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README.md

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+# rStar-Math Demonstrator
+
+An AI Agent that demonstrates the principles and performance of the rStar-Math framework, with capabilities to generate integration code for other chatbots and AI agents.
+
+The development of this GitHub Repository was inspired by the "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking" paper. To read the full paper, visit https://arxiv.org/pdf/2501.04519
+
+## Features
+
+- **Core Components**
+  - Monte Carlo Tree Search (MCTS) for step-by-step reasoning
+  - Process Preference Model (PPM) for evaluating solution quality
+  - Flexible model interface supporting multiple LLMs
+
+- **Model Support**
+  - OpenAI (GPT-4, GPT-3.5)
+  - Anthropic (Claude)
+  - Mistral AI
+  - Groq
+  - Google Gemini
+  - Local models via llama.cpp
+
+- **Integration Templates**
+  - Rasa chatbot framework
+  - LangChain
+  - Azure Bot Framework
+  - Streamlit
+  - Gradio
+
+- **Example Notebooks**
+  - Calculus with visualizations
+  - Geometry and proofs
+  - Linear algebra operations
+  - Statistics and probability
+  - Model comparison studies
+
+- **Development Tools**
+  - Comprehensive test suite
+  - Performance benchmarking
+  - Visualization components
+  - API documentation
+
+## Installation
+
+### Option 1: Install from PyPI
+
+```bash
+pip install rstar-math
+```
+
+### Option 2: Install from Source
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/rStar-Math.git
+cd rStar-Math
+```
+
+2. Create a virtual environment:
+```bash
+python -m venv venv
+
+# Windows
+venv\Scripts\activate
+
+# Unix/MacOS
+source venv/bin/activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Install in development mode:
+```bash
+pip install -e .
+```
+
+### Setting up API Keys
+
+Create a `.env` file in the project root:
+```bash
+OPENAI_API_KEY=your_openai_key
+ANTHROPIC_API_KEY=your_anthropic_key
+MISTRAL_API_KEY=your_mistral_key
+GROQ_API_KEY=your_groq_key
+GEMINI_API_KEY=your_gemini_key
+```
+
+## Running the Project
+
+### 1. Run Interactive Demos
+
+#### Gradio Interface
+```bash
+python examples/gradio_integration.py
+```
+
+#### Streamlit Dashboard
+```bash
+streamlit run examples/streamlit_integration.py
+```
+
+### 2. Run Example Notebooks
+
+```bash
+# Start Jupyter server
+jupyter lab
+
+# Navigate to examples/notebooks/
+# Open any of:
+# - calculus_examples.ipynb
+# - geometry_examples.ipynb
+# - linear_algebra_examples.ipynb
+# - statistics_examples.ipynb
+```
+
+### 3. Run Tests
+
+```bash
+# Run all tests
+pytest tests/
+
+# Run specific test suite
+pytest tests/test_new_models.py
+
+# Run with coverage report
+pytest --cov=src tests/
+```
+
+### 4. Run Benchmarks
+
+```bash
+# Run full benchmark suite
+python tools/benchmark.py
+
+# View results in browser
+python -m http.server 8000
+# Open http://localhost:8000/benchmark_results/
+```
+
+### 5. Framework Integrations
+
+#### Rasa Integration
+```bash
+# In your Rasa project
+pip install rstar-math
+cp examples/rasa_integration.py actions/
+```
+
+#### LangChain Integration
+```python
+from examples.langchain_integration import RStarMathChain
+chain = RStarMathChain()
+```
+
+#### Azure Bot Integration
+```bash
+# In your Azure Bot project
+pip install rstar-math
+cp examples/azure_bot_integration.py bot/
+```
+
+### 6. Local Model Setup
+
+1. Download a compatible model:
+```bash
+# Example: Download LLaMA model
+wget https://huggingface.co/models/llama-7b/resolve/main/model.bin -O models/llama-7b.bin
+```
+
+2. Run with local model:
+```python
+from examples.llama_cpp_integration import LlamaCppModel
+model = LlamaCppModel("models/llama-7b.bin")
+```
+
+## Quick Start
+
+```python
+from rstar_math.core import MCTS, PPM
+from rstar_math.models import ModelFactory
+
+# Initialize components
+mcts = MCTS.from_config_file('config/default.json')
+ppm = ProcessPreferenceModel.from_config_file('config/default.json')
+model = ModelFactory.create_model('openai', 'YOUR_API_KEY', 'config/default.json')
+
+# Solve a problem
+problem = "What is the derivative of f(x) = x^2 + 3x?"
+action, trajectory = mcts.search(problem)
+
+# Print solution steps with confidence scores
+for step in trajectory:
+    confidence = ppm.evaluate_step(step['state'], model)
+    print(f"Step: {step['state']}")
+    print(f"Confidence: {confidence:.2f}\n")
+```
+
+## Example Applications
+
+### 1. Interactive Web Interface
+
+```python
+from examples.gradio_integration import RStarMathGradio
+
+# Launch Gradio interface
+demo = RStarMathGradio()
+demo.launch()
+```
+
+### 2. Chatbot Integration
+
+```python
+from examples.rasa_integration import RStarMathAction
+
+# Use in Rasa custom action
+action = RStarMathAction()
+await action.run(dispatcher, tracker, domain)
+```
+
+### 3. Local Model Inference
+
+```python
+from examples.llama_cpp_integration import LlamaCppModel
+
+# Initialize local model
+model = LlamaCppModel("path/to/model.bin")
+response = model.generate_response("What is 2 + 2?")
+```
+
+## Documentation
+
+- [API Reference](docs/api.md)
+- [Model Integration Guide](docs/model_integration.md)
+- [Example Notebooks](examples/notebooks/)
+- [Benchmark Results](docs/benchmarks.md)
+
+## Benchmarking
+
+Run performance benchmarks:
+
+```bash
+python tools/benchmark.py
+```
+
+This will generate:
+- Execution time comparisons
+- Memory usage analysis
+- Token count statistics
+- Confidence score trends
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch
+3. Run tests: `pytest tests/`
+4. Submit a pull request
+
+## License
+
+MIT License - see LICENSE file for details
+
+## Citation
+
+If you use rStar-Math in your research, please cite:
+
+```bibtex
+@article{rstar2024,
+  title={rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking},
+  author={Original Authors},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+## Acknowledgments
+
+This project is inspired by the paper "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking" (https://arxiv.org/pdf/2501.04519).
+
+## Project Structure
+
+```
+rStar-Math/
+├── src/                    # Source code
+│   ├── core/              # Core components (MCTS, PPM)
+│   ├── models/            # Model implementations
+│   └── utils/             # Utility functions
+├── tests/                 # Test suites
+├── examples/              # Example integrations
+│   ├── notebooks/        # Jupyter notebooks
+│   └── frameworks/       # Framework integrations
+├── docs/                 # Documentation
+├── tools/                # Development tools
+└── config/               # Configuration files
+```
+
+## Development Workflow
+
+1. Create a new feature branch:
+```bash
+git checkout -b feature/your-feature-name
+```
+
+2. Make changes and run tests:
+```bash
+# Format code
+black src/ tests/
+
+# Run linter
+flake8 src/ tests/
+
+# Run tests
+pytest tests/
+```
+
+3. Submit a pull request:
+```bash
+git add .
+git commit -m "feat: your feature description"
+git push origin feature/your-feature-name
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. API Key Issues:
+```bash
+# Check if keys are loaded
+python -c "import os; print(os.getenv('OPENAI_API_KEY'))"
+```
+
+2. Model Loading Issues:
+```bash
+# Verify model files
+ls models/
+```
+
+3. CUDA Issues:
+```bash
+# Check CUDA availability
+python -c "import torch; print(torch.cuda.is_available())"
+```
+
+For more issues, check the [troubleshooting guide](docs/troubleshooting.md).