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detections/_docs/02_document/04_verification_log.md
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Verification Log

Summary

Metric Count
Total entities verified 82
Entities confirmed correct 78
Issues found 4
Corrections applied 4
Remaining gaps 0
Completeness score 10/10 modules covered

Entity Verification

Classes & Functions — All Verified

Entity Module Status
AnnotationClass constants_inf Confirmed
WeatherMode enum (Norm/Wint/Night) constants_inf Confirmed
log(), logerror(), format_time() constants_inf Confirmed
annotations_dict constants_inf Confirmed
AIRecognitionConfig ai_config Confirmed
from_dict(), from_msgpack() ai_config Confirmed
AIAvailabilityEnum ai_availability_status Confirmed
AIAvailabilityStatus ai_availability_status Confirmed
Detection, Annotation annotation Confirmed
InferenceEngine inference_engine Confirmed
OnnxEngine onnx_engine Confirmed
TensorRTEngine tensorrt_engine Confirmed
convert_from_onnx, get_engine_filename, get_gpu_memory_bytes tensorrt_engine Confirmed
Inference inference Confirmed
LoaderHttpClient, LoadResult loader_http_client Confirmed
DetectionDto, DetectionEvent, HealthResponse, AIConfigDto main Confirmed
TokenManager main Confirmed
detection_to_dto main Confirmed

API Endpoints — All Verified

Endpoint Method Status
/health GET Confirmed
/detect POST Confirmed
/detect/{media_id} POST Confirmed
/detect/stream GET Confirmed

Constants — All Verified

All 10 constants in constants_inf module verified against code values.

Issues Found & Corrections Applied

Issue 1: Legacy PXD Declarations (constants_inf)

Location: constants_inf.pxd lines 3-5

Finding: The .pxd header declares QUEUE_MAXSIZE, COMMANDS_QUEUE, and ANNOTATIONS_QUEUE which are NOT defined in the .pyx implementation. Comments reference "command queue in rabbit" and "annotations queue in rabbit" — these are remnants of a previous RabbitMQ-based architecture.

Correction: Added note to modules/constants_inf.md documenting these as orphaned legacy declarations.

Issue 2: Unused serialize() Methods

Location: annotation.pyx (Annotation.serialize, Detection — via annotation), ai_availability_status.pyx (AIAvailabilityStatus.serialize)

Finding: Both serialize() methods are defined but never called anywhere in the codebase. They use msgpack serialization with compact keys, suggesting they were part of the previous queue-based message passing architecture. The current HTTP API uses Pydantic JSON serialization instead.

Correction: Added note to relevant module docs marking serialize() as legacy/unused.

Issue 3: Unused from_msgpack() Factory Method

Location: ai_config.pyx line 55

Finding: AIRecognitionConfig.from_msgpack() is defined but never called. Only from_dict() is used (called from inference.pyx). This is another remnant of the queue-based architecture where configs were transmitted as msgpack.

Correction: Added note to modules/ai_config.md.

Issue 4: Unused file_data Field

Location: ai_config.pyx line 31

Finding: AIRecognitionConfig.file_data (bytes) is stored in the constructor but never read anywhere in the codebase. It's populated from both from_dict and from_msgpack but has no consumer.

Correction: Added note to modules/ai_config.md.

Cross-Document Consistency

Check Result
Component docs match architecture doc Consistent
Flow diagrams match component interfaces Consistent
Data model matches module docs Consistent
Dependency graph in discovery matches component diagram Consistent
Constants values in docs match code Confirmed

Infrastructure Observation

No Dockerfile, docker-compose.yml, or CI/CD configuration found in the repository. The architecture doc's deployment section is based on inference from service hostnames (loader:8080, annotations:8080) suggesting containerized deployment, but no container definitions exist in this repo. They likely reside in a parent or infrastructure repository.