📚 LibreRAG — An accessible & local RAG Pipeline

This project is a complete Retrieval-Augmented Generation (RAG) pipeline that turns a domain description into a searchable research assistant powered by arXiv papers.

You describe a domain (for example: "An expert on machine learning especially on memory") and the system will:

1. Generate ranked search keywords using OpenAI
2. Search arXiv using those keywords
3. Download and parse research PDFs
4. Embed the papers locally using all-MiniLM-L6-v2
5. Store them in Chroma vector database
6. Let you ask questions over the papers using ChatGPT

The result is a domain-specific research assistant that answers questions using real academic sources.

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🧠 How It Works

Domain description → Keyword generation → arXiv search → PDF download → Parsing → Embeddings → Chroma → RAG QA

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🚀 Features

• Domain → ranked search keywords
• Automatic arXiv ingestion
• PDF parsing
• Local embeddings (no cloud vector DB)
• Chroma vector store
• RAG-based question answering

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📦 Requirements

• Python 3.9+
• OpenAI API key
• Internet connection

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🔧 Installation

pip install openai chromadb sentence-transformers pypdf requests

Set your API key:

export OPENAI_API_KEY="your-api-key"

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▶️ Running

python librerag.py

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⭐ Why This Exists

This project is for researchers, engineers, and builders who want their own domain-specific AI assistant grounded in real academic literature — not hallucinations.

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📜 License

MIT