StackRAG

This is a Retrieval Augmented Generation (RAG)-based ai-agent designed for utilizing advanced reasoning capabilities of GPT-4 as well as community knowledge available in StackOverflow to provide well-grounded responses to user-given questions. Our agent is built using Langchain Agent Framework. The overall architecture diagram is presented below.

Architecture

Before staring make sure you have the following:

1. OpenAI API Key (https://platform.openai.com/docs/quickstart?context=python)
2. Cohere AI API Key (https://docs.cohere.com/docs)
3. An index in Pinecone (https://www.pinecone.io/)
4. StackAPI API Key (Follow instructions presented in the answer of this StackOverflow Post: https://stackoverflow.com/questions/61240974/how-to-retrieve-data-from-stack-exchange-api-with-user-authentication)

How to Setup

Follow these instructions to run the program

Setup the environment variables

Copy .env.example file into .env

cp .env.example .env

After copying the environment variables into .env file, fill them in

OPENAI_API_KEY=Your OpenAI API Key

PINECONE_ENVIRONMENT=Your Pinecone Environment for storing data ("gcp-starter" if using the default free version)

PINECONE_API_KEY=The API Key of the index that is going to store data

PINECONE_INDEX_NAME=The name of the index where you store data

SO_CLIENT_SECRET = Your StackOverflow secret client key

SO_KEY = Your StackOverflow API key

Install Necessary Dependencies

pip3 install -r requirements.txt

Get the USer-Interface

In order to get the UI where you can interact with our agent, run the following command

streamlit run ai-agent/ai-agent-ui.py

Citation

@article{abrahamyanStackRAG,
  title={StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation},
  author={Abrahamyan, Davit and Fard, Fatemeh Hendijani},
  journal={arXiv preprint arXiv:2406.13840},
  year={2024}
}