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Phase A — voyage-3 migration (executed): - VOYAGE_MODEL=voyage-3 set in Coolify (legal-ai app) and ~/.env - scripts/reembed_voyage.py: re-embeds document_chunks (6157), case_law_embeddings (9), precedent_chunks (385), and halachot (400) using the new model. paragraph_embeddings was empty. 6951 rows re-embedded in 93s, ~75 rows/sec. - Same 1024 dim → no schema change needed. Why voyage-3 over voyage-law-2: benchmark on 3 Hebrew legal queries with real passages from the corpus gave voyage-3 perfect ordering on 3/3 tests AND the largest separation (+0.483 vs voyage-law-2's +0.238). voyage-4 family had bigger separation but missed top-1 on the hardest test. Phase B (voyage-context-3) and Phase C (voyage-multimodal-3.5 for scanned + appraiser docs) are designed in docs/voyage-upgrades-plan.md but deferred — to be picked up in a fresh conversation. The plan includes: - Phase B: contextualized embeddings refactor (~49% recall lift on legal docs per Anthropic's research). Same dim, but ingestion pipeline must pass full doc context per chunk. - Phase C: page-level image embeddings via voyage-multimodal-3.5, stored in a parallel *_image_embeddings table. Hybrid text+image search. Targets appraiser report tables and scanned PDFs where current OCR loses layout. After this commit: MCP server needs a /mcp reconnect to pick up the new VOYAGE_MODEL env, and the legal-ai container will pick it up on its next redeploy. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>