# rag-azure-search This template performs RAG on documents using [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) as the vectorstore and Azure OpenAI chat and embedding models. For additional details on RAG with Azure AI Search, refer to [this notebook](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/vectorstores/azuresearch.ipynb). ## Environment Setup ***Prerequisites:*** Existing [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) and [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview) 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: ```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 rag-azure-search ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-azure-search ``` And add the following code to your `server.py` file: ```python 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. LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-azure-search/playground](http://127.0.0.1:8000/rag-azure-search/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-azure-search") ```