ResearchPilot-Agent-REST-API

Live Demo AI-powered research system with modular architecture, multi-agent collaboration, and real tool integration.

🌟 Features

• Multi-Agent System: 3 specialized AI agents working together
  • Researcher: Searches web for current information
  • Fact-Checker: Verifies claims and cross-checks sources
  • Summarizer: Creates professional reports
• Real Tool Access: Web search, webpage scraping, calculations
• JWT Authentication: Secure user authentication and authorization
• Database Storage: SQLite for research history and user management
• Modular Architecture: Clean separation of concerns
• RESTful API: FastAPI with automatic OpenAPI documentation
• Fully Tested: Unit tests with pytest

📁 Project Structure

research_assistant/
├── agent/              # AI agents and workflow logic
│   ├── tools.py        # Research tools (search, scrape, calculate)
│   ├── state.py        # Workflow state definition
│   ├── agents.py       # Agent classes
│   ├── router.py       # Routing logic
│   └── graph.py        # LangGraph workflow builder
├── api/                # FastAPI application
│   ├── main.py         # App entry point
│   ├── models.py       # Pydantic models
│   ├── auth_routes.py  # Authentication endpoints
│   └── research_routes.py # Research endpoints
├── database/           # Database layer
│   ├── db.py           # Database setup
│   └── models.py       # SQLAlchemy models
├── auth/               # Authentication
│   ├── security.py     # Password hashing, JWT
│   └── dependencies.py # Auth middleware
├── config/             # Configuration
│   └── settings.py     # Settings management
├── tests/              # Unit tests
│   └── test_agents.py  # Agent tests
├── requirements.txt    # Python dependencies
├── .env                # Environment variables
└── README.md          # This file

🚀 Quick Start

1. Clone and Setup

# Clone repository
git clone <your-repo-url>
cd research_assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configure Environment

Create .env file:

# API Keys
GOOGLE_API_KEY=your_google_gemini_api_key

# JWT Secret (generate with: openssl rand -hex 32)
SECRET_KEY=your_super_secret_jwt_key_here

# Database
DATABASE_URL=sqlite:///./research_assistant.db

# Agent Settings
MAX_RESEARCH_ITERATIONS=2
DEFAULT_LLM_TEMPERATURE=0.7

3. Run Application

# Start server
uvicorn api.main:app --reload

# Server will start at: http://localhost:8000

4. Access API Documentation

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• Health Check: http://localhost:8000/health

📚 API Usage

Register User

curl -X POST http://localhost:8000/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "username": "testuser",
    "email": "test@example.com",
    "password": "password123"
  }'

Login

curl -X POST http://localhost:8000/auth/login \
  -H "Content-Type: application/json" \
  -d '{
    "username": "testuser",
    "password": "password123"
  }'

Response:

{
  "access_token": "eyJhbGc...",
  "token_type": "bearer",
  "username": "testuser",
  "email": "test@example.com"
}

Create Research

curl -X POST http://localhost:8000/research/ \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -d '{
    "query": "What are the latest AI developments in 2024?",
    "max_iterations": 2
  }'

Get Research History

curl -X GET http://localhost:8000/research/history \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN"

Get Specific Research

curl -X GET http://localhost:8000/research/1 \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN"

🧪 Testing

# Run all tests
pytest tests/ -v

# Run specific test file
pytest tests/test_agents.py -v

# Run with coverage
pytest tests/ --cov=agent --cov-report=html

# View coverage report
open htmlcov/index.html

🔧 Configuration

Environment Variables

Variable	Description	Default
GOOGLE_API_KEY	Google AI API key	Required
SECRET_KEY	JWT secret key	Required
DATABASE_URL	Database connection	sqlite:///./research_assistant.db
MAX_RESEARCH_ITERATIONS	Max research cycles	2
ACCESS_TOKEN_EXPIRE_MINUTES	Token expiration	1440 (24h)

Agent Configuration

Edit config/settings.py:

MAX_RESEARCH_ITERATIONS: int = 2  # 1-5 iterations
DEFAULT_LLM_TEMPERATURE: float = 0.7  # 0.0-1.0

🏗️ Architecture

Multi-Agent Workflow

User Query
    ↓
Researcher Agent (with tools)
    ↓ (searches web, scrapes pages)
Fact-Checker Agent (with tools)
    ↓ (verifies claims)
Summarizer Agent
    ↓ (creates report)
Final Report

Tools Available

1. Web Search: DuckDuckGo search for current information
2. Web Scraper: Extract content from URLs
3. Calculator: Perform mathematical calculations

Database Schema

Users Table:

• id, username, email, hashed_password, is_active, created_at

ResearchSessions Table:

• id, user_id, query, research_data, verified_facts, final_report
• status, agent_iterations, processing_time, created_at

📊 Project Stats

• Lines of Code: ~1,500+
• Modules: 10
• API Endpoints: 8
• Test Coverage: 80%+
• Tools Integrated: 3

🎯 Key Features for Portfolio

1. ✅ Modular Design: Clean separation of concerns
2. ✅ Multi-Agent System: LangGraph orchestration
3. ✅ Tool Integration: Real web search capabilities
4. ✅ Production Ready: Auth, DB, error handling
5. ✅ Well Tested: Unit tests with pytest
6. ✅ Documented: Comprehensive API docs
7. ✅ Scalable: Easy to extend with new agents/tools

🚀 Future Enhancements

• Add streaming responses
• Implement caching with Redis
• Add more tools (Wikipedia, arXiv, GitHub)
• Create admin dashboard
• Add usage analytics
• Implement rate limiting
• Deploy to cloud (AWS/GCP)

📝 License

MIT License

👤 Author

[Asadullah Shebaz]

🙏 Acknowledgments

• LangChain & LangGraph
• FastAPI
• GROQ API Key