templates/gemini-functions-agent/README.md create mode 100644 templates/gemini-functions-agent/gemini_functions_agent/__init__.py create mode 100644 templates/gemini-functions-agent/gemini_functions_agent/agent.py create mode 100644 templates/gemini-functions-agent/main.py create mode 100644 templates/gemini-functions-agent/poetry.lock create mode 100644 templates/gemini-functions-agent/pyproject.toml create mode 100644 templates/gemini-functions-agent/tests/__init__.py diff --git a/templates/gemini-functions-agent/LICENSE b/templates/gemini-functions-agent/LICENSE new file mode 100644 index 000000000000000..426b65090341f37 --- /dev/null +++ b/templates/gemini-functions-agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/gemini-functions-agent/README.md b/templates/gemini-functions-agent/README.md new file mode 100644 index 000000000000000..16935c6298eaab5 --- /dev/null +++ b/templates/gemini-functions-agent/README.md @@ -0,0 +1,76 @@ + +# gemini-functions-agent + +This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take. + +This example creates an agent that can optionally look up information on the internet using Tavily's search engine. + +## Environment Setup + +The following environment variables need to be set: + +Set the `TAVILY_API_KEY` environment variable to access Tavily. + +You will also need to authenticate with Google: + +```shell +gcloud auth application-default login +``` + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package gemini-functions-agent +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add gemini-functions-agent +``` + +And add the following code to your `server.py` file: +```python +from gemini_functions_agent import agent_executor as gemini_functions_agent_chain + +add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/gemini-functions-agent/playground](http://127.0.0.1:8000/gemini-functions-agent/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent") +``` \ No newline at end of file diff --git a/templates/gemini-functions-agent/gemini_functions_agent/__init__.py b/templates/gemini-functions-agent/gemini_functions_agent/__init__.py new file mode 100644 index 000000000000000..da6719519020f0f --- /dev/null +++ b/templates/gemini-functions-agent/gemini_functions_agent/__init__.py @@ -0,0 +1,3 @@ +from gemini_functions_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/gemini-functions-agent/gemini_functions_agent/agent.py b/templates/gemini-functions-agent/gemini_functions_agent/agent.py new file mode 100644 index 000000000000000..79aba8a9445bb95 --- /dev/null +++ b/templates/gemini-functions-agent/gemini_functions_agent/agent.py @@ -0,0 +1,81 @@ +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_to_openai_function_messages +from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser +from langchain.utilities.tavily_search import TavilySearchAPIWrapper +from langchain_community.tools.tavily_search import TavilySearchResults +from langchain_core.messages import AIMessage, HumanMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_google_genai import ChatGoogleGenerativeAI + +# Create the tool +search = TavilySearchAPIWrapper() +description = """"A search engine optimized for comprehensive, accurate, \ +and trusted results. Useful for when you need to answer questions \ +about current events or about recent information. \ +Input should be a search query. \ +If the user is asking about something that you don't know about, \ +you should probably use this tool to see if that can provide any information.""" +tavily_tool = TavilySearchResults(api_wrapper=search, description=description) + +tools = [tavily_tool] + +llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro") + +prompt = ChatPromptTemplate.from_messages( + [ + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] +) + +llm_with_tools = llm.bind( + functions=[ + { + "name": tavily_tool.name, + "description": tavily_tool.description, + "parameters": { + "type": "object", + "properties": {"query": {"type": "string"}}, + "required": ["query"], + }, + } + ] +) + + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +agent = ( + { + "input": lambda x: x["input"], + "chat_history": lambda x: _format_chat_history(x["chat_history"]), + "agent_scratchpad": lambda x: format_to_openai_function_messages( + x["intermediate_steps"] + ), + } + | prompt + | llm_with_tools + | OpenAIFunctionsAgentOutputParser() +) + + +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] = Field( + ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} + ) + + +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( + input_type=AgentInput +) diff --git a/templates/gemini-functions-agent/main.py b/templates/gemini-functions-agent/main.py new file mode 100644 index 000000000000000..f0e1f5963f9e6de --- /dev/null +++ b/templates/gemini-functions-agent/main.py @@ -0,0 +1,5 @@ +from openai_functions_agent.agent import agent_executor + +if __name__ == "__main__": + question = "who won the womens world cup in 2023?" + print(agent_executor.invoke({"input": question, "chat_history": []})) diff --git a/templates/gemini-functions-agent/poetry.lock b/templates/gemini-functions-agent/poetry.lock new file mode 100644 index 000000000000000..17c00ed6251b00f --- /dev/null +++ b/templates/gemini-functions-agent/poetry.lock @@ -0,0 +1,2002 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"}, + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"}, + {file = 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b/templates/gemini-functions-agent/pyproject.toml new file mode 100644 index 000000000000000..5648cd2fdc73816 --- /dev/null +++ b/templates/gemini-functions-agent/pyproject.toml @@ -0,0 +1,33 @@ +[tool.poetry] +name = "gemini-functions-agent" +version = "0.1.0" +description = "Agent using Gemini function calling to execute functions, including search" +authors = [ + "Harrison Chase", +] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.9,<4.0" +langchain = "^0.1" +tavily-python = "^0.1.9" +langchain-google-genai = ">=0.0.7,<0.1" + +[tool.poetry.group.dev.dependencies] +langchain-cli = ">=0.0.21" + +[tool.langserve] +export_module = "gemini_functions_agent" +export_attr = "agent_executor" + +[tool.templates-hub] +use-case = "research" +author = "LangChain" +integrations = ["Google", "Tavily"] +tags = ["search", "agents", "function-calling"] + +[build-system] +requires = [ + "poetry-core", +] +build-backend = "poetry.core.masonry.api" diff --git a/templates/gemini-functions-agent/tests/__init__.py b/templates/gemini-functions-agent/tests/__init__.py new file mode 100644 index 000000000000000..e69de29bb2d1d64 From f92738a6f6c37909d42cda9e0493e5ad05b9af6e Mon Sep 17 00:00:00 2001 From: Dmitry Kankalovich Date: Thu, 8 Feb 2024 04:06:09 +0100 Subject: [PATCH 150/157] langchain[minor], community[minor], core[minor]: Async Cache support and AsyncRedisCache (#15817) * This PR adds async methods to the LLM cache. * Adds an implementation using Redis called AsyncRedisCache. * Adds a docker compose file at the /docker to help spin up docker * Updates redis tests to use a context manager so flushing always happens by default --- docker/docker-compose.yml | 17 ++ libs/community/langchain_community/cache.py | 190 +++++++++++---- libs/core/langchain_core/caches.py | 15 ++ .../language_models/chat_models.py | 4 +- .../langchain_core/language_models/llms.py | 42 +++- libs/langchain/langchain/cache.py | 2 + .../cache/test_redis_cache.py | 218 ++++++++++++++---- libs/langchain/tests/unit_tests/test_cache.py | 93 ++++++-- 8 files changed, 460 insertions(+), 121 deletions(-) create mode 100644 docker/docker-compose.yml diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 000000000000000..ce680ccafda51ea --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,17 @@ +# docker-compose to make it easier to spin up integration tests. +# Services should use NON standard ports to avoid collision with +version: "3" +name: langchain-tests + +services: + redis: + image: redis/redis-stack-server:latest + # We use non standard ports since + # these instances are used for testing + # and users may already have existing + # redis instances set up locally + # for other projects + ports: + - "6020:6379" + volumes: + - ./redis-volume:/data diff --git a/libs/community/langchain_community/cache.py b/libs/community/langchain_community/cache.py index b20016829427979..54996071f865567 100644 --- a/libs/community/langchain_community/cache.py +++ b/libs/community/langchain_community/cache.py @@ -27,6 +27,7 @@ import logging import uuid import warnings +from abc import ABC from datetime import timedelta from functools import lru_cache from typing import ( @@ -351,8 +352,60 @@ def clear(self, **kwargs: Any) -> None: self.redis.flushdb(flush_type=asynchronous) -class RedisCache(BaseCache): - """Cache that uses Redis as a backend.""" +class _RedisCacheBase(BaseCache, ABC): + @staticmethod + def _key(prompt: str, llm_string: str) -> str: + """Compute key from prompt and llm_string""" + return _hash(prompt + llm_string) + + @staticmethod + def _ensure_generation_type(return_val: RETURN_VAL_TYPE) -> None: + for gen in return_val: + if not isinstance(gen, Generation): + raise ValueError( + "RedisCache only supports caching of normal LLM generations, " + f"got {type(gen)}" + ) + + @staticmethod + def _get_generations( + results: dict[str | bytes, str | bytes], + ) -> Optional[List[Generation]]: + generations = [] + if results: + for _, text in results.items(): + try: + generations.append(loads(text)) + except Exception: + logger.warning( + "Retrieving a cache value that could not be deserialized " + "properly. This is likely due to the cache being in an " + "older format. Please recreate your cache to avoid this " + "error." + ) + # In a previous life we stored the raw text directly + # in the table, so assume it's in that format. + generations.append(Generation(text=text)) + return generations if generations else None + + @staticmethod + def _configure_pipeline_for_update( + key: str, pipe: Any, return_val: RETURN_VAL_TYPE, ttl: Optional[int] = None + ) -> None: + pipe.hset( + key, + mapping={ + str(idx): dumps(generation) for idx, generation in enumerate(return_val) + }, + ) + if ttl is not None: + pipe.expire(key, ttl) + + +class RedisCache(_RedisCacheBase): + """ + Cache that uses Redis as a backend. Allows to use a sync `redis.Redis` client. + """ def __init__(self, redis_: Any, *, ttl: Optional[int] = None): """ @@ -360,12 +413,12 @@ def __init__(self, redis_: Any, *, ttl: Optional[int] = None): This method initializes an object with Redis caching capabilities. It takes a `redis_` parameter, which should be an instance of a Redis - client class, allowing the object to interact with a Redis - server for caching purposes. + client class (`redis.Redis`), allowing the object + to interact with a Redis server for caching purposes. Parameters: redis_ (Any): An instance of a Redis client class - (e.g., redis.Redis) used for caching. + (`redis.Redis`) to be used for caching. This allows the object to communicate with a Redis server for caching operations. ttl (int, optional): Time-to-live (TTL) for cached items in seconds. @@ -377,61 +430,27 @@ def __init__(self, redis_: Any, *, ttl: Optional[int] = None): from redis import Redis except ImportError: raise ValueError( - "Could not import redis python package. " + "Could not import `redis` python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): - raise ValueError("Please pass in Redis object.") + raise ValueError("Please pass a valid `redis.Redis` client.") self.redis = redis_ self.ttl = ttl - def _key(self, prompt: str, llm_string: str) -> str: - """Compute key from prompt and llm_string""" - return _hash(prompt + llm_string) - def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" - generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) - if results: - for _, text in results.items(): - try: - generations.append(loads(text)) - except Exception: - logger.warning( - "Retrieving a cache value that could not be deserialized " - "properly. This is likely due to the cache being in an " - "older format. Please recreate your cache to avoid this " - "error." - ) - # In a previous life we stored the raw text directly - # in the table, so assume it's in that format. - generations.append(Generation(text=text)) - return generations if generations else None + return self._get_generations(results) # type: ignore[arg-type] def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" - for gen in return_val: - if not isinstance(gen, Generation): - raise ValueError( - "RedisCache only supports caching of normal LLM generations, " - f"got {type(gen)}" - ) - # Write to a Redis HASH + self._ensure_generation_type(return_val) key = self._key(prompt, llm_string) with self.redis.pipeline() as pipe: - pipe.hset( - key, - mapping={ - str(idx): dumps(generation) - for idx, generation in enumerate(return_val) - }, - ) - if self.ttl is not None: - pipe.expire(key, self.ttl) - + self._configure_pipeline_for_update(key, pipe, return_val, self.ttl) pipe.execute() def clear(self, **kwargs: Any) -> None: @@ -440,6 +459,89 @@ def clear(self, **kwargs: Any) -> None: self.redis.flushdb(asynchronous=asynchronous, **kwargs) +class AsyncRedisCache(_RedisCacheBase): + """ + Cache that uses Redis as a backend. Allows to use an + async `redis.asyncio.Redis` client. + """ + + def __init__(self, redis_: Any, *, ttl: Optional[int] = None): + """ + Initialize an instance of AsyncRedisCache. + + This method initializes an object with Redis caching capabilities. + It takes a `redis_` parameter, which should be an instance of a Redis + client class (`redis.asyncio.Redis`), allowing the object + to interact with a Redis server for caching purposes. + + Parameters: + redis_ (Any): An instance of a Redis client class + (`redis.asyncio.Redis`) to be used for caching. + This allows the object to communicate with a + Redis server for caching operations. + ttl (int, optional): Time-to-live (TTL) for cached items in seconds. + If provided, it sets the time duration for how long cached + items will remain valid. If not provided, cached items will not + have an automatic expiration. + """ + try: + from redis.asyncio import Redis + except ImportError: + raise ValueError( + "Could not import `redis.asyncio` python package. " + "Please install it with `pip install redis`." + ) + if not isinstance(redis_, Redis): + raise ValueError("Please pass a valid `redis.asyncio.Redis` client.") + self.redis = redis_ + self.ttl = ttl + + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + raise NotImplementedError( + "This async Redis cache does not implement `lookup()` method. " + "Consider using the async `alookup()` version." + ) + + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string. Async version.""" + results = await self.redis.hgetall(self._key(prompt, llm_string)) + return self._get_generations(results) # type: ignore[arg-type] + + def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: + """Update cache based on prompt and llm_string.""" + raise NotImplementedError( + "This async Redis cache does not implement `update()` method. " + "Consider using the async `aupdate()` version." + ) + + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string. Async version.""" + self._ensure_generation_type(return_val) + key = self._key(prompt, llm_string) + + async with self.redis.pipeline() as pipe: + self._configure_pipeline_for_update(key, pipe, return_val, self.ttl) + await pipe.execute() # type: ignore[attr-defined] + + def clear(self, **kwargs: Any) -> None: + """Clear cache. If `asynchronous` is True, flush asynchronously.""" + raise NotImplementedError( + "This async Redis cache does not implement `clear()` method. " + "Consider using the async `aclear()` version." + ) + + async def aclear(self, **kwargs: Any) -> None: + """ + Clear cache. If `asynchronous` is True, flush asynchronously. + Async version. + """ + asynchronous = kwargs.get("asynchronous", False) + await self.redis.flushdb(asynchronous=asynchronous, **kwargs) + + class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" diff --git a/libs/core/langchain_core/caches.py b/libs/core/langchain_core/caches.py index c14959c8f9c6b59..626670950ab7d34 100644 --- a/libs/core/langchain_core/caches.py +++ b/libs/core/langchain_core/caches.py @@ -4,6 +4,7 @@ from typing import Any, Optional, Sequence from langchain_core.outputs import Generation +from langchain_core.runnables import run_in_executor RETURN_VAL_TYPE = Sequence[Generation] @@ -22,3 +23,17 @@ def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> N @abstractmethod def clear(self, **kwargs: Any) -> None: """Clear cache that can take additional keyword arguments.""" + + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + return await run_in_executor(None, self.lookup, prompt, llm_string) + + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string.""" + return await run_in_executor(None, self.update, prompt, llm_string, return_val) + + async def aclear(self, **kwargs: Any) -> None: + """Clear cache that can take additional keyword arguments.""" + return await run_in_executor(None, self.clear, **kwargs) diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py index aaf61a7810e1cae..6279116093e5f86 100644 --- a/libs/core/langchain_core/language_models/chat_models.py +++ b/libs/core/langchain_core/language_models/chat_models.py @@ -622,7 +622,7 @@ async def _agenerate_with_cache( else: llm_string = self._get_llm_string(stop=stop, **kwargs) prompt = dumps(messages) - cache_val = llm_cache.lookup(prompt, llm_string) + cache_val = await llm_cache.alookup(prompt, llm_string) if isinstance(cache_val, list): return ChatResult(generations=cache_val) else: @@ -632,7 +632,7 @@ async def _agenerate_with_cache( ) else: result = await self._agenerate(messages, stop=stop, **kwargs) - llm_cache.update(prompt, llm_string, result.generations) + await llm_cache.aupdate(prompt, llm_string, result.generations) return result @abstractmethod diff --git a/libs/core/langchain_core/language_models/llms.py b/libs/core/langchain_core/language_models/llms.py index 7f987baf88df024..ec2af1b91e08868 100644 --- a/libs/core/langchain_core/language_models/llms.py +++ b/libs/core/langchain_core/language_models/llms.py @@ -139,6 +139,26 @@ def get_prompts( return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts +async def aget_prompts( + params: Dict[str, Any], prompts: List[str] +) -> Tuple[Dict[int, List], str, List[int], List[str]]: + """Get prompts that are already cached. Async version.""" + llm_string = str(sorted([(k, v) for k, v in params.items()])) + missing_prompts = [] + missing_prompt_idxs = [] + existing_prompts = {} + llm_cache = get_llm_cache() + for i, prompt in enumerate(prompts): + if llm_cache: + cache_val = await llm_cache.alookup(prompt, llm_string) + if isinstance(cache_val, list): + existing_prompts[i] = cache_val + else: + missing_prompts.append(prompt) + missing_prompt_idxs.append(i) + return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts + + def update_cache( existing_prompts: Dict[int, List], llm_string: str, @@ -157,6 +177,24 @@ def update_cache( return llm_output +async def aupdate_cache( + existing_prompts: Dict[int, List], + llm_string: str, + missing_prompt_idxs: List[int], + new_results: LLMResult, + prompts: List[str], +) -> Optional[dict]: + """Update the cache and get the LLM output. Async version""" + llm_cache = get_llm_cache() + for i, result in enumerate(new_results.generations): + existing_prompts[missing_prompt_idxs[i]] = result + prompt = prompts[missing_prompt_idxs[i]] + if llm_cache: + await llm_cache.aupdate(prompt, llm_string, result) + llm_output = new_results.llm_output + return llm_output + + class BaseLLM(BaseLanguageModel[str], ABC): """Base LLM abstract interface. @@ -869,7 +907,7 @@ async def agenerate( llm_string, missing_prompt_idxs, missing_prompts, - ) = get_prompts(params, prompts) + ) = await aget_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" @@ -917,7 +955,7 @@ async def agenerate( new_results = await self._agenerate_helper( missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs ) - llm_output = update_cache( + llm_output = await aupdate_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = ( diff --git a/libs/langchain/langchain/cache.py b/libs/langchain/langchain/cache.py index fae1d1cf032618f..3c249e964c5a8d1 100644 --- a/libs/langchain/langchain/cache.py +++ b/libs/langchain/langchain/cache.py @@ -1,6 +1,7 @@ from langchain_community.cache import ( AstraDBCache, AstraDBSemanticCache, + AsyncRedisCache, CassandraCache, CassandraSemanticCache, FullLLMCache, @@ -22,6 +23,7 @@ "SQLAlchemyCache", "SQLiteCache", "UpstashRedisCache", + "AsyncRedisCache", "RedisCache", "RedisSemanticCache", "GPTCache", diff --git a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py index 7b44ade026dab02..846c709b9716892 100644 --- a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py +++ b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py @@ -1,6 +1,7 @@ """Test Redis cache functionality.""" import uuid -from typing import List, cast +from contextlib import asynccontextmanager, contextmanager +from typing import AsyncGenerator, Generator, List, Optional, cast import pytest from langchain_core.embeddings import Embeddings @@ -8,7 +9,7 @@ from langchain_core.messages import AIMessage, BaseMessage, HumanMessage from langchain_core.outputs import ChatGeneration, Generation, LLMResult -from langchain.cache import RedisCache, RedisSemanticCache +from langchain.cache import AsyncRedisCache, RedisCache, RedisSemanticCache from langchain.globals import get_llm_cache, set_llm_cache from tests.integration_tests.cache.fake_embeddings import ( ConsistentFakeEmbeddings, @@ -17,65 +18,176 @@ from tests.unit_tests.llms.fake_chat_model import FakeChatModel from tests.unit_tests.llms.fake_llm import FakeLLM -REDIS_TEST_URL = "redis://localhost:6379" +# Using a non-standard port to avoid conflicts with potentially local running +# redis instances +# You can spin up a local redis using docker compose +# cd [repository-root]/docker +# docker-compose up redis +REDIS_TEST_URL = "redis://localhost:6020" def random_string() -> str: return str(uuid.uuid4()) -def test_redis_cache_ttl() -> None: +@contextmanager +def get_sync_redis(*, ttl: Optional[int] = 1) -> Generator[RedisCache, None, None]: + """Get a sync RedisCache instance.""" import redis - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=1)) - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.update("foo", "bar", [Generation(text="fizz")]) - key = llm_cache._key("foo", "bar") - assert llm_cache.redis.pttl(key) > 0 + cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=ttl) + try: + yield cache + finally: + cache.clear() -def test_redis_cache() -> None: - import redis +@asynccontextmanager +async def get_async_redis( + *, ttl: Optional[int] = 1 +) -> AsyncGenerator[AsyncRedisCache, None]: + """Get an async RedisCache instance.""" + from redis.asyncio import Redis - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))) - llm = FakeLLM() - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - output = llm.generate(["foo"]) - expected_output = LLMResult( - generations=[[Generation(text="fizz")]], - llm_output={}, - ) - assert output == expected_output - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.redis.flushall() + cache = AsyncRedisCache(redis_=Redis.from_url(REDIS_TEST_URL), ttl=ttl) + try: + yield cache + finally: + await cache.aclear() + + +def test_redis_cache_ttl() -> None: + from redis import Redis + + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm_cache.update("foo", "bar", [Generation(text="fizz")]) + key = llm_cache._key("foo", "bar") + assert isinstance(llm_cache.redis, Redis) + assert llm_cache.redis.pttl(key) > 0 + + +async def test_async_redis_cache_ttl() -> None: + from redis.asyncio import Redis as AsyncRedis + + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate("foo", "bar", [Generation(text="fizz")]) + key = llm_cache._key("foo", "bar") + assert isinstance(llm_cache.redis, AsyncRedis) + assert await llm_cache.redis.pttl(key) > 0 + + +def test_sync_redis_cache() -> None: + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache.update("prompt", llm_string, [Generation(text="fizz0")]) + output = llm.generate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz0")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_sync_in_async_redis_cache() -> None: + """Test the sync RedisCache invoked with async methods""" + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + # llm_cache.update("meow", llm_string, [Generation(text="meow")]) + await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz1")]) + output = await llm.agenerate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz1")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_async_redis_cache() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz2")]) + output = await llm.agenerate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz2")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_async_in_sync_redis_cache() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache = cast(RedisCache, get_llm_cache()) + with pytest.raises(NotImplementedError): + llm_cache.update("foo", llm_string, [Generation(text="fizz")]) def test_redis_cache_chat() -> None: - import redis + with get_sync_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeChatModel() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + prompt: List[BaseMessage] = [HumanMessage(content="foo")] + llm_cache = cast(RedisCache, get_llm_cache()) + llm_cache.update( + dumps(prompt), + llm_string, + [ChatGeneration(message=AIMessage(content="fizz"))], + ) + output = llm.generate([prompt]) + expected_output = LLMResult( + generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], + llm_output={}, + ) + assert output == expected_output - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))) - llm = FakeChatModel() - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - prompt: List[BaseMessage] = [HumanMessage(content="foo")] - get_llm_cache().update( - dumps(prompt), llm_string, [ChatGeneration(message=AIMessage(content="fizz"))] - ) - output = llm.generate([prompt]) - expected_output = LLMResult( - generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], - llm_output={}, - ) - assert output == expected_output - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.redis.flushall() + +async def test_async_redis_cache_chat() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeChatModel() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + prompt: List[BaseMessage] = [HumanMessage(content="foo")] + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate( + dumps(prompt), + llm_string, + [ChatGeneration(message=AIMessage(content="fizz"))], + ) + output = await llm.agenerate([prompt]) + expected_output = LLMResult( + generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], + llm_output={}, + ) + assert output == expected_output def test_redis_semantic_cache() -> None: + """Test redis semantic cache functionality.""" set_llm_cache( RedisSemanticCache( embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1 @@ -85,7 +197,8 @@ def test_redis_semantic_cache() -> None: params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update("foo", llm_string, [Generation(text="fizz")]) output = llm.generate( ["bar"] ) # foo and bar will have the same embedding produced by FakeEmbeddings @@ -95,13 +208,13 @@ def test_redis_semantic_cache() -> None: ) assert output == expected_output # clear the cache - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) output = llm.generate( ["bar"] ) # foo and bar will have the same embedding produced by FakeEmbeddings # expect different output now without cached result assert output != expected_output - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) def test_redis_semantic_cache_multi() -> None: @@ -114,7 +227,8 @@ def test_redis_semantic_cache_multi() -> None: params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update( + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update( "foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")] ) output = llm.generate( @@ -126,7 +240,7 @@ def test_redis_semantic_cache_multi() -> None: ) assert output == expected_output # clear the cache - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) def test_redis_semantic_cache_chat() -> None: @@ -140,7 +254,8 @@ def test_redis_semantic_cache_chat() -> None: params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) prompt: List[BaseMessage] = [HumanMessage(content="foo")] - get_llm_cache().update( + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update( dumps(prompt), llm_string, [ChatGeneration(message=AIMessage(content="fizz"))] ) output = llm.generate([prompt]) @@ -149,7 +264,7 @@ def test_redis_semantic_cache_chat() -> None: llm_output={}, ) assert output == expected_output - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) @pytest.mark.parametrize("embedding", [ConsistentFakeEmbeddings()]) @@ -192,10 +307,11 @@ def test_redis_semantic_cache_hit( ] for prompt_i_generations in generations ] + llm_cache = cast(RedisSemanticCache, get_llm_cache()) for prompt_i, llm_generations_i in zip(prompts, llm_generations): print(prompt_i) print(llm_generations_i) - get_llm_cache().update(prompt_i, llm_string, llm_generations_i) + llm_cache.update(prompt_i, llm_string, llm_generations_i) llm.generate(prompts) assert llm.generate(prompts) == LLMResult( generations=llm_generations, llm_output={} diff --git a/libs/langchain/tests/unit_tests/test_cache.py b/libs/langchain/tests/unit_tests/test_cache.py index 88260a6f71cfa9c..5b2e1a4da4e2c7f 100644 --- a/libs/langchain/tests/unit_tests/test_cache.py +++ b/libs/langchain/tests/unit_tests/test_cache.py @@ -1,4 +1,5 @@ """Test caching for LLMs and ChatModels.""" +import sqlite3 from typing import Dict, Generator, List, Union import pytest @@ -21,7 +22,11 @@ def get_sqlite_cache() -> SQLAlchemyCache: - return SQLAlchemyCache(engine=create_engine("sqlite://")) + return SQLAlchemyCache( + engine=create_engine( + "sqlite://", creator=lambda: sqlite3.connect("file::memory:?cache=shared") + ) + ) CACHE_OPTIONS = [ @@ -35,33 +40,41 @@ def set_cache_and_teardown(request: FixtureRequest) -> Generator[None, None, Non # Will be run before each test cache_instance = request.param set_llm_cache(cache_instance()) - if get_llm_cache(): - get_llm_cache().clear() + if llm_cache := get_llm_cache(): + llm_cache.clear() else: raise ValueError("Cache not set. This should never happen.") yield # Will be run after each test - if get_llm_cache(): - get_llm_cache().clear() + if llm_cache: + llm_cache.clear() set_llm_cache(None) else: raise ValueError("Cache not set. This should never happen.") -def test_llm_caching() -> None: +async def test_llm_caching() -> None: prompt = "How are you?" response = "Test response" cached_response = "Cached test response" llm = FakeListLLM(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=prompt, llm_string=create_llm_string(llm), return_val=[Generation(text=cached_response)], ) assert llm(prompt) == cached_response + # async test + await llm_cache.aupdate( + prompt=prompt, + llm_string=create_llm_string(llm), + return_val=[Generation(text=cached_response)], + ) + assert await llm.ainvoke(prompt) == cached_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -90,14 +103,15 @@ def test_old_sqlite_llm_caching() -> None: assert llm(prompt) == cached_response -def test_chat_model_caching() -> None: +async def test_chat_model_caching() -> None: prompt: List[BaseMessage] = [HumanMessage(content="How are you?")] response = "Test response" cached_response = "Cached test response" cached_message = AIMessage(content=cached_response) llm = FakeListChatModel(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=dumps(prompt), llm_string=llm._get_llm_string(), return_val=[ChatGeneration(message=cached_message)], @@ -105,6 +119,16 @@ def test_chat_model_caching() -> None: result = llm(prompt) assert isinstance(result, AIMessage) assert result.content == cached_response + + # async test + await llm_cache.aupdate( + prompt=dumps(prompt), + llm_string=llm._get_llm_string(), + return_val=[ChatGeneration(message=cached_message)], + ) + result = await llm.ainvoke(prompt) + assert isinstance(result, AIMessage) + assert result.content == cached_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -112,25 +136,38 @@ def test_chat_model_caching() -> None: ) -def test_chat_model_caching_params() -> None: +async def test_chat_model_caching_params() -> None: prompt: List[BaseMessage] = [HumanMessage(content="How are you?")] response = "Test response" cached_response = "Cached test response" cached_message = AIMessage(content=cached_response) llm = FakeListChatModel(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=dumps(prompt), llm_string=llm._get_llm_string(functions=[]), return_val=[ChatGeneration(message=cached_message)], ) result = llm(prompt, functions=[]) + result_no_params = llm(prompt) assert isinstance(result, AIMessage) assert result.content == cached_response - result_no_params = llm(prompt) assert isinstance(result_no_params, AIMessage) assert result_no_params.content == response + # async test + await llm_cache.aupdate( + prompt=dumps(prompt), + llm_string=llm._get_llm_string(functions=[]), + return_val=[ChatGeneration(message=cached_message)], + ) + result = await llm.ainvoke(prompt, functions=[]) + result_no_params = await llm.ainvoke(prompt) + assert isinstance(result, AIMessage) + assert result.content == cached_response + assert isinstance(result_no_params, AIMessage) + assert result_no_params.content == response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -138,19 +175,31 @@ def test_chat_model_caching_params() -> None: ) -def test_llm_cache_clear() -> None: +async def test_llm_cache_clear() -> None: prompt = "How are you?" - response = "Test response" + expected_response = "Test response" cached_response = "Cached test response" - llm = FakeListLLM(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + llm = FakeListLLM(responses=[expected_response]) + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( + prompt=prompt, + llm_string=create_llm_string(llm), + return_val=[Generation(text=cached_response)], + ) + llm_cache.clear() + response = llm(prompt) + assert response == expected_response + + # async test + await llm_cache.aupdate( prompt=prompt, llm_string=create_llm_string(llm), return_val=[Generation(text=cached_response)], ) - get_llm_cache().clear() - assert llm(prompt) == response + await llm_cache.aclear() + response = await llm.ainvoke(prompt) + assert response == expected_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " From 34d2daffb3169f2d8f83d0c02d6b8159c138f733 Mon Sep 17 00:00:00 2001 From: Luiz Ferreira Date: Thu, 8 Feb 2024 00:08:26 -0300 Subject: [PATCH 151/157] community[patch]: Fix chat openai unit test (#17124) - **Description:** Actually the test named `test_openai_apredict` isn't testing the apredict method from ChatOpenAI. - **Twitter handle:** https://twitter.com/OAlmofadas --- .../tests/unit_tests/chat_models/test_openai.py | 14 +++++++------- .../tests/unit_tests/embeddings/test_openai.py | 6 +----- .../community/tests/unit_tests/llms/test_openai.py | 12 ++++-------- 3 files changed, 12 insertions(+), 20 deletions(-) diff --git a/libs/community/tests/unit_tests/chat_models/test_openai.py b/libs/community/tests/unit_tests/chat_models/test_openai.py index 22fde4f335d9f5f..ad7033e4ea8a443 100644 --- a/libs/community/tests/unit_tests/chat_models/test_openai.py +++ b/libs/community/tests/unit_tests/chat_models/test_openai.py @@ -17,9 +17,9 @@ @pytest.mark.requires("openai") def test_openai_model_param() -> None: - llm = ChatOpenAI(model="foo") + llm = ChatOpenAI(model="foo", openai_api_key="foo") assert llm.model_name == "foo" - llm = ChatOpenAI(model_name="foo") + llm = ChatOpenAI(model_name="foo", openai_api_key="foo") assert llm.model_name == "foo" @@ -81,7 +81,7 @@ def mock_completion() -> dict: @pytest.mark.requires("openai") def test_openai_predict(mock_completion: dict) -> None: - llm = ChatOpenAI() + llm = ChatOpenAI(openai_api_key="foo") mock_client = MagicMock() completed = False @@ -103,11 +103,11 @@ def mock_create(*args: Any, **kwargs: Any) -> Any: @pytest.mark.requires("openai") async def test_openai_apredict(mock_completion: dict) -> None: - llm = ChatOpenAI() + llm = ChatOpenAI(openai_api_key="foo") mock_client = MagicMock() completed = False - def mock_create(*args: Any, **kwargs: Any) -> Any: + async def mock_create(*args: Any, **kwargs: Any) -> Any: nonlocal completed completed = True return mock_completion @@ -115,9 +115,9 @@ def mock_create(*args: Any, **kwargs: Any) -> Any: mock_client.create = mock_create with patch.object( llm, - "client", + "async_client", mock_client, ): - res = llm.predict("bar") + res = await llm.apredict("bar") assert res == "Bar Baz" assert completed diff --git a/libs/community/tests/unit_tests/embeddings/test_openai.py b/libs/community/tests/unit_tests/embeddings/test_openai.py index e9c3979138be404..c9499722ff5ec7f 100644 --- a/libs/community/tests/unit_tests/embeddings/test_openai.py +++ b/libs/community/tests/unit_tests/embeddings/test_openai.py @@ -1,11 +1,7 @@ -import os - import pytest from langchain_community.embeddings.openai import OpenAIEmbeddings -os.environ["OPENAI_API_KEY"] = "foo" - @pytest.mark.requires("openai") def test_openai_invalid_model_kwargs() -> None: @@ -16,5 +12,5 @@ def test_openai_invalid_model_kwargs() -> None: @pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): - llm = OpenAIEmbeddings(foo="bar") + llm = OpenAIEmbeddings(foo="bar", openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} diff --git a/libs/community/tests/unit_tests/llms/test_openai.py b/libs/community/tests/unit_tests/llms/test_openai.py index 3b1c4d8dfed23a2..73f6fad2834839d 100644 --- a/libs/community/tests/unit_tests/llms/test_openai.py +++ b/libs/community/tests/unit_tests/llms/test_openai.py @@ -1,12 +1,8 @@ -import os - import pytest from langchain_community.llms.openai import OpenAI from langchain_community.utils.openai import is_openai_v1 -os.environ["OPENAI_API_KEY"] = "foo" - def _openai_v1_installed() -> bool: try: @@ -17,15 +13,15 @@ def _openai_v1_installed() -> bool: @pytest.mark.requires("openai") def test_openai_model_param() -> None: - llm = OpenAI(model="foo") + llm = OpenAI(model="foo", openai_api_key="foo") assert llm.model_name == "foo" - llm = OpenAI(model_name="foo") + llm = OpenAI(model_name="foo", openai_api_key="foo") assert llm.model_name == "foo" @pytest.mark.requires("openai") def test_openai_model_kwargs() -> None: - llm = OpenAI(model_kwargs={"foo": "bar"}) + llm = OpenAI(model_kwargs={"foo": "bar"}, openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} @@ -42,7 +38,7 @@ def test_openai_invalid_model_kwargs() -> None: @pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): - llm = OpenAI(foo="bar") + llm = OpenAI(foo="bar", openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} From d903fa313e338ed1474bc2473222989815ad1740 Mon Sep 17 00:00:00 2001 From: Leonid Ganeline Date: Wed, 7 Feb 2024 19:09:34 -0800 Subject: [PATCH 152/157] docs: titles fix (#17206) Several notebooks have Title != file name. That results in corrupted sorting in Navbar (ToC). - Fixed titles and file names. - Changed text formats to the consistent form - Redirected renamed files in the `Vercel.json` --- ...nb => alibabacloud_pai_eas_endpoint.ipynb} | 16 +-- .../{watsonxllm.ipynb => ibm_watsonx.ipynb} | 10 +- ...{hanavector.ipynb => sap_hanavector.ipynb} | 105 ++++++++++-------- .../vectorstores/thirdai_neuraldb.ipynb | 32 ++++-- docs/vercel.json | 12 ++ 5 files changed, 109 insertions(+), 66 deletions(-) rename docs/docs/integrations/llms/{pai_eas_endpoint.ipynb => alibabacloud_pai_eas_endpoint.ipynb} (62%) rename docs/docs/integrations/llms/{watsonxllm.ipynb => ibm_watsonx.ipynb} (96%) rename docs/docs/integrations/vectorstores/{hanavector.ipynb => sap_hanavector.ipynb} (92%) diff --git a/docs/docs/integrations/llms/pai_eas_endpoint.ipynb b/docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb similarity index 62% rename from docs/docs/integrations/llms/pai_eas_endpoint.ipynb rename to docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb index 6fd85b7d58f65e3..db4d8d588ac68b5 100644 --- a/docs/docs/integrations/llms/pai_eas_endpoint.ipynb +++ b/docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb @@ -4,8 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# AliCloud PAI EAS\n", - "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system." + "# Alibaba Cloud PAI EAS\n", + "\n", + ">[Machine Learning Platform for AI of Alibaba Cloud](https://www.alibabacloud.com/help/en/pai) is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, `Machine Learning Platform for AI` provides whole-process AI engineering capabilities including data labeling (`PAI-iTAG`), model building (`PAI-Designer` and `PAI-DSW`), model training (`PAI-DLC`), compilation optimization, and inference deployment (`PAI-EAS`). `PAI-EAS` supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system." ] }, { @@ -29,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information," + "One who wants to use EAS LLMs must set up EAS service first. When the EAS service is launched, `EAS_SERVICE_URL` and `EAS_SERVICE_TOKEN` can be obtained. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information," ] }, { @@ -74,7 +75,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -88,10 +89,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/docs/integrations/llms/watsonxllm.ipynb b/docs/docs/integrations/llms/ibm_watsonx.ipynb similarity index 96% rename from docs/docs/integrations/llms/watsonxllm.ipynb rename to docs/docs/integrations/llms/ibm_watsonx.ipynb index f0b142cf96d462b..32ecdc34fc3e075 100644 --- a/docs/docs/integrations/llms/watsonxllm.ipynb +++ b/docs/docs/integrations/llms/ibm_watsonx.ipynb @@ -7,8 +7,9 @@ "source": [ "# IBM watsonx.ai\n", "\n", - "[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n", - "This example shows how to communicate with watsonx.ai models using LangChain." + ">[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n", + "\n", + "This example shows how to communicate with `watsonx.ai` models using `LangChain`." ] }, { @@ -16,6 +17,8 @@ "id": "ea35b2b7", "metadata": {}, "source": [ + "## Setting up\n", + "\n", "Install the package [`ibm-watsonx-ai`](https://ibm.github.io/watsonx-ai-python-sdk/install.html)." ] }, @@ -60,6 +63,7 @@ "metadata": {}, "source": [ "## Load the model\n", + "\n", "You might need to adjust model `parameters` for different models or tasks. For details, refer to [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)." ] }, @@ -328,7 +332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/docs/integrations/vectorstores/hanavector.ipynb b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb similarity index 92% rename from docs/docs/integrations/vectorstores/hanavector.ipynb rename to docs/docs/integrations/vectorstores/sap_hanavector.ipynb index 0cc9f61d31d1194..63fe3add655ff02 100644 --- a/docs/docs/integrations/vectorstores/hanavector.ipynb +++ b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb @@ -6,13 +6,15 @@ "source": [ "# SAP HANA Cloud Vector Engine\n", "\n", - ">SAP HANA Cloud Vector Engine is a vector store fully integrated into the SAP HANA Cloud database." + ">[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is a vector store fully integrated into the `SAP HANA Cloud` database." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## Setting up\n", + "\n", "Installation of the HANA database driver." ] }, @@ -32,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use `OpenAIEmbeddings` so we use the OpenAI API Key." + "To use `OpenAIEmbeddings` we use the OpenAI API Key." ] }, { @@ -55,63 +57,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load the sample document \"state_of_the_union.txt\" and create chunks from it." + "Create a database connection to a HANA Cloud instance" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "ExecuteTime": { - "end_time": "2023-09-09T08:02:25.452472Z", - "start_time": "2023-09-09T08:02:25.441563Z" + "end_time": "2023-09-09T08:02:28.174088Z", + "start_time": "2023-09-09T08:02:28.162698Z" } }, "outputs": [], "source": [ - "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores.hanavector import HanaDB\n", - "from langchain_openai import OpenAIEmbeddings\n", - "\n", - "text_documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n", - "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", - "text_chunks = text_splitter.split_documents(text_documents)\n", - "print(f\"Number of document chunks: {len(text_chunks)}\")\n", + "from hdbcli import dbapi\n", "\n", - "embeddings = OpenAIEmbeddings()" + "# Use connection settings from the environment\n", + "connection = dbapi.connect(\n", + " address=os.environ.get(\"HANA_DB_ADDRESS\"),\n", + " port=os.environ.get(\"HANA_DB_PORT\"),\n", + " user=os.environ.get(\"HANA_DB_USER\"),\n", + " password=os.environ.get(\"HANA_DB_PASSWORD\"),\n", + " autocommit=True,\n", + " sslValidateCertificate=False,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Create a database connection to a HANA Cloud instance" + "## Example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the sample document \"state_of_the_union.txt\" and create chunks from it." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2023-09-09T08:02:28.174088Z", - "start_time": "2023-09-09T08:02:28.162698Z" + "end_time": "2023-09-09T08:02:25.452472Z", + "start_time": "2023-09-09T08:02:25.441563Z" } }, "outputs": [], "source": [ - "from hdbcli import dbapi\n", + "from langchain.docstore.document import Document\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_community.document_loaders import TextLoader\n", + "from langchain_community.vectorstores.hanavector import HanaDB\n", + "from langchain_openai import OpenAIEmbeddings\n", "\n", - "# Use connection settings from the environment\n", - "connection = dbapi.connect(\n", - " address=os.environ.get(\"HANA_DB_ADDRESS\"),\n", - " port=os.environ.get(\"HANA_DB_PORT\"),\n", - " user=os.environ.get(\"HANA_DB_USER\"),\n", - " password=os.environ.get(\"HANA_DB_PASSWORD\"),\n", - " autocommit=True,\n", - " sslValidateCertificate=False,\n", - ")" + "text_documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n", + "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", + "text_chunks = text_splitter.split_documents(text_documents)\n", + "print(f\"Number of document chunks: {len(text_chunks)}\")\n", + "\n", + "embeddings = OpenAIEmbeddings()" ] }, { @@ -161,7 +170,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Perform a query to get the two best matching document chunks from the ones that we added in the previous step.\n", + "Perform a query to get the two best-matching document chunks from the ones that we added in the previous step.\n", "By default \"Cosine Similarity\" is used for the search." ] }, @@ -211,12 +220,15 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ - "Maximal Marginal Relevance Search (MMR)\n", + "## Maximal Marginal Relevance Search (MMR)\n", "\n", - "Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. First 20 (fetch_k) items will be retrieved from the DB. The MMR algorithm will then find the best 2 (k) matches." + "`Maximal marginal relevance` optimizes for similarity to query AND diversity among selected documents. The first 20 (fetch_k) items will be retrieved from the DB. The MMR algorithm will then find the best 2 (k) matches." ] }, { @@ -227,7 +239,10 @@ "end_time": "2023-09-09T08:05:23.276819Z", "start_time": "2023-09-09T08:05:21.972256Z" }, - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -346,7 +361,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)\n" + "## Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)" ] }, { @@ -505,7 +520,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Standard tables vs. \"custom\" tables with vector data" + "## Standard tables vs. \"custom\" tables with vector data" ] }, { @@ -513,9 +528,9 @@ "metadata": {}, "source": [ "As default behaviour, the table for the embeddings is created with 3 columns\n", - "* A column \"VEC_TEXT\", which contains the text of the Document\n", - "* A column \"VEC_METADATA\", which contains the metadata of the Document\n", - "* A column \"VEC_VECTOR\", which contains the embeddings-vector of the document's text" + "* A column `VEC_TEXT`, which contains the text of the Document\n", + "* A column `VEC_METADATA`, which contains the metadata of the Document\n", + "* A column `VEC_VECTOR`, which contains the embeddings-vector of the document's text" ] }, { @@ -594,11 +609,11 @@ "metadata": {}, "source": [ "Custom tables must have at least three columns that match the semantics of a standard table\n", - "* A column with type \"NCLOB\" or \"NVARCHAR\" for the text/context of the embeddings\n", - "* A column with type \"NCLOB\" or \"NVARCHAR\" for the metadata \n", + "* A column with type `NCLOB` or `NVARCHAR` for the text/context of the embeddings\n", + "* A column with type `NCLOB` or `NVARCHAR` for the metadata \n", "* A column with type REAL_VECTOR for the embedding vector\n", "\n", - "The table can contain additional columns. When new Documents are inserted to the table, these addtional columns must allow NULL values." + "The table can contain additional columns. When new Documents are inserted into the table, these additional columns must allow NULL values." ] }, { @@ -654,7 +669,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Add another document and perform a similarity search on the custom table" + "Add another document and perform a similarity search on the custom table." ] }, { diff --git a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb b/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb index f1f2461076eea58..137e501897cf1c4 100644 --- a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb +++ b/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb @@ -4,16 +4,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **NeuralDB**\n", - "NeuralDB is a CPU-friendly and fine-tunable vector store developed by ThirdAI.\n", + "# ThirdAI NeuralDB\n", + "\n", + ">[NeuralDB](https://www.thirdai.com/neuraldb-enterprise/) is a CPU-friendly and fine-tunable vector store developed by [ThirdAI](https://www.thirdai.com/).\n", + "\n", + "## Initialization\n", "\n", - "### **Initialization**\n", "There are three initialization methods:\n", "- From Scratch: Basic model\n", "- From Bazaar: Download a pretrained base model from our model bazaar for better performance\n", "- From Checkpoint: Load a model that was previously saved\n", "\n", - "For all of the following initialization methods, the `thirdai_key` parameter can be ommitted if the `THIRDAI_KEY` environment variable is set.\n", + "For all of the following initialization methods, the `thirdai_key` parameter can be omitted if the `THIRDAI_KEY` environment variable is set.\n", "\n", "ThirdAI API keys can be obtained at https://www.thirdai.com/try-bolt/" ] @@ -55,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Inserting document sources**" + "## Inserting document sources" ] }, { @@ -96,7 +98,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Similarity search**\n", + "## Similarity search\n", + "\n", "To query the vectorstore, you can use the standard LangChain vectorstore method `similarity_search`, which returns a list of LangChain Document objects. Each document object represents a chunk of text from the indexed files. For example, it may contain a paragraph from one of the indexed PDF files. In addition to the text, the document's metadata field contains information such as the document's ID, the source of this document (which file it came from), and the score of the document." ] }, @@ -114,7 +117,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Fine tuning**\n", + "## Fine tuning\n", + "\n", "NeuralDBVectorStore can be fine-tuned to user behavior and domain-specific knowledge. It can be fine-tuned in two ways:\n", "1. Association: the vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase.\n", "2. Upvoting: the vectorstore upweights the score of a document for a specific query. This is useful when you want to fine-tune the vectorstore to user behavior. For example, if a user searches \"how is a car manufactured\" and likes the returned document with id 52, then we can upvote the document with id 52 for the query \"how is a car manufactured\"." @@ -146,15 +150,23 @@ ], "metadata": { "kernelspec": { - "display_name": "langchain", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/vercel.json b/docs/vercel.json index 3e6b4a78f3a05c5..fe74510af2b88dd 100644 --- a/docs/vercel.json +++ b/docs/vercel.json @@ -1,5 +1,17 @@ { "redirects": [ + { + "source": "/docs/integrations/llms/watsonxllm", + "destination": "/docs/integrations/llms/ibm_watsonx" + }, + { + "source": "/docs/integrations/llms/pai_eas_endpoint", + "destination": "/docs/integrations/llms/alibabacloud_pai_eas_endpoint" + }, + { + "source": "/docs/integrations/vectorstores/hanavector", + "destination": "/docs/integrations/vectorstores/sap_hanavector" + }, { "source": "/docs/use_cases/qa_structured/sql", "destination": "/docs/use_cases/sql/" From 7e4b676d534b2a63a00706d7fcb6cef204f289f2 Mon Sep 17 00:00:00 2001 From: Tomaz Bratanic Date: Thu, 8 Feb 2024 04:16:14 +0100 Subject: [PATCH 153/157] community[patch]: Better error propagation for neo4jgraph (#17190) There are other errors that could happen when refreshing the schema, so we want to propagate specific errors for more clarity --- .../langchain_community/graphs/neo4j_graph.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py index 125fd96b151c5d9..9df427e38c72122 100644 --- a/libs/community/langchain_community/graphs/neo4j_graph.py +++ b/libs/community/langchain_community/graphs/neo4j_graph.py @@ -133,12 +133,14 @@ def __init__( # Set schema try: self.refresh_schema() - except neo4j.exceptions.ClientError: - raise ValueError( - "Could not use APOC procedures. " - "Please ensure the APOC plugin is installed in Neo4j and that " - "'apoc.meta.data()' is allowed in Neo4j configuration " - ) + except neo4j.exceptions.ClientError as e: + if e.code == "Neo.ClientError.Procedure.ProcedureNotFound": + raise ValueError( + "Could not use APOC procedures. " + "Please ensure the APOC plugin is installed in Neo4j and that " + "'apoc.meta.data()' is allowed in Neo4j configuration " + ) + raise e @property def get_schema(self) -> str: From 780e84ae79b4c9d958110c3a5e89d5e31e457361 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 7 Feb 2024 22:23:43 -0500 Subject: [PATCH 154/157] community[minor]: SQLDatabase Add fetch mode `cursor`, query parameters, query by selectable, expose execution options, and documentation (#17191) - **Description:** Improve `SQLDatabase` adapter component to promote code re-use, see [suggestion](https://github.com/langchain-ai/langchain/pull/16246#pullrequestreview-1846590962). - **Needed by:** GH-16246 - **Addressed to:** @baskaryan, @cbornet ## Details - Add `cursor` fetch mode - Accept SQL query parameters - Accept both `str` and SQLAlchemy selectables as query expression - Expose `execution_options` - Documentation page (notebook) about `SQLDatabase` [^1] See [About SQLDatabase](https://github.com/langchain-ai/langchain/blob/c1c7b763/docs/docs/integrations/tools/sql_database.ipynb). [^1]: Apparently there hasn't been any yet? --------- Co-authored-by: Andreas Motl --- .../integrations/tools/sql_database.ipynb | 440 ++++++++++++++++++ .../tools/sql_database/tool.py | 6 +- .../utilities/sql_database.py | 102 +++- .../langchain_experimental/sql/base.py | 2 +- .../langchain_experimental/sql/vector_sql.py | 1 + .../tests/unit_tests/test_sql_database.py | 75 ++- 6 files changed, 600 insertions(+), 26 deletions(-) create mode 100644 docs/docs/integrations/tools/sql_database.ipynb diff --git a/docs/docs/integrations/tools/sql_database.ipynb b/docs/docs/integrations/tools/sql_database.ipynb new file mode 100644 index 000000000000000..eec9027e60597d1 --- /dev/null +++ b/docs/docs/integrations/tools/sql_database.ipynb @@ -0,0 +1,440 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# SQL Database\n", + "\n", + "::: {.callout-note}\n", + "The `SQLDatabase` adapter utility is a wrapper around a database connection.\n", + "\n", + "For talking to SQL databases, it uses the [SQLAlchemy] Core API .\n", + ":::\n", + "\n", + "\n", + "This notebook shows how to use the utility to access an SQLite database.\n", + "It uses the example [Chinook Database], and demonstrates those features:\n", + "\n", + "- Query using SQL\n", + "- Query using SQLAlchemy selectable\n", + "- Fetch modes `cursor`, `all`, and `one`\n", + "- Bind query parameters\n", + "\n", + "[Chinook Database]: https://github.com/lerocha/chinook-database\n", + "[SQLAlchemy]: https://www.sqlalchemy.org/\n", + "\n", + "\n", + "You can use the `Tool` or `@tool` decorator to create a tool from this utility.\n", + "\n", + "\n", + "::: {.callout-caution}\n", + "If creating a tool from the SQLDatbase utility and combining it with an LLM or exposing it to an end user\n", + "remember to follow good security practices.\n", + "\n", + "See security information: https://python.langchain.com/docs/security\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "!wget 'https://github.com/lerocha/chinook-database/releases/download/v1.4.2/Chinook_Sqlite.sql'" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1|AC/DC\r\n", + "2|Accept\r\n", + "3|Aerosmith\r\n", + "4|Alanis Morissette\r\n", + "5|Alice In Chains\r\n", + "6|Antônio Carlos Jobim\r\n", + "7|Apocalyptica\r\n", + "8|Audioslave\r\n", + "9|BackBeat\r\n", + "10|Billy Cobham\r\n", + "11|Black Label Society\r\n", + "12|Black Sabbath\r\n" + ] + } + ], + "source": [ + "!sqlite3 -bail -cmd '.read Chinook_Sqlite.sql' -cmd 'SELECT * FROM Artist LIMIT 12;' -cmd '.quit'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "!sqlite3 -bail -cmd '.read Chinook_Sqlite.sql' -cmd '.save Chinook.db' -cmd '.quit'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Initialize Database" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "import sqlalchemy as sa\n", + "from langchain.sql_database import SQLDatabase\n", + "\n", + "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query as cursor\n", + "\n", + "The fetch mode `cursor` returns results as SQLAlchemy's\n", + "`CursorResult` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[{'ArtistId': 1, 'Name': 'AC/DC'},\n", + " {'ArtistId': 2, 'Name': 'Accept'},\n", + " {'ArtistId': 3, 'Name': 'Aerosmith'},\n", + " {'ArtistId': 4, 'Name': 'Alanis Morissette'},\n", + " {'ArtistId': 5, 'Name': 'Alice In Chains'},\n", + " {'ArtistId': 6, 'Name': 'Antônio Carlos Jobim'},\n", + " {'ArtistId': 7, 'Name': 'Apocalyptica'},\n", + " {'ArtistId': 8, 'Name': 'Audioslave'},\n", + " {'ArtistId': 9, 'Name': 'BackBeat'},\n", + " {'ArtistId': 10, 'Name': 'Billy Cobham'},\n", + " {'ArtistId': 11, 'Name': 'Black Label Society'},\n", + " {'ArtistId': 12, 'Name': 'Black Sabbath'}]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"cursor\")\n", + "print(type(result))\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query as string payload\n", + "\n", + "The fetch modes `all` and `one` return results in string format." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham'), (11, 'Black Label Society'), (12, 'Black Sabbath')]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"all\")\n", + "print(type(result))\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[(1, 'AC/DC')]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"one\")\n", + "print(type(result))\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with parameters\n", + "\n", + "In order to bind query parameters, use the optional `parameters` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'ArtistId': 35, 'Name': 'Pedro Luís & A Parede'},\n", + " {'ArtistId': 115, 'Name': 'Page & Plant'},\n", + " {'ArtistId': 116, 'Name': 'Passengers'},\n", + " {'ArtistId': 117, 'Name': \"Paul D'Ianno\"},\n", + " {'ArtistId': 118, 'Name': 'Pearl Jam'},\n", + " {'ArtistId': 119, 'Name': 'Peter Tosh'},\n", + " {'ArtistId': 120, 'Name': 'Pink Floyd'},\n", + " {'ArtistId': 121, 'Name': 'Planet Hemp'},\n", + " {'ArtistId': 186, 'Name': 'Pedro Luís E A Parede'},\n", + " {'ArtistId': 256, 'Name': 'Philharmonia Orchestra & Sir Neville Marriner'},\n", + " {'ArtistId': 275, 'Name': 'Philip Glass Ensemble'}]\n" + ] + } + ], + "source": [ + "result = db.run(\n", + " \"SELECT * FROM Artist WHERE Name LIKE :search;\",\n", + " parameters={\"search\": \"p%\"},\n", + " fetch=\"cursor\",\n", + ")\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with SQLAlchemy selectable\n", + "\n", + "Other than plain-text SQL statements, the adapter also accepts SQLAlchemy selectables." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'ArtistId': 35, 'Name': 'Pedro Luís & A Parede'},\n", + " {'ArtistId': 115, 'Name': 'Page & Plant'},\n", + " {'ArtistId': 116, 'Name': 'Passengers'},\n", + " {'ArtistId': 117, 'Name': \"Paul D'Ianno\"},\n", + " {'ArtistId': 118, 'Name': 'Pearl Jam'},\n", + " {'ArtistId': 119, 'Name': 'Peter Tosh'},\n", + " {'ArtistId': 120, 'Name': 'Pink Floyd'},\n", + " {'ArtistId': 121, 'Name': 'Planet Hemp'},\n", + " {'ArtistId': 186, 'Name': 'Pedro Luís E A Parede'},\n", + " {'ArtistId': 256, 'Name': 'Philharmonia Orchestra & Sir Neville Marriner'},\n", + " {'ArtistId': 275, 'Name': 'Philip Glass Ensemble'}]\n" + ] + } + ], + "source": [ + "# In order to build a selectable on SA's Core API, you need a table definition.\n", + "metadata = sa.MetaData()\n", + "artist = sa.Table(\n", + " \"Artist\",\n", + " metadata,\n", + " sa.Column(\"ArtistId\", sa.INTEGER, primary_key=True),\n", + " sa.Column(\"Name\", sa.TEXT),\n", + ")\n", + "\n", + "# Build a selectable with the same semantics of the recent query.\n", + "query = sa.select(artist).where(artist.c.Name.like(\"p%\"))\n", + "result = db.run(query, fetch=\"cursor\")\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with execution options\n", + "\n", + "It is possible to augment the statement invocation with custom execution options.\n", + "For example, when applying a schema name translation, subsequent statements will\n", + "fail, because they try to hit a non-existing table." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "ename": "OperationalError", + "evalue": "(sqlite3.OperationalError) no such table: bar.Artist\n[SQL: SELECT bar.\"Artist\".\"ArtistId\", bar.\"Artist\".\"Name\" \nFROM bar.\"Artist\" \nWHERE bar.\"Artist\".\"Name\" LIKE ?]\n[parameters: ('p%',)]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1969\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m evt_handled:\n\u001b[0;32m-> 1969\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1970\u001b[0m \u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_events \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_has_events:\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:922\u001b[0m, in \u001b[0;36mDefaultDialect.do_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdo_execute\u001b[39m(\u001b[38;5;28mself\u001b[39m, cursor, statement, parameters, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 922\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(statement, parameters)\n", + "\u001b[0;31mOperationalError\u001b[0m: no such table: bar.Artist", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Build a selectable with the same semantics of the recent query.\u001b[39;00m\n\u001b[1;32m 2\u001b[0m query \u001b[38;5;241m=\u001b[39m sa\u001b[38;5;241m.\u001b[39mselect(artist)\u001b[38;5;241m.\u001b[39mwhere(artist\u001b[38;5;241m.\u001b[39mc\u001b[38;5;241m.\u001b[39mName\u001b[38;5;241m.\u001b[39mlike(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfetch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresult\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mschema_translate_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/libs/community/langchain_community/utilities/sql_database.py:484\u001b[0m, in \u001b[0;36mSQLDatabase.run\u001b[0;34m(self, command, fetch, parameters, execution_options, include_columns)\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m 472\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 473\u001b[0m command: Union[\u001b[38;5;28mstr\u001b[39m, Executable],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 477\u001b[0m include_columns: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 478\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mstr\u001b[39m, Result]:\n\u001b[1;32m 479\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Execute a SQL command and return a string representing the results.\u001b[39;00m\n\u001b[1;32m 480\u001b[0m \n\u001b[1;32m 481\u001b[0m \u001b[38;5;124;03m If the statement returns rows, a string of the results is returned.\u001b[39;00m\n\u001b[1;32m 482\u001b[0m \u001b[38;5;124;03m If the statement returns no rows, an empty string is returned.\u001b[39;00m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 484\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 485\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfetch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexecution_options\u001b[49m\n\u001b[1;32m 486\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fetch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/libs/community/langchain_community/utilities/sql_database.py:450\u001b[0m, in \u001b[0;36mSQLDatabase._execute\u001b[0;34m(self, command, fetch, parameters, execution_options)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery expression has unknown type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(command)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 450\u001b[0m cursor \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexecution_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor\u001b[38;5;241m.\u001b[39mreturns_rows:\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fetch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1416\u001b[0m, in \u001b[0;36mConnection.execute\u001b[0;34m(self, statement, parameters, execution_options)\u001b[0m\n\u001b[1;32m 1414\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mObjectNotExecutableError(statement) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1415\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1417\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1418\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1419\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mNO_OPTIONS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1420\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/sql/elements.py:516\u001b[0m, in \u001b[0;36mClauseElement._execute_on_connection\u001b[0;34m(self, connection, distilled_params, execution_options)\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, Executable)\n\u001b[0;32m--> 516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_clauseelement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 517\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistilled_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\n\u001b[1;32m 518\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mObjectNotExecutableError(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1639\u001b[0m, in \u001b[0;36mConnection._execute_clauseelement\u001b[0;34m(self, elem, distilled_parameters, execution_options)\u001b[0m\n\u001b[1;32m 1627\u001b[0m compiled_cache: Optional[CompiledCacheType] \u001b[38;5;241m=\u001b[39m execution_options\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1628\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompiled_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_compiled_cache\n\u001b[1;32m 1629\u001b[0m )\n\u001b[1;32m 1631\u001b[0m compiled_sql, extracted_params, cache_hit \u001b[38;5;241m=\u001b[39m elem\u001b[38;5;241m.\u001b[39m_compile_w_cache(\n\u001b[1;32m 1632\u001b[0m dialect\u001b[38;5;241m=\u001b[39mdialect,\n\u001b[1;32m 1633\u001b[0m compiled_cache\u001b[38;5;241m=\u001b[39mcompiled_cache,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1637\u001b[0m linting\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdialect\u001b[38;5;241m.\u001b[39mcompiler_linting \u001b[38;5;241m|\u001b[39m compiler\u001b[38;5;241m.\u001b[39mWARN_LINTING,\n\u001b[1;32m 1638\u001b[0m )\n\u001b[0;32m-> 1639\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_context\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1640\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1641\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecution_ctx_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_compiled\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1642\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompiled_sql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1643\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1644\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1645\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompiled_sql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1646\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1647\u001b[0m \u001b[43m \u001b[49m\u001b[43melem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1648\u001b[0m \u001b[43m \u001b[49m\u001b[43mextracted_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1649\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_hit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_hit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1650\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_events:\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch\u001b[38;5;241m.\u001b[39mafter_execute(\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1654\u001b[0m elem,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1658\u001b[0m ret,\n\u001b[1;32m 1659\u001b[0m )\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1848\u001b[0m, in \u001b[0;36mConnection._execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m 1843\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exec_insertmany_context(\n\u001b[1;32m 1844\u001b[0m dialect,\n\u001b[1;32m 1845\u001b[0m context,\n\u001b[1;32m 1846\u001b[0m )\n\u001b[1;32m 1847\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_exec_single_context\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1849\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\n\u001b[1;32m 1850\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1988\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1985\u001b[0m result \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39m_setup_result_proxy()\n\u001b[1;32m 1987\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1988\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_dbapi_exception\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1989\u001b[0m \u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1990\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2343\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001b[0m\n\u001b[1;32m 2341\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m should_wrap:\n\u001b[1;32m 2342\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m sqlalchemy_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2343\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m sqlalchemy_exception\u001b[38;5;241m.\u001b[39mwith_traceback(exc_info[\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2345\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m exc_info[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1969\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1967\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 1968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m evt_handled:\n\u001b[0;32m-> 1969\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1970\u001b[0m \u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_events \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_has_events:\n\u001b[1;32m 1974\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch\u001b[38;5;241m.\u001b[39mafter_cursor_execute(\n\u001b[1;32m 1975\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1976\u001b[0m cursor,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1980\u001b[0m context\u001b[38;5;241m.\u001b[39mexecutemany,\n\u001b[1;32m 1981\u001b[0m )\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:922\u001b[0m, in \u001b[0;36mDefaultDialect.do_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdo_execute\u001b[39m(\u001b[38;5;28mself\u001b[39m, cursor, statement, parameters, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 922\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(statement, parameters)\n", + "\u001b[0;31mOperationalError\u001b[0m: (sqlite3.OperationalError) no such table: bar.Artist\n[SQL: SELECT bar.\"Artist\".\"ArtistId\", bar.\"Artist\".\"Name\" \nFROM bar.\"Artist\" \nWHERE bar.\"Artist\".\"Name\" LIKE ?]\n[parameters: ('p%',)]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)" + ] + } + ], + "source": [ + "query = sa.select(artist).where(artist.c.Name.like(\"p%\"))\n", + "db.run(query, fetch=\"cursor\", execution_options={\"schema_translate_map\": {None: \"bar\"}})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/libs/community/langchain_community/tools/sql_database/tool.py b/libs/community/langchain_community/tools/sql_database/tool.py index 850cecb45107b63..4c7fe4726a05506 100644 --- a/libs/community/langchain_community/tools/sql_database/tool.py +++ b/libs/community/langchain_community/tools/sql_database/tool.py @@ -1,6 +1,8 @@ # flake8: noqa """Tools for interacting with a SQL database.""" -from typing import Any, Dict, Optional, Type +from typing import Any, Dict, Optional, Sequence, Type, Union + +from sqlalchemy import Result from langchain_core.pydantic_v1 import BaseModel, Field, root_validator @@ -42,7 +44,7 @@ def _run( self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute the query, return the results or an error message.""" return self.db.run_no_throw(query) diff --git a/libs/community/langchain_community/utilities/sql_database.py b/libs/community/langchain_community/utilities/sql_database.py index 298b8ca7e224c98..087ac44bc112f89 100644 --- a/libs/community/langchain_community/utilities/sql_database.py +++ b/libs/community/langchain_community/utilities/sql_database.py @@ -1,12 +1,21 @@ """SQLAlchemy wrapper around a database.""" from __future__ import annotations -from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence +from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Union import sqlalchemy from langchain_core._api import deprecated from langchain_core.utils import get_from_env -from sqlalchemy import MetaData, Table, create_engine, inspect, select, text +from sqlalchemy import ( + Executable, + MetaData, + Result, + Table, + create_engine, + inspect, + select, + text, +) from sqlalchemy.engine import Engine from sqlalchemy.exc import ProgrammingError, SQLAlchemyError from sqlalchemy.schema import CreateTable @@ -373,67 +382,113 @@ def _get_sample_rows(self, table: Table) -> str: def _execute( self, - command: str, - fetch: Literal["all", "one"] = "all", - ) -> Sequence[Dict[str, Any]]: + command: Union[str, Executable], + fetch: Literal["all", "one", "cursor"] = "all", + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[Sequence[Dict[str, Any]], Result]: """ Executes SQL command through underlying engine. If the statement returns no rows, an empty list is returned. """ + parameters = parameters or {} + execution_options = execution_options or {} with self._engine.begin() as connection: # type: Connection # type: ignore[name-defined] if self._schema is not None: if self.dialect == "snowflake": connection.exec_driver_sql( - "ALTER SESSION SET search_path = %s", (self._schema,) + "ALTER SESSION SET search_path = %s", + (self._schema,), + execution_options=execution_options, ) elif self.dialect == "bigquery": - connection.exec_driver_sql("SET @@dataset_id=?", (self._schema,)) + connection.exec_driver_sql( + "SET @@dataset_id=?", + (self._schema,), + execution_options=execution_options, + ) elif self.dialect == "mssql": pass elif self.dialect == "trino": - connection.exec_driver_sql("USE ?", (self._schema,)) + connection.exec_driver_sql( + "USE ?", + (self._schema,), + execution_options=execution_options, + ) elif self.dialect == "duckdb": # Unclear which parameterized argument syntax duckdb supports. # The docs for the duckdb client say they support multiple, # but `duckdb_engine` seemed to struggle with all of them: # https://github.com/Mause/duckdb_engine/issues/796 - connection.exec_driver_sql(f"SET search_path TO {self._schema}") + connection.exec_driver_sql( + f"SET search_path TO {self._schema}", + execution_options=execution_options, + ) elif self.dialect == "oracle": connection.exec_driver_sql( - f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}" + f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}", + execution_options=execution_options, ) elif self.dialect == "sqlany": # If anybody using Sybase SQL anywhere database then it should not # go to else condition. It should be same as mssql. pass elif self.dialect == "postgresql": # postgresql - connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) + connection.exec_driver_sql( + "SET search_path TO %s", + (self._schema,), + execution_options=execution_options, + ) + + if isinstance(command, str): + command = text(command) + elif isinstance(command, Executable): + pass + else: + raise TypeError(f"Query expression has unknown type: {type(command)}") + cursor = connection.execute( + command, + parameters, + execution_options=execution_options, + ) - cursor = connection.execute(text(command)) if cursor.returns_rows: if fetch == "all": result = [x._asdict() for x in cursor.fetchall()] elif fetch == "one": first_result = cursor.fetchone() result = [] if first_result is None else [first_result._asdict()] + elif fetch == "cursor": + return cursor else: - raise ValueError("Fetch parameter must be either 'one' or 'all'") + raise ValueError( + "Fetch parameter must be either 'one', 'all', or 'cursor'" + ) return result return [] def run( self, - command: str, - fetch: Literal["all", "one"] = "all", + command: Union[str, Executable], + fetch: Literal["all", "one", "cursor"] = "all", include_columns: bool = False, - ) -> str: + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute a SQL command and return a string representing the results. If the statement returns rows, a string of the results is returned. If the statement returns no rows, an empty string is returned. """ - result = self._execute(command, fetch) + result = self._execute( + command, fetch, parameters=parameters, execution_options=execution_options + ) + + if fetch == "cursor": + return result res = [ { @@ -472,7 +527,10 @@ def run_no_throw( command: str, fetch: Literal["all", "one"] = "all", include_columns: bool = False, - ) -> str: + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute a SQL command and return a string representing the results. If the statement returns rows, a string of the results is returned. @@ -481,7 +539,13 @@ def run_no_throw( If the statement throws an error, the error message is returned. """ try: - return self.run(command, fetch, include_columns) + return self.run( + command, + fetch, + parameters=parameters, + execution_options=execution_options, + include_columns=include_columns, + ) except SQLAlchemyError as e: """Format the error message""" return f"Error: {e}" diff --git a/libs/experimental/langchain_experimental/sql/base.py b/libs/experimental/langchain_experimental/sql/base.py index 1785598e36ae21a..b6de5e67c9d45f3 100644 --- a/libs/experimental/langchain_experimental/sql/base.py +++ b/libs/experimental/langchain_experimental/sql/base.py @@ -173,7 +173,7 @@ def _call( sql_cmd = checked_sql_command _run_manager.on_text("\nSQLResult: ", verbose=self.verbose) - _run_manager.on_text(result, color="yellow", verbose=self.verbose) + _run_manager.on_text(str(result), color="yellow", verbose=self.verbose) # If return direct, we just set the final result equal to # the result of the sql query result, otherwise try to get a human readable # final answer diff --git a/libs/experimental/langchain_experimental/sql/vector_sql.py b/libs/experimental/langchain_experimental/sql/vector_sql.py index 1b0db9590441679..a21a6bae8682e04 100644 --- a/libs/experimental/langchain_experimental/sql/vector_sql.py +++ b/libs/experimental/langchain_experimental/sql/vector_sql.py @@ -78,6 +78,7 @@ def parse(self, text: str) -> str: def get_result_from_sqldb(db: SQLDatabase, cmd: str) -> Sequence[Dict[str, Any]]: result = db._execute(cmd, fetch="all") + assert isinstance(result, Sequence) return result diff --git a/libs/langchain/tests/unit_tests/test_sql_database.py b/libs/langchain/tests/unit_tests/test_sql_database.py index fea54e74c25489f..c593faae8e821ae 100644 --- a/libs/langchain/tests/unit_tests/test_sql_database.py +++ b/libs/langchain/tests/unit_tests/test_sql_database.py @@ -1,16 +1,19 @@ -# flake8: noqa=E501 +# flake8: noqa: E501 """Test SQL database wrapper.""" - +import pytest +import sqlalchemy as sa from langchain_community.utilities.sql_database import SQLDatabase, truncate_word from sqlalchemy import ( Column, Integer, MetaData, + Result, String, Table, Text, create_engine, insert, + select, ) metadata_obj = MetaData() @@ -108,8 +111,8 @@ def test_table_info_w_sample_rows() -> None: assert sorted(output.split()) == sorted(expected_output.split()) -def test_sql_database_run() -> None: - """Test that commands can be run successfully and returned in correct format.""" +def test_sql_database_run_fetch_all() -> None: + """Verify running SQL expressions returning results as strings.""" engine = create_engine("sqlite:///:memory:") metadata_obj.create_all(engine) stmt = insert(user).values( @@ -131,6 +134,52 @@ def test_sql_database_run() -> None: assert full_output == expected_full_output +def test_sql_database_run_fetch_result() -> None: + """Verify running SQL expressions returning results as SQLAlchemy `Result` instances.""" + engine = create_engine("sqlite:///:memory:") + metadata_obj.create_all(engine) + stmt = insert(user).values(user_id=17, user_name="hwchase") + with engine.begin() as conn: + conn.execute(stmt) + db = SQLDatabase(engine) + command = "select user_id, user_name, user_bio from user where user_id = 17" + + result = db.run(command, fetch="cursor", include_columns=True) + expected = [{"user_id": 17, "user_name": "hwchase", "user_bio": None}] + assert isinstance(result, Result) + assert result.mappings().fetchall() == expected + + +def test_sql_database_run_with_parameters() -> None: + """Verify running SQL expressions with query parameters.""" + engine = create_engine("sqlite:///:memory:") + metadata_obj.create_all(engine) + stmt = insert(user).values(user_id=17, user_name="hwchase") + with engine.begin() as conn: + conn.execute(stmt) + db = SQLDatabase(engine) + command = "select user_id, user_name, user_bio from user where user_id = :user_id" + + full_output = db.run(command, parameters={"user_id": 17}, include_columns=True) + expected_full_output = "[{'user_id': 17, 'user_name': 'hwchase', 'user_bio': None}]" + assert full_output == expected_full_output + + +def test_sql_database_run_sqlalchemy_selectable() -> None: + """Verify running SQL expressions using SQLAlchemy selectable.""" + engine = create_engine("sqlite:///:memory:") + metadata_obj.create_all(engine) + stmt = insert(user).values(user_id=17, user_name="hwchase") + with engine.begin() as conn: + conn.execute(stmt) + db = SQLDatabase(engine) + command = select(user).where(user.c.user_id == 17) + + full_output = db.run(command, include_columns=True) + expected_full_output = "[{'user_id': 17, 'user_name': 'hwchase', 'user_bio': None}]" + assert full_output == expected_full_output + + def test_sql_database_run_update() -> None: """Test commands which return no rows return an empty string.""" engine = create_engine("sqlite:///:memory:") @@ -145,6 +194,24 @@ def test_sql_database_run_update() -> None: assert output == expected_output +def test_sql_database_schema_translate_map() -> None: + """Verify using statement-specific execution options.""" + + engine = create_engine("sqlite:///:memory:") + db = SQLDatabase(engine) + + # Define query using SQLAlchemy selectable. + command = select(user).where(user.c.user_id == 17) + + # Define statement-specific execution options. + execution_options = {"schema_translate_map": {None: "bar"}} + + # Verify the schema translation is applied. + with pytest.raises(sa.exc.OperationalError) as ex: + db.run(command, execution_options=execution_options, fetch="cursor") + assert ex.match("no such table: bar.user") + + def test_truncate_word() -> None: assert truncate_word("Hello World", length=5) == "He..." assert truncate_word("Hello World", length=0) == "Hello World" From 4e3ed7f043ce0f7ee14cd96c8fd7b820a61979d3 Mon Sep 17 00:00:00 2001 From: Bassem Yacoube <125713079+AI-Bassem@users.noreply.github.com> Date: Wed, 7 Feb 2024 19:25:52 -0800 Subject: [PATCH 155/157] community[patch]: octoai embeddings bug fix (#17216) fixes a bug in octoa_embeddings provider --- .../langchain_community/embeddings/octoai_embeddings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/community/langchain_community/embeddings/octoai_embeddings.py b/libs/community/langchain_community/embeddings/octoai_embeddings.py index a93fa4e03ad18dd..f006a437a4ee33e 100644 --- a/libs/community/langchain_community/embeddings/octoai_embeddings.py +++ b/libs/community/langchain_community/embeddings/octoai_embeddings.py @@ -80,7 +80,7 @@ def _compute_embeddings( json_data = resp_json["data"] for item in json_data: if "embedding" in item: - embedding.append(item["embedding"]) + embedding = item["embedding"] except Exception as e: raise ValueError(f"Error raised by the inference endpoint: {e}") from e From 7f55c957900e6cd4693e6943dc2bb47b6f60d504 Mon Sep 17 00:00:00 2001 From: sana-google <135305956+sana-google@users.noreply.github.com> Date: Thu, 8 Feb 2024 03:26:10 +0000 Subject: [PATCH 156/157] docs: add missing link to Quickstart (#17085) Replace this entire comment with: - **Description:** Added missing link for Quickstart in Model IO documentation, - **Issue:** N/A, - **Dependencies:** N/A, - **Twitter handle:** N/A Co-authored-by: Eugene Yurtsev --- docs/docs/modules/model_io/index.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/modules/model_io/index.mdx b/docs/docs/modules/model_io/index.mdx index eeb94b485056f5d..a5cf4e81fd49c85 100644 --- a/docs/docs/modules/model_io/index.mdx +++ b/docs/docs/modules/model_io/index.mdx @@ -17,7 +17,7 @@ A conceptual explanation of messages, prompts, LLMs vs ChatModels, and output pa ## [Quickstart](/docs/modules/model_io/quick_start) -Covers the basics of getting started working with different types of models. You should walk through [this section] if you want to get an overview of the functionality. +Covers the basics of getting started working with different types of models. You should walk through [this section](/docs/modules/model_io/quick_start) if you want to get an overview of the functionality. ## [Prompts](/docs/modules/model_io/prompts/) From 00a09e1b7117f3bde14a44748510fcccc95f9de5 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Wed, 7 Feb 2024 19:52:42 -0800 Subject: [PATCH 157/157] docs: use PromptTemplate.from_template (#17218) Ran ```python import glob import re def update_prompt(x): return re.sub( r"(?P\b)PromptTemplate\(template=(?P