Execution Control

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Control Parameters

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Performance Controls

• Timeout Settings: Maximum execution time
• Parallel Processing: Enable concurrent operations
• Cache Usage: Control response caching

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Quality Controls

• Model Selection: Choose specific AI models
• Temperature: Control response creativity
• Token Limits: Manage response length

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Resource Controls

• Compute Limits: Cap resource usage
• Priority Levels: Set execution priority
• Retry Policies: Configure failure handling

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Implementation Example

response = client.rag.chat(
    messages=[{"role": "user", "content": "Complex query"}],
    execution_control={
        "enable_tracing": True,
        "timeout": 30,
        "max_tokens": 1000,
        "temperature": 0.7,
        "parallel_retrieval": True,
        "cache_enabled": True
    }
)

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Advanced Features

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Conditional Execution

• Set conditions for different processing paths
• Implement fallback strategies
• Define success criteria

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Resource Optimization

• Automatic scaling based on load
• Cost-aware processing
• Performance monitoring

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Best Practices

1. Start with default settings
2. Monitor performance metrics
3. Adjust based on your use case
4. Document your configurations

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Next Steps

• Explore the Architecture
• Try Document Processing
• View API Reference