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- Added vector_search_service.py with Qdrant integration * store_canvas_embedding() for storing 512-dim CLIP embeddings * search_by_embedding() for similarity search * batch operations and collection management - Updated search_algorithms.py to use real embeddings * Replaced random score stubs with CLIP-based semantic search * text_search() now uses embed_text() + vector similarity * image_search() now uses embed_image() + vector similarity * Graceful fallback to random if AI services unavailable - Added Qdrant configuration to config.py - Added dependencies: qdrant-client, torch, open_clip_torch, pillow - Added Qdrant service to docker-compose.yml - Created setup guides for local development Ready for testing - NOT pushed to remote yet
Outlines how to get the embedding worker up / qdrant db for working ai search example
Removed detailed next steps for AI search implementation, including completed tasks, missing features, and implementation order.
This reverts commit 34658c7.
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Resolves issues #24, #28, and #29