Document Qna

A full-stack system that transforms raw documents into AI-powered embeddings, ready for semantic search, RAG pipelines, and intelligent assistants. From ingestion to storage, it provides a streamlined pipeline to make your unstructured data instantly searchable and meaningful. Key Features Flexible Document Ingestion: Upload and parse PDFs, text, and more with automated preprocessing. Smart Chunking: Break large documents into context-aware segments for better retrieval. Embedding Generation: Leverages HuggingFace models to create high-quality vector representations. Vector Database Integration: Store and query embeddings via Weaviate (or other pluggable vector DBs). Search & Retrieval: Perform lightning-fast, semantically aware searches across ingested content. Modern Interface: Built with React + Tailwind + FastAPI for a clean, responsive user experience. This project is your end-to-end pipeline for turning static documents into dynamic, AI-ready knowledge bases — perfect for RAG systems, enterprise search, and personal knowledge assistants. 🚀