Case Study

AI PDF Chatbot

An AI PDF chatbot that lets users ask questions in plain English and get answers grounded in the exact document — built end-to-end: ingestion pipeline, vector search, chat UI, and deploy.

The challenge

Users don't want to read a 60-page PDF to find one answer. But naive LLM approaches hallucinate, and dumping the whole PDF into a prompt is expensive and slow. The product needed reliable retrieval, low-latency chat, and answers grounded in the source — not made up.

The approach

FastAPI ingestion pipeline chunks PDFs and stores OpenAI embeddings in PostgreSQL for semantic search. On query, the closest chunks are retrieved and passed as context to the model, so answers stay grounded in the actual document. Next.js frontend handles upload, streaming chat, and multi-document sessions.

What shipped

Production RAG pipeline: chunking, embeddings, vector search, and grounded generation
Sub-second retrieval on typical documents with PostgreSQL as the vector store — no separate infra
Streaming chat interface with context-aware follow-ups
End-to-end delivery solo: backend, vector search, LLM integration, frontend, and deploy

Project details

Next.jsOpenAIFastAPIPostgreSQL

Key features

PDF document processing and chunking
Vector embeddings for semantic search
Real-time chat interface
Context-aware responses

Best fit services

Next.js full-stack development
FastAPI backend development
Supabase and PostgreSQL architecture

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