feat(embeddings): cache persistent de vectori in SQLite + warmup in fundal

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>

app/schema.sql

@@ -281,6 +281,17 @@ CREATE TABLE IF NOT EXISTS shared_mappings (
     updated_at           TEXT NOT NULL DEFAULT (datetime('now'))
 );
 
+-- Cache persistent vectori embeddings corpus (mapping_suggestions). Cheia (model,
+-- text_hash) leaga cache-ul de numele modelului: schimbare model = randuri vechi inerte,
+-- fara sa fie folosite (curatate ulterior de purge_stale).
+CREATE TABLE IF NOT EXISTS embedding_cache (
+    text_hash   TEXT NOT NULL,
+    model       TEXT NOT NULL,
+    vector      BLOB NOT NULL,
+    created_at  TEXT NOT NULL DEFAULT (datetime('now')),
+    PRIMARY KEY (model, text_hash)
+);
+
 -- Heartbeat worker (un singur rand, id=1). /healthz citeste de aici.
 CREATE TABLE IF NOT EXISTS worker_heartbeat (
     id                  INTEGER PRIMARY KEY CHECK (id = 1),