ad502e8d50
Thank you for contributing to LangChain! **Description:** update to the Vectara / Langchain integration to integrate new Vectara capabilities: - Full RAG implemented as a Runnable with as_rag() - Vectara chat supported with as_chat() - Both support streaming response - Updated documentation and example notebook to reflect all the changes - Updated Vectara templates **Twitter handle:** ofermend **Add tests and docs**: no new tests or docs, but updated both existing tests and existing docs |
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| .. | ||
| rag_vectara_multiquery | ||
| tests | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| rag_vectara_multiquery.ipynb | ||
| README.md | ||
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_IDVECTARA_CORPUS_IDVECTARA_API_KEY
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-vectara-multiquery
If you want to add this to an existing project, you can just run:
langchain app add rag-vectara-multiquery
And add the following code to your server.py file:
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. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "vectara-demo"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-vectara-multiquery/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-vectara-multiquery")