langchain/templates/rag-redis/README.md
Erick Friis a1fae1fddd
Readme rewrite (#12615)
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-10-31 00:06:02 -07:00

2.9 KiB

rag-redis

This template performs RAG using Redis and OpenAI 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.

The following Redis environment variables need to be set:

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 installed:

pip install -U "langchain-cli[serve]"

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 to your 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. LangSmith is currently in private beta, you can sign up 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")