Vectorii corpusului k-NN persista in tabela embedding_cache (PK model+text_hash,
blob float32 LE); la warmup se vectorizeaza doar textele lipsa din cache, deci
restartul cu corpus neschimbat nu mai plateste ~1-2 min de embed (embed=0).
- app/embedding_cache.py: serializare array('f'), load/save/purge chunk 500 cu
BEGIN/COMMIT explicit (conexiuni autocommit), validare dimensiune la scriere
si citire, orchestrare sync_corpus_vectors cu embed_fn injectat
- index_corpus(vectors=): vectori precalculati cu validare aliniere; mismatch
-> fallback embed complet
- ensure_embeddings_corpus: warmup in thread la startup (block=True), calea de
request ne-blocanta (acquire non-blocking pe lock; warmup in curs -> return
imediat); purjare orfane + modele vechi doar dupa indexare reusita
- log warmup: cache=N embed=M in Xs
- 31 teste noi (cold/warm/incremental, model schimbat, concurenta, ranking
exact, echivalenta float32); suita completa 1596 passed
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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