GovWatch

GovWatch is a modern web application that brings transparency to government contract spending through AI-powered search and data visualization.

Project Overview

GovWatch makes government contract data accessible to everyone through:

• Natural Language Search: Ask questions in plain English about government contracts
• Automated Data Collection: Regular scraping of defense.gov contract announcements
• AI-Powered Analysis: Vector embeddings and semantic search to find relevant contracts
• User-Friendly Interface: Clean, responsive design that works on all devices

Repository Structure

This monorepo contains both the frontend and backend components:

govwatch/
├── frontend/           # Next.js frontend application
│   ├── app/            # Pages and layouts
│   ├── components/     # Reusable UI components
│   └── ...
├── backend/            # FastAPI backend application
│   ├── app/            # API code
│   │   ├── routes/     # API endpoints
│   │   ├── services/   # Business logic
│   │   └── ...
│   └── ...
└── ...

Getting Started

Prerequisites

• Node.js 18.x+ (frontend)
• Python 3.11+ (backend)
• Poetry (Python dependency management)
• Pinecone account (vector database)
• Google Gemini API key

Running the Frontend

cd frontend
npm install
npm run dev

The frontend will be available at http://localhost:3000

Running the Backend

cd backend
poetry install
poetry run start

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

Environment Setup

Frontend Environment

Create a .env.local file in the frontend directory:

NEXT_PUBLIC_API_URL=http://localhost:8000

Backend Environment

Create a .env file in the backend directory:

PINECONE_API_KEY=your_pinecone_api_key
GEMINI_API_KEY=your_gemini_api_key

Deployment

• Frontend: Deployed on Vercel
• Backend: Deployed on Render.com

Architecture

GovWatch uses a modern architecture:

1. Data Collection:

   • Python scraper collects contract data from defense.gov
   • Data is processed and structured

2. Vector Embeddings:

   • Contract text is converted to vector embeddings using Google's Gemini API
   • Embeddings are stored in Pinecone vector database

3. Search API:

   • FastAPI backend provides endpoints for searching contracts
   • User queries are converted to embeddings and compared with stored contract embeddings

4. Frontend:

   • Next.js application provides a user-friendly interface
   • Communicates with the backend API to retrieve search results

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

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

Contact

For questions or feedback, please open an issue on this repository.