mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
69 lines
2.2 KiB
Markdown
69 lines
2.2 KiB
Markdown
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# rag-pinecone-multi-query
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This template performs RAG using Pinecone and OpenAI with a multi-query retriever.
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It uses an LLM to generate multiple queries from different perspectives based on the user's input query.
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For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
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## Environment Setup
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This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set.
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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## Usage
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To use this package, you should first install the LangChain CLI:
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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 package, do:
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```shell
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langchain app new my-app --package rag-pinecone-multi-query
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```
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To add this package to an existing project, run:
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```shell
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langchain app add rag-pinecone-multi-query
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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_pinecone_multi_query import chain as rag_pinecone_multi_query_chain
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add_routes(app, rag_pinecone_multi_query_chain, path="/rag-pinecone-multi-query")
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```
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(Optional) Now, let's 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
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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 running locally at [http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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You can access the playground at [http://127.0.0.1:8000/rag-pinecone-multi-query/playground](http://127.0.0.1:8000/rag-pinecone-multi-query/playground)
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To access the template from code, use:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-pinecone-multi-query")
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``` |