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Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
87 lines
3.0 KiB
Markdown
87 lines
3.0 KiB
Markdown
# RAG - Azure AI 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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You can sign up for LangSmith [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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``` |