MultiMind SDK Examples Documentation

This documentation provides detailed information about the example implementations in the MultiMind SDK, including usage patterns, best practices, and advanced features.

Table of Contents

1. Getting Started
2. Model Management Examples
3. Multi-Modal Examples
4. Advanced Features
5. Testing
6. Best Practices

Getting Started

Prerequisites

1. Install the SDK:

pip install multimind-sdk

2. Set up environment variables:

export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"
export HUGGINGFACE_API_KEY="your-key"

3. Install example dependencies:

pip install -r examples/requirements.txt

Basic Usage

1. Model Management:

from multimind.models.factory import ModelFactory
from multimind.models.multi_model import MultiModelWrapper

# Initialize
factory = ModelFactory()
wrapper = MultiModelWrapper(
    model_factory=factory,
    primary_model="gpt-3.5-turbo",
    fallback_models=["gpt-4", "claude"]
)

async def example_generate_text():
    # Generate text
    response = await wrapper.generate("Explain quantum computing")

2. Multi-Modal Processing:

async def example_multi_modal():
    from multimind.router.multi_modal_router import MultiModalRouter

    # Initialize
    router = MultiModalRouter()

    # Process multi-modal request
    request = {
        "content": {
            "image": "base64_encoded_image",
            "text": "Describe this image"
        },
        "modalities": ["image", "text"]
    }
    response = await router.process(request)

Model Management Examples

Basic Usage

1. Text Generation:

# examples/model_management/basic/basic_usage.py
async def generate_text():
    wrapper = MultiModelWrapper(
        model_factory=factory,
        primary_model="gpt-3.5-turbo"
    )
    response = await wrapper.generate(
        prompt="Write a short story",
        temperature=0.7
    )

2. Chat Completion:

# examples/model_management/basic/basic_usage.py
async def chat_completion():
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather like?"}
    ]
    response = await wrapper.chat(messages=messages)

Advanced Features

1. Cost Optimization:

# examples/model_management/advanced/cost_optimization.py
wrapper = CostOptimizedWrapper(
    model_factory=factory,
    primary_model="gpt-3.5-turbo",
    fallback_models=["gpt-4", "claude"],
    budget=0.1
)
response = await wrapper.generate("Complex query")

2. Intelligent Switching:

# examples/model_management/advanced/intelligent_switching.py
wrapper = IntelligentSwitchingWrapper(
    model_factory=factory,
    performance_threshold=0.8
)
response = await wrapper.generate("Query")

Multi-Modal Examples

Basic Processing

1. Image Captioning:

# examples/multi_modal/basic/process_request.py
async def process_image():
    request = UnifiedRequest(
        inputs=[
            ModalityInput(
                modality="image",
                content=image_data
            ),
            ModalityInput(
                modality="text",
                content="Describe this image"
            )
        ]
    )
    response = await router.process(request)

2. Audio Transcription:

# examples/multi_modal/basic/process_request.py
async def process_audio():
    request = UnifiedRequest(
        inputs=[
            ModalityInput(
                modality="audio",
                content=audio_data
            )
        ]
    )
    response = await router.process(request)

Advanced Workflows

1. Cross-Modal Retrieval:

# examples/multi_modal/workflows/cross_modal_retrieval.py
workflow = CrossModalRetrievalWorkflow(
    models=router.models,
    integrations={}
)
result = await workflow.execute(request)

2. Multi-Modal Analysis:

# examples/multi_modal/workflows/workflows.py
workflow = MultiModalAnalysisWorkflow(
    models=router.models,
    integrations={}
)
result = await workflow.execute(request)

Advanced Features

Cost Tracking

from multimind.metrics.cost_tracker import CostTracker

tracker = CostTracker()
tracker.track_cost("gpt-4", 0.05)
total_cost = tracker.get_total_cost()

Performance Metrics

from multimind.metrics.performance import PerformanceTracker

tracker = PerformanceTracker()
tracker.track_latency("gpt-4", 0.5)
metrics = tracker.get_metrics()

Model Management

from multimind.models.factory import ModelFactory

factory = ModelFactory()
factory.register_model(
    name="custom-model",
    type="openai",
    config={"temperature": 0.7}
)

Testing

Running Tests

# Run all tests
pytest tests/examples/

# Run specific test categories
pytest tests/examples/model_management/
pytest tests/examples/multi_modal/

# Run with coverage
pytest --cov=examples tests/examples/

Writing Tests

@pytest.mark.asyncio
async def test_example():
    # Setup
    wrapper = MultiModelWrapper(...)
    
    # Execute
    response = await wrapper.generate("test")
    
    # Assert
    assert response is not None
    assert len(response) > 0

Best Practices

1. Error Handling

   • Always use try-except blocks
   • Implement proper fallback strategies
   • Log errors appropriately

2. Resource Management

   • Set appropriate timeouts
   • Implement rate limiting
   • Monitor API usage

3. Performance Optimization

   • Use appropriate model sizes
   • Implement caching where possible
   • Monitor latency and costs

4. Security

   • Never hardcode API keys
   • Validate input data
   • Implement proper access controls

5. Testing

   • Write comprehensive tests
   • Use mock data for testing
   • Test error conditions

Contributing

1. Fork the repository
2. Create a feature branch
3. Add your example
4. Write tests
5. Update documentation
6. Submit a pull request

Support

For issues and questions:

1. Open an issue on GitHub
2. Contact contact@multimind.dev