Metadata-Version: 2.4
Name: promptopt
Version: 0.1.0
Summary: Enterprise prompt optimization framework combining DSPy and GRPO approaches
Home-page: https://github.com/promptopt/promptopt
Author: promptopt team
Author-email: PromptOpt Team <team@promptopt.dev>
License: MIT
Project-URL: Homepage, https://github.com/srepho/promptopt
Project-URL: Bug Tracker, https://github.com/srepho/promptopt/issues
Project-URL: Documentation, https://github.com/srepho/promptopt/blob/main/DOCUMENTATION.md
Keywords: prompt optimization,dspy,grpo,llm,prompt engineering,enterprise,ai
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: openai>=1.0.0
Requires-Dist: anthropic>=0.3.0
Requires-Dist: dspy-ai>=2.0.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: pydantic>=2.0.0
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Requires-Dist: seaborn>=0.11.0
Requires-Dist: tqdm>=4.62.0
Requires-Dist: requests>=2.26.0
Requires-Dist: tenacity>=8.0.0
Requires-Dist: rich>=10.0.0
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Requires-Dist: ipywidgets>=7.6.0; extra == "colab"
Dynamic: author
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Dynamic: requires-python

# PromptOpt: Enterprise Prompt Optimization Framework

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A unified framework for testing, comparing, and hybridizing DSPy and GRPO approaches to prompt optimization, with special focus on enterprise deployment via synthetic data and Colab accessibility.

## 🌟 Key Features

- **🤖 Multiple Optimization Strategies**: DSPy, GRPO, and hybrid approaches
- **💰 Cost-Aware Optimization**: Built-in budget management and tracking
- **🏢 Enterprise-Ready**: Compliance support, ROI analysis, deployment tools
- **☁️ Colab-Optimized**: Works perfectly in Google Colab (no GPU needed)
- **📊 Tournament Evaluation**: Head-to-head prompt comparison system
- **🎯 Synthetic Data**: Generate realistic business scenarios
- **🔧 API-Based**: Works with OpenAI and Anthropic APIs

## Installation

### Using Conda (Recommended)

```bash
# Create a new conda environment
conda create -n promptopt python=3.9
conda activate promptopt

# Install the package
pip install -e .

# For development
pip install -r requirements-dev.txt
```

### Using pip only

```bash
pip install promptopt
```

For development:
```bash
pip install -e ".[dev]"
```

For Colab environments:
```bash
pip install promptopt[colab]
```

## Quick Start

```python
from promptopt import EnterprisePOC
from promptopt.colab import ColabManager

# Set up environment
manager = ColabManager()
manager.setup_enterprise_environment()

# Run a complete POC
poc = EnterprisePOC()
results = poc.run_complete_poc(
    business_scenario="customer_support",
    company_context={"industry": "tech", "size": "enterprise"},
    budget_limit=500.0
)

# View results
print(f"Optimization improvements: {results.optimization_improvements}")
print(f"Projected ROI: {results.roi_projections}")
```

## 📚 Documentation

See [DOCUMENTATION.md](DOCUMENTATION.md) for comprehensive guides and API reference.

## 🚀 Quick Start in Google Colab

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/promptopt/promptopt/blob/main/notebooks/Enterprise_Quickstart.ipynb)

## 🧪 Running Tests

```bash
conda activate promptopt
pytest tests/
```

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- DSPy framework for few-shot optimization techniques
- GRPO methodology for tournament-based optimization
- OpenAI and Anthropic for LLM APIs

## 📊 Example Results

Using PromptOpt, teams typically see:
- **30-40% improvement** in response quality
- **85%+ consistency** across team members
- **$2000+/month savings** from optimized prompts
- **2-week ROI** for enterprise deployments
