🤖 AI Research Assistant

📚 A powerful, AI-driven research assistant that leverages Ollama LLM to perform intelligent, iterative research on any topic.

Transforming the way we conduct research with AI

✨ Key Features

• 🧠 Advanced LLM Integration - Seamless integration with Ollama for state-of-the-art language processing
• 🔄 Iterative Research - Conducts multi-step research with progressive refinement
• 📊 Smart Analysis - Intelligent processing and synthesis of research findings
• 📝 Comprehensive Logging - Detailed tracking of research progress and findings
• ⚡ High Performance - Optimized for efficient processing and quick results
• 🛡️ Full Test Coverage - Ensuring reliability and stability
• ⚙️ Flexible Configuration - Easy customization through environment variables

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Ollama server (local or remote)
• Git

Installation

1. Clone the repository:

git clone <repository-url>
cd agents-lllm

2. Set up virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

⚙️ Configuration

Create a .env file in the project root:

# Ollama Configuration
OLLAMA_BASE_URL=http://localhost:11434
MODEL_NAME=llama3.2:3b

# Agent Configuration
TEMPERATURE=0.7
MAX_ITERATIONS=3

💻 Usage

from src import ResearchAssistant

# Initialize the research assistant
assistant = ResearchAssistant()

# Start researching
findings = assistant.research_topic("Artificial Intelligence in Healthcare")

# Process findings
for finding in findings:
    print(f"Finding {finding['iteration']}: {finding['content']}")

🧪 Testing

Run the comprehensive test suite:

# Run tests
python -m pytest src/tests/

# Generate coverage report
python -m pytest --cov=src src/tests/

📁 Project Structure

agents-lllm/
├── src/
│   ├── core/           # Core functionality
│   ├── utils/          # Utility functions
│   └── tests/          # Test suite
├── research_data/      # Research data storage
├── textbook_knowledge/ # Knowledge base
├── uploaded_books/     # Book storage
├── .env               # Environment configuration
├── requirements.txt   # Project dependencies
└── README.md         # This file

🤝 Contributing

Contributions are welcome! Feel free to:

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.

🙏 Acknowledgments

• Ollama team for their amazing LLM
• All contributors who help improve this project
• Open source community for various tools and libraries used

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Made with ❤️ by the AI Research Assistant Team