From 510224f3c16b343d393f26e56ff370b0b96c82df Mon Sep 17 00:00:00 2001 From: emrgnt-cmplxty <68796651+emrgnt-cmplxty@users.noreply.github.com> Date: Tue, 23 Apr 2024 20:14:38 -0700 Subject: [PATCH] Dev rebased (#332) * update the default embd. (#310) * Feature/add jina reranker rebased (#312) * Add jina reranker * fix oai * Revampt client & server approach. (#316) * Revampt client & server approach. * cleanups * tweak hyde prompt * Feature/add agent provider (#317) * update pipeline (#315) * Update CONTRIBUTING.md * Delete CONTRIBUTOR.md * Adding agent provider * Feature/add agent provider rebased v2 (#319) * modify prompt provider workflow, add agent * fix run qna client * add provider abstraction * tweaks and cleans * move text splitter config locale * Feature/modify get all uniq values (#325) * refine * update * format * fix * Feature/dev merge rebased (#329) * Update local_rag.mdx * Update llms.mdx (#322) * update the default embd. (#310) * Feature/add agent provider (#317) * update pipeline (#315) * Update CONTRIBUTING.md * Delete CONTRIBUTOR.md * Adding agent provider * Feature/modify get all uniq values (#325) * refine * update * format * fix * dev merge * Feature/fix dev merge mistakes rebased (#330) * Feature/add agent provider (#317) * update pipeline (#315) * Update CONTRIBUTING.md * Delete CONTRIBUTOR.md * Adding agent provider * Feature/modify get all uniq values (#325) * refine * update * format * fix * dev merge * cleanup * Feature/dev cleanup (#331) * final pub * final pub * json clean * fix sentence transformer issue * include rerank * fix llama cpp * rebase * fix rerank * small tweaks * rollbk config * cleanup --- .gitignore | 7 +- README.md | 48 +- config.json | 34 +- docs/pages/deep-dive/app.mdx | 12 +- docs/pages/deep-dive/config.mdx | 14 +- docs/pages/deep-dive/embedding.mdx | 12 +- docs/pages/deep-dive/factory.mdx | 20 +- docs/pages/deep-dive/rag.mdx | 30 +- docs/pages/providers/embeddings.mdx | 2 +- docs/pages/providers/llms.mdx | 2 +- .../configuring_your_rag_pipeline.mdx | 2 +- docs/pages/tutorials/local_rag.mdx | 10 +- poetry.lock | 1838 ++++++++++------- pyproject.toml | 56 +- r2r/client/base.py | 28 +- r2r/core/__init__.py | 44 +- r2r/core/abstractions/output.py | 7 +- r2r/core/abstractions/tool.py | 21 + r2r/core/abstractions/vector.py | 64 + r2r/core/adapters/__init__.py | 18 +- r2r/core/adapters/base.py | 102 +- r2r/core/agent/base.py | 75 + r2r/core/pipelines/embedding.py | 17 +- r2r/core/pipelines/eval.py | 14 +- r2r/core/pipelines/ingestion.py | 13 +- r2r/core/pipelines/pipeline.py | 10 +- r2r/core/pipelines/rag.py | 92 +- r2r/core/pipelines/scraping.py | 17 +- r2r/core/providers/base.py | 31 + r2r/core/providers/embedding.py | 67 +- r2r/core/providers/eval.py | 52 +- r2r/core/providers/llm.py | 51 +- r2r/core/providers/prompt.py | 53 +- r2r/core/providers/vector_db.py | 94 +- r2r/core/{providers => utils}/logging.py | 0 r2r/core/utils/splitter/text.py | 2 +- r2r/embeddings/modal/base.py | 55 - r2r/embeddings/openai/base.py | 91 +- r2r/embeddings/setence_transformer/base.py | 155 +- r2r/eval/deepeval/base.py | 6 +- r2r/eval/parea/base.py | 6 +- r2r/examples/clients/run_qna_client.py | 35 +- .../clients/run_synthetic_query_client.py | 43 - r2r/examples/clients/run_test_client.py | 16 +- r2r/examples/configs/local_llama_cpp.json | 36 +- r2r/examples/configs/local_ollama.json | 36 +- r2r/examples/configs/local_ollama_qdrant.json | 67 +- .../configs/local_ollama_with_rerank.json | 39 + r2r/examples/servers/config_pipeline.py | 24 +- r2r/llms/__init__.py | 8 +- r2r/llms/litellm/base.py | 40 +- r2r/llms/llama_cpp/base.py | 34 +- r2r/llms/openai/base.py | 43 +- r2r/main/app.py | 51 +- r2r/main/factory.py | 158 +- r2r/main/models.py | 3 +- r2r/main/utils.py | 7 +- r2r/pipelines/__init__.py | 8 +- r2r/pipelines/agent/rag.py | 11 +- r2r/pipelines/core/embedding.py | 21 +- r2r/pipelines/core/eval.py | 39 +- r2r/pipelines/core/ingestion.py | 12 + .../core/{prompt_provider.py => prompt.py} | 20 +- r2r/pipelines/core/scraping.py | 28 +- .../hyde/rag.py} | 115 +- r2r/pipelines/qna/rag.py | 42 +- r2r/pipelines/web/rag.py | 19 +- r2r/tests/end_to_end.py | 1 - r2r/vecs/adapter/text.py | 2 +- r2r/vecs/collection.py | 33 + r2r/vector_dbs/__init__.py | 4 +- r2r/vector_dbs/local/base.py | 77 +- r2r/vector_dbs/pg_vector/base.py | 52 +- r2r/vector_dbs/qdrant/base.py | 68 +- 74 files changed, 2692 insertions(+), 1772 deletions(-) create mode 100644 r2r/core/abstractions/tool.py create mode 100644 r2r/core/abstractions/vector.py create mode 100644 r2r/core/agent/base.py create mode 100644 r2r/core/providers/base.py rename r2r/core/{providers => utils}/logging.py (100%) delete mode 100644 r2r/embeddings/modal/base.py delete mode 100644 r2r/examples/clients/run_synthetic_query_client.py create mode 100644 r2r/examples/configs/local_ollama_with_rerank.json rename r2r/pipelines/core/{prompt_provider.py => prompt.py} (58%) rename r2r/{examples/servers/synthetic_query_pipeline.py => pipelines/hyde/rag.py} (62%) diff --git a/.gitignore b/.gitignore index 28370b8b..4bb43196 100644 --- a/.gitignore +++ b/.gitignore @@ -11,7 +11,8 @@ env/ node_modules -**/*.sqlite -**/*.sqlite3 +**/*.sqlite* +**/*.sqlite3* -qdrant_storage/ \ No newline at end of file +qdrant_storage/ +r2r/examples/data/* \ No newline at end of file diff --git a/README.md b/README.md index a673fd16..f1dd37d7 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,17 @@ R2R was conceived to bridge the gap between local LLM experimentation and scalab - **⚖️ Autoscale**: Scale your pipeline effortlessly in the cloud using [SciPhi](https://sciphi.ai/). - **🤖 OSS**: Benefit from a framework developed by the open-source community, designed to simplify RAG deployment. +# Table of Contents +1. [Demo(s)](#demos) +2. [Links](#links) +3. [Quick Install](#quick-install) +4. [Docker](#docker) +5. [Q&A Example](#q&a-example) +6. [HyDE Example](#hyde-example) +7. [Running Local RAG](#running-local-rag) +8. [Core Abstractions](#core-abstractions) + + ## Demo(s) Using the cloud application to deploy the pre-built basic pipeline: @@ -73,13 +84,13 @@ docker pull emrgntcmplxty/r2r:latest docker run -d --name r2r_container -p 8000:8000 -e CONFIG_OPTION=local_ollama emrgntcmplxty/r2r:latest ``` -## Basic Example +## Q&A Example [`Configurable Pipeline`](r2r/examples/servers/config_pipeline.py): Execute this script to select and serve a **Q&A RAG**, **Web RAG**, or **Agent RAG** pipeline. This starter pipeline supports ingestion, embedding, and question and the specified RAG, all accessible via a REST API. ```bash # launch the server - # For ex., do `export CONFIG_OPTION=local_ollama` or ``--config=local_ollama` to run fully locally - # For ex., do `export PIPELINE_OPTION=web` or ``--pipeline=web` to run WebRAG pipeline + # For ex., do `export CONFIG_OPTION=local_ollama` or `--config=local_ollama` to run fully locally + # For ex., do `export PIPELINE_OPTION=web` or `--pipeline=web` to run WebRAG pipeline python -m r2r.examples.servers.config_pipeline --config=default --pipeline=qna ``` @@ -113,39 +124,34 @@ docker run -d --name r2r_container -p 8000:8000 -e CONFIG_OPTION=local_ollama e python -m r2r.examples.clients.run_qna_client rag_completion_streaming --query="What was lyfts profit in 2020?" # [{"id": "a0f6b427-9083-5ef2-aaa1-024b6cebbaee", "score": 0.6862949051074227, "metadata": {"user_id": "df7021ed-6e66-5581-bd69-d4e9ac1e5ada", "pipeline_run_id": "0c2c9a81-0720-4e34-8736-b66189956013", "text": "Title: Lyft 10k 2021\nNet loss was $ ... - + # # Title: Lyft 10k 2021 ... - + # # Lyft's net loss in 2020 was $1.8 billion. ``` -### Running Local RAG -[Refer here](https://r2r-docs.sciphi.ai/tutorials/local_rag) for a tutorial on how to modify the commands above to use local providers. - -## Synthetic Queries Example +## HyDE Example -[`Synthetic Query Pipeline`](r2r/examples/servers/synthetic_query_pipeline.py): Execute this script to start a backend server equipped with more advanced synthetic query pipeline. This pipeline is designed to create synthetic queries, enhancing the RAG system's learning and performance. +[`HyDE Pipeline`](r2r/examples/servers/hyde_pipeline.py): Execute this script to start a backend server equipped with more advanced synthetic query pipeline. This pipeline is designed to create synthetic queries, enhancing the RAG system's learning and performance. ```bash # launch the server - python -m r2r.examples.servers.synthetic_query_pipeline + python -m r2r.examples.servers.config_pipeline --config=default --pipeline=hyde ``` -[`Synthetic Query Client`](r2r/examples/clients/run_synthetic_query_client.py): Use this client script after the synthetic query pipeline is running. It's tailored for use with the synthetic query pipeline, demonstrating the improved features of the RAG system. - ```bash + # ingests Lyft 10K, Uber 10K, and others + python -m r2r.examples.clients.run_qna_client ingest --document_filter=all + # run the client - python -m r2r.examples.clients.run_synthetic_query_client - ``` + python -m r2r.examples.clients.run_qna_client search --query="What was lyft and ubers profit in 2020?" -## Extra Examples + # {... 'message': {'content': 'In 2020, Lyft reported a net loss of $1.7529 billion [8]. Uber also reported a significant loss for the year 2020, with its net loss improving by $1.8 billion from 2020, indicating a substantial loss for the year as well [38]. Neither company achieved a profit in 2020; instead, they both experienced considerable losses.' ...} + ``` -[`Reducto Pipeline`](r2r/examples/servers/reducto_pipeline.py): Launch this script to activate a backend server that integrates a Reducto adapter for enhanced PDF ingestion. +## Running Local RAG - ```bash - # launch the server - python -m r2r.examples.servers.reducto_pipeline - ``` +[Refer here](https://r2r-docs.sciphi.ai/tutorials/local_rag) for a tutorial on how to modify the commands above to use local providers. ## Core Abstractions diff --git a/config.json b/config.json index 97e5fbb7..6e53a040 100644 --- a/config.json +++ b/config.json @@ -1,36 +1,36 @@ { - "embedding": { - "provider": "openai", - "model": "text-embedding-3-small", - "dimension": 1536, - "batch_size": 32 - }, - "evals": { - "provider": "parea", - "frequency": 1.0 - }, "language_model": { "provider": "litellm" }, - "logging_database": { + "vector_database": { "provider": "local", - "collection_name": "demo_logs", - "level": "INFO" + "collection_name": "demo_vecs" }, "ingestion":{ - "provider": "local", + "provider": "local" + }, + "embedding": { + "provider": "openai", + "search_model": "text-embedding-3-small", + "search_dimension": 512, + "batch_size": 32, "text_splitter": { "type": "recursive_character", "chunk_size": 512, "chunk_overlap": 20 } }, - "vector_database": { - "provider": "local", - "collection_name": "demo_vecs" + "evals": { + "provider": "parea", + "frequency": 0.0 }, "app": { "max_logs": 100, "max_file_size_in_mb": 100 + }, + "logging_database": { + "provider": "local", + "collection_name": "demo_logs", + "level": "INFO" } } \ No newline at end of file diff --git a/docs/pages/deep-dive/app.mdx b/docs/pages/deep-dive/app.mdx index 712cdca2..ca39f550 100644 --- a/docs/pages/deep-dive/app.mdx +++ b/docs/pages/deep-dive/app.mdx @@ -83,7 +83,12 @@ The application uses a configuration file (`config.json`) to set various setting "provider": "openai", "model": "text-embedding-3-small", "dimension": 1536, - "batch_size": 32 + "batch_size": 32, + "text_splitter": { + "type": "recursive_character", + "chunk_size": 512, + "chunk_overlap": 20 + } }, "evals": { "provider": "deepeval", @@ -99,11 +104,6 @@ The application uses a configuration file (`config.json`) to set various setting }, "ingestion": { "provider": "local", - "text_splitter": { - "type": "recursive_character", - "chunk_size": 512, - "chunk_overlap": 20 - } }, "vector_database": { "provider": "local", diff --git a/docs/pages/deep-dive/config.mdx b/docs/pages/deep-dive/config.mdx index 4c5bce4b..79204571 100644 --- a/docs/pages/deep-dive/config.mdx +++ b/docs/pages/deep-dive/config.mdx @@ -17,7 +17,12 @@ The default values for the config are shown below: "provider": "openai", "model": "text-embedding-ada-002", "dimension": 1536, - "batch_size": 32 + "batch_size": 32, + "text_splitter": { + "type": "recursive_character", + "chunk_size": 512, + "chunk_overlap": 20 + } }, "evals": { "provider": "deepeval", @@ -32,12 +37,7 @@ The default values for the config are shown below: "level": "INFO" }, "ingestion": { - "provider": "local", - "text_splitter": { - "type": "recursive_character", - "chunk_size": 512, - "chunk_overlap": 20 - } + "provider": "local" }, "vector_database": { "provider": "local", diff --git a/docs/pages/deep-dive/embedding.mdx b/docs/pages/deep-dive/embedding.mdx index 58ce6241..75bfa89c 100644 --- a/docs/pages/deep-dive/embedding.mdx +++ b/docs/pages/deep-dive/embedding.mdx @@ -8,7 +8,7 @@ The `BasicEmbeddingPipeline` is a simple implementation of the `EmbeddingPipelin ### Initialization -The `BasicEmbeddingPipeline` is initialized with the `embedding_model`, `embeddings_provider`, `db`, `text_splitter`, an optional `logging_connection`, `embedding_batch_size`, and `id_prefix`. +The `BasicEmbeddingPipeline` is initialized with an `embedding_provider`, `vector_db_provider`, `text_splitter`, an optional `logging_connection`, `embedding_batch_size`, and `id_prefix`. ### Text Extraction @@ -70,18 +70,16 @@ import textstat class CustomEmbeddingPipeline(BasicEmbeddingPipeline): def __init__( self, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, - db: VectorDBProvider, + embedding_provider: OpenAIEmbeddingProvider, + vector_db_provider: VectorDBProvider, text_splitter: TextSplitter, logging_connection: Optional[LoggingDatabaseConnection] = None, embedding_batch_size: int = 1, id_prefix: str = "demo", ): super().__init__( - embedding_model, - embeddings_provider, - db, + embedding_provider, + vector_db_provider, text_splitter, logging_connection, embedding_batch_size, diff --git a/docs/pages/deep-dive/factory.mdx b/docs/pages/deep-dive/factory.mdx index 0746be4f..40f4082c 100644 --- a/docs/pages/deep-dive/factory.mdx +++ b/docs/pages/deep-dive/factory.mdx @@ -11,10 +11,9 @@ from r2r.main import E2EPipelineFactory, R2RConfig app = E2EPipelineFactory.create_pipeline( config=R2RConfig.load_config(), - db=None, - embeddings_provider=None, - llm=None, - text_splitter=None, + vector_db_provider=None, + embedding_provider=None, + llm_provider=None, adapters=None, ingestion_pipeline_impl=BasicIngestionPipeline, embedding_pipeline_impl=BasicEmbeddingPipeline, @@ -37,7 +36,7 @@ This method retrieves the appropriate vector database based on the `database_con - `pgvector`: Returns an instance of `PGVectorDB`. - `local`: Returns an instance of `LocalVectorDB`. -### `get_embeddings_provider(embedding_config: dict[str, Any])` +### `get_embedding_provider(embedding_config: dict[str, Any])` This method retrieves the appropriate embeddings provider based on the `embedding_config`. It supports the following embeddings providers: - `openai`: Returns an instance of `OpenAIEmbeddingProvider`. @@ -46,8 +45,8 @@ This method retrieves the appropriate embeddings provider based on the `embeddin ### `get_llm(llm_config: dict[str, Any])` This method retrieves the appropriate language model based on the `llm_config`. It supports the following language models: -- `openai`: Returns an instance of `OpenAILLM` with the `OpenAIConfig`. -- `litellm`: Returns an instance of `LiteLLM` with the `LiteLLMConfig`. +- `openai`: Returns an instance of `OpenAILLM`. +- `litellm`: Returns an instance of `LiteLLM`. ### `get_text_splitter(text_splitter_config: dict[str, Any])` @@ -58,13 +57,12 @@ This method retrieves the appropriate text splitter based on the `text_splitter_ This method creates the end-to-end pipeline by assembling various components based on the provided `config` and optional parameters. It performs the following steps: 1. Sets up logging based on the `logging_database` configuration. -2. Retrieves the embeddings provider using `get_embeddings_provider` or uses the provided `embeddings_provider`. -3. Retrieves the vector database using `get_vector_db` or uses the provided `db`. +2. Retrieves the embeddings provider using `get_embedding_provider` or uses the provided `embedding_provider`. +3. Retrieves the vector database using `get_vector_db_provider` or uses the provided `vector_db_provider`. 4. Initializes the vector database collection with the specified `collection_name` and `embedding_dimension`. -5. Retrieves the language model using `get_llm` or uses the provided `llm`. +5. Retrieves the language model using `get_llm_provider` or uses the provided `llm`. 6. Creates a `LoggingDatabaseConnection` instance for logging purposes. 7. Creates an instance of the `rag_pipeline_impl` (default: `QnARAGPipeline`) with the language model, vector database, embedding model, embeddings provider, and logging connection. -8. Retrieves the text splitter using `get_text_splitter` or uses the provided `text_splitter`. 9. Creates an instance of the `embedding_pipeline_impl` (default: `BasicEmbeddingPipeline`) with the embedding model, embeddings provider, vector database, logging connection, text splitter, and embedding batch size. 10. Creates an instance of the `eval_pipeline_impl` (default: `BasicEvalPipeline`) with the evaluation configuration and logging connection. 11. Creates an instance of the `ingestion_pipeline_impl` (default: `BasicIngestionPipeline`) with the specified adapters. diff --git a/docs/pages/deep-dive/rag.mdx b/docs/pages/deep-dive/rag.mdx index 79ff93bf..4c65a41a 100644 --- a/docs/pages/deep-dive/rag.mdx +++ b/docs/pages/deep-dive/rag.mdx @@ -9,10 +9,9 @@ The `QnARAGPipeline` is a simple implementation of the `RAGPipeline` abstract ba ### Initialization The `QnARAGPipeline` is initialized with the following parameters: -- `llm`: An instance of `LLMProvider` for generating completions. -- `db`: An instance of `VectorDBProvider` for searching and retrieving documents. -- `embedding_model`: The name of the embedding model to use. -- `embeddings_provider`: An instance of `OpenAIEmbeddingProvider` for generating embeddings. +- `llm_provider`: An instance of `LLMProvider` for generating completions. +- `vector_db_provider`: An instance of `VectorDBProvider` for searching and retrieving documents. +- `embedding_provider`: An instance of `OpenAIEmbeddingProvider` for generating embeddings. - `prompt_provider` (optional): An instance of `PromptProvider` for providing prompts (default is `BasicPromptProvider`). - `logging_connection` (optional): An instance of `LoggingDatabaseConnection` for logging. @@ -40,10 +39,9 @@ To create a custom RAG pipeline, you can subclass the `RAGPipeline` abstract bas class CustomRAGPipeline(RAGPipeline): def __init__( self, - llm: LLMProvider, - db: VectorDBProvider, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, + llm_provider: LLMProvider, + vector_db_provider: VectorDBProvider, + embedding_provider: OpenAIEmbeddingProvider, prompt_provider: Optional[PromptProvider] = None, logging_connection: Optional[LoggingDatabaseConnection] = None, ) -> None: @@ -52,13 +50,12 @@ class CustomRAGPipeline(RAGPipeline): self.prompt_provider = prompt_provider super().__init__( - llm, + llm_provider=llm_provider, prompt_provider=prompt_provider, + embedding_provider=embedding_provider, + vector_db_provider=vector_db_provider, logging_connection=logging_connection, ) - self.embedding_model = embedding_model - self.embeddings_provider = embeddings_provider - self.db = db self.pipeline_run_info = None def transform_query(self, query: str) -> str: @@ -75,10 +72,9 @@ class CustomRAGPipeline(RAGPipeline): **kwargs, ) -> list[VectorSearchResult]: # Custom document retrieval logic - results = self.db.search( - query_vector=self.embeddings_provider.get_embedding( + results = self.vector_db_provider.search( + query_vector=self.embedding_provider.get_embedding( transformed_query, - self.embedding_model, ), filters=filters, limit=limit, @@ -86,10 +82,10 @@ class CustomRAGPipeline(RAGPipeline): return results def rerank_results( - self, results: list[VectorSearchResult] + self, transformed_query: str, results: list[VectorSearchResult], limit ) -> list[VectorSearchResult]: # Custom result reranking logic - return list(reversed(results)) + return list(reversed(results))[0:limit] def _format_results(self, results: list[VectorSearchResult]) -> str: # Custom result formatting logic diff --git a/docs/pages/providers/embeddings.mdx b/docs/pages/providers/embeddings.mdx index 218da282..294bad27 100644 --- a/docs/pages/providers/embeddings.mdx +++ b/docs/pages/providers/embeddings.mdx @@ -35,4 +35,4 @@ Anything supported by OpenAI, such as: - **Pricing**: Approximately 12,500 pages per dollar. Balances cost and performance effectively. - **More**: [Embeddings Guide](https://platform.openai.com/docs/guides/embeddings) -Lastly, the `sentence_transformer` package from HuggingFace is also supported as a provider. For example, one such popular model is [`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). \ No newline at end of file +Lastly, the `sentence_transformer` package from HuggingFace is also supported as a provider. For example, one such popular model is [`mixedbread-ai/mxbai-embed-large-v1`](https://huggingface.co/sentence-transformers/mixedbread-ai/mxbai-embed-large-v1). \ No newline at end of file diff --git a/docs/pages/providers/llms.mdx b/docs/pages/providers/llms.mdx index 1a94418e..a4d136af 100644 --- a/docs/pages/providers/llms.mdx +++ b/docs/pages/providers/llms.mdx @@ -93,7 +93,7 @@ pip install llama-cpp-python # or poetry install ... -E local-llm To configure the LlamaCpp provider, you need to set the following options in the `language_model` section of the configuration file (`config.json`): -- `provider_name`: Set it to `"llama-cpp"` to use the LlamaCpp provider. +- `provider`: Set it to `"llama-cpp"` to use the LlamaCpp provider. - `model_path`: Specify the path where the LlamaCpp models are located. If not provided, it defaults to `~/.cache/models`. - `model_name`: Specify the name of the LlamaCpp model to use. If not provided, it defaults to `"tinyllama-1.1b-chat-v1.0.Q2_K.gguf"`. diff --git a/docs/pages/tutorials/configuring_your_rag_pipeline.mdx b/docs/pages/tutorials/configuring_your_rag_pipeline.mdx index c9d8d7d3..e66bbaf0 100644 --- a/docs/pages/tutorials/configuring_your_rag_pipeline.mdx +++ b/docs/pages/tutorials/configuring_your_rag_pipeline.mdx @@ -226,7 +226,7 @@ Set the `provider` field under `vector_database` in `config.json` to specify you #### Embedding Provider R2R supports OpenAI and local inference embedding providers: - `openai`: OpenAI models like `text-embedding-3-small` -- `sentence-transformers`: HuggingFace models like `all-MiniLM-L6-v2` +- `sentence-transformers`: HuggingFace models like `mixedbread-ai/mxbai-embed-large-v1` Configure the `embedding` section to set your desired embedding model, dimension, and batch size. diff --git a/docs/pages/tutorials/local_rag.mdx b/docs/pages/tutorials/local_rag.mdx index 6833382a..5a1ed139 100644 --- a/docs/pages/tutorials/local_rag.mdx +++ b/docs/pages/tutorials/local_rag.mdx @@ -59,7 +59,7 @@ To streamline this process, we've provided pre-configured local settings in the { "embedding": { "provider": "sentence-transformers", - "model": "all-MiniLM-L6-v2", + "model": "mixedbread-ai/mxbai-embed-large-v1", "dimension": 384, "batch_size": 32 }, @@ -78,7 +78,7 @@ To streamline this process, we've provided pre-configured local settings in the You may also modify the configuration defaults for ingestion, logging, and your vector database provider in a similar manner. More information on this follows below. -This chosen config modification above instructs R2R to use the `sentence-transformers` library for embeddings with the `all-MiniLM-L6-v2` model, turns off evals, and sets the LLM provider to `ollama`. During ingestion, the default is to split documents into chunks of 512 characters with 20 characters of overlap between chunks. +This chosen config modification above instructs R2R to use the `sentence-transformers` library for embeddings with the `mixedbread-ai/mxbai-embed-large-v1` model, turns off evals, and sets the LLM provider to `ollama`. During ingestion, the default is to split documents into chunks of 512 characters with 20 characters of overlap between chunks. A local vector database will be used to store the embeddings. The current default is a minimal sqlite implementation, with plans to migrate the tutorial to LanceDB shortly. @@ -117,7 +117,7 @@ The output should look something like this: Here's what's happening under the hood: 1. R2R loads the included PDF and converts it to text using PyPDF2. 2. It splits the text into chunks of 512 characters each, with 20 characters overlapping between chunks. -3. Each chunk is embedded using the `all-MiniLM-L6-v2` model from `sentence-transformers`. +3. Each chunk is embedded using the `mixedbread-ai/mxbai-embed-large-v1` model from `sentence-transformers`. 4. The chunks and embeddings are stored in the specified vector database, which defaults to a local SQLite database. With just one command, we've gone from a raw document to an embedded knowledge base we can query. In addition to the raw chunks, metadata such as user ID or document ID can be attached to enable easy filtering later. @@ -151,7 +151,7 @@ python -m r2r.examples.clients.run_qna_client rag_completion \ ``` This command tells R2R to use the specified model to generate a completion for the given query. R2R will: -1. Embed the query using `all-MiniLM-L6-v2`. +1. Embed the query using `mixedbread-ai/mxbai-embed-large-v1`. 2. Find the chunks most similar to the query embedding. 3. 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[[package]] @@ -6599,13 +6832,13 @@ text-embedding = ["sentence-transformers (==2.*)"] [[package]] name = "virtualenv" -version = "20.25.1" +version = "20.25.3" description = "Virtual Python Environment builder" optional = true python-versions = ">=3.7" files = [ - {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"}, - {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"}, + {file = "virtualenv-20.25.3-py3-none-any.whl", hash = "sha256:8aac4332f2ea6ef519c648d0bc48a5b1d324994753519919bddbb1aff25a104e"}, + {file = "virtualenv-20.25.3.tar.gz", hash = "sha256:7bb554bbdfeaacc3349fa614ea5bff6ac300fc7c335e9facf3a3bcfc703f45be"}, ] [package.dependencies] @@ -6614,7 +6847,7 @@ filelock = ">=3.12.2,<4" platformdirs = ">=3.9.1,<5" [package.extras] -docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] [[package]] @@ -6833,6 +7066,17 @@ cffi = ">=1.16.0" [package.extras] test = ["pytest"] +[[package]] +name = "xlsxwriter" +version = "3.2.0" +description = "A Python module for creating Excel XLSX files." +optional = false +python-versions = ">=3.6" +files = [ + {file = "XlsxWriter-3.2.0-py3-none-any.whl", hash = "sha256:ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e"}, + {file = "XlsxWriter-3.2.0.tar.gz", hash = "sha256:9977d0c661a72866a61f9f7a809e25ebbb0fb7036baa3b9fe74afcfca6b3cb8c"}, +] + [[package]] name = "xxhash" version = "3.4.1" @@ -7069,21 +7313,19 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] -all = ["boto3", "datasets", "exa-py", "ionic-api-sdk", "llama-cpp-python", "parea-ai", "psycopg2-binary", "qdrant_client", "sentry-sdk", "tiktoken"] +all = ["boto3", "exa-py", "ionic-api-sdk", "llama-cpp-python", "parea-ai", "psycopg2-binary", "qdrant_client", "tiktoken"] deepeval = ["deepeval"] -embedding = ["tiktoken"] eval = ["parea-ai"] exa = ["exa-py"] ionic = ["ionic-api-sdk"] local-llm = ["llama-cpp-python", "sentence-transformers"] -monitoring = ["sentry-sdk"] postgres = ["psycopg2-binary"] +pptx = ["python-pptx"] qdrant = ["qdrant_client"] reducto = ["boto3"] -sentence-transformers = ["sentence-transformers"] -streaming = ["datasets"] +sentence-transformers-only = ["sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "80577f56bfd4bbc7b7d3e2758949c939cffc4bade811b95f412280a956d6a18d" +content-hash = "55c4e2e8cb59a4de4deefebf58afe97fcc55643aa352bd736b42bc8937d0e8d5" diff --git a/pyproject.toml b/pyproject.toml index f7e4ebe2..8316dfd9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "r2r" -version = "0.1.35" +version = "0.1.36" description = "SciPhi R2R" authors = ["Owen Colegrove "] license = "MIT" @@ -16,47 +16,59 @@ include = ["config.json"] python = ">=3.9,<3.13" # Required dependencies -bs4 = "^0.0.2" -fastapi = "^0.109.2" -fire = "^0.5.0" -gunicorn = "^21.2.0" -litellm = "^1.34.0" -openai = "^1.11.1" + +# python pydantic = "^2.6.3" -pypdf = "^4.2.0" python-dotenv = "^1.0.1" python-multipart = "^0.0.9" + +# infrastructure +fastapi = "^0.109.2" +fire = "^0.5.0" +gunicorn = "^21.2.0" +psycopg2-binary = {version = "^2.9.9", optional = true} requests = "^2.31.0" types-requests = "^2.31.0" uvicorn = "^0.27.0.post1" -vecs = "^0.4.0" +# ingestion +boto3 = {version = "^1.34.71", optional = true} +bs4 = "^0.0.2" +openpyxl = "^3.1.2" +markdown = "^3.6" +pypdf = "^4.2.0" +python-pptx = "^0.6.23" +python-docx = "^1.1.0" -# Optional dependencies +# embedding providers tiktoken = {version = "^0.5.2", optional = true} -datasets = {version = "^2.16.1", optional = true} +sentence-transformers = {version = "^2.7.0", optional = true} + +# vector db providers +vecs = "^0.4.0" qdrant_client = {version = "^1.7.0", optional = true} -psycopg2-binary = {version = "^2.9.9", optional = true} -numpy = {version = "^1.26.4", optional = true} -sentry-sdk = {version = "^1.40.4", optional = true} + +# llm providers +litellm = "^1.35.18" +openai = "^1.11.1" +llama-cpp-python = {version = "^0.2.57", optional = true} + +# eval providers deepeval = {version ="^0.20.88", optional = true} parea-ai = {version = "^0.2.86", optional = true} + +# integrations ionic-api-sdk = {version = "0.9.3", optional = true} -boto3 = {version = "^1.34.71", optional = true} exa-py = {version = "^1.0.9", optional = true} -llama-cpp-python = {version = "^0.2.57", optional = true} -sentence-transformers = {version = "^2.7.0", optional = true} [tool.poetry.extras] -embedding = ["tiktoken"] -streaming = ["datasets"] +reducto = ["boto3"] qdrant = ["qdrant_client"] postgres = ["psycopg2-binary"] -monitoring = ["sentry-sdk"] deepeval = ["deepeval"] eval = ["parea-ai"] +pptx = ["python-pptx"] ionic = ["ionic-api-sdk"] -reducto = ["boto3"] exa = ["exa-py"] sentence-transformers-only = ["sentence-transformers"] local-llm = ["llama-cpp-python", "sentence-transformers"] @@ -77,4 +89,4 @@ exclude = 'playground/.*|deprecated/.*|dump/.*|docs/source|vecs/*' [[tool.mypy.overrides]] module = "yaml" -ignore_missing_imports = true +ignore_missing_imports = true \ No newline at end of file diff --git a/r2r/client/base.py b/r2r/client/base.py index 42724916..ebd2cf71 100644 --- a/r2r/client/base.py +++ b/r2r/client/base.py @@ -76,7 +76,8 @@ def add_entries( def search( self, query: str, - limit: Optional[int] = 10, + search_limit: Optional[int] = 25, + rerank_limit: Optional[int] = 15, filters: Optional[Dict[str, Any]] = None, settings: Optional[Dict[str, Any]] = None, ): @@ -84,7 +85,8 @@ def search( json_data = { "query": query, "filters": filters or {}, - "limit": limit, + "search_limit": search_limit, + "rerank_limit": rerank_limit, "settings": settings or {}, } response = requests.post(url, json=json_data) @@ -96,7 +98,8 @@ def search( def rag_completion( self, query: str, - limit: Optional[int] = 10, + search_limit: Optional[int] = 25, + rerank_limit: Optional[int] = 15, filters: Optional[Dict[str, Any]] = None, settings: Optional[Dict[str, Any]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -114,7 +117,8 @@ def rag_completion( json_data = { "query": query, "filters": filters or {}, - "limit": limit, + "search_limit": search_limit, + "rerank_limit": rerank_limit, "settings": settings or {}, "generation_config": generation_config or {}, } @@ -143,7 +147,8 @@ def eval( async def stream_rag_completion( self, query: str, - limit: Optional[int] = 10, + search_limit: Optional[int] = 25, + rerank_limit: Optional[int] = 15, filters: Optional[Dict[str, Any]] = None, settings: Optional[Dict[str, Any]] = None, generation_config: Optional[Dict[str, Any]] = None, @@ -165,7 +170,8 @@ async def stream_rag_completion( json_data = { "query": query, "filters": filters or {}, - "limit": limit, + "search_limit": search_limit, + "rerank_limit": rerank_limit, "settings": settings or {}, "generation_config": generation_config or {}, } @@ -191,3 +197,13 @@ def get_logs_summary(self): url = f"{self.base_url}/logs_summary" response = requests.get(url) return response.json() + + def get_user_ids(self): + url = f"{self.base_url}/get_user_ids/" + response = requests.get(url) + return response.json() + + def get_user_documents(self, user_id: str): + url = f"{self.base_url}/get_user_documents/" + response = requests.get(url, params={"user_id": user_id}) + return response.json() diff --git a/r2r/core/__init__.py b/r2r/core/__init__.py index 31acee4c..9efbda4f 100644 --- a/r2r/core/__init__.py +++ b/r2r/core/__init__.py @@ -1,39 +1,47 @@ from .abstractions.document import DocumentPage -from .abstractions.output import RAGPipelineOutput +from .abstractions.output import ( + LLMChatCompletion, + LLMChatCompletionChunk, + RAGPipelineOutput, +) +from .abstractions.vector import VectorEntry, VectorSearchResult +from .agent.base import Agent from .pipelines.embedding import EmbeddingPipeline from .pipelines.eval import EvalPipeline from .pipelines.ingestion import IngestionPipeline from .pipelines.rag import RAGPipeline from .pipelines.scraping import ScraperPipeline -from .providers.embedding import EmbeddingProvider -from .providers.eval import EvalProvider +from .providers.embedding import EmbeddingConfig, EmbeddingProvider +from .providers.eval import EvalConfig, EvalProvider from .providers.llm import GenerationConfig, LLMConfig, LLMProvider -from .providers.logging import LoggingDatabaseConnection, log_execution_to_db -from .providers.prompt import DefaultPromptProvider, PromptProvider -from .providers.vector_db import ( - VectorDBProvider, - VectorEntry, - VectorSearchResult, -) +from .providers.prompt import PromptConfig, PromptProvider +from .providers.vector_db import VectorDBConfig, VectorDBProvider +from .utils.logging import LoggingDatabaseConnection, log_execution_to_db __all__ = [ + "LoggingDatabaseConnection", + "log_execution_to_db", "DocumentPage", - "DefaultPromptProvider", "RAGPipelineOutput", + "VectorSearchResult", + "VectorEntry", + "Agent", "EmbeddingPipeline", "EvalPipeline", "IngestionPipeline", - "ScraperPipeline", "RAGPipeline", - "LoggingDatabaseConnection", - "log_execution_to_db", - "PromptProvider", - "EvalProvider", + "ScraperPipeline", + "EmbeddingConfig", "EmbeddingProvider", + "EvalConfig", + "EvalProvider", + "PromptConfig", + "PromptProvider", "GenerationConfig", + "LLMChatCompletion", + "LLMChatCompletionChunk", "LLMConfig", "LLMProvider", - "VectorSearchResult", - "VectorEntry", + "VectorDBConfig", "VectorDBProvider", ] diff --git a/r2r/core/abstractions/output.py b/r2r/core/abstractions/output.py index 0e14fb5c..822f0d21 100644 --- a/r2r/core/abstractions/output.py +++ b/r2r/core/abstractions/output.py @@ -1,6 +1,9 @@ from typing import Optional -from openai.types.chat import ChatCompletion +from openai.types.chat import ChatCompletion, ChatCompletionChunk + +LLMChatCompletion = ChatCompletion +LLMChatCompletionChunk = ChatCompletionChunk class RAGPipelineOutput: @@ -8,7 +11,7 @@ def __init__( self, search_results: list, context: Optional[str] = None, - completion: Optional[ChatCompletion] = None, + completion: Optional[LLMChatCompletion] = None, ): self.search_results = search_results self.context = context diff --git a/r2r/core/abstractions/tool.py b/r2r/core/abstractions/tool.py new file mode 100644 index 00000000..04e0ed16 --- /dev/null +++ b/r2r/core/abstractions/tool.py @@ -0,0 +1,21 @@ +"""Interface for tools.""" + +from typing import Awaitable, Callable, Dict, Optional + +from pydantic import BaseModel, Extra + + +class Tool(BaseModel): + """`Tool` exposes a function or coroutine directly.""" + + class Config: + extra = Extra.forbid + arbitrary_types_allowed = True + + function: Callable[..., str] + name: str = "" + description: str = "" + coroutine: Optional[Callable[..., Awaitable[str]]] = None + + def run(self, tool_input: Dict[str, str]) -> str: + return self.function(**tool_input) diff --git a/r2r/core/abstractions/vector.py b/r2r/core/abstractions/vector.py new file mode 100644 index 00000000..d9627bfe --- /dev/null +++ b/r2r/core/abstractions/vector.py @@ -0,0 +1,64 @@ +import json +from typing import Any +from uuid import UUID + + +class VectorEntry: + def __init__( + self, entry_id: UUID, vector: list[float], metadata: dict[str, Any] + ): + """Create a new VectorEntry object.""" + self.vector = vector + self.id = entry_id + self.metadata = metadata + + def to_json(self) -> str: + """Serialize the object to a JSON string.""" + return json.dumps( + {"id": self.id, "vector": self.vector, "metadata": self.metadata}, + default=lambda o: o.__dict__, + ) + + @staticmethod + def from_json(json_str: str) -> "VectorEntry": + """Deserialize a JSON string into a VectorEntry object.""" + data = json.loads(json_str) + return VectorEntry( + vector=data["vector"], + entry_id=data["id"], + metadata=data["metadata"], + ) + + def __str__(self) -> str: + """Return a string representation of the VectorEntry.""" + return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})" + + def __repr__(self) -> str: + """Return an unambiguous string representation of the VectorEntry.""" + return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})" + + +class VectorSearchResult: + def __init__( + self, entry_id: str, score: float, metadata: dict[str, Any] + ) -> None: + """Create a new VectorSearchResult object.""" + self.id = entry_id + self.score = score + self.metadata = metadata + + def __str__(self) -> str: + """Return a string representation of the VectorSearchResult.""" + return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})" + + def __repr__(self) -> str: + """Return an unambiguous string representation of the VectorSearchResult for debugging.""" + return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})" + + def to_dict(self) -> dict: + """Return a dictionary representation of the VectorSearchResult.""" + return { + "id": self.id, + "score": self.score, + "metadata": self.metadata, + } diff --git a/r2r/core/adapters/__init__.py b/r2r/core/adapters/__init__.py index ada8fb2b..0e2d1ce5 100644 --- a/r2r/core/adapters/__init__.py +++ b/r2r/core/adapters/__init__.py @@ -1,11 +1,25 @@ from .advanced.reducto import ReductoAdapter -from .base import Adapter, HTMLAdapter, JSONAdapter, PDFAdapter, TextAdapter +from .base import ( + Adapter, + CSVAdapter, + HTMLAdapter, + JSONAdapter, + MarkdownAdapter, + PDFAdapter, + PPTAdapter, + TextAdapter, + XLSXAdapter, +) __all__ = [ "Adapter", + "CSVAdapter", "PDFAdapter", - "TextAdapter", + "PPTAdapter", "JSONAdapter", + "MarkdownAdapter", "HTMLAdapter", "ReductoAdapter", + "TextAdapter", + "XLSXAdapter", ] diff --git a/r2r/core/adapters/base.py b/r2r/core/adapters/base.py index f1c63810..30c66d7a 100644 --- a/r2r/core/adapters/base.py +++ b/r2r/core/adapters/base.py @@ -2,7 +2,7 @@ import string from abc import ABC, abstractmethod from io import BytesIO -from typing import Any, Generic, TypeVar, Union +from typing import Any, Generator, Generic, TypeVar, Union from bs4 import BeautifulSoup @@ -11,20 +11,20 @@ class Adapter(ABC, Generic[T]): @abstractmethod - def adapt(self, data: Any) -> T: + def adapt(self, data: Any) -> Generator[str, None, None]: pass class TextAdapter(Adapter[str]): - def adapt(self, data: str) -> list[str]: - return [data] + def adapt(self, data: str) -> Generator[str, None, None]: + yield data class JSONAdapter(Adapter[str]): - def adapt(self, data: Union[str, bytes]) -> list[str]: + def adapt(self, data: Union[str, bytes]) -> Generator[str, None, None]: if isinstance(data, bytes): data = data.decode("utf-8") - return [self._parse_json(json.loads(data))] + yield self._parse_json(json.loads(data)) def _parse_json(self, data: dict) -> str: def remove_objects_with_null(obj): @@ -64,7 +64,7 @@ def format_json_as_text(obj, indent=0): class HTMLAdapter(Adapter[str]): def adapt(self, data: str) -> list[str]: soup = BeautifulSoup(data, "html.parser") - return [soup.get_text()] + yield soup.get_text() class PDFAdapter(Adapter[str]): @@ -78,14 +78,94 @@ def __init__(self): "Error, `pypdf` is required to run `PyPDFAdapter`. Please install it using `pip install pypdf`." ) - def adapt(self, data: bytes) -> list[str]: + def adapt(self, data: bytes) -> Generator[str, None, None]: pdf = self.PdfReader(BytesIO(data)) - text = "" for page in pdf.pages: page_text = page.extract_text() if page_text is not None: page_text = "".join( filter(lambda x: x in string.printable, page_text) ) - text += page_text + " " - return [text] + yield page_text + + +class PPTAdapter(Adapter[str]): + def __init__(self): + try: + from pptx import Presentation + + self.Presentation = Presentation + except ImportError: + raise ValueError( + "Error, `python-pptx` is required to run `PPTAdapter`. Please install it using `pip install python-pptx`." + ) + + def adapt(self, data: bytes) -> list[str]: + prs = self.Presentation(BytesIO(data)) + for slide in prs.slides: + for shape in slide.shapes: + if hasattr(shape, "text"): + yield shape.text + + +class DOCXAdapter(Adapter[str]): + def __init__(self): + try: + from docx import Document + + self.Document = Document + except ImportError: + raise ValueError( + "Error, `python-docx` is required to run `DOCXAdapter`. Please install it using `pip install python-docx`." + ) + + def adapt(self, data: bytes) -> Generator[str, None, None]: + doc = self.Document(BytesIO(data)) + for paragraph in doc.paragraphs: + yield paragraph.text + + +class CSVAdapter(Adapter[str]): + def __init__(self): + import csv + from io import StringIO + + self.csv = csv + self.StringIO = StringIO + + def adapt(self, data: Union[str, bytes]) -> Generator[str, None, None]: + if isinstance(data, bytes): + data = data.decode("utf-8") + csv_reader = self.csv.reader(self.StringIO(data)) + for row in csv_reader: + yield ", ".join(row) + + +class XLSXAdapter(Adapter[str]): + def __init__(self): + try: + from openpyxl import load_workbook + + self.load_workbook = load_workbook + except ImportError: + raise ValueError( + "Error, `openpyxl` is required to run `XLSXAdapter`. Please install it using `pip install openpyxl`." + ) + + def adapt(self, data: bytes) -> Generator[str, None, None]: + wb = self.load_workbook(filename=BytesIO(data)) + for sheet in wb.worksheets: + for row in sheet.iter_rows(values_only=True): + yield ", ".join(map(str, row)) + + +class MarkdownAdapter(Adapter[str]): + def __init__(self): + import markdown + + self.markdown = markdown + + def adapt(self, data: str) -> Generator[str, None, None]: + html = self.markdown.markdown(data) + soup = BeautifulSoup(html, "html.parser") + yield soup.get_text() diff --git a/r2r/core/agent/base.py b/r2r/core/agent/base.py new file mode 100644 index 00000000..54a4d9c0 --- /dev/null +++ b/r2r/core/agent/base.py @@ -0,0 +1,75 @@ +"""Defines the abstract base classes and enums for agent objects""" + +import logging +import logging.config +from abc import ABC, abstractmethod +from typing import Sequence + +from ..abstractions.output import LLMChatCompletion +from ..abstractions.tool import Tool +from ..providers.llm import LLMProvider +from ..providers.prompt import PromptProvider + +logger = logging.getLogger(__name__) + + +class Agent(ABC): + """ + An abstract class for implementing a agent. + + An agent is an autonomous entity that can perform actions and communicate + with other providers. + """ + + def __init__( + self, prompt_provider: PromptProvider, llm_provider: LLMProvider + ) -> None: + self.llm_provider = llm_provider + self.prompt_provider = prompt_provider + + self._initialized = False + + @abstractmethod + def __iter__(self): + pass + + @abstractmethod + def __next__(self) -> LLMChatCompletion: + """ + Iterates the agent by performing a single step of its task. + + A single step is a new conversation turn, which consists of generating + a new 'asisstant' message, and parsing the reply from the 'user'. + + Raises: + AgentStopIterationError: If the agent has already completed its task + or exceeded the maximum number of iterations. + """ + pass + + @property + @abstractmethod + def tools(self) -> Sequence[Tool]: + """An abstract property for getting the tools associated with the agent.""" + pass + + @abstractmethod + def run(self, message: str) -> str: + """ + Runs the agent until it completes its task. + + The task is complete when next returns None. + """ + pass + + @abstractmethod + def run_stream(self, message: str): + """ + Runs the agent until it completes its task. Stream the result when it is ready. + """ + pass + + @abstractmethod + def _setup(self) -> None: + """An abstract method for setting up the agent before running.""" + pass diff --git a/r2r/core/pipelines/embedding.py b/r2r/core/pipelines/embedding.py index 6126239d..e838d385 100644 --- a/r2r/core/pipelines/embedding.py +++ b/r2r/core/pipelines/embedding.py @@ -7,9 +7,9 @@ from typing import Any, Optional from ..providers.embedding import EmbeddingProvider -from ..providers.logging import LoggingDatabaseConnection from ..providers.vector_db import VectorDBProvider, VectorEntry from ..utils import generate_run_id +from ..utils.logging import LoggingDatabaseConnection from .pipeline import Pipeline logger = logging.getLogger(__name__) @@ -18,16 +18,14 @@ class EmbeddingPipeline(Pipeline): def __init__( self, - embedding_model: str, - embeddings_provider: EmbeddingProvider, - db: VectorDBProvider, + embedding_provider: EmbeddingProvider, + vector_db_provider: VectorDBProvider, logging_connection: Optional[LoggingDatabaseConnection] = None, *args, **kwargs, ): - self.embedding_model = embedding_model - self.embeddings_provider = embeddings_provider - self.db = db + self.embedding_provider = embedding_provider + self.vector_db_provider = vector_db_provider super().__init__(logging_connection=logging_connection, **kwargs) def initialize_pipeline(self, *args, **kwargs) -> None: @@ -60,3 +58,8 @@ def store_chunks(self, chunks: list[VectorEntry], *args, **kwargs) -> None: def run(self, document: Any, **kwargs): pass + + def run_stream(self, document: Any, **kwargs): + raise NotImplementedError( + "Streaming mode not supported for `EmbeddingPipeline`." + ) diff --git a/r2r/core/pipelines/eval.py b/r2r/core/pipelines/eval.py index 379b6e45..4d51748d 100644 --- a/r2r/core/pipelines/eval.py +++ b/r2r/core/pipelines/eval.py @@ -3,8 +3,8 @@ from abc import abstractmethod from typing import Any, Optional -from ..providers.logging import LoggingDatabaseConnection from ..utils import generate_run_id +from ..utils.logging import LoggingDatabaseConnection from .pipeline import Pipeline logger = logging.getLogger(__name__) @@ -48,3 +48,15 @@ def run( if random.random() < self.frequency: return self.evaluate(query, context, completion) return None + + def run_stream( + self, + query: str, + context: str, + completion: str, + run_id: Optional[str], + **kwargs, + ): + raise NotImplementedError( + "Streaming mode not supported for `EvalPipeline`." + ) diff --git a/r2r/core/pipelines/ingestion.py b/r2r/core/pipelines/ingestion.py index d6e9808c..b23f8c1d 100644 --- a/r2r/core/pipelines/ingestion.py +++ b/r2r/core/pipelines/ingestion.py @@ -3,8 +3,8 @@ from ..abstractions.document import DocumentPage from ..adapters import Adapter -from ..providers.logging import LoggingDatabaseConnection from ..utils import generate_run_id +from ..utils.logging import LoggingDatabaseConnection from .pipeline import Pipeline @@ -62,3 +62,14 @@ def run( Yields the processed DocumentPage objects. """ pass + + def run_stream( + self, + document_id: str, + blobs: dict[str, Any], + metadata: Optional[dict] = None, + **kwargs, + ): + raise NotImplementedError( + "Streaming mode not supported for `IngestionPipeline`." + ) diff --git a/r2r/core/pipelines/pipeline.py b/r2r/core/pipelines/pipeline.py index 29dc66dc..fc4586a1 100644 --- a/r2r/core/pipelines/pipeline.py +++ b/r2r/core/pipelines/pipeline.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Optional -from ..providers.logging import LoggingDatabaseConnection +from ..utils.logging import LoggingDatabaseConnection class Pipeline(ABC): @@ -32,6 +32,7 @@ def __getattribute__(self, name): "__getattribute__", "initialize_pipeline", "run", + "run_stream", ]: def newfunc(*args, **kwargs): @@ -53,3 +54,10 @@ def initialize_pipeline(self, *args, **kwargs): @abstractmethod def run(self, *args, **kwargs): pass + + @abstractmethod + def run_stream(self, *args, **kwargs): + """ + Runs the pipeline in streaming mode. + """ + pass diff --git a/r2r/core/pipelines/rag.py b/r2r/core/pipelines/rag.py index b799433c..04a0a34a 100644 --- a/r2r/core/pipelines/rag.py +++ b/r2r/core/pipelines/rag.py @@ -8,12 +8,12 @@ from abc import abstractmethod from typing import Any, Generator, Optional, Union -from openai.types.chat import ChatCompletion - -from ..abstractions.output import RAGPipelineOutput +from ..abstractions.output import LLMChatCompletion, RAGPipelineOutput +from ..providers.embedding import EmbeddingProvider from ..providers.llm import GenerationConfig, LLMProvider -from ..providers.logging import LoggingDatabaseConnection, log_execution_to_db from ..providers.prompt import PromptProvider +from ..providers.vector_db import VectorDBProvider +from ..utils.logging import LoggingDatabaseConnection, log_execution_to_db from .pipeline import Pipeline logger = logging.getLogger(__name__) @@ -26,14 +26,19 @@ class RAGPipeline(Pipeline): def __init__( self, - llm: "LLMProvider", prompt_provider: PromptProvider, + embedding_provider: EmbeddingProvider, + llm_provider: LLMProvider, + vector_db_provider: VectorDBProvider, logging_connection: Optional[LoggingDatabaseConnection] = None, *args, **kwargs, ): - self.llm = llm self.prompt_provider = prompt_provider + self.llm_provider = llm_provider + self.embedding_provider = embedding_provider + self.vector_db_provider = vector_db_provider + super().__init__(logging_connection=logging_connection, **kwargs) def initialize_pipeline( @@ -63,7 +68,7 @@ def transform_query(self, query: str) -> Any: @abstractmethod def search( self, - transformed_query, + transformed_query: Any, filters: dict[str, Any], limit: int, *args, @@ -76,7 +81,9 @@ def search( pass @abstractmethod - def rerank_results(self, results: list) -> list: + def rerank_results( + self, transformed_query: Any, results: list, limit: int + ) -> list: """ Reranks the retrieved results based on relevance or other criteria. """ @@ -94,8 +101,7 @@ def construct_context( self, results: list, ) -> str: - reranked_results = self.rerank_results(results) - return self._format_results(reranked_results) + return self._format_results(results) @log_execution_to_db def construct_prompt(self, inputs: dict[str, str]) -> str: @@ -112,7 +118,7 @@ def generate_completion( prompt: str, generation_config: GenerationConfig, conversation: list[dict] = None, - ) -> Union[Generator[str, None, None], ChatCompletion]: + ) -> Union[Generator[str, None, None], LLMChatCompletion]: """ Generates a completion based on the prompt. """ @@ -141,14 +147,16 @@ def generate_completion( ] if not generation_config.stream: - return self.llm.get_completion(messages, generation_config) + return self.llm_provider.get_completion( + messages, generation_config + ) return self._stream_generate_completion(messages, generation_config) def _stream_generate_completion( self, messages: list[dict], generation_config: GenerationConfig ) -> Generator[str, None, None]: - for result in self.llm.get_completion_stream( + for result in self.llm_provider.get_completion_stream( messages, generation_config ): yield result.choices[0].delta.content or "" # type: ignore @@ -157,39 +165,73 @@ def run( self, query, filters={}, - limit=10, + search_limit=25, + rerank_limit=15, search_only=False, generation_config: Optional[GenerationConfig] = None, *args, **kwargs, - ) -> Union[Generator[str, None, None], RAGPipelineOutput]: + ) -> Union[RAGPipelineOutput, LLMChatCompletion]: """ - Runs the completion pipeline. + Runs the completion pipeline for non-streaming execution. """ + if generation_config and generation_config.stream: + raise ValueError( + "Streaming mode must be enabled when running `run_stream`." + ) self.initialize_pipeline(query, search_only) transformed_query = self.transform_query(query) - search_results = self.search(transformed_query, filters, limit) + search_results = self.search(transformed_query, filters, search_limit) + search_results = self.rerank_results( + transformed_query, search_results, rerank_limit + ) + if search_only: return RAGPipelineOutput(search_results, None, None) - elif not generation_config: + if not generation_config: raise ValueError( "GenerationConfig is required for completion generation." ) - elif search_only and generation_config: - raise ValueError( - "GenerationConfig is not required for search only." - ) context = self.construct_context(search_results) - prompt = self.construct_prompt( {"query": transformed_query, "context": context} ) + completion = self.generate_completion(prompt, generation_config) + return RAGPipelineOutput(search_results, context, completion) + + def run_stream( + self, + query, + generation_config: GenerationConfig, + filters={}, + search_limit=25, + rerank_limit=15, + *args, + **kwargs, + ) -> Generator[str, None, None]: + """ + Runs the completion pipeline for streaming execution. + """ if not generation_config.stream: - completion = self.generate_completion(prompt, generation_config) - return RAGPipelineOutput(search_results, context, completion) + raise ValueError( + "Streaming mode must be enabled when running `run_stream." + ) + + self.initialize_pipeline(query, search_only=False) + + transformed_query = self.transform_query(query) + search_results = self.search(transformed_query, filters, search_limit) + search_results = self.rerank_results( + transformed_query, search_results, rerank_limit + ) + + context = self.construct_context(search_results) + prompt = self.construct_prompt( + {"query": transformed_query, "context": context} + ) return self._stream_run( search_results, context, prompt, generation_config diff --git a/r2r/core/pipelines/scraping.py b/r2r/core/pipelines/scraping.py index 63b94448..99070739 100644 --- a/r2r/core/pipelines/scraping.py +++ b/r2r/core/pipelines/scraping.py @@ -1,9 +1,8 @@ -from abc import abstractmethod -from typing import Any, Iterator, Optional +from typing import Iterator, Optional from ..abstractions.document import DocumentPage -from ..providers.logging import LoggingDatabaseConnection from ..utils import generate_run_id +from ..utils.logging import LoggingDatabaseConnection from .pipeline import Pipeline @@ -41,3 +40,15 @@ def run( raise ValueError("No URL provided to scrape.") yield from self.scrape_url(url) + + def run_stream( + self, + query: str, + context: str, + completion: str, + run_id: Optional[str], + **kwargs, + ): + raise NotImplementedError( + "Streaming mode not supported for `ScraperPipeline`." + ) diff --git a/r2r/core/providers/base.py b/r2r/core/providers/base.py new file mode 100644 index 00000000..61f8c927 --- /dev/null +++ b/r2r/core/providers/base.py @@ -0,0 +1,31 @@ +from abc import ABC, abstractmethod, abstractproperty +from dataclasses import fields +from typing import List, Optional + + +class ProviderConfig(ABC): + """A base provider configuration class""" + + @abstractmethod + def validate(self) -> None: + pass + + @classmethod + def create(cls, **kwargs): + valid_keys = {f.name for f in fields(cls)} + filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_keys} + return cls(**filtered_kwargs) + + @abstractproperty + def supported_providers(self) -> List[str]: + """Define a list of supported providers.""" + pass + + +class Provider(ABC): + """A base provider class to provide a common interface for all providers.""" + + def __init__(self, config: Optional[ProviderConfig] = None): + if config: + config.validate() + self.config = config diff --git a/r2r/core/providers/embedding.py b/r2r/core/providers/embedding.py index 9ab36fad..5b4a7244 100644 --- a/r2r/core/providers/embedding.py +++ b/r2r/core/providers/embedding.py @@ -1,33 +1,74 @@ -from abc import ABC, abstractmethod -from typing import Optional +from abc import abstractmethod +from dataclasses import dataclass +from enum import Enum +from typing import List, Optional +from .base import Provider, ProviderConfig +from .vector_db import VectorSearchResult -class EmbeddingProvider(ABC): - supported_providers = ["openai", "sentence-transformers"] - def __init__(self, provider: str): - if provider not in EmbeddingProvider.supported_providers: +@dataclass +class EmbeddingConfig(ProviderConfig): + """A base embedding configuration class""" + + provider: Optional[str] = None + search_model: Optional[str] = None + search_dimension: Optional[int] = None + rerank_model: Optional[str] = None + + def validate(self) -> None: + if not self.provider: raise ValueError( - f"Error, `{provider}` is not in EmbeddingProvider's list of supported providers." + "The 'provider' field must be set for EmbeddingConfig." ) + if self.provider not in self.supported_providers: + raise ValueError(f"Provider '{self.provider}' is not supported.") - @abstractmethod - def _check_inputs(self, model: str, dimensions: Optional[int]) -> None: - pass + @property + def supported_providers(self) -> List[str]: + return ["openai", "sentence-transformers"] + + +class EmbeddingProvider(Provider): + """An abstract class to provide a common interface for embedding providers.""" + + class PipelineStage(Enum): + SEARCH = 1 + RERANK = 2 + + def __init__(self, config: EmbeddingConfig): + if not isinstance(config, EmbeddingConfig): + raise ValueError( + "EmbeddingProvider must be initialized with a `EmbeddingConfig`." + ) + + super().__init__(config) @abstractmethod def get_embedding( - self, text: str, model: str, dimensions: Optional[int] = None + self, text: str, stage: PipelineStage = PipelineStage.SEARCH ): pass @abstractmethod def get_embeddings( - self, texts: list[str], model: str, dimensions: Optional[int] = None + self, texts: list[str], stage: PipelineStage = PipelineStage.SEARCH + ): + pass + + @abstractmethod + def rerank( + self, + query: str, + documents: list[VectorSearchResult], + stage: PipelineStage = PipelineStage.RERANK, + limit: int = 10, ): pass @abstractmethod - def tokenize_string(self, text: str, model: str) -> list[int]: + def tokenize_string( + self, text: str, model: str, stage: PipelineStage + ) -> list[int]: """Tokenizes the input string.""" pass diff --git a/r2r/core/providers/eval.py b/r2r/core/providers/eval.py index 9001f299..f671e41f 100644 --- a/r2r/core/providers/eval.py +++ b/r2r/core/providers/eval.py @@ -1,31 +1,51 @@ import random -from abc import ABC, abstractmethod -from typing import Optional, Union +from abc import abstractmethod +from dataclasses import dataclass +from typing import List, Optional, Union +from .base import Provider, ProviderConfig -class EvalProvider(ABC): - providers = ["deepeval", "parea", "none"] - def __init__(self, provider: str, sampling_fraction: float = 1.0): - if provider not in self.providers: - raise ValueError(f"Provider {provider} not supported.") - if provider == "none" and sampling_fraction != 0.0: +@dataclass +class EvalConfig(ProviderConfig): + """A base eval config class""" + + provider: Optional[str] = None + sampling_fraction: float = 1.0 + + def validate(self) -> None: + if self.provider not in self.supported_providers: + raise ValueError(f"Provider {self.provider} not supported.") + if self.provider == "none" and self.sampling_fraction != 0.0: raise ValueError( f"Sampling fraction must be 0.0 when setting evaluation provider to None." ) - self.provider = provider - self.sampling_fraction = sampling_fraction + @property + def supported_providers(self) -> List[str]: + return ["deepeval", "parea", "none"] - def evaluate( - self, query: str, context: str, completion: str - ) -> Optional[dict]: - if random.random() < self.sampling_fraction: - return self._evaluate(query, context, completion) - return None + +class EvalProvider(Provider): + """An abstract class to provide a common interface for evaluation providers.""" + + def __init__(self, config: EvalConfig): + if not isinstance(config, EvalConfig): + raise ValueError( + "EvalProvider must be initialized with a `EvalConfig`." + ) + + super().__init__(config) @abstractmethod def _evaluate( self, query: str, context: str, completion: str ) -> dict[str, dict[str, Union[str, float]]]: pass + + def evaluate( + self, query: str, context: str, completion: str + ) -> Optional[dict]: + if random.random() < self.config.sampling_fraction: + return self._evaluate(query, context, completion) + return None diff --git a/r2r/core/providers/llm.py b/r2r/core/providers/llm.py index 448ecc7e..a6ae790f 100644 --- a/r2r/core/providers/llm.py +++ b/r2r/core/providers/llm.py @@ -1,22 +1,11 @@ """Base classes for language model providers.""" from abc import ABC, abstractmethod -from dataclasses import dataclass, field, fields -from typing import Optional +from dataclasses import dataclass, field +from typing import List, Optional -from openai.types.chat import ChatCompletion, ChatCompletionChunk - - -@dataclass -class LLMConfig(ABC): - provider: Optional[str] = None - version: str = "0.1.0" - - @classmethod - def create(cls, **kwargs): - valid_keys = {f.name for f in fields(cls)} - filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_keys} - return cls(**filtered_kwargs) +from ..abstractions.output import LLMChatCompletion, LLMChatCompletionChunk +from .base import Provider, ProviderConfig @dataclass @@ -38,13 +27,37 @@ class GenerationConfig(ABC): api_base: Optional[str] = None -class LLMProvider(ABC): +@dataclass +class LLMConfig(ProviderConfig): + """A base LLM config class""" + + provider: Optional[str] = None + + def validate(self) -> None: + if not self.provider: + raise ValueError("Provider must be set.") + + if self.provider and self.provider not in self.supported_providers: + raise ValueError(f"Provider '{self.provider}' is not supported.") + + @property + def supported_providers(self) -> List[str]: + return ["litellm", "llama-cpp", "openai"] + + +class LLMProvider(Provider): """An abstract class to provide a common interface for LLMs.""" def __init__( self, + config: LLMConfig, ) -> None: - pass + if not isinstance(config, LLMConfig): + raise ValueError( + "LLMProvider must be initialized with a `LLMConfig`." + ) + + super().__init__(config) @abstractmethod def get_completion( @@ -52,7 +65,7 @@ def get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletion: + ) -> LLMChatCompletion: """Abstract method to get a chat completion from the provider.""" pass @@ -62,6 +75,6 @@ def get_completion_stream( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletionChunk: + ) -> LLMChatCompletionChunk: """Abstract method to get a completion stream from the provider.""" pass diff --git a/r2r/core/providers/prompt.py b/r2r/core/providers/prompt.py index 8ddcb030..6fe43994 100644 --- a/r2r/core/providers/prompt.py +++ b/r2r/core/providers/prompt.py @@ -1,42 +1,41 @@ -from abc import ABC, abstractmethod -from typing import Any, Optional +from abc import abstractmethod +from dataclasses import dataclass +from typing import Any, List, Optional +from .base import Provider, ProviderConfig -class PromptProvider(ABC): - @abstractmethod - def add_prompt(self, prompt_name: str, prompt: str) -> None: - pass - @abstractmethod - def get_prompt( - self, prompt_name: str, inputs: Optional[dict[str, Any]] = None - ) -> str: +@dataclass +class PromptConfig(ProviderConfig): + def validate(self) -> None: pass - @abstractmethod - def get_all_prompts(self) -> dict[str, str]: - pass + @property + def supported_providers(self) -> List[str]: + # Return a list of supported prompt providers + return ["default_prompt_provider"] -class DefaultPromptProvider(PromptProvider): - def __init__(self) -> None: - self.prompts: dict[str, str] = {} +class PromptProvider(Provider): + def __init__(self, config: Optional[PromptConfig] = None): + if config is None: + config = PromptConfig() + elif not isinstance(config, PromptConfig): + raise ValueError( + "PromptProvider must be initialized with a `PromptConfig`." + ) + super().__init__(config) + @abstractmethod def add_prompt(self, prompt_name: str, prompt: str) -> None: - self.prompts[prompt_name] = prompt + pass + @abstractmethod def get_prompt( self, prompt_name: str, inputs: Optional[dict[str, Any]] = None ) -> str: - prompt = self.prompts.get(prompt_name) - if prompt is None: - raise ValueError(f"Prompt '{prompt_name}' not found.") - return prompt.format(**(inputs or {})) - - def set_prompt(self, prompt_name: str, prompt: str) -> None: - if prompt_name not in self.prompts: - raise ValueError(f"Prompt '{prompt_name}' not found.") - self.prompts[prompt_name] = prompt + pass + @abstractmethod def get_all_prompts(self) -> dict[str, str]: - return self.prompts.copy() + pass diff --git a/r2r/core/providers/vector_db.py b/r2r/core/providers/vector_db.py index 59f69e16..15fc2e6d 100644 --- a/r2r/core/providers/vector_db.py +++ b/r2r/core/providers/vector_db.py @@ -1,77 +1,36 @@ -import json from abc import ABC, abstractmethod -from typing import Any, Union -from uuid import UUID - - -class VectorEntry: - def __init__( - self, entry_id: UUID, vector: list[float], metadata: dict[str, Any] - ): - self.vector = vector - self.id = entry_id - self.metadata = metadata - - def to_json(self) -> str: - """Serialize the object to a JSON string.""" - return json.dumps( - {"id": self.id, "vector": self.vector, "metadata": self.metadata}, - default=lambda o: o.__dict__, - ) - - @staticmethod - def from_json(json_str: str) -> "VectorEntry": - """Deserialize a JSON string into a VectorEntry object.""" - data = json.loads(json_str) - return VectorEntry( - vector=data["vector"], - entry_id=data["id"], - metadata=data["metadata"], - ) - - def __str__(self) -> str: - """Return a string representation of the VectorEntry.""" - return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})" - - def __repr__(self) -> str: - """Return an unambiguous string representation of the VectorEntry.""" - return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})" - - -class VectorSearchResult: - def __init__( - self, entry_id: str, score: float, metadata: dict[str, Any] - ) -> None: - self.id = entry_id - self.score = score - self.metadata = metadata +from dataclasses import dataclass +from typing import Any, Optional, Union + +from r2r.vecs.collection import MetadataValues + +from ..abstractions.vector import VectorEntry, VectorSearchResult +from .base import Provider, ProviderConfig - def __str__(self) -> str: - """Return a string representation of the VectorSearchResult.""" - return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})" - def __repr__(self) -> str: - """Return an unambiguous string representation of the VectorSearchResult for debugging.""" - return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})" +@dataclass +class VectorDBConfig(ProviderConfig): + provider: str + collection_name: str - def to_dict(self) -> dict: - """Return a dictionary representation of the VectorSearchResult.""" - return { - "id": self.id, - "score": self.score, - "metadata": self.metadata, - } + def validate(self) -> None: + if self.provider not in self.supported_providers: + raise ValueError(f"Provider '{self.provider}' is not supported.") + @property + def supported_providers(self) -> list[str]: + return ["local", "pgvector", "qdrant"] -class VectorDBProvider(ABC): - supported_providers = ["local", "pgvector", "qdrant"] - def __init__(self, provider: str): - if provider not in VectorDBProvider.supported_providers: +class VectorDBProvider(Provider, ABC): + def __init__(self, config: VectorDBConfig): + if not isinstance(config, VectorDBConfig): raise ValueError( - f"Error, `{provider}` is not in VectorDBProvider's list of supported providers." + "VectorDBProvider must be initialized with a `VectorDBConfig`." ) + super().__init__(config) + @abstractmethod def initialize_collection( self, collection_name: str, dimension: int @@ -125,6 +84,9 @@ def filtered_deletion( @abstractmethod def get_all_unique_values( - self, metadata_field: str, filters: dict = {} - ) -> list: + self, + metadata_field: str, + filter_field: Optional[str] = None, + filter_value: Optional[str] = None, + ) -> list[str]: pass diff --git a/r2r/core/providers/logging.py b/r2r/core/utils/logging.py similarity index 100% rename from r2r/core/providers/logging.py rename to r2r/core/utils/logging.py diff --git a/r2r/core/utils/splitter/text.py b/r2r/core/utils/splitter/text.py index f6b3fdf5..d5540d76 100644 --- a/r2r/core/utils/splitter/text.py +++ b/r2r/core/utils/splitter/text.py @@ -1132,7 +1132,7 @@ def __init__( ) self.model = model - self._model = SentenceTransformer(self.model) + self._model = SentenceTransformer(self.model, trust_remote_code=True) self.tokenizer = self._model.tokenizer self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk) diff --git a/r2r/embeddings/modal/base.py b/r2r/embeddings/modal/base.py deleted file mode 100644 index f3e1824b..00000000 --- a/r2r/embeddings/modal/base.py +++ /dev/null @@ -1,55 +0,0 @@ -import logging -from typing import Optional - -import modal - -from r2r.core import EmbeddingProvider - -logger = logging.getLogger(__name__) - - -class ModalEmbeddingProvider(EmbeddingProvider): - def __init__( - self, - modal_app_name: str, - modal_class_name: str, - dimension: int, - batch: int, - provider: str = "modal", - ): - logger.info( - "Initializing `ModalEmbeddingProvider` to provide embeddings." - ) - super().__init__(provider) - try: - cls = modal.Cls.lookup(modal_app_name, modal_class_name) - model = cls() - except ImportError: - raise ValueError("Unable to get modal's model cls") - self.model = model - self.dimension = dimension - self.batch = batch - - def _check_inputs(self, model: str, dimensions: Optional[int]) -> None: - if dimensions and dimensions != self.dimension: - raise ValueError( - f"Dimensions {dimensions} for {model} are not supported" - ) - - def get_embedding( - self, text: str, model: str, dimensions: Optional[int] = None - ) -> list[float]: - raise ValueError( - "ModalEmbeddingProvider does not support `get_embedding`." - ) - - def get_embeddings( - self, texts: list[str], model: str, dimensions: Optional[int] = None - ) -> list[list[float]]: - self._check_inputs(model, dimensions) - return self.model.completion_stream.remote(texts) - - def tokenize_string(self, text: str, model: str) -> list[int]: - raise ValueError( - "ModalEmbeddingProvider does not support `tokenize_string`." - ) diff --git a/r2r/embeddings/openai/base.py b/r2r/embeddings/openai/base.py index d8089413..a2365b11 100644 --- a/r2r/embeddings/openai/base.py +++ b/r2r/embeddings/openai/base.py @@ -1,10 +1,9 @@ import logging import os -from typing import Optional from openai import OpenAI -from r2r.core import EmbeddingProvider +from r2r.core import EmbeddingProvider, VectorSearchResult logger = logging.getLogger(__name__) @@ -21,12 +20,17 @@ class OpenAIEmbeddingProvider(EmbeddingProvider): "text-embedding-3-large": [256, 1024, 3072], } - def __init__(self, provider: str = "openai"): + def __init__(self, config: dict): logger.info( "Initializing `OpenAIEmbeddingProvider` to provide embeddings." ) + super().__init__(config) + provider = config.provider + if not provider: + raise ValueError( + "Must set provider in order to initialize SentenceTransformerEmbeddingProvider." + ) - super().__init__(provider) if provider != "openai": raise ValueError( "OpenAIEmbeddingProvider must be initialized with provider `openai`." @@ -36,49 +40,92 @@ def __init__(self, provider: str = "openai"): "Must set OPENAI_API_KEY in order to initialize OpenAIEmbeddingProvider." ) self.client = OpenAI() + if config.rerank_model: + raise ValueError( + "OpenAIEmbeddingProvider does not support separate reranking." + ) + self.search_model = config.search_model + self.search_dimension = config.search_dimension - def _check_inputs(self, model: str, dimensions: Optional[int]) -> None: - if model not in OpenAIEmbeddingProvider.MODEL_TO_TOKENIZER: - raise ValueError(f"OpenAI embedding model {model} not supported.") + if self.search_model not in OpenAIEmbeddingProvider.MODEL_TO_TOKENIZER: + raise ValueError( + f"OpenAI embedding model {self.search_model} not supported." + ) if ( - dimensions - and dimensions - not in OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[model] + self.search_dimension + and self.search_dimension + not in OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[ + self.search_model + ] ): raise ValueError( - f"Dimensions {dimensions} for {model} are not supported" + f"Dimensions {self.dimension} for {self.search_model} are not supported" + ) + + if not self.search_model or not self.search_dimension: + raise ValueError( + "Must set search_model and search_dimension in order to initialize OpenAIEmbeddingProvider." + ) + + if config.rerank_model: + raise ValueError( + "OpenAIEmbeddingProvider does not support separate reranking." ) - # TODO - Check max length of tokenized documents? def get_embedding( - self, text: str, model: str, dimensions: Optional[int] = None + self, + text: str, + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.SEARCH, ) -> list[float]: - self._check_inputs(model, dimensions) + if stage != EmbeddingProvider.PipelineStage.SEARCH: + raise ValueError( + "OpenAIEmbeddingProvider only supports search stage." + ) + return ( self.client.embeddings.create( input=[text], - model=model, - dimensions=dimensions - or OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[model][-1], + model=self.search_model, + dimensions=self.search_dimension + or OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[ + self.search_model + ][-1], ) .data[0] .embedding ) def get_embeddings( - self, texts: list[str], model: str, dimensions: Optional[int] = None + self, + texts: list[str], + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.SEARCH, ) -> list[list[float]]: - self._check_inputs(model, dimensions) + if stage != EmbeddingProvider.PipelineStage.SEARCH: + raise ValueError( + "OpenAIEmbeddingProvider only supports search stage." + ) + return [ ele.embedding for ele in self.client.embeddings.create( input=texts, - model=model, - dimensions=dimensions - or OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[model][-1], + model=self.search_model, + dimensions=self.search_dimension + or OpenAIEmbeddingProvider.MODEL_TO_DIMENSIONS[ + self.search_model + ][-1], ).data ] + def rerank( + self, + transformed_query: str, + texts: list[VectorSearchResult], + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.RERANK, + limit: int = 10, + ): + return texts[:limit] + def tokenize_string(self, text: str, model: str) -> list[int]: try: import tiktoken diff --git a/r2r/embeddings/setence_transformer/base.py b/r2r/embeddings/setence_transformer/base.py index e51d6f74..160d25f2 100644 --- a/r2r/embeddings/setence_transformer/base.py +++ b/r2r/embeddings/setence_transformer/base.py @@ -1,54 +1,163 @@ import logging -from typing import Optional +from dataclasses import asdict -from r2r.core import EmbeddingProvider +from r2r.core import EmbeddingConfig, EmbeddingProvider, VectorSearchResult logger = logging.getLogger(__name__) class SentenceTransformerEmbeddingProvider(EmbeddingProvider): def __init__( - self, embedding_model: str, provider: str = "sentence-transformers" + self, + config: EmbeddingConfig, ): + super().__init__(config) logger.info( - "Initializing `SentenceTransformerEmbeddingProvider` to provide embeddings." + "Initializing `SentenceTransformerEmbeddingProvider` with separate models for search and rerank." ) - - super().__init__(provider) + provider = config.provider + if not provider: + raise ValueError( + "Must set provider in order to initialize SentenceTransformerEmbeddingProvider." + ) if provider != "sentence-transformers": raise ValueError( "SentenceTransformerEmbeddingProvider must be initialized with provider `sentence-transformers`." ) try: - from sentence_transformers import SentenceTransformer + from sentence_transformers import CrossEncoder, SentenceTransformer + + self.SentenceTransformer = SentenceTransformer + # TODO - Modify this to be configurable, as `bge-reranker-large` is a `SentenceTransformer` model + self.CrossEncoder = CrossEncoder except ImportError: raise ValueError( "Must download sentence-transformers library to run `SentenceTransformerEmbeddingProvider`." ) - self.encoder = SentenceTransformer(embedding_model) - def _check_inputs(self, model: str, dimensions: Optional[int]) -> None: - if ( - dimensions - and dimensions != self.encoder.get_sentence_embedding_dimension() - ): - raise ValueError( - f"Dimensions {dimensions} for {model} are not supported" + # Initialize separate models for search and rerank + self.do_search = False + self.do_rerank = False + + self.search_encoder = self._init_model( + config, EmbeddingProvider.PipelineStage.SEARCH + ) + self.rerank_encoder = self._init_model( + config, EmbeddingProvider.PipelineStage.RERANK + ) + + def _init_model(self, config: EmbeddingConfig, stage: str): + stage_name = stage.name.lower() + model = asdict(config).get(f"{stage_name}_model", None) + dimension = asdict(config).get(f"{stage_name}_dimension", None) + transformer_type = asdict(config).get( + f"{stage_name}_transformer_type", "SentenceTransformer" + ) + + if stage == EmbeddingProvider.PipelineStage.SEARCH: + self.do_search = True + # Check if a model is set for the stage + if not (model and dimension and transformer_type): + raise ValueError( + f"Must set {stage}_model and {stage}_dimension for {stage} stage in order to initialize SentenceTransformerEmbeddingProvider." + ) + + if stage == EmbeddingProvider.PipelineStage.RERANK: + # Check if a model is set for the stage + if not (model and dimension and transformer_type): + return None + + self.do_rerank = True + if transformer_type == "SentenceTransformer": + raise ValueError( + f"`SentenceTransformer` models are not yet supported for {stage} stage in SentenceTransformerEmbeddingProvider." + ) + + # Save the model_key and dimension into instance variables + setattr(self, f"{stage_name}_model", model) + setattr(self, f"{stage_name}_dimension", dimension) + setattr(self, f"{stage_name}_transformer_type", transformer_type) + + # Initialize the model + encoder = ( + self.SentenceTransformer( + model, truncate_dim=dimension, trust_remote_code=True ) + if transformer_type == "SentenceTransformer" + else self.CrossEncoder(model, trust_remote_code=True) + ) + return encoder def get_embedding( - self, text: str, model: str, dimensions: Optional[int] = None + self, + text: str, + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.SEARCH, ) -> list[float]: - self._check_inputs(model, dimensions) - return self.encoder.encode([text]).tolist()[0] + if stage != EmbeddingProvider.PipelineStage.SEARCH: + raise ValueError("`get_embedding` only supports `SEARCH` stage.") + if not self.do_search: + raise ValueError( + "`get_embedding` can only be called for the search stage if a search model is set." + ) + encoder = self.search_encoder + return encoder.encode([text]).tolist()[0] def get_embeddings( - self, texts: list[str], model: str, dimensions: Optional[int] = None + self, + texts: list[str], + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.SEARCH, + ) -> list[list[float]]: + if stage != EmbeddingProvider.PipelineStage.SEARCH: + raise ValueError("`get_embeddings` only supports `SEARCH` stage.") + if not self.do_search: + raise ValueError( + "`get_embeddings` can only be called for the search stage if a search model is set." + ) + encoder = ( + self.search_encoder + if stage == EmbeddingProvider.PipelineStage.SEARCH + else self.rerank_encoder + ) + return encoder.encode(texts).tolist() + + def rerank( + self, + query: str, + documents: list[VectorSearchResult], + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.RERANK, + limit: int = 10, ) -> list[list[float]]: - self._check_inputs(model, dimensions) - return self.encoder.encode(texts).tolist() + if stage != EmbeddingProvider.PipelineStage.RERANK: + raise ValueError("`rerank` only supports `RERANK` stage.") + if not self.do_rerank: + return documents[:limit] + + from copy import copy + + texts = copy([doc.metadata["text"] for doc in documents]) + # Use the rank method from the rerank_encoder, which is a CrossEncoder model + reranked_scores = self.rerank_encoder.rank( + query, texts, return_documents=False, top_k=limit + ) + # Map the reranked scores back to the original documents + reranked_results = [] + for score in reranked_scores: + corpus_id = score["corpus_id"] + new_result = documents[corpus_id] + new_result.score = float(score["score"]) + reranked_results.append(new_result) + + # Sort the documents by the new scores in descending order + reranked_results.sort(key=lambda doc: doc.score, reverse=True) + import pdb + + pdb.set_trace() + return reranked_results - def tokenize_string(self, text: str, model: str) -> list[int]: + def tokenize_string( + self, + stage: EmbeddingProvider.PipelineStage = EmbeddingProvider.PipelineStage.SEARCH, + ) -> list[int]: raise ValueError( - "SentenceTransformerEmbeddingProvider does not support `tokenize_string`." + "SentenceTransformerEmbeddingProvider does not support tokenize_string." ) diff --git a/r2r/eval/deepeval/base.py b/r2r/eval/deepeval/base.py index 629507df..b7ab6e56 100644 --- a/r2r/eval/deepeval/base.py +++ b/r2r/eval/deepeval/base.py @@ -1,11 +1,11 @@ import os -from r2r.core import EvalProvider +from r2r.core import EvalConfig, EvalProvider class DeepEvalProvider(EvalProvider): - def __init__(self, provider: str, sampling_fraction: float = 1.0): - super().__init__(provider, sampling_fraction) + def __init__(self, config: EvalConfig): + super().__init__(config) try: from deepeval import evaluate from deepeval.metrics import ( diff --git a/r2r/eval/parea/base.py b/r2r/eval/parea/base.py index 1799e0d3..a7ce1082 100644 --- a/r2r/eval/parea/base.py +++ b/r2r/eval/parea/base.py @@ -1,12 +1,12 @@ import os from typing import Union -from r2r.core.providers.eval import EvalProvider +from r2r.core.providers.eval import EvalConfig, EvalProvider class PareaEvalProvider(EvalProvider): - def __init__(self, provider: str, sampling_fraction: float = 1.0): - super().__init__(provider, sampling_fraction) + def __init__(self, config: EvalConfig): + super().__init__(config) try: from parea.evals.general import answer_relevancy_factory from parea.evals.rag import ( diff --git a/r2r/examples/clients/run_qna_client.py b/r2r/examples/clients/run_qna_client.py index 67d0b7ca..25897a0c 100644 --- a/r2r/examples/clients/run_qna_client.py +++ b/r2r/examples/clients/run_qna_client.py @@ -1,6 +1,5 @@ import asyncio import glob -import json import os import fire @@ -27,7 +26,10 @@ def ingest(self, document_filter="lyft_2021.pdf"): data_path = os.path.join(current_file_directory, "..", "data") for file_path in glob.glob(os.path.join(data_path, "*.pdf")): file_name = file_path.split(os.path.sep)[-1] - if document_filter.lower() == "all" or file_name.lower().startswith(document_filter.lower()): + if ( + document_filter.lower() == "all" + or file_name.lower().startswith(document_filter.lower()) + ): if file_name in self.titles: document_id = generate_id_from_label(file_path) metadata = { @@ -39,11 +41,12 @@ def ingest(self, document_filter="lyft_2021.pdf"): document_id, file_path, metadata, settings ) print("Upload response = ", upload_response) - - def search(self, query): + + def search(self, query, search_limit=25, rerank_limit=15): search_response = self.client.search( query, - 5, + search_limit=search_limit, + rerank_limit=rerank_limit, filters={"user_id": self.user_id}, ) for i, response in enumerate(search_response): @@ -53,10 +56,17 @@ def search(self, query): print(body[:500]) print("\n") - def rag_completion(self, query, model="gpt-4-turbo-preview"): + def rag_completion( + self, + query, + model="gpt-4-turbo-preview", + search_limit=25, + rerank_limit=15, + ): rag_response = self.client.rag_completion( query, - 5, + search_limit=search_limit, + rerank_limit=rerank_limit, filters={"user_id": self.user_id}, generation_config={"model": model}, ) @@ -89,6 +99,17 @@ def get_logs_summary(self): logs_summary_response = self.client.get_logs_summary() print(f"Logs summary response:\n{logs_summary_response}\n") + def list_user_ids(self): + user_ids_response = self.client.get_user_ids() + print("User IDs response = ", user_ids_response) + + def list_user_documents(self): + if not self.user_id: + print("User ID is not set. Cannot fetch documents.") + return + user_documents_response = self.client.get_user_documents(self.user_id) + print(f"Documents for user {self.user_id} = ", user_documents_response) + if __name__ == "__main__": fire.Fire(QnAClient) diff --git a/r2r/examples/clients/run_synthetic_query_client.py b/r2r/examples/clients/run_synthetic_query_client.py deleted file mode 100644 index ae5b47ef..00000000 --- a/r2r/examples/clients/run_synthetic_query_client.py +++ /dev/null @@ -1,43 +0,0 @@ -import glob -import os - -from r2r.client import R2RClient -from r2r.core.utils import generate_id_from_label - -# Initialize the client with the base URL of your API -base_url = "http://localhost:8000" # Change this to your actual API base URL -client = R2RClient(base_url) - -titles = { - "meditations.pdf": "Title: Meditations - Marcus Aurelius", - "the_republic.pdf": "Title: The Republic - Plato", - "test.pdf": "Title: A Test Document", -} - -user_id_0 = generate_id_from_label("user_0") - -current_file_directory = os.path.dirname(os.path.abspath(__file__)) -data_path = os.path.join(current_file_directory, "..", "data") -print("data_path = ", data_path) -# Use this directory in the glob pattern -for file_path in glob.glob(os.path.join(data_path, "*.pdf")): - file_name = file_path.split(os.path.sep)[-1] - print(f"Uploading and processing file: {file_path}") - # # Upload and process a file - document_id = str(generate_id_from_label(file_path)) - metadata = {"user_id": user_id_0, "chunk_prefix": titles[file_name]} - settings: dict = {} - upload_response = client.upload_and_process_file( - document_id, file_path, metadata, settings - ) - -prompt = """You are given a user query {query} and a user context {context}. Use the context to answer the given query. """ -formatted_prompt = prompt.format( - query="What are the key themes of these books?", - context="User Uploads:\nTitle: Meditations - Marcus Aurelius\nTitle: The Republic - Plato", -) -# Perform a search on this file -search_response = client.rag_completion( - formatted_prompt, 5, filters={"user_id": user_id_0} -) -print(search_response) diff --git a/r2r/examples/clients/run_test_client.py b/r2r/examples/clients/run_test_client.py index fae9e7b4..81885986 100644 --- a/r2r/examples/clients/run_test_client.py +++ b/r2r/examples/clients/run_test_client.py @@ -8,10 +8,8 @@ base_url = "http://localhost:8000" client = R2RClient(base_url) -from r2r.client import R2RClient -# print('eval_result:', eval_result) -print("Upserting entry to remote db...") +print("Upserting entry to remote vector db...") # Upsert a single entry entry_response = client.add_entry( @@ -32,7 +30,7 @@ print(f"Copy same entry response:\n{entry_response}\n\n") -print("Upserting entries to remote db...") +print("Upserting entries to remote vector db...") # Upsert multiple entries entries = [ { @@ -50,23 +48,23 @@ print(f"Upsert entries response:\n{bulk_upsert_response}\n\n") # Perform a search -print("Searching remote db...") +print("Searching remote vector db...") search_response = client.search("test", 5) print(f"Search response:\n{search_response}\n\n") -print("Searching remote db with filter...") +print("Searching remote vector db with filter...") # Perform a search w/ filter filtered_search_response = client.search("test", 5, filters={"tags": "bulk"}) print(f"Search response w/ filter:\n{filtered_search_response}\n\n") -print("Deleting sample document in remote db...") +print("Deleting sample document in remote vector db...") # Delete a document response = client.filtered_deletion( "document_id", generate_id_from_label("doc 2") ) print(f"Deletion response:\n{response}\n\n") -print("Searching remote db with filter after deletion...") +print("Searching remote vector db with filter after deletion...") # Perform a search w/ filter after deletion post_deletion_filtered_search_response = client.search( "test", 5, filters={"tags": "bulk"} @@ -89,7 +87,7 @@ ) print(f"Upload test pdf response:\n{upload_pdf_response}\n\n") -print("Searching remote db after upload...") +print("Searching remote vector db after upload...") # Perform a search on this file pdf_filtered_search_response = client.search( "what is a cool physics equation?", diff --git a/r2r/examples/configs/local_llama_cpp.json b/r2r/examples/configs/local_llama_cpp.json index a159c226..791cbb71 100644 --- a/r2r/examples/configs/local_llama_cpp.json +++ b/r2r/examples/configs/local_llama_cpp.json @@ -1,36 +1,36 @@ { - "embedding": { - "provider": "sentence-transformers", - "model": "all-MiniLM-L6-v2", - "dimension": 384, - "batch_size": 32 - }, - "evals": { - "provider": "none", - "frequency": 0.0 - }, "language_model": { "provider": "llama-cpp" }, - "logging_database": { + "vector_database": { "provider": "local", - "collection_name": "demo_logs", - "level": "INFO" + "collection_name": "demo_vecs" }, "ingestion":{ - "provider": "local", + "provider": "local" + }, + "embedding": { + "provider": "sentence-transformers", + "search_model": "mixedbread-ai/mxbai-embed-large-v1", + "search_dimension": 512, + "batch_size": 32, "text_splitter": { "type": "recursive_character", "chunk_size": 512, "chunk_overlap": 20 } }, - "vector_database": { - "provider": "local", - "collection_name": "demo_vecs" + "evals": { + "provider": "parea", + "frequency": 0.0 }, "app": { "max_logs": 100, "max_file_size_in_mb": 100 + }, + "logging_database": { + "provider": "local", + "collection_name": "demo_logs", + "level": "INFO" } -} +} \ No newline at end of file diff --git a/r2r/examples/configs/local_ollama.json b/r2r/examples/configs/local_ollama.json index 82c2beb1..cbc81a6d 100644 --- a/r2r/examples/configs/local_ollama.json +++ b/r2r/examples/configs/local_ollama.json @@ -1,36 +1,36 @@ { - "embedding": { - "provider": "sentence-transformers", - "model": "all-MiniLM-L6-v2", - "dimension": 384, - "batch_size": 32 - }, - "evals": { - "provider": "none", - "frequency": 0.0 - }, "language_model": { "provider": "litellm" }, - "logging_database": { + "vector_database": { "provider": "local", - "collection_name": "demo_logs", - "level": "INFO" + "collection_name": "demo_vecs" }, "ingestion":{ - "provider": "local", + "provider": "local" + }, + "embedding": { + "provider": "sentence-transformers", + "search_model": "mixedbread-ai/mxbai-embed-large-v1", + "search_dimension": 512, + "batch_size": 32, "text_splitter": { "type": "recursive_character", "chunk_size": 512, "chunk_overlap": 20 } }, - "vector_database": { - "provider": "local", - "collection_name": "demo_vecs" + "evals": { + "provider": "parea", + "frequency": 0.0 }, "app": { "max_logs": 100, "max_file_size_in_mb": 100 + }, + "logging_database": { + "provider": "local", + "collection_name": "demo_logs", + "level": "INFO" } -} +} \ No newline at end of file diff --git a/r2r/examples/configs/local_ollama_qdrant.json b/r2r/examples/configs/local_ollama_qdrant.json index ce9d4e86..e9d834a6 100644 --- a/r2r/examples/configs/local_ollama_qdrant.json +++ b/r2r/examples/configs/local_ollama_qdrant.json @@ -1,37 +1,36 @@ { - "embedding": { - "provider": "sentence-transformers", - "model": "all-MiniLM-L6-v2", - "dimension": 384, - "batch_size": 32 - }, - "evals": { - "provider": "none", - "frequency": 0.0 - }, - "language_model": { - "provider": "litellm" - }, - "logging_database": { - "provider": "local", - "collection_name": "demo_logs", - "level": "INFO" - }, - "ingestion":{ - "provider": "local", - "text_splitter": { - "type": "recursive_character", - "chunk_size": 512, - "chunk_overlap": 20 - } - }, - "vector_database": { - "provider": "qdrant", - "collection_name": "demo_vecs" - }, - "app": { - "max_logs": 100, - "max_file_size_in_mb": 100 + "language_model": { + "provider": "litellm" + }, + "vector_database": { + "provider": "qdrant", + "collection_name": "demo_vecs" + }, + "ingestion":{ + "provider": "local" + }, + "embedding": { + "provider": "sentence-transformers", + "search_model": "mixedbread-ai/mxbai-embed-large-v1", + "search_dimension": 512, + "batch_size": 32, + "text_splitter": { + "type": "recursive_character", + "chunk_size": 512, + "chunk_overlap": 20 } + }, + "evals": { + "provider": "parea", + "frequency": 0.0 + }, + "app": { + "max_logs": 100, + "max_file_size_in_mb": 100 + }, + "logging_database": { + "provider": "local", + "collection_name": "demo_logs", + "level": "INFO" } - \ No newline at end of file +} \ No newline at end of file diff --git a/r2r/examples/configs/local_ollama_with_rerank.json b/r2r/examples/configs/local_ollama_with_rerank.json new file mode 100644 index 00000000..226f57f7 --- /dev/null +++ b/r2r/examples/configs/local_ollama_with_rerank.json @@ -0,0 +1,39 @@ +{ + "embedding": { + "provider": "sentence-transformers", + "search_model": "mixedbread-ai/mxbai-embed-large-v1", + "search_dimension": 512, + "rerank_model": "jinaai/jina-reranker-v1-turbo-en", + "rerank_dimension": 384, + "rerank_transformer_type": "CrossEncoder", + "batch_size": 32, + "text_splitter": { + "type": "recursive_character", + "chunk_size": 512, + "chunk_overlap": 20 + } + }, + "evals": { + "provider": "none", + "frequency": 0.0 + }, + "language_model": { + "provider": "litellm" + }, + "vector_database": { + "provider": "local", + "collection_name": "demo_vecs" + }, + "ingestion":{ + "provider": "local" + }, + "app": { + "max_logs": 100, + "max_file_size_in_mb": 100 + }, + "logging_database": { + "provider": "local", + "collection_name": "demo_logs", + "level": "INFO" + } +} \ No newline at end of file diff --git a/r2r/examples/servers/config_pipeline.py b/r2r/examples/servers/config_pipeline.py index 62b7fd80..05959f82 100644 --- a/r2r/examples/servers/config_pipeline.py +++ b/r2r/examples/servers/config_pipeline.py @@ -4,7 +4,12 @@ import uvicorn from r2r.main import E2EPipelineFactory, R2RConfig -from r2r.pipelines import AgentRAGPipeline, QnARAGPipeline, WebRAGPipeline +from r2r.pipelines import ( + AgentRAGPipeline, + HyDEPipeline, + QnARAGPipeline, + WebRAGPipeline, +) current_file_path = os.path.dirname(__file__) configs_path = os.path.join(current_file_path, "..", "configs") @@ -12,16 +17,20 @@ CONFIG_OPTIONS = { "default": None, "local_ollama": os.path.join(configs_path, "local_ollama.json"), - "local_llama_cpp": os.path.join(configs_path, "local_llama_cpp.json"), "local_ollama_qdrant": os.path.join( configs_path, "local_ollama_qdrant.json" ), + "local_ollama_with_rerank": os.path.join( + configs_path, "local_ollama_with_rerank.json" + ), + "local_llama_cpp": os.path.join(configs_path, "local_llama_cpp.json"), } PIPELINE_OPTIONS = { "qna": QnARAGPipeline, "web": WebRAGPipeline, "agent": AgentRAGPipeline, + "hyde": HyDEPipeline, } @@ -55,7 +64,16 @@ def create_app(config_name: str = "default", pipeline_name: str = "qna"): choices=PIPELINE_OPTIONS.keys(), help="Pipeline implementation to be deployed", ) + parser.add_argument( + "--port", + type=str, + default="8000", + help="Port to serve deployed pipeline on.", + ) + args, _ = parser.parse_known_args() + port = os.getenv("PORT") or args.port + app = create_app(args.config, args.pipeline) - uvicorn.run(app, host="0.0.0.0", port=8000) + uvicorn.run(app, host="0.0.0.0", port=int(port)) diff --git a/r2r/llms/__init__.py b/r2r/llms/__init__.py index 0116598d..08b89eb7 100644 --- a/r2r/llms/__init__.py +++ b/r2r/llms/__init__.py @@ -1,12 +1,10 @@ -from .litellm.base import LiteLLM, LiteLLMConfig +from .litellm.base import LiteLLM from .llama_cpp.base import LlamaCPP, LlamaCppConfig -from .openai.base import OpenAIConfig, OpenAILLM +from .openai.base import OpenAILLM __all__ = [ - "LiteLLMConfig", "LiteLLM", - "OpenAIConfig", - "OpenAILLM", "LlamaCPP", "LlamaCppConfig", + "OpenAILLM", ] diff --git a/r2r/llms/litellm/base.py b/r2r/llms/litellm/base.py index 20e98814..ba0ab05a 100644 --- a/r2r/llms/litellm/base.py +++ b/r2r/llms/litellm/base.py @@ -2,37 +2,29 @@ from dataclasses import dataclass from typing import Any, Generator, Union -from openai.types.chat import ChatCompletion, ChatCompletionChunk - -from r2r.core import GenerationConfig, LLMConfig, LLMProvider +from r2r.core import ( + GenerationConfig, + LLMChatCompletion, + LLMChatCompletionChunk, + LLMConfig, + LLMProvider, +) logger = logging.getLogger(__name__) -@dataclass -class LiteLLMConfig(LLMConfig): - """Configuration for LiteLLM models.""" - - provider_name: str = "litellm" - - class LiteLLM(LLMProvider): """A concrete class for creating LiteLLM models.""" def __init__( self, - config: LiteLLMConfig, + config: LLMConfig, *args, **kwargs, ) -> None: logger.info(f"Initializing `LiteLLM` with config: {config}") - super().__init__() - if not isinstance(config, LiteLLMConfig): - raise ValueError( - "The provided config must be an instance of LiteLLMConfig." - ) - self.config: LiteLLMConfig = config + super().__init__(config) try: from litellm import completion @@ -48,7 +40,7 @@ def get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletion: + ) -> LLMChatCompletion: if generation_config.stream: raise ValueError( "Stream must be set to False to use the `get_completion` method." @@ -60,7 +52,7 @@ def get_completion_stream( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> Generator[ChatCompletionChunk, None, None]: + ) -> Generator[LLMChatCompletionChunk, None, None]: if not generation_config.stream: raise ValueError( "Stream must be set to True to use the `get_completion_stream` method." @@ -72,7 +64,9 @@ def _get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]]: + ) -> Union[ + LLMChatCompletion, Generator[LLMChatCompletionChunk, None, None] + ]: # Create a dictionary with the default arguments args = self._get_base_args(generation_config) args["messages"] = messages @@ -85,16 +79,16 @@ def _get_completion( response = self.litellm_completion(**args) if not generation_config.stream: - return ChatCompletion(**response.dict()) + return LLMChatCompletion(**response.dict()) else: return self._get_chat_completion(response) def _get_chat_completion( self, response: Any, - ) -> Generator[ChatCompletionChunk, None, None]: + ) -> Generator[LLMChatCompletionChunk, None, None]: for part in response: - yield ChatCompletionChunk(**part.dict()) + yield LLMChatCompletionChunk(**part.dict()) def _get_base_args( self, diff --git a/r2r/llms/llama_cpp/base.py b/r2r/llms/llama_cpp/base.py index 33ef85c1..bfe5b06b 100644 --- a/r2r/llms/llama_cpp/base.py +++ b/r2r/llms/llama_cpp/base.py @@ -6,9 +6,13 @@ from dataclasses import dataclass from typing import Union -from openai.types.chat import ChatCompletion, ChatCompletionChunk - -from r2r.core import GenerationConfig, LLMConfig, LLMProvider +from r2r.core import ( + GenerationConfig, + LLMChatCompletion, + LLMChatCompletionChunk, + LLMConfig, + LLMProvider, +) logger = logging.getLogger(__name__) @@ -18,17 +22,17 @@ class LlamaCppConfig(LLMConfig): """Configuration for LlamaCpp models.""" # Base - provider_name: str = "llama-cpp" + provider: str = "llama-cpp" + model: str = "" model_path: str = "" - model_name: str = "" def __post_init__(self): if not self.model_path or self.model_path == "": self.model_path = os.path.join( os.path.expanduser("~"), ".cache", "models" ) - if not self.model_name or self.model_name == "": - self.model_name = "tinyllama-1.1b-chat-v1.0.Q2_K.gguf" + if not self.model or self.model == "": + self.model = "tinyllama-1.1b-chat-v1.0.Q2_K.gguf" class LlamaCPP(LLMProvider): @@ -41,13 +45,11 @@ def __init__( **kwargs, ) -> None: logger.info(f"Initializing `LlamaCPP` with config: {config}") - super().__init__() - if not isinstance(config, LlamaCppConfig): raise ValueError( "The provided config must be an instance of LlamaCppConfig." ) - + super().__init__(config) self.config: LlamaCppConfig = config try: @@ -57,7 +59,7 @@ def __init__( "Error, `llama-cpp-python` is required to run a LlamaCPP. Please install it using `pip install llama-cpp-python`." ) - path = os.path.join(self.config.model_path, self.config.model_name) + path = os.path.join(self.config.model_path, self.config.model) self.client = Llama(path, n_ctx=2048) def get_completion( @@ -65,7 +67,7 @@ def get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletion: + ) -> LLMChatCompletion: if generation_config.stream: raise ValueError( "Stream must be set to False to use the `get_completion` method." @@ -77,7 +79,7 @@ def get_completion_stream( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletionChunk: + ) -> LLMChatCompletionChunk: if not generation_config.stream: raise ValueError( "Stream must be set to True to use the `get_completion_stream` method." @@ -89,7 +91,7 @@ def _get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> Union[ChatCompletion, ChatCompletionChunk]: + ) -> Union[LLMChatCompletion, LLMChatCompletionChunk]: """Get a completion from the LlamaCpp model based on the provided messages.""" args = self._get_base_args(generation_config) @@ -99,7 +101,7 @@ def _get_completion( response = self.client(prompt, **args) if not generation_config.stream: - return ChatCompletion( + return LLMChatCompletion( # TODO - Set an intelligent id id="777", object="chat.completion", @@ -117,7 +119,7 @@ def _get_completion( ], ) else: - return ChatCompletionChunk( + return LLMChatCompletionChunk( choices=[ { "message": { diff --git a/r2r/llms/openai/base.py b/r2r/llms/openai/base.py index 8e213d3c..f41d63e5 100644 --- a/r2r/llms/openai/base.py +++ b/r2r/llms/openai/base.py @@ -2,56 +2,49 @@ import logging import os -from dataclasses import dataclass from typing import Union -from openai.types.chat import ChatCompletion, ChatCompletionChunk - -from r2r.core import GenerationConfig, LLMConfig, LLMProvider +from r2r.core import ( + GenerationConfig, + LLMChatCompletion, + LLMChatCompletionChunk, + LLMConfig, + LLMProvider, +) logger = logging.getLogger(__name__) -@dataclass -class OpenAIConfig(LLMConfig): - """Configuration for OpenAI models.""" - - # Base - provider_name: str = "openai" - - class OpenAILLM(LLMProvider): """A concrete class for creating OpenAI models.""" def __init__( self, - config: OpenAIConfig, + config: LLMConfig, *args, **kwargs, ) -> None: logger.info(f"Initializing `OpenAILLM` with config: {config}") - super().__init__() - if not isinstance(config, OpenAIConfig): + if not isinstance(config, LLMConfig): raise ValueError( "The provided config must be an instance of OpenAIConfig." ) - self.config: OpenAIConfig = config - + super().__init__(config) try: from openai import OpenAI # noqa except ImportError: raise ImportError( "Error, `openai` is required to run an OpenAILLM. Please install it using `pip install openai`." ) - if config.provider_name != "openai" or not os.getenv("OPENAI_API_KEY"): + if config.provider_name != "openai": raise ValueError( - "OpenAI API key not found. Please set the OPENAI_API_KEY environment variable." + "OpenAILLM must be initialized with config with `openai` provider." ) - # set the config here, again, for typing purposes - if not isinstance(self.config, OpenAIConfig): + if not os.getenv("OPENAI_API_KEY"): raise ValueError( - "The provided config must be an instance of OpenAIConfig." + "OpenAI API key not found. Please set the OPENAI_API_KEY environment variable." ) + self.config: LLMConfig = config self.client = OpenAI() def get_completion( @@ -59,7 +52,7 @@ def get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletion: + ) -> LLMChatCompletion: if not generation_config.stream: raise ValueError( "Stream must be set to False to use the `get_completion` method." @@ -71,7 +64,7 @@ def get_completion_stream( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> ChatCompletionChunk: + ) -> LLMChatCompletionChunk: if not generation_config.stream: raise ValueError( "Stream must be set to True to use the `get_completion_stream` method." @@ -83,7 +76,7 @@ def _get_completion( messages: list[dict], generation_config: GenerationConfig, **kwargs, - ) -> Union[ChatCompletion, ChatCompletionChunk]: + ) -> Union[LLMChatCompletion, LLMChatCompletionChunk]: """Get a completion from the OpenAI API based on the provided messages.""" # Create a dictionary with the default arguments diff --git a/r2r/main/app.py b/r2r/main/app.py index 547377b6..4bb309f9 100644 --- a/r2r/main/app.py +++ b/r2r/main/app.py @@ -1,7 +1,6 @@ import json import logging import os -import re from pathlib import Path from typing import AsyncGenerator, Generator, Optional, Union, cast @@ -28,7 +27,7 @@ RAGPipelineOutput, ScraperPipeline, ) -from r2r.main.utils import ( # configure_logging, +from r2r.main.utils import ( R2RConfig, apply_cors, find_project_root, @@ -209,7 +208,11 @@ async def add_entries(entries_req: AddEntriesRequest): async def search(query: RAGQueryModel): try: rag_completion = rag_pipeline.run( - query.query, query.filters, query.limit, search_only=True + query.query, + query.filters, + query.search_limit, + query.rerank_limit, + search_only=True, ) return rag_completion.search_results except Exception as e: @@ -226,9 +229,10 @@ async def rag_completion( stream = query.generation_config.stream if not stream: untyped_completion = rag_pipeline.run( - query.query, - query.filters, - query.limit, + query=query.query, + filters=query.filters, + search_limit=query.search_limit, + rerank_limit=query.rerank_limit, generation_config=query.generation_config, ) # Tell the type checker that rag_completion is a RAGPipelineOutput @@ -282,10 +286,11 @@ async def _stream_rag_completion( ) completion_generator = cast( Generator[str, None, None], - rag_pipeline.run( - query.query, - query.filters, - query.limit, + rag_pipeline.run_stream( + query=query.query, + filters=query.filters, + search_limit=query.search_limit, + rerank_limit=query.rerank_limit, generation_config=gen_config, ), ) @@ -296,7 +301,7 @@ async def _stream_rag_completion( current_marker = None logging.info( - f"Streaming RAG completion results to client for query ={query.query}." + f"Streaming RAG completion results to client for query = {query.query}." ) for item in completion_generator: @@ -361,9 +366,10 @@ async def _stream_rag_completion( logging.info( f"Performing evaluation with payload: {payload} to url: {url}/eval" ) - background_tasks.add_task( - requests.post, f"{url}/eval", json=payload - ) + if config.evals.get("frequency", 0.0) > 0.0: + background_tasks.add_task( + requests.post, f"{url}/eval", json=payload + ) return StreamingResponse( _stream_rag_completion(query, rag_pipeline), @@ -401,7 +407,7 @@ async def eval(payload: EvalPayloadModel): @app.delete("/filtered_deletion/") async def filtered_deletion(key: str, value: Union[bool, int, str]): try: - embedding_pipeline.db.filtered_deletion(key, value) + embedding_pipeline.vector_db_provider.filtered_deletion(key, value) return {"message": "Entries deleted successfully."} except Exception as e: logging.error( @@ -412,9 +418,12 @@ async def filtered_deletion(key: str, value: Union[bool, int, str]): @app.get("/get_user_ids/") async def get_user_ids(): try: - user_ids = embedding_pipeline.db.get_all_unique_values( - metadata_field="user_id" + user_ids = ( + embedding_pipeline.vector_db_provider.get_all_unique_values( + metadata_field="user_id" + ) ) + return {"user_ids": user_ids} except Exception as e: logging.error(f":get_user_ids: [Error](error={str(e)})") @@ -423,8 +432,12 @@ async def get_user_ids(): @app.get("/get_user_documents/") async def get_user_documents(user_id: str): try: - document_ids = embedding_pipeline.db.get_all_unique_values( - metadata_field="document_id", filters={"user_id": user_id} + document_ids = ( + embedding_pipeline.vector_db_provider.get_all_unique_values( + metadata_field="document_id", + filter_field="user_id", + filter_value=user_id, + ) ) return {"document_ids": document_ids} except Exception as e: diff --git a/r2r/main/factory.py b/r2r/main/factory.py index ddab98a2..3a087d8b 100644 --- a/r2r/main/factory.py +++ b/r2r/main/factory.py @@ -3,16 +3,15 @@ import dotenv -from r2r.core import LoggingDatabaseConnection -from r2r.core.utils import RecursiveCharacterTextSplitter -from r2r.llms import ( - LiteLLM, - LiteLLMConfig, - LlamaCPP, - LlamaCppConfig, - OpenAIConfig, - OpenAILLM, +from r2r.core import ( + EmbeddingConfig, + EvalConfig, + LLMConfig, + LoggingDatabaseConnection, + VectorDBConfig, ) +from r2r.core.utils import RecursiveCharacterTextSplitter +from r2r.llms import LiteLLM, LlamaCPP, LlamaCppConfig, OpenAILLM from r2r.pipelines import ( BasicEmbeddingPipeline, BasicEvalPipeline, @@ -28,54 +27,83 @@ class E2EPipelineFactory: + """Factory class to create the end-to-end pipeline.""" + @staticmethod - def get_vector_db(database_config: dict[str, Any]): + def get_vector_db_provider(database_config: dict[str, Any]): + """Get the vector database provider based on the provided database_config.""" if database_config["provider"] == "qdrant": from r2r.vector_dbs import QdrantDB - return QdrantDB() + return QdrantDB(VectorDBConfig.create(**database_config)) elif database_config["provider"] == "pgvector": from r2r.vector_dbs import PGVectorDB - return PGVectorDB() + return PGVectorDB(VectorDBConfig.create(**database_config)) elif database_config["provider"] == "local": - from r2r.vector_dbs import LocalVectorDB + from r2r.vector_dbs import LocalDBConfig, LocalVectorDB - return LocalVectorDB() + return LocalVectorDB(LocalDBConfig.create(**database_config)) @staticmethod - def get_embeddings_provider(embedding_config: dict[str, Any]): - if embedding_config["provider"] == "openai": + def get_embedding_provider(embedding_config: dict[str, Any]): + """Get the embedding provider based on the provided embedding_config.""" + embedding_config = EmbeddingConfig.create(**embedding_config) + + if embedding_config.provider == "openai": from r2r.embeddings import OpenAIEmbeddingProvider - return OpenAIEmbeddingProvider() - elif embedding_config["provider"] == "sentence-transformers": + return OpenAIEmbeddingProvider(embedding_config) + elif embedding_config.provider == "sentence-transformers": from r2r.embeddings import SentenceTransformerEmbeddingProvider - return SentenceTransformerEmbeddingProvider( - embedding_config["model"] - ) + return SentenceTransformerEmbeddingProvider(embedding_config) else: raise ValueError( - f"Embedding provider {embedding_config['provider']} not supported" + f"Embedding provider {embedding_config.provider} not supported" ) @staticmethod - def get_llm(llm_config: dict[str, Any]): + def get_eval_provider(eval_config: dict[str, Any]): + """Get the evaluation provider based on the provided eval_config.""" + eval_config = EvalConfig.create(**eval_config) + + if eval_config.provider == "deepeval": + try: + from r2r.eval import DeepEvalProvider + except ImportError: + raise ImportError( + "DeepEval is not installed. Please install it using `pip install deepeval`." + ) + eval_provider = DeepEvalProvider(eval_config) + + elif eval_config.provider == "parea": + try: + from r2r.eval import PareaEvalProvider + except ImportError: + raise ImportError( + "Parea is not installed. Please install it using `pip install parea-ai`." + ) + eval_provider = PareaEvalProvider(eval_config) + elif eval_config.provider == "none": + eval_provider = None + return eval_provider + + @staticmethod + def get_llm_provider(llm_config: dict[str, Any]): + """Get the language model provider based on the provided llm_config.""" + if llm_config["provider"] == "openai": - return OpenAILLM(OpenAIConfig()) + return OpenAILLM(LLMConfig.create(**llm_config)) elif llm_config["provider"] == "litellm": - return LiteLLM(LiteLLMConfig()) + return LiteLLM(LLMConfig.create(**llm_config)) elif llm_config["provider"] == "llama-cpp": - return LlamaCPP( - LlamaCppConfig( - llm_config.get("model_path", ""), - llm_config.get("model_name", ""), - ) - ) + return LlamaCPP(LlamaCppConfig.create(**llm_config)) @staticmethod def get_text_splitter(text_splitter_config: dict[str, Any]): + """Get the text splitter based on the provided text_splitter_config.""" + if text_splitter_config["type"] != "recursive_character": raise ValueError( "Only recursive character text splitter is supported" @@ -90,10 +118,9 @@ def get_text_splitter(text_splitter_config: dict[str, Any]): @staticmethod def create_pipeline( config: R2RConfig, - db=None, - embeddings_provider=None, - llm=None, - text_splitter=None, + vector_db_provider=None, + embedding_provider=None, + llm_provider=None, adapters=None, scraper_pipeline_impl=BasicScraperPipeline, ingestion_pipeline_impl=BasicIngestionPipeline, @@ -104,51 +131,50 @@ def create_pipeline( ): logging.basicConfig(level=config.logging_database["level"]) - embeddings_provider = ( - embeddings_provider - or E2EPipelineFactory.get_embeddings_provider(config.embedding) + embedding_provider = ( + embedding_provider + or E2EPipelineFactory.get_embedding_provider(config.embedding) + ) + llm_provider = llm_provider or E2EPipelineFactory.get_llm_provider( + config.language_model + ) + vector_db_provider = ( + vector_db_provider + or E2EPipelineFactory.get_vector_db_provider( + config.vector_database + ) + ) + vector_db_provider.initialize_collection( + embedding_provider.search_dimension ) - embedding_model = config.embedding["model"] - embedding_dimension = config.embedding["dimension"] - embedding_batch_size = config.embedding["batch_size"] - - db = db or E2EPipelineFactory.get_vector_db(config.vector_database) - collection_name = config.vector_database["collection_name"] - db.initialize_collection(collection_name, embedding_dimension) - llm = llm or E2EPipelineFactory.get_llm(config.language_model) + eval_provider = E2EPipelineFactory.get_eval_provider(config.evals) logging_connection = LoggingDatabaseConnection( config.logging_database["provider"], config.logging_database["collection_name"], ) - cmpl_pipeline = rag_pipeline_impl( - llm, - db=db, - embedding_model=embedding_model, - embeddings_provider=embeddings_provider, + scrpr_pipeline = scraper_pipeline_impl() + ingst_pipeline = ingestion_pipeline_impl(adapters=adapters) + embd_pipeline = embedding_pipeline_impl( + embedding_provider=embedding_provider, + vector_db_provider=vector_db_provider, logging_connection=logging_connection, + text_splitter=E2EPipelineFactory.get_text_splitter( + config.embedding["text_splitter"] + ), + embedding_batch_size=config.embedding.get("batch_size", 1), ) - - text_splitter = text_splitter or E2EPipelineFactory.get_text_splitter( - config.ingestion["text_splitter"] - ) - - embd_pipeline = embedding_pipeline_impl( - embedding_model, - embeddings_provider, - db, + cmpl_pipeline = rag_pipeline_impl( + embedding_provider=embedding_provider, + llm_provider=llm_provider, + vector_db_provider=vector_db_provider, logging_connection=logging_connection, - text_splitter=text_splitter, - embedding_batch_size=embedding_batch_size, ) - eval_pipeline = eval_pipeline_impl( - config.evals, logging_connection=logging_connection + eval_provider, logging_connection=logging_connection ) - ingst_pipeline = ingestion_pipeline_impl(adapters=adapters) - scrpr_pipeline = scraper_pipeline_impl() app = app_fn( scraper_pipeline=scrpr_pipeline, diff --git a/r2r/main/models.py b/r2r/main/models.py index 93f06283..1370a68e 100644 --- a/r2r/main/models.py +++ b/r2r/main/models.py @@ -74,7 +74,8 @@ class GenerationConfigModel(BaseModel): class RAGQueryModel(BaseModel): query: str - limit: Optional[int] = 10 + search_limit: Optional[int] = 25 + rerank_limit: Optional[int] = 15 filters: dict = {} settings: SettingsModel = SettingsModel() generation_config: GenerationConfigModel = GenerationConfigModel() diff --git a/r2r/main/utils.py b/r2r/main/utils.py index 477beea5..42f647d5 100644 --- a/r2r/main/utils.py +++ b/r2r/main/utils.py @@ -21,7 +21,12 @@ def find_project_root(current_dir): REQUIRED_KEYS: dict[str, list] = { - "embedding": ["provider", "model"], + "embedding": [ + "provider", + "search_model", + "search_dimension", + "batch_size", + ], "evals": ["provider", "frequency"], "language_model": ["provider"], "logging_database": ["provider", "collection_name", "level"], diff --git a/r2r/pipelines/__init__.py b/r2r/pipelines/__init__.py index f98bcee6..6f426f16 100644 --- a/r2r/pipelines/__init__.py +++ b/r2r/pipelines/__init__.py @@ -2,19 +2,21 @@ from .core.embedding import BasicEmbeddingPipeline from .core.eval import BasicEvalPipeline from .core.ingestion import BasicIngestionPipeline, IngestionType -from .core.prompt_provider import BasicPromptProvider +from .core.prompt import BasicPromptProvider from .core.scraping import BasicScraperPipeline +from .hyde.rag import HyDEPipeline from .qna.rag import QnARAGPipeline from .web.rag import WebRAGPipeline __all__ = [ + "AgentRAGPipeline", "BasicEmbeddingPipeline", "BasicEvalPipeline", - "IngestionType", "BasicScraperPipeline", "BasicIngestionPipeline", "BasicPromptProvider", + "HyDEPipeline", + "IngestionType", "QnARAGPipeline", "WebRAGPipeline", - "AgentRAGPipeline", ] diff --git a/r2r/pipelines/agent/rag.py b/r2r/pipelines/agent/rag.py index f16ec2f5..f68786de 100644 --- a/r2r/pipelines/agent/rag.py +++ b/r2r/pipelines/agent/rag.py @@ -59,7 +59,8 @@ def run( self, query, filters={}, - limit=10, + search_limit=25, + rerank_limit=15, search_only=False, generation_config: Optional[GenerationConfig] = None, *args, @@ -91,5 +92,11 @@ def run( if not search_query: return RAGPipelineOutput(search_results=[], completion=completion) - search_results = self.search(search_query, filters={}, limit=5) + search_results = self.search( + search_query, filters={}, limit=search_limit + ) + search_results = self.rerank_results( + search_query, search_results, rerank_limit + ) + return RAGPipelineOutput(search_results, None, None) diff --git a/r2r/pipelines/core/embedding.py b/r2r/pipelines/core/embedding.py index b006c6bd..8a74d68e 100644 --- a/r2r/pipelines/core/embedding.py +++ b/r2r/pipelines/core/embedding.py @@ -9,6 +9,7 @@ from r2r.core import ( DocumentPage, EmbeddingPipeline, + EmbeddingProvider, LoggingDatabaseConnection, VectorDBProvider, VectorEntry, @@ -27,13 +28,14 @@ class BasicEmbeddingPipeline(EmbeddingPipeline): def __init__( self, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, - db: VectorDBProvider, + embedding_provider: OpenAIEmbeddingProvider, + vector_db_provider: VectorDBProvider, text_splitter: TextSplitter, logging_connection: Optional[LoggingDatabaseConnection] = None, embedding_batch_size: int = 1, id_prefix: str = "demo", + *args, + **kwargs, ): """ Initializes the embedding pipeline with necessary components and configurations. @@ -43,9 +45,8 @@ def __init__( ) super().__init__( - embedding_model, - embeddings_provider, - db, + embedding_provider, + vector_db_provider, logging_connection, ) self.text_splitter = text_splitter @@ -106,8 +107,8 @@ def embed_chunks(self, chunks: list[str]) -> list[list[float]]: """ Generates embeddings for each text chunk using the embedding model. """ - return self.embeddings_provider.get_embeddings( - chunks, self.embedding_model + return self.embedding_provider.get_embeddings( + chunks, EmbeddingProvider.PipelineStage.SEARCH ) def store_chunks( @@ -117,9 +118,9 @@ def store_chunks( Stores the embedded chunks in the database, with an option to upsert. """ if do_upsert: - self.db.upsert_entries(chunks) + self.vector_db_provider.upsert_entries(chunks) else: - self.db.copy_entries(chunks) + self.vector_db_provider.copy_entries(chunks) def run( self, diff --git a/r2r/pipelines/core/eval.py b/r2r/pipelines/core/eval.py index d66999f0..a6ee16b7 100644 --- a/r2r/pipelines/core/eval.py +++ b/r2r/pipelines/core/eval.py @@ -2,54 +2,21 @@ from r2r.core import ( EvalPipeline, + EvalProvider, LoggingDatabaseConnection, log_execution_to_db, ) class BasicEvalPipeline(EvalPipeline): - eval_providers = ["deepeval", "parea", "none"] - def __init__( self, - eval_config: dict, + eval_provider: Optional[EvalProvider] = None, logging_connection: Optional[LoggingDatabaseConnection] = None, *args, **kwargs, ): - frequency = eval_config["frequency"] - super().__init__(frequency, logging_connection, *args, **kwargs) - provider = eval_config["provider"] - if provider not in self.eval_providers: - raise ValueError( - f"EvalProvider {provider} not supported in `BasicEvalPipeline`." - ) - - if provider == "deepeval": - try: - from r2r.eval import DeepEvalProvider - except ImportError: - raise ImportError( - "DeepEval is not installed. Please install it using `pip install deepeval`." - ) - self.eval_provider = DeepEvalProvider( - provider, - eval_config.get("sampling_fraction", 1.0), - ) - - elif provider == "parea": - try: - from r2r.eval import PareaEvalProvider - except ImportError: - raise ImportError( - "Parea is not installed. Please install it using `pip install parea-ai`." - ) - self.eval_provider = PareaEvalProvider( - provider, - eval_config.get("sampling_fraction", 1.0), - ) - else: - self.eval_provider = None + self.eval_provider = eval_provider @log_execution_to_db def evaluate(self, query: str, context: str, completion: str) -> Any: diff --git a/r2r/pipelines/core/ingestion.py b/r2r/pipelines/core/ingestion.py index 1b361411..cbc8d963 100644 --- a/r2r/pipelines/core/ingestion.py +++ b/r2r/pipelines/core/ingestion.py @@ -9,10 +9,14 @@ from r2r.core import DocumentPage, IngestionPipeline, LoggingDatabaseConnection from r2r.core.adapters import ( Adapter, + CSVAdapter, HTMLAdapter, JSONAdapter, + MarkdownAdapter, PDFAdapter, + PPTAdapter, TextAdapter, + XLSXAdapter, ) logger = logging.getLogger(__name__) @@ -23,6 +27,10 @@ class IngestionType(Enum): JSON = "json" HTML = "html" PDF = "pdf" + PPTX = "pptx" + CSV = "csv" + XLSX = "xlsx" + MD = "md" class BasicIngestionPipeline(IngestionPipeline): @@ -49,6 +57,10 @@ def __init__( IngestionType.JSON: JSONAdapter(), IngestionType.HTML: HTMLAdapter(), IngestionType.PDF: PDFAdapter(), + IngestionType.PPTX: PPTAdapter(), + IngestionType.CSV: CSVAdapter(), + IngestionType.XLSX: XLSXAdapter(), + IngestionType.MD: MarkdownAdapter(), } self.adapters = self.default_adapters if adapters is not None: diff --git a/r2r/pipelines/core/prompt_provider.py b/r2r/pipelines/core/prompt.py similarity index 58% rename from r2r/pipelines/core/prompt_provider.py rename to r2r/pipelines/core/prompt.py index 7b9f1ea4..c84d1a04 100644 --- a/r2r/pipelines/core/prompt_provider.py +++ b/r2r/pipelines/core/prompt.py @@ -1,9 +1,9 @@ from typing import Any, Optional -from r2r.core import DefaultPromptProvider +from r2r.core.providers.prompt import PromptProvider -class BasicPromptProvider(DefaultPromptProvider): +class BasicPromptProvider(PromptProvider): BASIC_SYSTEM_PROMPT = "You are a helpful assistant." BASIC_TASK_PROMPT = """ ## Task: @@ -26,12 +26,26 @@ def __init__( system_prompt: Optional[str] = None, task_prompt: Optional[str] = None, ): - super().__init__() + self.prompts: dict[str, str] = {} self.add_prompt( "system_prompt", system_prompt or self.BASIC_SYSTEM_PROMPT ) self.add_prompt("task_prompt", task_prompt or self.BASIC_TASK_PROMPT) + def add_prompt(self, prompt_name: str, prompt: str) -> None: + self.prompts[prompt_name] = prompt + + def get_prompt( + self, prompt_name: str, inputs: Optional[dict[str, Any]] = None + ) -> str: + prompt = self.prompts.get(prompt_name) + if prompt is None: + raise ValueError(f"Prompt '{prompt_name}' not found.") + return prompt.format(**(inputs or {})) + + def get_all_prompts(self) -> dict[str, str]: + return self.prompts.copy() + def get_system_prompt(self, inputs: dict[str, Any]) -> str: return self.get_prompt("system_prompt", inputs) diff --git a/r2r/pipelines/core/scraping.py b/r2r/pipelines/core/scraping.py index 178f3262..4be6074e 100644 --- a/r2r/pipelines/core/scraping.py +++ b/r2r/pipelines/core/scraping.py @@ -9,29 +9,27 @@ logger = logging.getLogger(__name__) -def get_raw_html_from_url(url: str) -> str: - try: - response = requests.get(url) - - if response.status_code == 200: - return response.text - else: - return ( - f"Failed to retrieve HTML. Status code: {response.status_code}" - ) - except requests.exceptions.RequestException as e: - return f"An error occurred: {e}" - - class BasicScraperPipeline(ScraperPipeline): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + @staticmethod + def _get_raw_html_from_url(url: str) -> str: + try: + response = requests.get(url) + + if response.status_code == 200: + return response.text + else: + return f"Failed to retrieve HTML. Status code: {response.status_code}" + except requests.exceptions.RequestException as e: + return f"An error occurred: {e}" + def scrape_url(self, url: str) -> Iterator[DocumentPage]: """ Scrape the given URL and return the raw data. """ - response = requests.get(url) + response = BasicScraperPipeline._get_raw_html_from_url(url) if response.status_code == 200: soup = BeautifulSoup(response.text, "html.parser") yield DocumentPage( diff --git a/r2r/examples/servers/synthetic_query_pipeline.py b/r2r/pipelines/hyde/rag.py similarity index 62% rename from r2r/examples/servers/synthetic_query_pipeline.py rename to r2r/pipelines/hyde/rag.py index 6173b72f..431b7a92 100644 --- a/r2r/examples/servers/synthetic_query_pipeline.py +++ b/r2r/pipelines/hyde/rag.py @@ -3,18 +3,20 @@ from typing import Generator, Optional from r2r.core import ( + EmbeddingProvider, GenerationConfig, LLMProvider, LoggingDatabaseConnection, + PromptProvider, RAGPipeline, RAGPipelineOutput, VectorDBProvider, VectorSearchResult, log_execution_to_db, ) -from r2r.embeddings import OpenAIEmbeddingProvider -from r2r.main import E2EPipelineFactory, R2RConfig -from r2r.pipelines import BasicPromptProvider, QnARAGPipeline + +from ..core.prompt import BasicPromptProvider +from ..qna.rag import QnARAGPipeline logger = logging.getLogger(__name__) @@ -35,50 +37,72 @@ REMINDER - Use line item references to like [1], [2], ... refer to specifically numbered items in the provided context. ## Response: """ +DEFAULT_HYDE_PROMPT = """ +### Instruction: + +Given the following query that follows to write a double newline separated list of up to {num_answers} single paragraph attempted answers. +DO NOT generate any single answer which is likely to require information from multiple distinct documents, +EACH single answer will be used to carry out a cosine similarity semantic search over distinct indexed documents, such as varied medical documents. +FOR EXAMPLE if asked `how do the key themes of Great Gatsby compare with 1984`, the two attempted answers would be +`The key themes of Great Gatsby are ... ANSWER_CONTINUED` and `The key themes themes of 1984 are ... ANSWER_CONTINUED`, where `ANSWER_CONTINUED` IS TO BE COMPLETED BY YOU in your response. +Here is the original user query to be transformed into answers: + +{query} + +### Response: +""" -class SyntheticRAGPipeline(QnARAGPipeline): +class HyDEPipeline(QnARAGPipeline): def __init__( self, - llm: LLMProvider, - db: VectorDBProvider, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, + embedding_provider: EmbeddingProvider, + llm_provider: LLMProvider, + vector_db_provider: VectorDBProvider, + prompt_provider: Optional[PromptProvider] = None, logging_connection: Optional[LoggingDatabaseConnection] = None, system_prompt: Optional[str] = DEFAULT_SYSTEM_PROMPT, task_prompt: Optional[str] = DEFAULT_TASK_PROMPT, + hyde_prompt: Optional[str] = DEFAULT_HYDE_PROMPT, ) -> None: - logger.debug(f"Initalizing `SyntheticRAGPipeline`") + logger.debug(f"Initalizing `HydePipeline`") + + if not prompt_provider: + prompt_provider = BasicPromptProvider + self.prompt_provider = prompt_provider(system_prompt, task_prompt) + self.prompt_provider.add_prompt("hyde_prompt", hyde_prompt) super().__init__( - llm, - db, - embedding_model, - embeddings_provider, + llm_provider=llm_provider, + vector_db_provider=vector_db_provider, + embedding_provider=embedding_provider, logging_connection=logging_connection, - prompt_provider=BasicPromptProvider(system_prompt, task_prompt), + prompt_provider=self.prompt_provider, ) def transform_query(self, query: str, generation_config: GenerationConfig) -> list[str]: # type: ignore """ - Transforms the query into a list of strings. + Transforms the query into a list of hypothetical queries. """ - print(f"Transforming query: {query}") - prompt = ( - "Use the query that follows to write a comma separated three queries " - "that will be used to retrieve the relevant materials. DO NOT generate " - "queries which require multiple document types. E.g. ask for `lecture notes " - "with information about X` or `readings that cover topic Y`.\n\n## Query:\n" - "{query}\n\n## Response:".format(query=query) - ) + self._check_pipeline_initialized() orig_stream = generation_config.stream generation_config.stream = False - completion = self.generate_completion(prompt, generation_config) + + num_answers = generation_config.add_generation_kwargs.get( + "num_answers", "three" + ) + + formatted_prompt = self.prompt_provider.get_prompt( + "hyde_prompt", {"query": query, "num_answers": num_answers} + ) + + completion = self.generate_completion( + formatted_prompt, generation_config + ) transformed_queries = ( - completion.choices[0].message.content.strip().split("\n") + completion.choices[0].message.content.strip().split("\n\n") ) generation_config.stream = orig_stream - print(f"Transformed Queries: {transformed_queries}") return transformed_queries @log_execution_to_db @@ -87,13 +111,14 @@ def search( transformed_query: str, filters: dict, limit: int, + *args, + **kwargs, ) -> list[VectorSearchResult]: logger.debug(f"Retrieving results for query: {transformed_query}") - results = self.db.search( - query_vector=self.embeddings_provider.get_embedding( + results = self.vector_db_provider.search( + query_vector=self.embedding_provider.get_embedding( transformed_query, - self.embedding_model, ), filters=filters, limit=limit, @@ -108,23 +133,20 @@ def construct_context( ) -> str: queries = [ele[0] for ele in results] search_results = [ele[1] for ele in results] - reranked_results = [ - self.rerank_results(search_result) - for search_result in search_results - ] context = "" offset = 1 - for query, results in zip(queries, reranked_results): + for query, results in zip(queries, search_results): context += f"## Query:\n{query}\n\n## Context:\n{self._format_results(results, offset)}\n\n" offset += len(results) return context - # Modifies `SyntheticRAGPipeline` run to return search_results and completion + # Modifies `HydePipeline` run to return search_results and completion def run( self, query, filters={}, - limit=5, + search_limit=25, + rerank_limit=15, search_only=False, generation_config: Optional[GenerationConfig] = None, *args, @@ -136,24 +158,26 @@ def run( if not generation_config: generation_config = GenerationConfig(model="gpt-3.5-turbo") - print("generation_config = ", generation_config) self.initialize_pipeline(query, search_only) transformed_queries = self.transform_query(query, generation_config) search_results = [ - (transformed_query, self.search(transformed_query, filters, limit)) + ( + transformed_query, + self.rerank_results( + transformed_query, + self.search(transformed_query, filters, search_limit), + rerank_limit, + ), + ) for transformed_query in transformed_queries ] if search_only: - print("returning here...") return RAGPipelineOutput(search_results, None, None) - context = self.construct_context(search_results) prompt = self.construct_prompt({"query": query, "context": context}) - print("generation_config.stream = ", generation_config.stream) if not generation_config.stream: completion = self.generate_completion(prompt, generation_config) - print("returning there...") return RAGPipelineOutput(search_results, context, completion) return self._stream_run( @@ -187,10 +211,3 @@ def _stream_run( for chunk in self.generate_completion(prompt, generation_config): yield chunk yield f"" - - -# Creates a pipeline using the `E2EPipelineFactory` -app = E2EPipelineFactory.create_pipeline( - rag_pipeline_impl=SyntheticRAGPipeline, - config=R2RConfig.load_config("config.json"), -) diff --git a/r2r/pipelines/qna/rag.py b/r2r/pipelines/qna/rag.py index b15a45c4..f5581ad3 100644 --- a/r2r/pipelines/qna/rag.py +++ b/r2r/pipelines/qna/rag.py @@ -6,6 +6,7 @@ from typing import Optional from r2r.core import ( + EmbeddingProvider, LLMProvider, LoggingDatabaseConnection, PromptProvider, @@ -14,9 +15,8 @@ VectorSearchResult, log_execution_to_db, ) -from r2r.embeddings import OpenAIEmbeddingProvider -from ..core.prompt_provider import BasicPromptProvider +from ..core.prompt import BasicPromptProvider logger = logging.getLogger(__name__) @@ -28,29 +28,32 @@ class QnARAGPipeline(RAGPipeline): def __init__( self, - llm: LLMProvider, - db: VectorDBProvider, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, + embedding_provider: EmbeddingProvider, + llm_provider: LLMProvider, + vector_db_provider: VectorDBProvider, prompt_provider: Optional[PromptProvider] = None, logging_connection: Optional[LoggingDatabaseConnection] = None, + system_prompt: Optional[str] = BasicPromptProvider.BASIC_SYSTEM_PROMPT, + task_prompt: Optional[str] = BasicPromptProvider.BASIC_TASK_PROMPT, ) -> None: """ Initializes the RAG pipeline with necessary components and configurations. """ logger.debug(f"Initalizing `QnARAGPipeline`.") if not prompt_provider: - prompt_provider = BasicPromptProvider() + prompt_provider = BasicPromptProvider( + system_prompt, + task_prompt, + ) self.prompt_provider = prompt_provider super().__init__( - llm, - prompt_provider=prompt_provider, + embedding_provider=embedding_provider, + llm_provider=llm_provider, + vector_db_provider=vector_db_provider, logging_connection=logging_connection, + prompt_provider=prompt_provider, ) - self.embedding_model = embedding_model - self.embeddings_provider = embeddings_provider - self.db = db self.pipeline_run_info = None def transform_query(self, query: str) -> str: @@ -74,10 +77,9 @@ def search( """ logger.debug(f"Retrieving results for query: {transformed_query}") self._check_pipeline_initialized() - results = self.db.search( - query_vector=self.embeddings_provider.get_embedding( + results = self.vector_db_provider.search( + query_vector=self.embedding_provider.get_embedding( transformed_query, - self.embedding_model, ), filters=filters, limit=limit, @@ -88,14 +90,18 @@ def search( return results def rerank_results( - self, results: list[VectorSearchResult] + self, + transformed_query: str, + results: list[VectorSearchResult], + limit: int, ) -> list[VectorSearchResult]: """ Reranks the retrieved documents based on relevance, if necessary. """ self._check_pipeline_initialized() - # Placeholder for reranking logic - A unit transformation. - return results + return self.embedding_provider.rerank( + transformed_query, results, limit=limit + ) def _format_results(self, results: list[VectorSearchResult]) -> str: """ diff --git a/r2r/pipelines/web/rag.py b/r2r/pipelines/web/rag.py index 0c74c698..dbfe2ed6 100644 --- a/r2r/pipelines/web/rag.py +++ b/r2r/pipelines/web/rag.py @@ -16,7 +16,8 @@ ) from r2r.embeddings import OpenAIEmbeddingProvider from r2r.integrations import SerperClient -from ..core.prompt_provider import BasicPromptProvider + +from ..core.prompt import BasicPromptProvider from ..qna.rag import QnARAGPipeline WEB_RAG_SYSTEM_PROMPT = "You are a helpful assistant." @@ -42,10 +43,9 @@ class WebRAGPipeline(QnARAGPipeline): def __init__( self, - llm: LLMProvider, - db: VectorDBProvider, - embedding_model: str, - embeddings_provider: OpenAIEmbeddingProvider, + llm_provider: LLMProvider, + embedding_provider: OpenAIEmbeddingProvider, + vector_db_provider: VectorDBProvider, logging_connection: Optional[LoggingDatabaseConnection] = None, prompt_provider: Optional[BasicPromptProvider] = BasicPromptProvider( WEB_RAG_SYSTEM_PROMPT, WEB_RAG_TASK_PROMPT @@ -53,12 +53,11 @@ def __init__( ) -> None: logger.debug(f"Initalizing `WebRAGPipeline`.") super().__init__( - llm=llm, - logging_connection=logging_connection, - db=db, - embedding_model=embedding_model, - embeddings_provider=embeddings_provider, + llm_provider=llm_provider, + vector_db_provider=vector_db_provider, + embedding_provider=embedding_provider, prompt_provider=prompt_provider, + logging_connection=logging_connection, ) self.serper_client = SerperClient() diff --git a/r2r/tests/end_to_end.py b/r2r/tests/end_to_end.py index 5c453286..2c9a626e 100644 --- a/r2r/tests/end_to_end.py +++ b/r2r/tests/end_to_end.py @@ -79,7 +79,6 @@ def test_process_url(client): 5, filters={"document_id": document_id}, ) - print("url search response: ", search_response) assert len(search_response) > 0 assert url in search_response[0]["metadata"]["url"] assert "Google" in search_response[0]["metadata"]["text"] diff --git a/r2r/vecs/adapter/text.py b/r2r/vecs/adapter/text.py index 411b03d6..78ae7732 100644 --- a/r2r/vecs/adapter/text.py +++ b/r2r/vecs/adapter/text.py @@ -18,7 +18,7 @@ "all-distilroberta-v1", "all-MiniLM-L12-v2", "multi-qa-distilbert-cos-v1", - "all-MiniLM-L6-v2", + "mixedbread-ai/mxbai-embed-large-v1", "multi-qa-MiniLM-L6-cos-v1", "paraphrase-multilingual-mpnet-base-v2", "paraphrase-albert-small-v2", diff --git a/r2r/vecs/collection.py b/r2r/vecs/collection.py index 7d3faec6..96ab72b5 100644 --- a/r2r/vecs/collection.py +++ b/r2r/vecs/collection.py @@ -35,6 +35,7 @@ and_, cast, delete, + distinct, func, or_, select, @@ -334,6 +335,38 @@ def _drop(self): return self + def get_unique_metadata_values( + self, + field: str, + filter_field: Optional[str] = None, + filter_value: Optional[MetadataValues] = None, + ) -> List[MetadataValues]: + """ + Fetches all unique metadata values of a specific field, optionally filtered by another metadata field. + Args: + field (str): The metadata field for which to fetch unique values. + filter_field (Optional[str], optional): The metadata field to filter on. Defaults to None. + filter_value (Optional[MetadataValues], optional): The value to filter the metadata field with. Defaults to None. + Returns: + List[MetadataValues]: A list of unique metadata values for the specified field. + """ + with self.client.Session() as sess: + with sess.begin(): + stmt = select( + distinct(self.table.c.metadata[field].astext) + ).where(self.table.c.metadata[field] != None) + + if filter_field is not None and filter_value is not None: + stmt = stmt.where( + self.table.c.metadata[filter_field].astext + == str(filter_value) + ) + + result = sess.execute(stmt) + unique_values = result.scalars().all() + + return unique_values + def copy( self, records: Iterable[Tuple[str, Any, Metadata]], diff --git a/r2r/vector_dbs/__init__.py b/r2r/vector_dbs/__init__.py index 419a0431..1be7733e 100644 --- a/r2r/vector_dbs/__init__.py +++ b/r2r/vector_dbs/__init__.py @@ -1,5 +1,5 @@ -from .local.base import LocalVectorDB +from .local.base import LocalDBConfig, LocalVectorDB from .pg_vector.base import PGVectorDB from .qdrant.base import QdrantDB -__all__ = ["LocalVectorDB", "PGVectorDB", "QdrantDB"] +__all__ = ["LocalDBConfig", "LocalVectorDB", "PGVectorDB", "QdrantDB"] diff --git a/r2r/vector_dbs/local/base.py b/r2r/vector_dbs/local/base.py index afe0fe05..e83e51ed 100644 --- a/r2r/vector_dbs/local/base.py +++ b/r2r/vector_dbs/local/base.py @@ -2,54 +2,61 @@ import logging import os import sqlite3 -from typing import Optional, Union +from dataclasses import dataclass +from typing import List, Optional, Union -from r2r.core import VectorDBProvider, VectorEntry, VectorSearchResult +from r2r.core import ( + VectorDBConfig, + VectorDBProvider, + VectorEntry, + VectorSearchResult, +) logger = logging.getLogger(__name__) +@dataclass +class LocalDBConfig(VectorDBConfig): + db_path: Optional[str] = None + + @property + def supported_providers(self) -> List[str]: + return ["local"] + + class LocalVectorDB(VectorDBProvider): - def __init__( - self, provider: str = "local", db_path: Optional[str] = None - ) -> None: + def __init__(self, config: LocalDBConfig) -> None: logger.info( "Initializing `LocalVectorDB` to store and retrieve embeddings." ) - super().__init__(provider) - if provider != "local": + super().__init__(config) + if config.provider != "local": raise ValueError( "LocalVectorDB must be initialized with provider `local`." ) - self.db_path = db_path - self.collection_name: Optional[str] = None - def _get_conn(self): conn = sqlite3.connect( - self.db_path or os.getenv("LOCAL_DB_PATH", "local.sqlite") + self.config.db_path or os.getenv("LOCAL_DB_PATH", "local.sqlite") ) return conn def _get_cursor(self, conn): return conn.cursor() - def initialize_collection( - self, collection_name: str, dimension: int - ) -> None: + def initialize_collection(self, dimension: int) -> None: conn = self._get_conn() cursor = self._get_cursor(conn) cursor.execute( f""" - CREATE TABLE IF NOT EXISTS "{collection_name}" ( + CREATE TABLE IF NOT EXISTS "{self.config.collection_name}" ( id TEXT PRIMARY KEY, vector TEXT, metadata TEXT ) """ ) - self.collection_name = collection_name conn.commit() conn.close() @@ -59,7 +66,7 @@ def create_index(self, index_type, column_name, index_options): ) def copy(self, entry: VectorEntry, commit=True) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Collection name is not set. Please call `initialize_collection` first." ) @@ -68,7 +75,7 @@ def copy(self, entry: VectorEntry, commit=True) -> None: cursor = self._get_cursor(conn) cursor.execute( f""" - INSERT OR IGNORE INTO "{self.collection_name}" (id, vector, metadata) + INSERT OR IGNORE INTO "{self.config.collection_name}" (id, vector, metadata) VALUES (?, ?, ?) """, ( @@ -82,7 +89,7 @@ def copy(self, entry: VectorEntry, commit=True) -> None: conn.close() def upsert(self, entry: VectorEntry, commit=True) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Collection name is not set. Please call `initialize_collection` first." ) @@ -91,7 +98,7 @@ def upsert(self, entry: VectorEntry, commit=True) -> None: cursor = self._get_cursor(conn) cursor.execute( f""" - INSERT OR REPLACE INTO "{self.collection_name}" (id, vector, metadata) + INSERT OR REPLACE INTO "{self.config.collection_name}" (id, vector, metadata) VALUES (?, ?, ?) """, ( @@ -120,13 +127,13 @@ def search( *args, **kwargs, ) -> list[VectorSearchResult]: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Collection name is not set. Please call `initialize_collection` first." ) conn = self._get_conn() cursor = self._get_cursor(conn) - cursor.execute(f'SELECT * FROM "{self.collection_name}"') + cursor.execute(f'SELECT * FROM "{self.config.collection_name}"') results = [] for id, vector, metadata in cursor.fetchall(): vector = json.loads(vector) @@ -142,37 +149,44 @@ def search( def filtered_deletion( self, key: str, value: Union[bool, int, str] ) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Collection name is not set. Please call `initialize_collection` first." ) conn = self._get_conn() cursor = self._get_cursor(conn) - cursor.execute(f'SELECT * FROM "{self.collection_name}"') + cursor.execute(f'SELECT * FROM "{self.config.collection_name}"') for id, vector, metadata in cursor.fetchall(): metadata = json.loads(metadata) if metadata.get(key) == value: cursor.execute( - f'DELETE FROM "{self.collection_name}" WHERE id = ?', (id,) + f'DELETE FROM "{self.config.collection_name}" WHERE id = ?', + (id,), ) conn.commit() conn.close() def get_all_unique_values( - self, metadata_field: str, filters: dict = {} - ) -> list: - if self.collection_name is None: + self, + metadata_field: str, + filter_field: Optional[str] = None, + filter_value: Optional[str] = None, + ) -> list[str]: + if self.config.collection_name is None: raise ValueError( "Collection name is not set. Please call `initialize_collection` first." ) conn = self._get_conn() cursor = self._get_cursor(conn) - cursor.execute(f'SELECT metadata FROM "{self.collection_name}"') + cursor.execute(f'SELECT metadata FROM "{self.config.collection_name}"') unique_values = set() for (metadata,) in cursor.fetchall(): metadata = json.loads(metadata) - if all(metadata.get(k) == v for k, v in filters.items()): + if ( + filter_field is None + or metadata.get(filter_field) == filter_value + ): if metadata_field in metadata: unique_values.add(metadata[metadata_field]) conn.close() @@ -180,3 +194,6 @@ def get_all_unique_values( def close(self): pass + + def close(self): + pass diff --git a/r2r/vector_dbs/pg_vector/base.py b/r2r/vector_dbs/pg_vector/base.py index becde3d2..b7f55102 100644 --- a/r2r/vector_dbs/pg_vector/base.py +++ b/r2r/vector_dbs/pg_vector/base.py @@ -2,24 +2,25 @@ import os from typing import Optional, Union -from r2r.core import VectorDBProvider, VectorEntry, VectorSearchResult +from r2r.core import ( + VectorDBConfig, + VectorDBProvider, + VectorEntry, + VectorSearchResult, +) from r2r.vecs.client import Client -from r2r.vecs.collection import Collection +from r2r.vecs.collection import Collection, MetadataValues logger = logging.getLogger(__name__) class PGVectorDB(VectorDBProvider): - def __init__(self, provider: str = "pgvector") -> None: + def __init__(self, config: VectorDBConfig) -> None: logger.info( "Initializing `PGVectorDB` to store and retrieve embeddings." ) - super().__init__(provider) - if provider != "pgvector": - raise ValueError( - "PGVectorDB must be initialized with provider `pgvector`." - ) + super().__init__(config) try: import r2r.vecs except ImportError: @@ -43,11 +44,9 @@ def __init__(self, provider: str = "pgvector") -> None: ) self.collection: Optional[Collection] = None - def initialize_collection( - self, collection_name: str, dimension: int - ) -> None: + def initialize_collection(self, dimension: int) -> None: self.collection = self.vx.get_or_create_collection( - name=collection_name, dimension=dimension + name=self.config.collection_name, dimension=dimension ) def copy(self, entry: VectorEntry, commit=True) -> None: @@ -145,28 +144,19 @@ def filtered_deletion( self.collection.delete(filters={key: {"$eq": value}}) # type: ignore def get_all_unique_values( - self, metadata_field: str, filters: dict = {} - ) -> list: + self, + metadata_field: str, + filter_field: Optional[str] = None, + filter_value: Optional[str] = None, + ) -> list[str]: if self.collection is None: raise ValueError( "Please call `initialize_collection` before attempting to run `get_all_unique_values`." ) - mapped_filters = { - key: {"$eq": value} for key, value in filters.items() - } - - # Pass an empty vector as the `data` argument - records = self.collection.query( - data=[0] * self.collection.dimension, # Empty vector - filters=mapped_filters, - include_metadata=True, - include_value=False, - # Remove the `limit` argument to retrieve all records + unique_values = self.collection.get_unique_metadata_values( + field=metadata_field, + filter_field=filter_field, + filter_value=filter_value, ) - - unique_values = set( - record[1].get(metadata_field) for record in records - ) - - return list(unique_values) + return unique_values diff --git a/r2r/vector_dbs/qdrant/base.py b/r2r/vector_dbs/qdrant/base.py index eca72109..77f391e1 100644 --- a/r2r/vector_dbs/qdrant/base.py +++ b/r2r/vector_dbs/qdrant/base.py @@ -2,22 +2,28 @@ import os from typing import Optional, Union -from r2r.core import VectorDBProvider, VectorEntry, VectorSearchResult +from r2r.core import ( + VectorDBConfig, + VectorDBProvider, + VectorEntry, + VectorSearchResult, +) logger = logging.getLogger(__name__) class QdrantDB(VectorDBProvider): - def __init__(self, provider: str = "qdrant") -> None: + def __init__(self, config: VectorDBConfig) -> None: logger.info( "Initializing `QdrantDB` to store and retrieve embeddings." ) - super().__init__(provider) - if provider != "qdrant": + super().__init__(config) + if config.provider != "qdrant": raise ValueError( "QdrantDB must be initialized with provider `qdrant`." ) + try: from qdrant_client import QdrantClient from qdrant_client.http import models @@ -42,22 +48,18 @@ def __init__(self, provider: str = "qdrant") -> None: raise ValueError( f"Error {e} occurred while attempting to connect to the qdrant provider." ) - self.collection_name: Optional[str] = None - def initialize_collection( - self, collection_name: str, dimension: int - ) -> None: - self.collection_name = collection_name + def initialize_collection(self, dimension: int) -> None: try: result = self.client.create_collection( - collection_name=f"{collection_name}", + collection_name=f"{self.config.collection_name}", vectors_config=self.models.VectorParams( size=dimension, distance=self.models.Distance.COSINE ), ) if result is False: raise ValueError( - f"Error occurred while attempting to create collection {collection_name}." + f"Error occurred while attempting to create collection {self.config.collection_name}." ) except Exception: # TODO - Handle more appropriately - create collection fails when it already exists @@ -69,7 +71,7 @@ def copy(self, entry: VectorEntry, commit=True) -> None: ) def upsert(self, entry: VectorEntry, commit=True) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Please call `initialize_collection` before attempting to run `upsert`." ) @@ -81,14 +83,14 @@ def upsert(self, entry: VectorEntry, commit=True) -> None: ) ] self.client.upsert( - collection_name=f"{self.collection_name}", + collection_name=f"{self.config.collection_name}", points=points, ) def upsert_entries( self, entries: list[VectorEntry], commit: bool = True ) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Please call `initialize_collection` before attempting to run `upsert_entries`." ) @@ -101,7 +103,7 @@ def upsert_entries( for entry in entries ] self.client.upsert( - collection_name=f"{self.collection_name}", + collection_name=f"{self.config.collection_name}", points=points, ) @@ -113,13 +115,13 @@ def search( *args, **kwargs, ) -> list[VectorSearchResult]: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Please call `initialize_collection` before attempting to run `search`." ) results = self.client.search( - collection_name=f"{self.collection_name}", + collection_name=f"{self.config.collection_name}", query_filter=self.models.Filter( must=[ self.models.FieldCondition( @@ -151,13 +153,13 @@ def close(self): def filtered_deletion( self, key: str, value: Union[bool, int, str] ) -> None: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Please call `initialize_collection` before attempting to run `filtered_deletion`." ) self.client.delete( - collection_name=self.collection_name, + collection_name=self.config.collection_name, points_selector=self.models.FilterSelector( filter=self.models.Filter( must=[ @@ -172,23 +174,27 @@ def filtered_deletion( return def get_all_unique_values( - self, collection_name: str, metadata_field: str, filters: dict = {} + self, + metadata_field: str, + filter_field: Optional[str] = None, + filter_value: Optional[str] = None, ) -> list: - if self.collection_name is None: + if self.config.collection_name is None: raise ValueError( "Please call `initialize_collection` before attempting to run `get_all_unique_values`." ) - # Create a scroll filter based on the provided filters + # Create a scroll filter based on the provided filter field and value scroll_filter = None - if filters: - filter_conditions = [ - self.models.FieldCondition( - key=key, match=self.models.MatchValue(value=value) - ) - for key, value in filters.items() - ] - scroll_filter = self.models.Filter(must=filter_conditions) + if filter_field and filter_value: + scroll_filter = self.models.Filter( + must=[ + self.models.FieldCondition( + key=filter_field, + match=self.models.MatchValue(value=filter_value), + ) + ] + ) unique_values = set() @@ -196,7 +202,7 @@ def get_all_unique_values( next_page_offset = None while True: records, next_page_offset = self.scroll( - collection_name=collection_name, + collection_name=self.config.collection_name, scroll_filter=scroll_filter, offset=next_page_offset, limit=100, # Adjust the batch size as needed