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Document Q&A Chatbot
RAG-powered chatbot that lets businesses query internal PDF documents using natural language — no manual search required.
The Problem
A business needed to query internal PDF documents without manual search.
The Solution
Built a RAG pipeline using LangChain, ChromaDB, and Gemini API with a FastAPI backend.
What I Built
PDF ingestion and chunking pipeline with LangChain
ChromaDB vector store for semantic retrieval
Gemini API for answer generation with source citations
FastAPI backend with React chat UI
Tech Stack
- Python
- LangChain
- ChromaDB
- Gemini API
- FastAPI
- React
Challenges
Keeping retrieval accurate across large PDFs with mixed formatting — solved by tuning chunk size and overlap.
What I Learned
Chunk strategy and embedding model choice matter more than the LLM for RAG accuracy. Source citation builds user trust significantly.
Key Metrics
Reduced document lookup time
Supports 50+ page PDFs
Sub-3s response time
Links
Source Code
github.com/dharmapra2/document-qa-chatbot
Open on GitHub →Live Deployment
No live deployment yet. Check the GitHub repository for setup instructions.