update langchain code and notebook (#3201)

sdk/python/endpoints/online/llm/langchain/requirements.txt

@@ -6,9 +6,12 @@ azure-keyvault-secrets
 azure-mgmt-keyvault
 azure-keyvault
 azure-search-documents==11.4.0b3
-langchain==0.0.164
+langchain
+langchain-cli
 openai==0.27.6
 parse
 requests
 pyyaml
-azure-cli
+azure-cli
+langserve
+fastapi

sdk/python/endpoints/online/llm/src/langchain/.gitignore

@@ -0,0 +1 @@
+__pycache__

sdk/python/endpoints/online/llm/src/langchain/Dockerfile

@@ -0,0 +1,21 @@
+FROM python:3.11-slim
+
+RUN pip install poetry==1.6.1
+
+RUN poetry config virtualenvs.create false
+
+WORKDIR /code
+
+COPY ./pyproject.toml ./README.md ./poetry.lock* ./
+
+COPY ./package[s] ./packages
+
+RUN poetry install  --no-interaction --no-ansi --no-root
+
+COPY ./app ./app
+
+RUN poetry install --no-interaction --no-ansi
+
+EXPOSE 8080
+
+CMD exec uvicorn app.server:app --host 0.0.0.0 --port 8080

sdk/python/endpoints/online/llm/src/langchain/README.md

@@ -0,0 +1,83 @@
+# demo
+This sample based on https://github.com/langchain-ai/langchain/blob/master/templates/openai-functions-agent. The modification we have are:
+* Switch to use **Prompty**
+* Use **AzureChatOpenAI** instead of **ChatOpenAI**
+* Change tool to ElasticSearh
+
+## Installation
+
+Install the LangChain CLI if you haven't yet
+
+```bash
+pip install -U langchain-cli
+```
+
+## Adding packages
+
+```bash
+# adding packages from 
+# https://github.com/langchain-ai/langchain/tree/master/templates
+langchain app add $PROJECT_NAME
+
+# adding custom GitHub repo packages
+langchain app add --repo $OWNER/$REPO
+# or with whole git string (supports other git providers):
+# langchain app add git+https://github.com/hwchase17/chain-of-verification
+
+# with a custom api mount point (defaults to `/{package_name}`)
+langchain app add $PROJECT_NAME --api_path=/my/custom/path/rag
+```
+
+Note: you remove packages by their api path
+
+```bash
+langchain app remove my/custom/path/rag
+```
+
+## Setup LangSmith (Optional)
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+## Launch LangServe
+
+```bash
+langchain serve
+```
+
+## Running in Docker
+
+This project folder includes a Dockerfile that allows you to easily build and host your LangServe app.
+
+### Building the Image
+
+To build the image, you simply:
+
+```shell
+docker build . -t my-langserve-app
+```
+
+If you tag your image with something other than `my-langserve-app`,
+note it for use in the next step.
+
+### Running the Image Locally
+
+To run the image, you'll need to include any environment variables
+necessary for your application.
+
+In the below example, we inject the `OPENAI_API_KEY` environment
+variable with the value set in my local environment
+(`$OPENAI_API_KEY`)
+
+We also expose port 8080 with the `-p 8080:8080` option.
+
+```shell
+docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -p 8080:8080 my-langserve-app
+```

sdk/python/endpoints/online/llm/src/langchain/app/server.py

@@ -0,0 +1,24 @@
+from fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+from langserve import add_routes
+
+app = FastAPI()
+
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+
+
+@app.get("/")
+async def redirect_root_to_docs():
+    return RedirectResponse("/docs")
+
+
+from openai_functions_agent import agent_executor as openai_functions_agent_chain
+
+add_routes(app, openai_functions_agent_chain, path="/openai-functions-agent")
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/README.md

@@ -0,0 +1,72 @@
+
+# openai-functions-agent
+
+This template creates an agent that uses OpenAI function calling to communicate its decisions on what actions to take. 
+
+This example creates an agent that can optionally look up information on the internet using Tavily's search engine.
+
+## Environment Setup
+
+The following environment variables need to be set:
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+Set the `TAVILY_API_KEY` environment variable to access Tavily.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package openai-functions-agent
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add openai-functions-agent
+```
+
+And add the following code to your `server.py` file:
+```python
+from openai_functions_agent import agent_executor as openai_functions_agent_chain
+
+add_routes(app, openai_functions_agent_chain, path="/openai-functions-agent")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/openai-functions-agent/playground](http://127.0.0.1:8000/openai-functions-agent/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/openai-functions-agent")
+```

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/main.py

@@ -0,0 +1,5 @@
+from openai_functions_agent.agent import agent_executor
+
+if __name__ == "__main__":
+    question = "who won the womens world cup in 2023?"
+    print(agent_executor.invoke({"input": question, "chat_history": []}))  # noqa: T201

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/openai_functions_agent/__init__.py

@@ -0,0 +1,3 @@
+from openai_functions_agent.agent import agent_executor
+
+__all__ = ["agent_executor"]

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/openai_functions_agent/agent.py

@@ -0,0 +1,64 @@
+from typing import List, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_function_messages
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_openai import AzureChatOpenAI
+import os
+from langchain_prompty import create_chat_prompt
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+
+
+# Define the arguments schema model
+class SearchQueryArgs(BaseModel):
+    query: str = Field(..., example="What is the current state of the stock market?")
+
+
+token_provider = get_bearer_token_provider(
+    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+)
+
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
+    azure_ad_token_provider=token_provider,
+)
+
+prompt = create_chat_prompt(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "basic_chat.prompty")
+)
+
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]):
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    return buffer
+
+
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
+        "agent_scratchpad": lambda x: format_to_openai_function_messages(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm
+    | OpenAIFunctionsAgentOutputParser()
+)
+
+
+class AgentInput(BaseModel):
+    input: str
+    chat_history: List[Tuple[str, str]] = Field(
+        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
+    )
+
+
+agent_executor = AgentExecutor(agent=agent, verbose=True, tools=[]).with_types(
+    input_type=AgentInput
+)

sdk/python/endpoints/online/llm/src/langchain/packages/openai-functions-agent/openai_functions_agent/basic_chat.prompty

@@ -0,0 +1,35 @@
+---
+name: Basic Prompt
+template:
+   type: mustache
+   parser: prompty
+description: A basic prompt that uses the GPT-3 chat API to answer questions
+authors:
+  - author_1
+  - author_2
+model:
+  api: chat
+  configuration:
+    azure_deployment: gpt-35-turbo
+sample:
+  firstName: Jane
+  lastName: Doe
+  input: What is the meaning of life?
+  chat_history: []
+---
+system:
+You are an AI assistant who helps people find information.
+As the assistant, you answer questions briefly, succinctly, 
+and in a personable manner using markdown and even add some personal flair with appropriate emojis.
+
+# Customer
+You are helping {{firstName}} {{lastName}} to find answers to their questions.
+Use their name to address them in your responses.
+
+{{#chat_history}}
+{{type}}:
+{{content}}
+{{/chat_history}}
+
+user:
+{{input}}