rag-redis

This template performs RAG using Redis (vector database) and OpenAI (LLM) on financial 10k filings docs for Nike.

It relies on the sentence transformer all-MiniLM-L6-v2 for embedding chunks of the pdf and user questions.

Environment Setup

Set the OPENAI_API_KEY environment variable to access the OpenAI models:

export OPENAI_API_KEY= <YOUR OPENAI API KEY>

Set the following Redis environment variables:

export REDIS_HOST = <YOUR REDIS HOST>
export REDIS_PORT = <YOUR REDIS PORT>
export REDIS_USER = <YOUR REDIS USER NAME>
export REDIS_PASSWORD = <YOUR REDIS PASSWORD>

Supported Settings

We use a variety of environment variables to configure this application

Environment Variable	Description	Default Value
DEBUG	Enable or disable Langchain debugging logs	True
REDIS_HOST	Hostname for the Redis server	"localhost"
REDIS_PORT	Port for the Redis server	6379
REDIS_USER	User for the Redis server	""
REDIS_PASSWORD	Password for the Redis server	""
REDIS_URL	Full URL for connecting to Redis	None, Constructed from user, password, host, and port if not provided
INDEX_NAME	Name of the vector index	"rag-redis"

Usage

To use this package, you should first have the LangChain CLI and Pydantic installed in a Python virtual environment:

pip install -U langchain-cli pydantic==1.10.13

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-redis

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

langchain app add rag-redis

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

from rag_redis.chain import chain as rag_redis_chain

add_routes(app, rag_redis_chain, path="/rag-redis")

(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-redis/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

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