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Oleksandr Bezdieniezhnykh
1fe9425aa8
- 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
51 lines
1.5 KiB
Markdown
51 lines
1.5 KiB
Markdown
# Module: media_hash
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## Purpose
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Content-based hashing for media files using XxHash64 with a deterministic sampling algorithm. Produces a stable, unique ID for any media file based on its content.
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## Public Interface
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| Function | Signature | Description |
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|----------|-----------|-------------|
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| `compute_media_content_hash` | `(data: bytes, virtual: bool = False) -> str` | Returns hex XxHash64 digest of sampled content. If `virtual=True`, prefixes with "V". |
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## Internal Logic
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### Sampling Algorithm (`_sampling_payload`)
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- **Small files** (< 3072 bytes): uses entire content
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- **Large files** (≥ 3072 bytes): samples 3 × 1024-byte windows: first 1024, middle 1024, last 1024
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- All payloads are prefixed with the 8-byte little-endian file size for collision resistance
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The sampling avoids reading the full file through the hash function while still providing high uniqueness — the head, middle, and tail capture format headers, content, and EOF markers.
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## Dependencies
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- **External**: `xxhash` (pinned at 3.5.0 in requirements.txt)
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- **Internal**: none (leaf module)
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## Consumers
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- `main` — computes content hash for uploaded media in `POST /detect` to use as the media record ID and storage filename
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## Data Models
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None.
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## Configuration
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None.
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## External Integrations
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None.
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## Security
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None. The hash is non-cryptographic (fast, not tamper-resistant).
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## Tests
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- `tests/test_media_hash.py` — covers small files, large files, and virtual prefix behavior
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