rag-redis-parea

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.

It uses Parea AI to instrument tracing and evaluations.

Updated from the original LangChain template rag-redis.

Environment Setup

Copy the .env.example file to .env and fill in the values. Or export values in shell.

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

Set the PAREA_API_KEY environment variable to access tracing with PareaAI:

export OPENAI_API_KEY= <YOUR OPENAI API KEY>
export PAREA_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_PASSWORD = <YOUR REDIS PASSWORD>

Supported Settings

We use a variety of environment variables to configure this application

Environment Variable	Description	Default Value
REDIS_HOST	Hostname for the Redis server	"localhost"
REDIS_PORT	Port for the Redis server	6379
REDIS_PASSWORD	Password for the Redis server	""
INDEX_NAME	Name of the vector index	"rag-redis"
TOKENIZERS_PARALLELISM	To avoid potential deadlocks	"False"

Usage

Load requirements:

poetry install

Start the Redis server:

redis-stack-server

Then, from the root directory run ingest-docs using the CLI helper to load your data into Redis.

python main.py --ingest-docs

Then run the chain (use --run-eval to also run evaluations defined in evals/evals.py):

python main.py --run-eval

Results

View trace logs on Parea AI.