FastAPI RAG Service

A high-performance document question-answering service built with FastAPI and LangChain, implementing Retrieval-Augmented Generation (RAG) for intelligent document analysis.

🚀 Live Demo

• API Docs: https://hackrx-rag-app.onrender.com/docs
• GitHub Repository: https://github.com/sujeetgund/fastapi-rag-service.git

📋 Overview

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.

✨ Features

• Document Processing: Support for various document formats (PDF, DOC, TXT, etc.)
• Multi-Question Support: Process multiple questions in a single request
• RESTful API: Clean, well-documented FastAPI endpoints
• RAG Pipeline: Advanced retrieval-augmented generation for accurate answers
• Dockerized: Easy deployment with Docker support
• Type Safety: Full Pydantic model validation
• Error Handling: Comprehensive exception handling and logging

🛠️ Tech Stack

• FastAPI: Modern, fast web framework for building APIs
• LangChain: Framework for developing applications with language models
• Pydantic: Data validation and settings management
• Docker: Containerization for easy deployment
• UV: Fast Python package installer and resolver

📁 Project Structure

fastapi-rag-service/
├── api/
│   └── routes.py              # API endpoint definitions
├── core/
│   ├── config.py              # Configuration settings
│   ├── exceptions.py          # Custom exception classes
│   └── security.py            # Security and authentication
├── models/
│   └── schemas.py             # Pydantic models and schemas
├── services/
│   ├── document_service.py    # Document processing logic
│   └── qa_service.py          # Question-answering service
├── .dockerignore
├── .gitignore
├── .python-version
├── Dockerfile                 # Docker configuration
├── main.py                    # FastAPI application entry point
├── pyproject.toml             # Project metadata and dependencies
├── README.md
├── requirements.txt           # Python dependencies
└── uv.lock                    # UV lock file

📊 API Models

QuestionAnswerRequest Model

The input structure for the primary /hackrx/run endpoint.

Field	Type	Description	Example
documents	str	URL of the document to process	"https://example.com/document.pdf"
questions	List[str]	List of questions to answer	["What is the main topic?", "What are the key findings?"]

QuestionAnswerResponse Model

The output structure from the RAG pipeline.

Field	Type	Description	Example
answers	List[str]	Generated answers in the same order as questions	["The main topic is...", "The key findings are..."]

🚀 Quick Start

Prerequisites

• Python 3.9+
• Docker (optional)
• UV package manager (recommended)

Installation

1. Clone the repository

git clone https://github.com/sujeetgund/fastapi-rag-service.git
cd fastapi-rag-service

2. Install dependencies

Using UV (recommended):

uv sync

Using pip:

pip install -r requirements.txt

3. Run the application

uvicorn main:app --reload

The API will be available at http://localhost:8000

Docker Deployment

1. Build the Docker image

docker build -t fastapi-rag-service .

2. Run the container

docker run -p 8000:8000 fastapi-rag-service

📖 Usage

API Endpoint

POST /hackrx/run

Process a document and answer questions about its content.

Request Example

curl -X POST "http://localhost:8000/hackrx/run" \
  -H "Content-Type: application/json" \
  -d '{
    "documents": "https://example.com/research-paper.pdf",
    "questions": [
      "What is the main research question?",
      "What methodology was used?",
      "What are the key conclusions?"
    ]
  }'

Response Example

{
  "answers": [
    "The main research question focuses on investigating the impact of machine learning algorithms on data processing efficiency.",
    "The study employed a mixed-methods approach combining quantitative analysis with qualitative interviews.",
    "The key conclusions indicate that ML algorithms can improve processing efficiency by up to 40% while maintaining data accuracy."
  ]
}

Interactive Documentation

Visit http://localhost:8000/docs to access the interactive Swagger UI documentation.

⚙️ Configuration

Configuration settings can be modified in core/config.py:

• API settings
• Model parameters
• Document processing options
• Security configurations

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.