savanty 0.2.1

An intelligent optimization problem solver using LLMs and Answer Set Programming

Savanty: Intelligent Optimization Problem Solver

Savanty is an intelligent Python library that leverages the power of Large Language Models (LLMs) and DSPy to parse, solve, and visualize optimization problems. It provides both a command-line interface and a web interface for users to input problem descriptions in natural language and automatically generates solutions using Answer Set Programming (ASP).

🌟 Features

• Natural Language Processing: Utilizes GPT-4 and DSPy for robust optimization problem parsing
• Code Generation: Automatically generates Clorm predicates and ASP programs
• Problem Validation: Validates if a problem can be solved with ASP before attempting to solve it
• Interactive Gap Filling: Asks for additional information when problem descriptions are incomplete
• Problem Solving: Implements optimization problem solving using Clingo
• Dual Interface: Command-line interface for direct usage and web interface for interactive solving
• Modern Python Package: Installable via pip with proper CLI integration

🚀 Quick Start

# Install Savanty
pip install savanty

# Set your OpenAI API key
export OPENAI_API_KEY=your_openai_api_key_here

# Solve a problem directly from the command line
savanty -p "Minimize x+y subject to x>=0, y>=0, x+y<=10"

# Or run the web interface
savanty --web --port 5000

📋 Prerequisites

• Python 3.8+
• An OpenAI API key

📦 Installation

From PyPI (Recommended)

pip install savanty

From Source

git clone https://github.com/terraprompt/savanty.git
cd savanty
pip install .

⚙️ Configuration

Savanty can be configured using environment variables:

# Required: Set your OpenAI API key
export OPENAI_API_KEY=your_openai_api_key_here

# Optional: Configure the LLM model (default: gpt-4o)
export SAVANTY_LLM_MODEL=gpt-4-turbo

# Optional: Set a custom Flask secret key
export FLASK_SECRET_KEY=your_flask_secret_key_here

🖥️ Usage

Command Line Interface

Solve optimization problems directly from the command line:

# Solve a problem directly
savanty -p "We have a task scheduling problem. Each task has a name, duration, and priority. We need to schedule tasks within a maximum time of 10 units. Tasks cannot overlap. We want to maximize the total priority of scheduled tasks. Available tasks are: Task1: duration 3, priority 5; Task2: duration 2, priority 3; Task3: duration 4, priority 7; Task4: duration 1, priority 2."

# Run the web interface on a custom port
savanty --web --port 8080

Interactive Problem Solving

When a problem description is incomplete, Savanty will ask for additional information:

savanty -p "Solve a scheduling problem"
# Savanty will ask: "What are the tasks and their properties?"
# You can then provide: "Task1: duration 3, priority 5; Task2: duration 2, priority 3"

Web Interface

Run the web interface to solve problems interactively:

# Run on default port 5000
savanty --web

# Run on a custom port
savanty --web --port 8080

Then open your browser to http://localhost:5000 (or your custom port) to access the web interface. The web interface also supports interactive gap filling - if your problem description is incomplete, Savanty will ask for additional information directly in the web interface.

Python API

Use Savanty directly in your Python code:

from savanty.solver import solve_optimization_problem

problem_description = """
We have a task scheduling problem. Each task has a name, duration, and priority.
We need to schedule tasks within a maximum time of 10 units.
Tasks cannot overlap.
We want to maximize the total priority of scheduled tasks.

Available tasks are:
Task1: duration 3, priority 5
Task2: duration 2, priority 3
Task3: duration 4, priority 7
Task4: duration 1, priority 2
"""

result = solve_optimization_problem(problem_description)

if result.needs_more_info:
    print("Please provide more information:")
    for question in result.questions:
        print(f"- {question}")
elif result.error:
    print(f"Error: {result.error}")
else:
    print(f"Solution: {result.solution}")

💡 Example Use Cases

1. Task Scheduling Problem

savanty -p "Schedule tasks to maximize priority. TaskA (duration 3, priority 5), TaskB (duration 2, priority 4), TaskC (duration 4, priority 7). Time limit is 8 units. Tasks cannot overlap."

2. Resource Allocation

savanty -p "Allocate 3 projects to 2 teams. Project1 (value 100, requires 2 team members), Project2 (value 150, requires 3 team members), Project3 (value 80, requires 1 team member). Total team members available: 4. Maximize total value."

3. Knapsack Problem

savanty -p "Knapsack with capacity 10. Item1 (weight 5, value 10), Item2 (weight 4, value 7), Item3 (weight 6, value 12), Item4 (weight 2, value 3). Maximize value without exceeding capacity."

4. Graph Coloring

savanty -p "Color a graph with 4 nodes and edges: (1,2), (2,3), (3,4), (1,4). Use minimum number of colors such that adjacent nodes have different colors."

5. Sudoku Solver

savanty -p "Solve a 4x4 Sudoku puzzle. Given: position (1,1) = 1, position (1,3) = 2, position (2,2) = 3, position (3,1) = 4. Find values for all positions respecting Sudoku rules."

📁 Project Structure

savanty/
├── savanty/
│   ├── __init__.py
│   ├── cli.py          # Command-line interface
│   ├── solver.py       # Core solver logic
│   ├── dspy_modules.py # DSPy modules for LLM processing
│   └── templates/
│       └── index.html  # Web interface template
├── tests/
├── pyproject.toml
├── README.md
└── LICENSE

🤝 Contributing

Contributions to Savanty are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch: git checkout -b feature-branch-name
3. Make your changes and commit them: git commit -m 'Add some feature'
4. Push to the original branch: git push origin feature-branch-name
5. Create the pull request

Alternatively, see the GitHub documentation on creating a pull request.

📄 License

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

📧 Contact

If you have any questions or feedback, please contact the maintainer:

Dipankar Sarkar - [e-mail]

Project Link: https://github.com/terraprompt/savanty

🙏 Acknowledgements

• OpenAI for providing the GPT-4 API
• DSPy for LLM prompting and optimization
• Clingo for the ASP solver
• Clorm for Object-Relational Mapping with Clingo
• Flask for the web framework
• HTMX for dynamic HTML capabilities
• Tailwind CSS for styling
• Click for CLI creation