📚 RAG System – URLs + Documents

A Retrieval-Augmented Generation (RAG) application built using LangChain, Chroma, HuggingFace embeddings, Groq LLM, and Streamlit.

✨ Features

• Ingest web URLs (Wikipedia, blogs, etc.)
• Upload documents (PDF, TXT, MD)
• Semantic search using vector embeddings
• Context-aware answers powered by LLM
• Streamlit-based interactive UI

🧠 Tech Stack

• LangChain
• ChromaDB
• HuggingFace Sentence Transformers
• Groq (LLaMA 3.1)
• Streamlit

🚀 How to Run

pip install -r requirements.txt export GROQ_API_KEY=your_key_here streamlit run app.py

📌 Use Cases

Study assistant Research Q&A Private knowledge base Document chatbot

🧾 Architecture

Documents + URLs → Chunking → Embeddings → Vector DB → Retrieval → LLM Answer

Commit README:

git add README.md git commit -m "Add README" git push