Test keywords in place of the CLIP embedding in a file's JSON

clipKeywords takes a file's ML data and whether it is a video, and gives the
titles of the Discover categories of Ente's photos app that its CLIP embedding
falls in, highest score first. The tests cover the rule with made-up vectors
and Ente's real categories with the CLIP embedding of a public-domain photo of
a sunset. The backup and backup-metadata tests now expect keywords in a file's
JSON and no CLIP embedding in its mlData. These tests fail until the
implementation lands.

Model: opus-5-5

test/cli/backup.test.ts

@@ -946,9 +946,19 @@ describe("the backup lock", () => {
     });
 });
 
-// Also serves ML data: file 100 has `ML_PAYLOAD`, the other two have none.
-// While `mlError` is set, every ML data request fails with it.
-const ML_PAYLOAD = { face: { faces: [] }, clip: { embedding: [0.5, 0.25] } };
+// Also serves ML data: file 100 has `ML_PAYLOAD`, whose CLIP embedding is that
+// of a photo of a sunset (see clip-keywords.test.ts), and the other two have
+// none. While `mlError` is set, every ML data request fails with it.
+const SUNSET_EMBEDDING: number[] = JSON.parse(
+    readFileSync(
+        new URL("../clip-keywords-sunset.json", import.meta.url),
+        "utf-8",
+    ),
+).embedding;
+const ML_PAYLOAD = {
+    face: { faces: [] },
+    clip: { embedding: SUNSET_EMBEDDING },
+};
 
 class MLClient extends MockClient {
     mlError?: string;
@@ -968,16 +978,20 @@ const fileJSON = (outDir: string, fileID: number): Record<string, unknown> =>
     JSON.parse(readFileSync(saved(outDir, `${fileID}.json`), "utf-8"));
 
 describe("ML data in each file's JSON", () => {
-    it("writes a file's ML data, and no ML field for a file that has none", async () => {
+    // The JSON holds the ML data without its CLIP embedding, and in its place
+    // the keywords the embedding gives.
+    it("writes a file's ML data and keywords, and no ML field for a file that has none", async () => {
         const lib = await openLibrary(stubSource(), new MLClient());
         const outDir = join(root, "backup");
 
         const result = await lib.backup({ downloadDirectory: outDir });
 
         expect(result.failed).toBe(0);
-        expect(fileJSON(outDir, 100).mlData).toEqual(ML_PAYLOAD);
+        expect(fileJSON(outDir, 100).mlData).toEqual({ face: { faces: [] } });
+        expect(fileJSON(outDir, 100).keywords).toEqual(["Sunset"]);
         expect(fileJSON(outDir, 101)).not.toHaveProperty("mlData");
         expect(fileJSON(outDir, 101)).not.toHaveProperty("mlDataError");
+        expect(fileJSON(outDir, 101)).not.toHaveProperty("keywords");
         await lib.close();
     });
 
@@ -993,7 +1007,7 @@ describe("ML data in each file's JSON", () => {
         const result = await lib.backup({ downloadDirectory: outDir });
 
         // File 100's ML data was cached by the first run and is kept.
-        expect(fileJSON(outDir, 100).mlData).toEqual(ML_PAYLOAD);
+        expect(fileJSON(outDir, 100).mlData).toEqual({ face: { faces: [] } });
         expect(fileJSON(outDir, 100)).not.toHaveProperty("mlDataError");
         for (const fileID of [101, 200]) {
             expect(fileJSON(outDir, fileID).mlDataError).toBe(
@@ -1026,7 +1040,7 @@ describe("ML data in each file's JSON", () => {
 
         expect(result.failed).toBe(0);
         expect(result.skipped).toBe(3);
-        expect(fileJSON(outDir, 100).mlData).toEqual(ML_PAYLOAD);
+        expect(fileJSON(outDir, 100).mlData).toEqual({ face: { faces: [] } });
         for (const fileID of [100, 101, 200]) {
             expect(fileJSON(outDir, fileID)).not.toHaveProperty("mlDataError");
         }