RAG_APP_README.md

Abalone RAG Chatbot

This project implements a Retrieval-Augmented Generation (RAG) chatbot about Abalone using LangChain + OpenAI with a Streamlit frontend. It's designed to be deployed on Hugging Face Spaces.

Contents

• app.py - Streamlit app entrypoint
• src/ingest.py - Ingest files from data/ into a persisted Chroma vectorstore
• src/vectorstore.py - Helpers to build/load the Chroma vectorstore and return a retriever
• src/qa_chain.py - Build the conversational retrieval QA chain
• data/ - Put Abalone source files here (CSV/MD/TXT/PDF)
• vectorstore/ - Persisted vectorstore directory (created by ingestion)

Quickstart (local)

1. Create a venv and install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Set your OpenAI API key:

export OPENAI_API_KEY="sk-..."

3. Add Abalone files into data/ (for example abalone.csv).

4. Build the vectorstore:

python -m src.ingest --data-dir ./data --persist-dir ./vectorstore

5. Run the Streamlit app:

streamlit run app.py

Deploying to Hugging Face Spaces

• Add OPENAI_API_KEY in the Spaces secrets (Settings -> Secrets).
• Push this repository to your HF Space. HF will install requirements.txt and run the Streamlit app.
• On first run, click the "Ingest data" button or allow the app to rebuild the index.

Security

• Do NOT commit your OpenAI API key. Use HF Spaces Secrets for deployment.

License

• MIT