146 PYDANTIC USE IN DSPY more

Documentation for 146_PYDANTIC_USE_IN_DSPY_more from the Ds ex repository.

Additional Complementary Integration Points

21. Prompt Engineering and Template System

Template Validation: Uses Pydantic models to validate prompt template parameters
Variable Substitution: Leverages Pydantic’s field system for template variable validation
Prompt Constraints: Uses Pydantic validators to enforce prompt formatting rules

class PromptTemplate(BaseModel):
    template: str = Field(..., min_length=1)
    variables: dict[str, Any] = Field(default_factory=dict)
    
    @model_validator(mode="after")
    def validate_template_variables(self):
        # Ensure all template variables are provided

22. Language Model Interface

LM Configuration: Uses Pydantic models for language model parameter validation
Response Parsing: Leverages Pydantic for structured response validation
Token Usage Tracking: Uses Pydantic models for usage statistics

class LMUsage(BaseModel):
    prompt_tokens: int = Field(ge=0)
    completion_tokens: int = Field(ge=0)
    total_tokens: int = Field(ge=0)
    cost: Optional[float] = Field(ge=0)

23. Retrieval System Integration

Document Schema: Uses Pydantic for document metadata validation
Query Validation: Leverages Pydantic for search query parameter validation
Result Formatting: Uses Pydantic models for standardizing retrieval results

class RetrievalResult(BaseModel):
    text: str
    score: float = Field(ge=0, le=1)
    metadata: dict[str, Any] = Field(default_factory=dict)
    source: Optional[str] = None

24. Evaluation Framework

Metric Definition: Uses Pydantic for metric configuration and validation
Result Aggregation: Leverages Pydantic models for evaluation result storage
Benchmark Configuration: Uses Pydantic for dataset and evaluation setup

class EvaluationResult(BaseModel):
    score: float
    predictions: list[dict[str, Any]]
    metadata: dict[str, Any] = Field(default_factory=dict)
    timestamp: datetime = Field(default_factory=datetime.now)

25. Fine-tuning and Training

Training Configuration: Uses Pydantic for hyperparameter validation
Data Format Validation: Leverages Pydantic for training data structure validation
Job Status Tracking: Uses Pydantic models for training job state management

class TrainingConfig(BaseModel):
    learning_rate: float = Field(gt=0, le=1)
    batch_size: int = Field(ge=1)
    epochs: int = Field(ge=1)
    validation_split: float = Field(ge=0, le=1, default=0.2)

26. Streaming and Real-time Processing

Stream Message Validation: Uses Pydantic for validating streaming message formats
Response Chunking: Leverages Pydantic for structured chunk validation
Status Updates: Uses Pydantic models for status message formatting

class StreamChunk(BaseModel):
    content: str
    chunk_type: Literal["text", "tool_call", "status"]
    metadata: dict[str, Any] = Field(default_factory=dict)
    is_final: bool = False

Media Validation: Uses Pydantic validators for image/audio format validation
Encoding Standards: Leverages Pydantic for enforcing encoding requirements
Size Constraints: Uses Pydantic field constraints for media file size limits

class MediaFile(BaseModel):
    content: bytes
    mime_type: str = Field(regex=r'^(image|audio|video)/.+')
    size_bytes: int = Field(ge=1, le=10_000_000)  # 10MB limit
    
    @model_validator(mode="after")
    def validate_content_type(self):
        # Validate content matches mime_type

28. Plugin and Extension System

Plugin Configuration: Uses Pydantic for plugin parameter validation
Extension Registry: Leverages Pydantic for extension metadata validation
Hook Definition: Uses Pydantic models for defining plugin hooks

class PluginConfig(BaseModel):
    name: str = Field(min_length=1, max_length=100)
    version: str = Field(regex=r'^\d+\.\d+\.\d+$')
    dependencies: list[str] = Field(default_factory=list)
    config: dict[str, Any] = Field(default_factory=dict)

29. Caching and Persistence

Cache Entry Validation: Uses Pydantic for cache data structure validation
Serialization Keys: Leverages Pydantic for generating consistent cache keys
Data Integrity: Uses Pydantic validators for ensuring cache data consistency

class CacheEntry(BaseModel):
    key: str = Field(min_length=1)
    value: Any
    timestamp: datetime = Field(default_factory=datetime.now)
    ttl: Optional[int] = Field(ge=0)
    
    @model_validator(mode="after")
    def validate_expiry(self):
        # Validate TTL logic

30. Distributed Processing

Task Definition: Uses Pydantic for distributed task parameter validation
Worker Configuration: Leverages Pydantic for worker node configuration
Result Aggregation: Uses Pydantic models for collecting distributed results

class DistributedTask(BaseModel):
    task_id: str = Field(min_length=1)
    worker_id: str
    parameters: dict[str, Any]
    priority: int = Field(ge=0, le=10, default=5)
    timeout: int = Field(ge=1, default=300)

31. API Integration and Web Services

Request Validation: Uses Pydantic for API request body validation
Response Formatting: Leverages Pydantic for consistent API response structure
Authentication: Uses Pydantic models for credential validation

class APIRequest(BaseModel):
    signature: str
    inputs: dict[str, Any]
    config: Optional[dict[str, Any]] = None
    
    class Config:
        extra = "forbid"
        
class APIResponse(BaseModel):
    outputs: dict[str, Any]
    metadata: dict[str, Any] = Field(default_factory=dict)
    success: bool = True
    error: Optional[str] = None

32. Workflow and Pipeline Management

Pipeline Definition: Uses Pydantic for workflow step validation
Dependency Management: Leverages Pydantic for step dependency validation
Execution Context: Uses Pydantic models for pipeline execution state

class PipelineStep(BaseModel):
    name: str = Field(min_length=1)
    module: str
    parameters: dict[str, Any] = Field(default_factory=dict)
    dependencies: list[str] = Field(default_factory=list)
    
    @model_validator(mode="after")
    def validate_dependencies(self):
        # Ensure no circular dependencies

33. Monitoring and Observability

Metrics Collection: Uses Pydantic for metrics data structure validation
Log Entry Validation: Leverages Pydantic for structured logging
Performance Tracking: Uses Pydantic models for performance metric storage

class PerformanceMetric(BaseModel):
    metric_name: str
    value: float
    unit: str
    timestamp: datetime = Field(default_factory=datetime.now)
    tags: dict[str, str] = Field(default_factory=dict)

34. Security and Access Control

Permission Validation: Uses Pydantic for access control rule validation
Token Management: Leverages Pydantic for API token structure validation
Audit Logging: Uses Pydantic models for security event logging

class SecurityPolicy(BaseModel):
    resource: str
    actions: list[str]
    principals: list[str]
    conditions: dict[str, Any] = Field(default_factory=dict)
    
    @model_validator(mode="after")
    def validate_policy(self):
        # Validate security policy rules

35. Version Control and Migrations

Schema Versioning: Uses Pydantic for model version validation
Migration Scripts: Leverages Pydantic for migration parameter validation
Backward Compatibility: Uses Pydantic’s aliasing for field migration

class ModelVersion(BaseModel):
    version: str = Field(regex=r'^\d+\.\d+$')
    schema_hash: str = Field(min_length=32, max_length=64)
    migration_path: Optional[str] = None
    deprecated: bool = False

36. Testing and Quality Assurance

Test Case Definition: Uses Pydantic for test parameter validation
Mock Data Generation: Leverages Pydantic factories for test data creation
Assertion Validation: Uses Pydantic models for test result validation

class TestCase(BaseModel):
    test_name: str = Field(min_length=1)
    inputs: dict[str, Any]
    expected_outputs: dict[str, Any]
    tolerance: float = Field(ge=0, default=0.01)
    
    @model_validator(mode="after")
    def validate_test_consistency(self):
        # Ensure inputs/outputs are consistent

37. Resource Management

Resource Allocation: Uses Pydantic for resource requirement validation
Quota Management: Leverages Pydantic for usage limit enforcement
Cost Tracking: Uses Pydantic models for cost calculation and tracking

class ResourceQuota(BaseModel):
    cpu_cores: int = Field(ge=1, le=64)
    memory_gb: int = Field(ge=1, le=512)
    gpu_count: int = Field(ge=0, le=8)
    storage_gb: int = Field(ge=10, le=10000)
    monthly_budget: float = Field(ge=0)

38. Custom Validators and Constraints

Domain-Specific Validation: Custom Pydantic validators for DSPy-specific constraints
Cross-Field Validation: Uses @model_validator for complex validation logic
Dynamic Constraints: Runtime constraint generation using Pydantic

@model_validator(mode="after")
def validate_signature_consistency(self):
    # Ensure input/output field consistency
    if len(self.input_fields) == 0:
        raise ValueError("Signature must have at least one input field")
    if len(self.output_fields) == 0:
        raise ValueError("Signature must have at least one output field")
    return self

These additional integration points demonstrate how Pydantic serves as the backbone for DSPy’s data validation, serialization, and type safety across virtually every component of the framework, from low-level data structures to high-level workflow management.