# ADR-002: Signature Programming Model Implementation

## Status
**Implemented** (2025-10-01)

## Update Summary

This ADR has been updated to reflect the actual implementation of Option 3 (DSPy-inspired signatures) which is now complete and deployed in Kaizen. SignatureBase has been completely removed from the codebase.

## Context

DSPy introduced signature-based programming where developers define input/output specifications rather than writing prompts. This approach has proven highly effective for AI development, but current implementations have limitations:

- **DSPy Signatures**: Limited to simple input/output definitions
- **Type Safety**: Minimal runtime type checking and validation
- **Enterprise Integration**: No support for security, audit trails, or compliance
- **Multi-modal Support**: Primarily text-focused with limited media handling

Kaizen needed a signature programming model that exceeds DSPy capabilities while integrating seamlessly with Kailash's enterprise infrastructure.

## Decision

We implemented **Option 3: DSPy-Inspired Class-Based Signatures** using `Signature`, `InputField`, and `OutputField` - a clean, Pythonic approach that leverages metaclasses for field extraction and integrates seamlessly with Kailash Core SDK.

### Core Design (Actual Implementation)

```python
from kaizen.signatures import Signature, InputField, OutputField

class DocumentAnalysis(Signature):
    """Analyze business documents for key insights and compliance."""

    # Input specifications
    document: str = InputField(
        desc="Business document content to analyze"
    )

    analysis_type: str = InputField(
        desc="Type of analysis: financial, legal, or operational",
        default="general"
    )

    # Output specifications
    key_insights: str = OutputField(
        desc="Main insights extracted from the document"
    )

    compliance_score: float = OutputField(
        desc="Compliance score from 0.0 to 1.0"
    )

    risk_factors: str = OutputField(
        desc="Identified risk factors with severity levels"
    )

# Usage with Kaizen framework
framework = Kaizen(signature_programming_enabled=True)

agent = framework.create_agent(
    "document_analyzer",
    signature=DocumentAnalysis,
    config={"model": "gpt-4", "temperature": 0.3}
)

# Execute using Core SDK runtime
runtime = LocalRuntime()
workflow = agent.to_workflow()
results, run_id = runtime.execute(
    workflow.build(),
    parameters={
        "document": contract_text,
        "analysis_type": "legal"
    }
)

print(f"Insights: {results['key_insights']}")
print(f"Compliance: {results['compliance_score']}")
print(f"Risks: {results['risk_factors']}")
```

### Implementation Architecture

The actual implementation uses:

1. **SignatureMeta** - Metaclass that extracts field definitions from class annotations
2. **InputField/OutputField** - Descriptors for defining signature fields with metadata
3. **Signature Base Class** - Provides instantiation, validation, and workflow compilation
4. **SignatureCompiler** - Converts signatures to Kailash WorkflowBuilder patterns
5. **Agent Integration** - Seamless execution through Core SDK runtime

### Architecture Components

1. **KaizenSignature Class**: Base class for all signature definitions
2. **Context System**: Rich input/output specifications with validation
3. **Type System**: Extended Python typing with AI-specific types
4. **Validation Engine**: Comprehensive input/output validation
5. **Security Layer**: Built-in security scanning and compliance
6. **Optimization System**: Automatic prompt optimization based on signatures

### Advanced Features

```python
# Multi-step workflows with intermediate signatures
@signature.workflow
class CustomerServicePipeline:
    # Step 1: Intent classification
    customer_message: str = context.input()
    intent: CustomerIntent = context.intermediate(
        signature=IntentClassification
    )

    # Step 2: Response generation
    response: str = context.output(
        signature=ResponseGeneration,
        depends_on=["intent"]
    )

    # Step 3: Sentiment tracking
    satisfaction_score: float = context.output(
        signature=SentimentAnalysis,
        depends_on=["response"]
    )

# Memory-aware signatures
@signature.stateful
class ConversationAgent:
    memory: ConversationMemory = context.memory(
        ttl="24h",
        encryption=True
    )

    user_input: str = context.input()
    response: str = context.output()

    async def execute(self, **inputs):
        # Access conversation history
        history = await self.memory.get_history()
        # Generate contextual response
        return await self.generate_response(inputs, history)
```

## Consequences

### Positive
- **Developer Experience**: Intuitive Python-native syntax with powerful capabilities
- **Type Safety**: Comprehensive validation at development and runtime
- **Enterprise Ready**: Built-in security, compliance, and audit capabilities
- **Multi-modal Native**: Support for text, images, audio, video from day one
- **Optimization Friendly**: Signatures provide clear optimization targets
- **Composable**: Complex workflows from simple signature building blocks

### Negative
- **Learning Curve**: New concepts beyond standard Python programming
- **Performance Overhead**: Additional validation and processing layers
- **Magic Behavior**: Decorator-based system may obscure execution flow
- **Debugging Complexity**: Multiple abstraction layers complicate troubleshooting

## Alternatives Considered

### Option 1: Direct DSPy Port
**Description**: Port DSPy signature system with minimal changes
- **Pros**: Proven approach, faster implementation
- **Cons**: Limited enterprise features, doesn't leverage Kailash capabilities
- **Why Rejected**: Insufficient differentiation and enterprise readiness

### Option 2: Pydantic-Based Signatures
**Description**: Use Pydantic models as basis for signatures
- **Pros**: Leverages existing validation ecosystem
- **Cons**: Not designed for AI workflows, limited optimization potential
- **Why Rejected**: Doesn't provide AI-specific capabilities needed

### Option 3: Custom DSL
**Description**: Create domain-specific language for AI workflows
- **Pros**: Complete control over syntax and features
- **Cons**: High learning curve, maintenance overhead, not Python-native
- **Why Rejected**: Would alienate Python developers and require extensive tooling

## Implementation Strategy

### Phase 1: Core Signature System
```python
# Basic signature infrastructure
class KaizenSignature:
    def __init__(self, **kwargs):
        self.inputs = {}
        self.outputs = {}
        self.metadata = {}

    @classmethod
    def execute(cls, **inputs):
        # Signature execution logic
        pass

# Context system for rich specifications
class ContextField:
    def __init__(self, description, validation=None, security=None):
        self.description = description
        self.validation = validation or []
        self.security = security or []
```

### Phase 2: Advanced Features
- Multi-step workflow signatures
- Memory-aware stateful signatures
- Cross-signature dependencies
- Optimization hook points

### Phase 3: Enterprise Integration
- Security scanning integration
- Compliance validation
- Audit trail generation
- Performance monitoring

### Phase 4: Optimization Engine
- ML-based prompt optimization
- Performance tuning
- Cost optimization
- A/B testing framework

## Integration Points

### With Core SDK
- Signatures compile to WorkflowBuilder patterns
- Leverage existing Node architecture for execution
- Maintain compatibility with LocalRuntime

### With DataFlow
- Database-backed memory system for stateful signatures
- Automatic persistence of signature results
- Query optimization for signature-based workflows

### With Nexus
- API endpoint generation from signatures
- CLI command creation from signatures
- MCP server integration for signature-based tools

## Success Criteria

- **Usability**: 90%+ developer satisfaction with signature syntax
- **Performance**: <10ms signature compilation time
- **Type Safety**: 99%+ validation accuracy at runtime
- **Enterprise Adoption**: Support for all major compliance frameworks
- **Competitive**: 5x faster development vs manual prompt engineering

## Related ADRs
- ADR-001: Kaizen Framework Architecture
- ADR-003: Memory System Architecture
- ADR-004: Model Orchestration Strategy
- ADR-005: Security and Compliance Framework
