feat: Phase 2 — embeddings + hybrid memory.search

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>

apps/web/scripts/migrate.ts

@@ -72,11 +72,88 @@ async function main() {
     }
 
     console.log("Migrations complete.");
+
+    if (process.env.EMBEDDER_URL) {
+      await backfillEmbeddings(sql);
+    } else {
+      console.log("EMBEDDER_URL not set — skipping embedding backfill.");
+    }
   } finally {
     await sql.end({ timeout: 5 });
   }
 }
 
+/**
+ * Backfill embedding column for any memory written before embeddings were
+ * online. Idempotent: only touches rows where embedding IS NULL. Runs on
+ * every migrator boot, so deploying Phase 2 — or recovering from an
+ * embedder outage that left fresh rows unembedded — needs no manual step.
+ */
+async function backfillEmbeddings(sql: ReturnType<typeof postgres>) {
+  const embedderUrl = process.env.EMBEDDER_URL!.replace(/\/$/, "");
+  const BATCH = 32;
+
+  // Wait for the embedder to report ready — its first boot has to download
+  // and load the model, which can take 30–60s on a cold container.
+  const waitDeadline = Date.now() + 180_000;
+  for (;;) {
+    try {
+      const res = await fetch(`${embedderUrl}/health`);
+      if (res.ok) {
+        const body = (await res.json()) as { ready?: boolean };
+        if (body.ready) break;
+      }
+    } catch {
+      /* embedder not up yet */
+    }
+    if (Date.now() > waitDeadline) {
+      throw new Error("embedder did not become ready within 180s");
+    }
+    await new Promise((r) => setTimeout(r, 2000));
+  }
+
+  let total = 0;
+  for (;;) {
+    const rows = await sql<{ id: string; content: string }[]>`
+      SELECT id, content FROM memories
+      WHERE embedding IS NULL AND deleted_at IS NULL
+      ORDER BY created_at
+      LIMIT ${BATCH}
+    `;
+    if (rows.length === 0) break;
+
+    const res = await fetch(`${embedderUrl}/embed`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({ texts: rows.map((r) => r.content) }),
+    });
+    if (!res.ok) {
+      const detail = await res.text().catch(() => "");
+      throw new Error(`embedder error ${res.status}: ${detail.slice(0, 200)}`);
+    }
+    const { vectors } = (await res.json()) as { vectors: number[][] };
+
+    await sql.begin(async (tx) => {
+      for (let i = 0; i < rows.length; i++) {
+        const id = rows[i]!.id;
+        const vec = vectors[i];
+        if (!vec) continue;
+        const literal = `[${vec.join(",")}]`;
+        await tx`UPDATE memories SET embedding = ${literal}::vector WHERE id = ${id}`;
+      }
+    });
+
+    total += rows.length;
+    console.log(`  embedded ${rows.length} memories (total: ${total})`);
+  }
+
+  if (total === 0) {
+    console.log("Embedding backfill: nothing to do.");
+  } else {
+    console.log(`Embedding backfill complete: ${total} memories embedded.`);
+  }
+}
+
 main().catch((err) => {
   console.error("Migration failed:", err);
   process.exit(1);