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[AZ-172] Update documentation for distributed architecture, add Update Docs step to workflow

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- Update module docs: main, inference, ai_config, loader_http_client
- Add new module doc: media_hash
- Update component docs: inference_pipeline, api
- Update system-flows (F2, F3) and data_parameters
- Add Task Mode to document skill for incremental doc updates
- Insert Step 11 (Update Docs) in existing-code flow, renumber 11-13 to 12-14

Made-with: Cursor
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-03-31 17:25:58 +03:00
parent e29606c313
commit 1fe9425aa8
12 changed files with 447 additions and 245 deletions
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_docs/00_problem/input_data/data_parameters.md
+15 -10
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@@ -2,23 +2,27 @@
## Media Input
### Single Image Detection (POST /detect)
### Upload Detection (POST /detect)
| Parameter | Type | Source | Description |
|-----------|------|--------|-------------|
| file | bytes (multipart) | Client upload | Image file (JPEG, PNG, etc. — any format OpenCV can decode) |
| config | JSON string (optional) | Query/form field | AIConfigDto overrides |
| file | bytes (multipart) | Client upload | Image or video file (JPEG, PNG, MP4, MOV, etc.) |
| config | JSON string (optional) | Form field | AIConfigDto overrides |
| Authorization header | Bearer token (optional) | HTTP header | JWT for media lifecycle management |
| x-refresh-token header | string (optional) | HTTP header | Refresh token for JWT renewal |
When auth headers are present, the service: computes an XxHash64 content hash, persists the file to `VIDEOS_DIR`/`IMAGES_DIR`, creates a media record via Annotations API, and tracks processing status.
### Media Detection (POST /detect/{media_id})
| Parameter | Type | Source | Description |
|-----------|------|--------|-------------|
| media_id | string | URL path | Identifier for media in the Loader service |
| AIConfigDto body | JSON (optional) | Request body | Configuration overrides |
| media_id | string | URL path | Identifier for media in the Annotations service |
| AIConfigDto body | JSON (optional) | Request body | Configuration overrides (merged with DB settings) |
| Authorization header | Bearer token | HTTP header | JWT for Annotations service |
| x-refresh-token header | string | HTTP header | Refresh token for JWT renewal |
Media files (images and videos) are resolved by the Inference pipeline via paths in the config. The Loader service provides model files, not media files directly.
Media path is resolved from the Annotations service via `GET /api/media/{media_id}`. AI settings are fetched from `GET /api/users/{user_id}/ai-settings` and merged with client overrides.
## Configuration Input (AIConfigDto / AIRecognitionConfig)
@@ -30,12 +34,13 @@ Media files (images and videos) are resolved by the Inference pipeline via paths
| tracking_distance_confidence | float | 0.0 | Movement threshold for tracking (model-width fraction) |
| tracking_probability_increase | float | 0.0 | Confidence increase threshold for tracking |
| tracking_intersection_threshold | float | 0.6 | Overlap ratio for NMS deduplication |
| model_batch_size | int | 1 | Inference batch size |
| model_batch_size | int | 8 | Inference batch size |
| big_image_tile_overlap_percent | int | 20 | Tile overlap for large images (0-100%) |
| altitude | float | 400 | Camera altitude in meters |
| focal_length | float | 24 | Camera focal length in mm |
| sensor_width | float | 23.5 | Camera sensor width in mm |
| paths | list[str] | [] | Media file paths to process |
`paths` field was removed in AZ-174 — media paths are now resolved via the Annotations service.
## Model Files
@@ -61,7 +66,7 @@ Array of 19 objects, each with:
## Data Volumes
- Single image: up to tens of megapixels (aerial imagery). Large images are tiled.
- Video: processed frame-by-frame with configurable sampling rate.
- Video: processed frame-by-frame with configurable sampling rate. Decoded from in-memory bytes via PyAV.
- Model file: ONNX model size depends on architecture (typically 10-100 MB). TensorRT engines are GPU-specific compiled versions.
- Detection output: up to 300 detections per frame (model limit).
@@ -73,4 +78,4 @@ Array of 19 objects, each with:
| API responses | JSON | Pydantic model_dump |
| SSE events | text/event-stream | JSON per event |
| Internal config | Python dict | AIRecognitionConfig.from_dict() |
| Legacy (unused) | msgpack | serialize() / from_msgpack() |
| Content hash | XxHash64 hex string | 16-char hex digest |