test: add result-level ANN plan validation above k threshold

tests/turso-vector-search.test.ts

@@ -100,4 +100,71 @@ describe("turso vector search", () => {
     }
     expect(observedSql[1]).toContain("m.container_tag = ?");
   });
+
+  it("returns correct tagged memories from ANN above the k threshold (result-level plan check)", async () => {
+    baseDir = mkdtempSync(join(tmpdir(), "turso-vector-ann-"));
+
+    const { CONFIG } = await import("../src/config.js");
+    CONFIG.storagePath = baseDir;
+
+    const { tursoConnectionManager } = await import("../src/services/turso/connection-manager.js");
+    const { tursoShardManager } = await import("../src/services/turso/shard-manager.js");
+    const { tursoVectorSearch } = await import("../src/services/turso/vector-search.js");
+
+    const dims = CONFIG.embeddingDimensions;
+    const scopeHash = "a1b2c3d4e5f67890";
+    const targetTag = `opencode_project_${scopeHash}`;
+    const otherTag = "opencode_project_0000000000000000";
+
+    const shard = await tursoShardManager.createShard("project", scopeHash, 0);
+    const db = await tursoConnectionManager.getConnection(shard.dbPath);
+
+    // Insert 200 memories across two container tags, well above the k=128
+    // threshold for container-tagged ANN searches. The target-tag memory at
+    // index 0 has the query vector itself (similarity 1.0), so it must rank
+    // first; other-tag memories must never appear in results.
+    const now = Date.now();
+    for (let i = 0; i < 200; i++) {
+      const vec = new Float32Array(dims);
+      // Spread signal across dimensions so vectors are distinguishable but
+      // the index-0 vector is closest to the query (also vec[0]=1).
+      vec[i % dims] = 1;
+      if (i > 0) vec[0] = 0.001;
+
+      const tag = i < 150 ? targetTag : otherTag;
+      await tursoVectorSearch.insertVector(db, {
+        id: `mem_ann_${i}`,
+        content: `Memory ${i}`,
+        vector: vec,
+        containerTag: tag,
+        tags: "",
+        createdAt: now - (200 - i),
+        updatedAt: now,
+      });
+    }
+
+    // Query vector matches mem_ann_0 (vec[0]=1, no other dimensions set).
+    const queryVector = new Float32Array(dims);
+    queryVector[0] = 1;
+
+    const results = await tursoVectorSearch.searchInShard(
+      shard,
+      queryVector,
+      targetTag,
+      10,
+      "turso"
+    );
+
+    expect(results.length).toBeGreaterThan(0);
+    // The closest target-tag memory must rank first.
+    expect(results[0]?.id).toBe("mem_ann_0");
+    expect(results[0]?.similarity).toBeGreaterThan(0.9);
+    // No other-container-tag memories (indices 150-199) should leak through.
+    for (const r of results) {
+      const idx = Number(r.id.replace("mem_ann_", ""));
+      expect(idx).toBeLessThan(150);
+    }
+    // Results should be limited to the requested limit.
+    expect(results.length).toBeLessThanOrEqual(10);
+  });
 });