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rag-azure-search

This template performs RAG on documents using Azure AI Search as the vectorstore and Azure OpenAI chat and embedding models.

For additional details on RAG with Azure AI Search, refer to this notebook.

Environment Setup​

Prerequisites: Existing Azure AI Search and Azure OpenAI resources.

Environment Variables:

To run this template, you'll need to set the following environment variables:

Required:

  • AZURE_SEARCH_ENDPOINT - The endpoint of the Azure AI Search service.
  • AZURE_SEARCH_KEY - The API key for the Azure AI Search service.
  • AZURE_OPENAI_ENDPOINT - The endpoint of the Azure OpenAI service.
  • AZURE_OPENAI_API_KEY - The API key for the Azure OpenAI service.
  • AZURE_EMBEDDINGS_DEPLOYMENT - Name of the Azure OpenAI deployment to use for embeddings.
  • AZURE_CHAT_DEPLOYMENT - Name of the Azure OpenAI deployment to use for chat.

Optional:

  • AZURE_SEARCH_INDEX_NAME - Name of an existing Azure AI Search index to use. If not provided, an index will be created with name "rag-azure-search".
  • OPENAI_API_VERSION - Azure OpenAI API version to use. Defaults to "2023-05-15".

Usage​

To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-azure-search

If you want to add this to an existing project, you can just run:

langchain app add rag-azure-search

And add the following code to your server.py file:

from rag_azure_search import chain as rag_azure_search_chain

add_routes(app, rag_azure_search_chain, path="/rag-azure-search")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

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:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-azure-search/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-azure-search")

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