TL;DR
This service allows users to upload documents via URL and ask multiple questions about the content. Using advanced RAG techniques with LangChain, it provides accurate, context-aware answers by retrieving relevant document sections and generating responses.
Problem → Solution
Problem The challenge was to build a RAG system to process insurance policy documents and answer questions against them, evaluated through multiple tests with documents and queries provided in each request.
Solution A robust FastAPI service that accepts a document URL and a list of questions. It uses LangChain for an efficient RAG pipeline to deliver accurate answers, all served through a scalable, containerized API.
Key Features
- Dynamic Document Processing
- Multi-Question Support
- RESTful API
- Advanced RAG Pipeline
- Dockerized Deployment
- Full Pydantic Type-Safety
Architecture
A clean, modular FastAPI application with dedicated services for document processing and Q&A, ensuring scalability and maintainability.
Role & Credits
HackRx 6.0 Hackathon (2025) This project was developed for the HackRx 6.0 hackathon organized by Bajaj Finserv Health Limited.
My Role: I was the sole developer responsible for designing the architecture, implementing the RAG pipeline with LangChain, building the FastAPI service, and containerizing the application with Docker.
API Usage
POST /hackrx/run
curl -X POST "https://hackrx-rag-app.onrender.com/hackrx/run" \
-H "Content-Type: application/json" \
-d '{
"documents": "https://example.com/document.pdf",
"questions": [
"What is the main topic?",
"What are the key findings?"
]
}'
Visit the interactive Swagger UI at the API Docs.