Dspy plugin implementation

Documentation for dspy_plugin_implementation from the Pipeline ex repository.

DSPy Plugin Implementation Prompt

Context Recontextualization

You are working on the pipeline_ex system, an Elixir-based pipeline generator that is being enhanced with a comprehensive plugin architecture. You now need to implement a complete DSPy plugin that integrates all the advanced infrastructure components we’ve designed.

System Architecture Context

Plugin Architecture: Dynamic plugin loading system with component registration
Python Bridge: Sophisticated Python integration for DSPy optimization
Real-Time Optimization: Async optimization with caching and fallback mechanisms
Training Data Management: Quality management and versioning system
Production Deployment: Phased rollout with A/B testing and monitoring

Current Infrastructure Components Available

Dynamic Step Registry: Pipeline.Enhanced.StepRegistry for runtime step registration
Provider Registry: Pipeline.Enhanced.ProviderRegistry for dynamic provider management
Configuration System: Pipeline.Enhanced.ConfigurationSystem with schema extensions
Plugin Manager: Pipeline.Enhanced.PluginManager for plugin lifecycle management
Python Bridge: Pipeline.DSPy.PythonBridge for Python integration
Quality Management: Pipeline.DSPy.DataQuality for training data management
Monitoring System: Pipeline.DSPy.ProductionMonitor for performance tracking

DSPy Integration Requirements

Signature System: Convert YAML signatures to DSPy format
Optimization Engine: Bootstrap, CoPro, and MIPro optimization strategies
Training Data Pipeline: Automated collection and quality management
Real-Time Execution: Async optimization with fallback to traditional execution
Production Monitoring: Comprehensive metrics and alerting
A/B Testing: Experiment management for DSPy vs traditional execution

Task

Implement a comprehensive DSPy plugin that integrates all infrastructure components into a cohesive, production-ready DSPy optimization system.

Required Components

Main DSPy Plugin (lib/pipeline/plugins/dspy_plugin.ex)
- Plugin behavior implementation
- Component registration and lifecycle management
- Configuration management
- Health monitoring and status reporting
DSPy Step Types (lib/pipeline/dspy/steps/)
- Optimized Claude step with DSPy integration
- Optimized Gemini step with DSPy integration
- Chain step for multi-step DSPy workflows
- Evaluation step for training data collection
DSPy Providers (lib/pipeline/dspy/providers/)
- Optimized Claude provider with Python bridge integration
- Optimized Gemini provider with Python bridge integration
- Provider selection and fallback strategies
- Performance monitoring integration
DSPy Signature System (lib/pipeline/dspy/signature.ex)
- YAML to DSPy signature conversion
- Signature validation and optimization
- Training data collection based on signatures
- Signature versioning and management
DSPy Optimization Engine (lib/pipeline/dspy/optimization_engine.ex)
- Integration with Python bridge
- Multiple optimization strategies
- Async optimization with caching
- Performance monitoring and metrics
DSPy Training Data Manager (lib/pipeline/dspy/training_data_manager.ex)
- Automated training data collection
- Quality management and validation
- Data versioning and lineage tracking
- Integration with execution history
DSPy Configuration Extensions (lib/pipeline/dspy/config_extension.ex)
- Schema extensions for DSPy configuration
- Validation rules for DSPy-specific options
- Configuration migration utilities
- Environment-specific configuration support

Implementation Requirements

Full Plugin Integration: Must integrate with all infrastructure components
Production Ready: Comprehensive error handling, monitoring, and logging
Performance Optimized: Efficient caching, async processing, and resource management
Backward Compatible: Seamless fallback to traditional execution
Extensively Tested: Unit tests, integration tests, and performance tests
Well Documented: Comprehensive documentation and usage examples

DSPy Plugin Architecture

The plugin must support this configuration:

# DSPy Plugin Configuration
plugins:
  dspy_plugin:
    module: Pipeline.Plugins.DSPyPlugin
    enabled: true
    config:
      # Python Integration
      python_bridge:
        pool_size: 3
        timeout: 30000
        max_retries: 3
        health_check_interval: 10000
      
      # Optimization Settings
      optimization:
        enabled: true
        strategies: ["bootstrap_few_shot", "copro", "mipro"]
        default_strategy: "bootstrap_few_shot"
        async_optimization: true
        cache_enabled: true
        cache_ttl: 3600
      
      # Training Data Management
      training_data:
        collection_enabled: true
        quality_threshold: 0.7
        bias_detection: true
        versioning_enabled: true
        sources:
          - execution_history
          - user_feedback
          - synthetic_generation
      
      # Real-Time Execution
      real_time:
        max_wait_time: 5000
        fallback_strategy: "traditional"
        cache_warming: true
        adaptive_thresholds: true
      
      # Production Monitoring
      monitoring:
        enabled: true
        metrics_collection: true
        alerting: true
        performance_comparison: true
        ab_testing: true
      
      # Deployment Configuration
      deployment:
        phase: "development"
        rollout_percentage: 100
        experiment_enabled: false
        feature_flags:
          - optimization_enabled
          - training_data_collection
          - real_time_optimization

Step Type Implementation

The plugin must register these step types:

# DSPy-optimized step types
step_types = [
  {"dspy_claude", Pipeline.DSPy.Steps.OptimizedClaudeStep},
  {"dspy_gemini", Pipeline.DSPy.Steps.OptimizedGeminiStep},
  {"dspy_chain", Pipeline.DSPy.Steps.ChainStep},
  {"dspy_evaluation", Pipeline.DSPy.Steps.EvaluationStep}
]

Provider Implementation

The plugin must register these providers:

# DSPy-optimized providers
providers = [
  {"dspy_claude", Pipeline.DSPy.Providers.OptimizedClaudeProvider, [:dspy_optimization, :claude_compatible]},
  {"dspy_gemini", Pipeline.DSPy.Providers.OptimizedGeminiProvider, [:dspy_optimization, :gemini_compatible]}
]

Configuration Schema Extensions

The plugin must register these schema extensions:

# DSPy configuration schema extensions
schema_extensions = [
  {"dspy", Pipeline.DSPy.ConfigExtension.get_dspy_schema_extension()},
  {"dspy_monitoring", Pipeline.DSPy.ConfigExtension.get_monitoring_schema_extension()},
  {"dspy_training", Pipeline.DSPy.ConfigExtension.get_training_schema_extension()}
]

Usage Examples

The plugin must support these usage patterns:

# DSPy-enhanced pipeline configuration
workflow:
  name: "dspy_code_analysis"
  
  dspy_config:
    optimization_enabled: true
    evaluation_mode: "bootstrap_few_shot"
    training_data_collection: true
    real_time_optimization: true
  
  steps:
    - name: "analyze_security"
      type: "dspy_claude"
      dspy_signature:
        input_fields:
          - name: "code"
            type: "string"
            description: "Source code to analyze"
        output_fields:
          - name: "security_analysis"
            type: "object"
            description: "Security analysis results"
            schema:
              type: "object"
              properties:
                vulnerabilities: {type: "array", items: {type: "string"}}
                risk_score: {type: "number", minimum: 0, maximum: 100}
                recommendations: {type: "array", items: {type: "string"}}
      
      dspy_config:
        optimization_enabled: true
        cache_enabled: true
        max_wait_time: 3000
        fallback_strategy: "traditional"
        collect_training_data: true
    
    - name: "generate_report"
      type: "dspy_chain"
      dspy_config:
        chain_steps:
          - signature: "security_analysis_to_summary"
          - signature: "summary_to_report"
        optimization_enabled: true

Testing Requirements

The plugin must include comprehensive tests:

# Test structure
test/
├── dspy_plugin_test.exs              # Main plugin tests
├── dspy/
│   ├── steps/
│   │   ├── optimized_claude_step_test.exs
│   │   ├── optimized_gemini_step_test.exs
│   │   └── chain_step_test.exs
│   ├── providers/
│   │   ├── optimized_claude_provider_test.exs
│   │   └── optimized_gemini_provider_test.exs
│   ├── signature_test.exs
│   ├── optimization_engine_test.exs
│   └── training_data_manager_test.exs
└── integration/
    ├── dspy_integration_test.exs
    ├── python_bridge_integration_test.exs
    └── end_to_end_test.exs

Integration Points

The plugin must integrate with:

Step Registry: Register all DSPy step types
Provider Registry: Register all DSPy providers
Configuration System: Extend schemas for DSPy configuration
Python Bridge: Use for optimization and evaluation
Training Data System: Collect and manage training data
Monitoring System: Report performance metrics
A/B Testing System: Support experimentation
Deployment System: Support phased rollouts

Error Handling

The plugin must provide comprehensive error handling:

Graceful Degradation: Fallback to traditional execution on errors
Detailed Logging: Comprehensive logging for debugging
Health Monitoring: Self-monitoring and health reporting
Recovery Mechanisms: Automatic recovery from transient failures
User-Friendly Messages: Clear error messages for users

Performance Requirements

The plugin must meet these performance requirements:

Optimization Latency: < 5 seconds for real-time optimization
Cache Hit Rate: > 80% for frequently used signatures
Fallback Time: < 100ms to switch to traditional execution
Memory Usage: < 100MB additional memory overhead
Startup Time: < 10 seconds for plugin initialization

Security Considerations

The plugin must implement security measures:

Input Validation: Validate all configuration and execution inputs
Python Process Isolation: Secure isolation of Python processes
Data Privacy: Protect training data and user information
Access Control: Proper authorization for plugin operations
Audit Logging: Comprehensive audit trail for all operations

Documentation Requirements

The plugin must include:

API Documentation: Complete API reference
Configuration Guide: Detailed configuration options
Usage Examples: Comprehensive usage examples
Troubleshooting Guide: Common issues and solutions
Performance Tuning: Optimization recommendations

Implement this DSPy plugin as a complete, production-ready solution that showcases the full capabilities of the enhanced pipeline_ex infrastructure while providing a robust, scalable, and maintainable DSPy integration.