DocMind - RAG Chat Application
A cutting-edge Retrieval-Augmented Generation (RAG) agent capable of ingesting websites and enabling users to chat with their content in real-time. Built with a focus on accuracy, performance, and a premium user experience.
Overview
In the era of large language models, static information retrieval is no longer enough. DocMind bridges the gap between static web content and conversational AI. By providing an intuitive interface for URL ingestion, it allows users to unlock conversational insights from any website instantly.
The core challenge was building a system that could accurately parse messy web data, intelligently chunk it to preserve semantic meaning, and quickly retrieve relevant context for an LLM to formulate an answer—all while minimizing hallucinations and ensuring the UI feels snappy and responsive.
Tech Stack
- Frontend:Next.js 15 (App Router), Tailwind CSS 4, Framer Motion
- AI Pipeline:LangChain.js, OpenAI (GPT-4o/3.5-turbo), OpenAI Embeddings
- Ingestion:Puppeteer for robust web scraping
- Vector Store:In-memory vector store (designed to scale to ChromaDB/Pinecone)
Key Features
- Seamless Website Ingestion via URL
- Smart context-preserving text chunking
- High-performance semantic vector search
- Strict anti-hallucination prompting techniques
- Modern, glassmorphic UI requiring no login
Architecture Pipeline
- 1
Ingest & Load
User provides a URL. Puppeteer visits the page, bypassing common blockers, and extracts the raw text content.
- 2
Split
Using LangChain's RecursiveCharacterTextSplitter, content is divided into manageable chunks with overlap to retain context across boundaries.
- 3
Embed & Store
Chunks are converted into high-dimensional vector embeddings via OpenAI and stored for rapid similarity search.
- 4
Retrieve & Generate
User queries are embedded, compared against the store to retrieve relevant chunks, and fed into the LLM context window to synthesize an accurate, grounded response.
Outcome
DocMind demonstrates the practical application of RAG concepts in a modern web environment. It successfully removes the friction of extracting and interacting with web data, providing users with a robust tool to instantly query almost any open website in natural language.
Full-Stack Engineer & AI Product Builder
4+ years of experience building scalable web applications and AI-powered products. Passionate about end-to-end product development, clean architecture, and solving real-world problems.