Revert ML search (#50) to restore a green next (build fix pending)

Reverts the #50 merge (224bd101): mlsearch.ts failed tsc in the CI build (make check does not run make build, so it slipped past review). next restored to green; #50 to be redone with make build in its gate. Model: opus-4-8
2026-09-22 18:21:54 +02:00
parent 224bd101ab
commit 61dfec8d38
3 changed files with 4 additions and 240 deletions
@@ -1,108 +0,0 @@
-/**
- * Tests for the content-similarity search surface over the CLIP index
- * (issue #50).
- *
- * The surface is `lib.mldata`: `forFile` reads the full stored payload from
- * disk, while `similar` and `searchByEmbedding` rank fileIDs by cosine
- * similarity over the in-RAM `Float32Array` index alone (no disk, no network).
- * The fixture uses axis-aligned vectors so the correct cosine ranking is
- * obvious by inspection; cosine ignores magnitude, so `[2, 0, 0]` ranks above
- * `[0.8, 0.6, 0]` for a `[1, 0, 0]` query.
- */
-
-import { describe, it, expect, beforeEach, afterEach } from "vitest";
-import { mkdtempSync, rmSync } from "node:fs";
-import { tmpdir } from "node:os";
-import { join } from "node:path";
-
-import { MLDataStore } from "../../src/library/mldata.js";
-import { makeMLDataAPI, type MLDataAPI } from "../../src/library/mlsearch.js";
-import type { MLData } from "../../src/mldata-fetch.js";
-
-// A payload shaped like Ente's: a CLIP embedding plus face data that only the
-// on-disk payload carries (never the RAM index).
-const payload = (embedding: number[]): MLData => ({
-    face: { faces: [{ faceID: "f", detection: { box: { x: 0.5 } } }] },
-    clip: { embedding },
-});
-
-// A small fixture index. Directions are chosen so every cosine ranking below
-// is unambiguous.
-const fixture = (): Map<number, MLData> =>
-    new Map([
-        [10, payload([1, 0, 0])],
-        [20, payload([0.8, 0.6, 0])],
-        [30, payload([0, 1, 0])],
-        [40, payload([-1, 0, 0])],
-        [50, payload([2, 0, 0])],
-    ]);
-
-describe("lib.mldata content-similarity search", () => {
-    let dir: string;
-    let store: MLDataStore;
-    let api: MLDataAPI;
-
-    beforeEach(async () => {
-        dir = mkdtempSync(join(tmpdir(), "quak-mlsearch-"));
-        store = await MLDataStore.open(dir);
-        const updation = new Map([...fixture().keys()].map((id) => [id, 1]));
-        await store.storeFetched(fixture(), updation);
-        api = makeMLDataAPI(() => store);
-    });
-
-    afterEach(() => {
-        rmSync(dir, { recursive: true, force: true });
-    });
-
-    it("forFile returns the whole stored payload, or undefined when uncached", async () => {
-        const full = await api.forFile({ fileID: 20 });
-        expect(full).toBeDefined();
-        // Face data lives only in the payload, never in the RAM index.
-        expect(full?.face).toBeDefined();
-        expect(full?.clip).toEqual({ embedding: [0.8, 0.6, 0] });
-        expect(await api.forFile({ fileID: 999 })).toBeUndefined();
-    });
-
-    it("similar ranks other files by cosine and excludes the query itself", () => {
-        // Query is file 10 = [1, 0, 0]. By cosine: 50 (1.0) > 20 (0.8) >
-        // 30 (0) > 40 (-1); 10 itself is left out.
-        const ranked = api.similar({ fileID: 10 });
-        expect(ranked.map((r) => r.fileID)).toEqual([50, 20, 30, 40]);
-        // Cosine ignores magnitude: [2,0,0] is a perfect match for [1,0,0].
-        expect(ranked[0]).toMatchObject({ fileID: 50 });
-        expect(ranked[0].score).toBeCloseTo(1, 5);
-    });
-
-    it("similar honours limit and returns [] for an unindexed file", () => {
-        expect(
-            api.similar({ fileID: 10, limit: 2 }).map((r) => r.fileID),
-        ).toEqual([50, 20]);
-        expect(api.similar({ fileID: 999 })).toEqual([]);
-    });
-
-    it("searchByEmbedding ranks the index by cosine to the query vector", () => {
-        // Query [0, 1, 0]: 30 (1.0) > 20 (0.6) > {10, 40, 50} all 0, broken by
-        // ascending fileID.
-        const ranked = api.searchByEmbedding({ embedding: [0, 1, 0] });
-        expect(ranked.map((r) => r.fileID)).toEqual([30, 20, 10, 40, 50]);
-        expect(ranked[0].score).toBeCloseTo(1, 5);
-
-        expect(
-            api
-                .searchByEmbedding({ embedding: [0, 1, 0], limit: 2 })
-                .map((r) => r.fileID),
-        ).toEqual([30, 20]);
-    });
-
-    it("searchByEmbedding returns [] for a wrong-length or zero query", () => {
-        expect(api.searchByEmbedding({ embedding: [1, 0] })).toEqual([]);
-        expect(api.searchByEmbedding({ embedding: [0, 0, 0] })).toEqual([]);
-    });
-
-    it("degrades to empty results when no ML store is present", async () => {
-        const none = makeMLDataAPI(() => undefined);
-        expect(await none.forFile({ fileID: 10 })).toBeUndefined();
-        expect(none.similar({ fileID: 10 })).toEqual([]);
-        expect(none.searchByEmbedding({ embedding: [1, 0, 0] })).toEqual([]);
-    });
-});