# rag-vectara-multiquery This template performs multiquery RAG with vectara. ## Environment Setup Set the `OPENAI_API_KEY` environment variable to access the OpenAI models for the multi-query processing. Also, ensure the following environment variables are set: * `VECTARA_CUSTOMER_ID` * `VECTARA_CORPUS_ID` * `VECTARA_API_KEY` ## 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-vectara-multiquery ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-vectara-multiquery ``` And add the following code to your `server.py` file: ```python from rag_vectara import chain as rag_vectara_chain add_routes(app, rag_vectara_chain, path="/rag-vectara-multiquery") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [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 "vectara-demo" ``` 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-vectara-multiquery/playground](http://127.0.0.1:8000/rag-vectara-multiquery/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-vectara-multiquery") ```