mirror of
https://github.com/hwchase17/langchain
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87 lines
3.0 KiB
Markdown
87 lines
3.0 KiB
Markdown
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# rag-azure-search
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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.
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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).
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## Environment Setup
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***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.
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***Environment Variables:***
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To run this template, you'll need to set the following environment variables:
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***Required:***
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- AZURE_SEARCH_ENDPOINT - The endpoint of the Azure AI Search service.
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- AZURE_SEARCH_KEY - The API key for the Azure AI Search service.
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- AZURE_OPENAI_ENDPOINT - The endpoint of the Azure OpenAI service.
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- AZURE_OPENAI_API_KEY - The API key for the Azure OpenAI service.
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- AZURE_EMBEDDINGS_DEPLOYMENT - Name of the Azure OpenAI deployment to use for embeddings.
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- AZURE_CHAT_DEPLOYMENT - Name of the Azure OpenAI deployment to use for chat.
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***Optional:***
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- 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".
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- OPENAI_API_VERSION - Azure OpenAI API version to use. Defaults to "2023-05-15".
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-azure-search
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-azure-search
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```
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And add the following code to your `server.py` file:
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```python
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from rag_azure_search import chain as rag_azure_search_chain
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add_routes(app, rag_azure_search_chain, path="/rag-azure-search")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-azure-search")
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```
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