LangChain RAG System

Developed a comprehensive RAG (Retrieval-Augmented Generation) system that combines the power of LangChain framework with Google's Gemini model and Pinecone vector database. This system enables intelligent document processing, semantic search, and context-aware question answering.

The RAG system processes documents by chunking them into smaller segments, creating vector embeddings using advanced language models, and storing them in Pinecone's vector database for efficient similarity search. When users ask questions, the system retrieves the most relevant document chunks and uses them as context for the Gemini model to generate accurate, informed responses.

This project demonstrates advanced AI/ML capabilities including vector embeddings, semantic search, document processing, and integration of multiple AI services. The system can handle various document types and provides intelligent responses based on the processed knowledge base.