RAG - Google Cloud Matching Engine

This template performs RAG using Google Cloud Vertex Matching Engine.

It utilizes a previously created index to retrieve relevant documents or contexts based on user-provided questions.

Environment Setup

An index should be created before running the code.

The process to create this index can be found here.

Environment variables for Vertex should be set:

PROJECT_ID
ME_REGION
GCS_BUCKET
ME_INDEX_ID
ME_ENDPOINT_ID

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-matching-engine

If you want to add this to an existing project, you can just run:

langchain app add rag-matching-engine

And add the following code to your server.py file:

from rag_matching_engine import chain as rag_matching_engine_chain

add_routes(app, rag_matching_engine_chain, path="/rag-matching-engine")

(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 "default"

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-matching-engine/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-matching-engine")

For more details on how to connect to the template, refer to the Jupyter notebook rag_matching_engine.