dtoro
ad007e4cd4
- Create face_embeddings table with pgvector Vector(128) + HNSW index - Implement extract_faces task (YuNet detection + SFace recognition) - Implement recluster_faces task (DBSCAN clustering → Tag(kind=face_cluster)) - Clusters are named "Person N" and get representative_photo_id - cluster_id FK → tags.id, SET NULL on delete for merge/rename support Migration 0005 creates the face_embeddings table. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
23 lines
512 B
Python
23 lines
512 B
Python
"""
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Database models for Mulita
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"""
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from app.models.photos import Photo
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from app.models.folders import Folder, SourceRoot
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from app.models.tags import Tag, PhotoTag
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from app.models.heaps import Heap, HeapPhoto
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from app.models.embeddings import Embedding
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from app.models.ocr_text import OCRText
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from app.models.face_embedding import FaceEmbedding
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__all__ = [
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'Photo',
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'Folder',
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'SourceRoot',
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'Tag',
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'PhotoTag',
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'Heap',
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'HeapPhoto',
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'Embedding',
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'OCRText',
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'FaceEmbedding',
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] |