docs/API_MODELS_SUMMARY.md

API Models Implementation Summary

Overview

Created a dedicated, rigorously defined Pydantic models module (ylff/api_models.py) for all API request/response schemas with comprehensive validation, documentation, and type safety.

✅ Completed

1. Created ylff/api_models.py

• ✅ All API models extracted and enhanced
• ✅ Comprehensive field validation
• ✅ Detailed descriptions and examples
• ✅ Custom validators for complex rules
• ✅ Type-safe enums
• ✅ JSON schema examples

2. Model Categories

Enums (Type Safety)

• ✅ JobStatus: Job execution status values
• ✅ DeviceType: Device selection (CPU, CUDA, MPS)
• ✅ UseCase: Use case for model selection

Request Models

• ✅ ValidateSequenceRequest: Sequence validation
• ✅ ValidateARKitRequest: ARKit validation
• ✅ BuildDatasetRequest: Dataset building
• ✅ TrainRequest: Model fine-tuning
• ✅ PretrainRequest: Model pre-training (ARKit-specific)
• ✅ EvaluateBAAgreementRequest: BA agreement evaluation
• ✅ VisualizeRequest: Result visualization

Response Models

• ✅ JobResponse: Standard job-based response
• ✅ ValidationStats: BA validation statistics
• ✅ HealthResponse: Health check response
• ✅ ModelsResponse: Models list response
• ✅ ErrorResponse: Standard error response

3. Validation Features

Range Constraints

• ✅ Numeric ranges: ge, le, gt, lt
• ✅ String lengths: min_length
• ✅ Angle ranges: 0-180 degrees
• ✅ Quality ranges: 0.0-1.0

Custom Validators

• ✅ reject_threshold > accept_threshold validation
• ✅ Path format validation
• ✅ Non-empty string validation

Type Safety

• ✅ Enums for categorical values
• ✅ Optional fields clearly marked
• ✅ Required fields explicitly defined

4. Documentation

• ✅ Field descriptions for all fields
• ✅ Examples for every field
• ✅ JSON schema examples in model_config
• ✅ Comprehensive model documentation

5. Updated ylff/api.py

• ✅ Removed inline model definitions
• ✅ Import all models from api_models
• ✅ All endpoints use imported models
• ✅ Maintained backward compatibility

Model Features

Field Validation Examples

# Range validation
accept_threshold: float = Field(
    2.0,
    ge=0.0,      # Greater than or equal to 0
    le=180.0,    # Less than or equal to 180
)

# String validation
sequence_dir: str = Field(
    ...,
    min_length=1,  # Cannot be empty
)

# Custom validator
@field_validator("reject_threshold")
@classmethod
def reject_greater_than_accept(cls, v, info):
    if v <= info.data["accept_threshold"]:
        raise ValueError("reject_threshold must be > accept_threshold")
    return v

Enum Usage

# Type-safe device selection
device: DeviceType = Field(
    DeviceType.CPU,
    examples=["cpu", "cuda", "mps"],
)

# Type-safe use case
use_case: UseCase = Field(
    UseCase.BA_VALIDATION,
    examples=["ba_validation", "mono_depth"],
)

Benefits

1. Type Safety: Catch errors at request validation time
2. Documentation: Auto-generated API docs with examples
3. Validation: Comprehensive input validation before processing
4. Consistency: Standardized request/response formats
5. Maintainability: Centralized model definitions
6. Developer Experience: Clear error messages and examples
7. API Discovery: JSON schema examples enable client generation

File Structure

ylff/
├── api.py                 # API endpoints (imports models)
├── api_models.py          # All Pydantic models (NEW)
└── api_middleware.py      # Middleware utilities

docs/
├── API_MODELS.md          # Comprehensive model documentation
└── API_MODELS_SUMMARY.md  # This file

Next Steps

The models are now:

• ✅ Rigorously defined with validation
• ✅ Well-documented with examples
• ✅ Type-safe with enums
• ✅ Ready for API documentation generation
• ✅ Ready for client SDK generation

Future enhancements:

• Add response models for all endpoints
• Add pagination models for list endpoints
• Add filter/sort models for query parameters
• Generate OpenAPI schema from models
• Create client SDK from models