Wire the full embedding flow:
- Rewrite Embedding model to use pgvector Vector(512) with HNSW index
- Add embed_photo, vision_fanout, backfill_vision Celery tasks on
dedicated `vision` queue
- Hook vision_fanout into generate_thumbnails completion
- Add POST /api/v1/photos/search with hybrid RRF ranking (semantic-only
for now; FTS leg added in PR5)
- Stub ocr_photo, detect_objects, extract_faces tasks for later PRs
Migration 0003 drops/recreates the embeddings table (was never populated).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Unify object detections, scene labels, and face clusters with user tags
via new columns on the existing Tag model:
- kind (user|object|scene|face_cluster), source, representative_photo_id
- photo_tags gains confidence, bbox (JSONB), source per-association
- Uniqueness moves from (name) to (name, kind) so ML labels coexist
with user tags without collision
Add Alembic migration 0002 with defensive IF NOT EXISTS guards.
Update tags router: kind filter on GET, merge endpoint for combining
auto-detected clusters/objects, include kind/source in list response.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Switch the default database from SQLite to Postgres + pgvector (via
pgvector/pgvector:pg16 Docker image) to support the upcoming vision
pipeline (embeddings, OCR, object detection, face clustering).
- Add `db` service to docker-compose.yml with healthcheck
- Wire `alembic upgrade head` into backend CMD before uvicorn
- Bootstrap empty 0001_baseline revision (schema still owned by create_all)
- Guard SQLite-only PRAGMAs and inline ALTERs behind _is_sqlite flag
- Run `CREATE EXTENSION IF NOT EXISTS vector` on Postgres init
- Add asyncpg, psycopg2-binary, pgvector to requirements
- Provide docker-compose.sqlite.yml escape hatch for legacy SQLite mode
Fresh DB + rescan assumed — no SQLite→Postgres data migration.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
