Document AI Chat: Intelligent RAG-Powered PDF Assistant
Built intelligent document chat system using RAG architecture with pgvector embeddings and Google Gemini, enabling natural Q&A with any PDF document.
Impact & Results
The Problem
Users struggle to extract specific information from lengthy PDF documents, often spending hours manually searching through pages. Traditional document search relies on exact keyword matching, missing contextual understanding. Existing solutions don't provide conversational interfaces or intelligent summarization capabilities. Most PDF tools lack the ability to understand document context and provide meaningful answers to complex queries.
The Solution
Developed intelligent RAG (Retrieval-Augmented Generation) system that processes PDFs through advanced chunking, creates 768-dimensional vector embeddings using Google Gemini, and stores them in PostgreSQL with pgvector extension. Built conversational interface enabling natural language queries against document content. Implemented cosine similarity search with ivfflat indexing for fast retrieval and context-aware response generation using Gemini Flashlight model.
The Results
Successfully deployed production-ready RAG application with live demo at document-ai-chat.theshikhar.com. Achieved fast document ingestion with drag-and-drop PDF upload and real-time embedding generation. Built responsive chat interface with light/dark theme support and stateless architecture. Implemented efficient vector search with PostgreSQL pgvector extension and automated data cleanup for demo environment. Created containerized deployment ready for cloud platforms.
Technical Challenges
Optimizing PDF text chunking strategies for maximum context preservation while maintaining embedding quality. Implementing efficient vector similarity search with proper indexing for fast retrieval at scale. Building stateless frontend architecture with localStorage persistence and 7-day expiration. Designing RAG prompt construction to provide accurate, contextual answers from retrieved document chunks. Ensuring production-ready deployment with Docker containerization and database migrations.
Screenshots & Gallery
