A local, AI-powered study assistant that uses Retrieval-Augmented Generation (RAG) to answer questions based on personal study materials. Built with Flask, LangChain, ChromaDB, and Groq (Llama 3).

• RAG Architecture: Retrieves relevant information from documents.
• Local Vector Store: Uses ChromaDB to store document embeddings.
• LLM: Llama 3 (via Groq API).
• Multi-Format Support: Ingests PDF, DOCX, and TXT files.

• Python, Flask
• Llama 3.1 8B (via Groq API)
• LangChain
• Chroma (Local)
• Embeddings- sentence-transformers/all-MiniLM-L6-v2 (Local)