dtoro
94c07b1d0d
Replace OpenCLIP ViT-B/32 (512-d, ~78% recall) with SigLIP2 ViT-B/16 (768-d, ~84% recall) as the default embedding model for significantly better image-text retrieval quality. - New SigLIP2Embedder class with 384px input and SigLIP normalization - ONNX export pipeline for SigLIP2 visual + textual encoders - Migration 0010: resize embeddings.vector from 512 to 768 dimensions - Config-driven model selection: "siglip2_vitb16" (default) or "openclip_vitb32" (legacy) — both models can coexist - Content classifier follows the configured embedder family - Existing embeddings cleared on migration; vision backfill regenerates Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
25 lines
950 B
Python
25 lines
950 B
Python
"""
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Embedding model — stores CLIP/SigLIP image embeddings via pgvector.
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Composite PK (photo_id, model) allows re-embedding with newer models
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without clobbering old vectors.
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Vector dimension is 768 to support SigLIP2 ViT-B/16 (the default).
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OpenCLIP ViT-B/32 (512-d) embeddings are zero-padded on insert so
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both models coexist in the same column. The padding is invisible to
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cosine similarity (zeros don't affect the angle).
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"""
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from sqlalchemy import Column, String, ForeignKey, DateTime, func
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from pgvector.sqlalchemy import Vector
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from app.database import Base
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class Embedding(Base):
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__tablename__ = 'embeddings'
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photo_id = Column(String, ForeignKey('photos.id', ondelete='CASCADE'), primary_key=True)
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model = Column(String(64), primary_key=True) # e.g. 'siglip2_vitb16'
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vector = Column(Vector(768)) # SigLIP2 ViT-B/16 → 768-d
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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