feat(search): semantic image search with CLIP embeddings - #3316
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🟢 Metrics 200 complexity · -95 duplication
Metric Results Complexity 200 Duplication -95
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semantic:"..." in KQL embeds the query text (immich-ml, multilingual CLIP) and ranks image vectors by cosine similarity: bleve via faiss KNN behind the new vectors build tag (RRF fusion, vector round-trip through a stored-only field), OpenSearch via knn_vector plus client-side RRF. The filter part of the query keeps its meaning and stays the only source of totals and facets.
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Semantic image search:
semantic:"dog on a beach"finds images by content, no tags needed, any language the model covers.Indexing: a
ClipExtractordecorator (aroundbasic/tika) embeds images via a CLIP inference service (immich machine-learning API). Querying: the clause text is embedded into the same space and images are ranked by cosine similarity, bleve via faiss KNN behind the newvectorsbuild tag, OpenSearch viaknn_vector. The clause is split off the parsed KQL tree and composes with the rest of the query: the filter part scopes the neighbor search and owns the totals, hybrid rankings fuse via RRF, purely semantic queries return just the ranking. Vector size is index schema (512, startup probe hard-fails on mismatch); in bleve the raw vector lives in a stored-only sibling field so it survives move/delete/restore.Enable with
SEARCH_EXTRACTOR_CLIP_URL; bleve needs a build withENABLE_VECTORS=true(faiss stage in Dockerfile.multiarch); existing content needs a force reindex.Open: configurable dims/model, originals vs thumbnails to the service, capability flag for clients, web search bar wraps input as
name:"*...*", OR/NOT position degrades to AND, faiss release/CI story, immich-ml is AGPL (external unmodified service).