RAG Document Assistant
Upload documents and chat with them via retrieval-augmented generation; sources cited and cached.
About This Project
An intelligent document assistant powered by Retrieval-Augmented Generation (RAG) technology. Users can upload various document formats and interact with their content through natural language queries. The system employs advanced chunking and embedding techniques to process documents, stores them in Pinecone vector database for efficient retrieval, and uses LangChain orchestration to generate accurate, contextual responses. Every answer includes proper source citations, allowing users to verify information. The assistant features hybrid search capabilities combining semantic and keyword-based retrieval, with intelligent caching to optimize performance for frequently asked questions.
Agentic Orchestration
Tools Used
- langchain
Flow Highlights
- chunking+embeddings
- hybrid search
- citation + fallback