Adds a small embedder sidecar (Xenova/bge-small-en-v1.5, ONNX, CPU-only)
that the web app calls inline on memory.write and memory.update, and on
demand from the new memory.search tool.
memory.search performs three candidate fetches in parallel — pgvector
cosine similarity, Postgres full-text via plainto_tsquery + ts_rank_cd,
and tag-set overlap — then fuses them with Reciprocal Rank Fusion
(k=60). Each result carries its per-source rank so the model can see
*why* a memory surfaced.
The migrator boot step gained an idempotent embedding backfill: any row
with embedding IS NULL is batched (32 at a time) through the embedder
after SQL migrations apply. Safe to run on every boot.
New tool memory.update fixes the missing edit path; centralises the
re-embed-on-content-change rule alongside write.
Stack additions:
- apps/embedder/ — Fastify server, persistent /data/models volume so the
~30 MB model only downloads once
- apps/web/lib/embedder.ts — typed HTTP client with batched embed +
health probe
- packages/schemas — MemoryUpdateInput, MemorySearchInput
- docker-compose — embedder service, healthcheck, app + migrator both
depend_on it healthy; EMBEDDER_URL promoted to a required env var
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
shadowdaoandClaude Opus 4.7
9a8b504f51