RAG-based QA Bot

Built a comprehensive RAG (Retrieval-Augmented Generation) pipeline using LangChain for orchestration, integrating Pinecone vector database for semantic document storage and retrieval, achieving high-relevance retrieval accuracy by combining dense vector search with keyword-based retrieval and re-ranking models.

Implemented context-aware answer generation using Cohere API, creating a system where questions find answers not just in text but in understanding, managing context window limitations through intelligent chunking and hybrid search approaches that ensure relevant information retrieval.

Developed a multi-document knowledge base system that brings information to life by connecting retrieval with generation, enabling accurate question answering through the fusion of retrieval and generation, where context meets intelligence to provide meaningful responses from document collections.