# 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](https://platform.openai.com) models: ```bash export OPENAI_API_KEY= ``` Set the following [Redis](https://redis.com/try-free) environment variables: ```bash export REDIS_HOST = export REDIS_PORT = export REDIS_USER = export 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: ```shell 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: ```shell langchain app new my-app --package rag-redis ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-redis ``` And add the following code snippet to your `app/server.py` file: ```python 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](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 "default" ``` 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-redis/playground](http://127.0.0.1:8000/rag-redis/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-redis") ```