Start empty when the cache directory holds another account's cache (closes #104)

When metadata.json in the cache directory was written for a different,
non-zero user ID than the client's, Library.open deletes it and mldata/
and loads an empty store, so the first refresh enumerates from 0 and none
of the other account's collections, files, keys or ML results are served.
Only reachable with --cache-dir or an explicit cacheDirectory.

Model: opus-5-5

README.md

@@ -471,11 +471,6 @@ accepted for backward compatibility but ignored. `backup-metadata --exif` (alias
 metadata. The listing and backup commands support `--json` for machine-readable
 output.
 
-`backup-metadata` fetches ML data in requests of up to 200 files. When a request
-still fails after its retries, the error is logged, each of its files is written
-with the reason in an `mlDataError` field instead of `mlData`, and the dump goes
-on. The exit code is non-zero if any ML data request failed.
-
 `helper fix-missing-thumbnails` regenerates thumbnails for baseline JPEG images
 only, because the bundled decoder (`jpeg-js`) decodes only JPEG. A non-JPEG
 image (PNG, HEIC) or a video is reported as `skipped` (unsupported format), kept

TODO.md

@@ -24,11 +24,6 @@ Tag v1.0.0.
   and starts empty, so the first refresh enumerates from 0. This only happens
   with `--cache-dir` or an explicit `cacheDirectory`; the default path already
   includes the user ID. A test opens one account's cache as another account.
-- 2026-09-23: `backup-metadata` no longer stops on one failed ML data request
-  (issue 101). Each request of up to 200 files is tried on its own; a failed one
-  is logged, its files are written with the reason in `mlDataError`, and the
-  command exits 1 once the dump is complete. `fetchMLData`, which only this
-  command used, is gone; the command calls `fetchMLDataBatch` per batch.
 - 2026-09-23: Single-sourced the version string (issue 5). `package.json` is the
   only place it is written: `src/index.ts` imports it for `VERSION` and
   `bin/quak.ts` passes `VERSION` to commander. tsc copies `package.json` to

src/cli-commands.ts

@@ -323,11 +323,11 @@ export const backupMetadataCommand = async (
     if (!client) return 1;
     const lib = await openReadLibrary(ctx, client);
     try {
-        const { failedMLBatches } = await runMetadataBackup(lib, client, dir, {
+        await runMetadataBackup(lib, client, dir, {
             exif: opts.exif || opts.all,
             onProgress: (msg) => ctx.stderr.write(msg + "\n"),
         });
-        return failedMLBatches > 0 ? 1 : 0;
+        return 0;
     } finally {
         await lib.close();
     }

src/metadata-backup.ts

@@ -5,11 +5,7 @@ import exifReader from "exif-reader";
 import type { Client } from "./client.js";
 import type { Library, Photo } from "./library/index.js";
 import { sanitizeFileName } from "./filename.js";
-import {
+import { fetchMLData } from "./mldata-fetch.js";
-    fetchMLDataBatch,
-    MLDATA_BATCH_SIZE,
-    type MLData,
-} from "./mldata-fetch.js";
 import type { EnteFile } from "./model/types.js";
 
 export type ProgressCallback = (message: string) => void;
@@ -141,14 +137,13 @@ const extractExif = async (
 // of plain JSON: account, per-collection, and per-file records including the
 // private and public magic metadata and (by default) the ML data. Collections
 // and files are enumerated from the library's cache rather than a fresh server
-// scan. Returns how many ML data requests failed; their files are still
+// scan; the ML fetch and EXIF extraction are unchanged.
-// written, with `mlDataError` in place of `mlData`.
 export const runMetadataBackup = async (
     lib: Library,
     client: Client,
     outDir: string,
     opts?: MetadataBackupOptions,
-): Promise<{ failedMLBatches: number }> => {
+): Promise<void> => {
     const log = opts?.onProgress ?? (() => {});
     const wantExif = opts?.exif ?? false;
 
@@ -213,31 +208,12 @@ export const runMetadataBackup = async (
         }
     }
 
-    // One failed request (retries exhausted) must not end the dump: its files
-    // get the reason in `mlDataError` and the other batches go on.
     log("Fetching ML data (face detections, CLIP embeddings)...");
-    const mlDataMap = new Map<number, MLData>();
+    const mlDataMap = await fetchMLData(
-    const mlDataErrors = new Map<number, string>();
+        client.getApiClient(),
-    let failedMLBatches = 0;
+        [...fileKeys.keys()],
-    const fileIDs = [...fileKeys.keys()];
+        fileKeys,
-    for (let i = 0; i < fileIDs.length; i += MLDATA_BATCH_SIZE) {
+    );
-        const batch = fileIDs.slice(i, i + MLDATA_BATCH_SIZE);
-        try {
-            const result = await fetchMLDataBatch(
-                client.getApiClient(),
-                batch,
-                fileKeys,
-            );
-            for (const [id, payload] of result) mlDataMap.set(id, payload);
-        } catch (err) {
-            const reason = err instanceof Error ? err.message : String(err);
-            failedMLBatches++;
-            log(
-                `ML data request for ${batch.length} file(s) failed: ${reason}`,
-            );
-            for (const id of batch) mlDataErrors.set(id, reason);
-        }
-    }
     log(`Got ML data for ${mlDataMap.size} file(s)`);
 
     const writtenFileIDs = new Set<number>();
@@ -257,8 +233,6 @@ export const runMetadataBackup = async (
 
         const ml = mlDataMap.get(file.id);
         if (ml) fileMeta.mlData = ml;
-        const mlError = mlDataErrors.get(file.id);
-        if (mlError) fileMeta.mlDataError = mlError;
 
         if (wantExif && !writtenFileIDs.has(file.id)) {
             log(`[${file.metadata.title}] Extracting EXIF...`);
@@ -279,5 +253,4 @@ export const runMetadataBackup = async (
     }
 
     log("Metadata backup complete.");
-    return { failedMLBatches };
 };

src/mldata-fetch.ts

@@ -5,8 +5,9 @@
 // comes back encrypted under the file's own key and gzipped; decrypting and
 // gunzipping yields the JSON payload
 // `{ face: { faces: [...] }, clip: { embedding } }`. Ente caps a request at 200
-// ids, so callers that want many at once split them into batches of
+// ids, so `fetchMLData` batches for callers that want many at once while
-// `MLDATA_BATCH_SIZE` and call `fetchMLDataBatch` once per batch.
+// `fetchMLDataBatch` is the single-request unit the library submits to its
+// request pool.
 
 import { gunzipSync } from "node:zlib";
 
@@ -68,3 +69,25 @@ export const fetchMLDataBatch = async (
     }
     return result;
 };
+
+// Fetch ML data for arbitrarily many ids, batching at `MLDATA_BATCH_SIZE`. Used
+// by the one-shot metadata backup; the library fetches through its request pool
+// with `fetchMLDataBatch` instead.
+export const fetchMLData = async (
+    api: ApiClient,
+    fileIDs: number[],
+    fileKeys: Map<number, Uint8Array>,
+): Promise<Map<number, MLData>> => {
+    const result = new Map<number, MLData>();
+    for (let i = 0; i < fileIDs.length; i += MLDATA_BATCH_SIZE) {
+        const batch = fileIDs.slice(i, i + MLDATA_BATCH_SIZE);
+        for (const [id, payload] of await fetchMLDataBatch(
+            api,
+            batch,
+            fileKeys,
+        )) {
+            result.set(id, payload);
+        }
+    }
+    return result;
+};

test/cli/metadata-backup.test.ts

@@ -38,7 +38,7 @@ import { join } from "node:path";
 import { tmpdir } from "node:os";
 import sodium from "libsodium-wrappers-sumo";
 import { SRP, SrpServer } from "fast-srp-hap";
-import { beforeAll, afterAll, describe, expect, it, vi } from "vitest";
+import { beforeAll, afterAll, describe, expect, it } from "vitest";
 import {
     init,
     toBase64,
@@ -53,16 +53,8 @@ import {
     runMetadataBackup,
     type MetadataBackupOptions,
 } from "../../src/metadata-backup.js";
-import { backupMetadataCommand } from "../../src/cli-commands.js";
 import type { KeyAttributes } from "../../src/auth/types.js";
 
-// One file per ML data request, so the two files of the mock account are
-// fetched in two requests and one of them can fail on its own.
-vi.mock("../../src/mldata-fetch.js", async (importOriginal) => ({
-    ...(await importOriginal<typeof import("../../src/mldata-fetch.js")>()),
-    MLDATA_BATCH_SIZE: 1,
-}));
-
 const TEST_EMAIL = "metabackup@example.com";
 const TEST_PASSWORD = "metapass";
 const TEST_OPS = 2;
@@ -355,8 +347,7 @@ const buildMetaMock = async (): Promise<MetaMockState> => {
     };
 };
 
-// `failMLDataFor`: answer 500 to every ML data request that asks for this file.
+const buildMetaFetch = (m: MetaMockState) => {
-const buildMetaFetch = (m: MetaMockState, failMLDataFor?: number) => {
     let srpServer: SrpServer;
     return (async (
         input: RequestInfo | URL,
@@ -412,8 +403,6 @@ const buildMetaFetch = (m: MetaMockState, failMLDataFor?: number) => {
         }
         if (path === "/files/data/fetch") {
             const body = JSON.parse(init?.body as string);
-            if ((body.fileIDs as number[]).includes(failMLDataFor!))
-                return new Response("server error", { status: 500 });
             const data = (body.fileIDs as number[])
                 .filter((id: number) => m.encryptedMLData[id])
                 .map((id: number) => ({
@@ -647,63 +636,3 @@ describe("quak backup-metadata", () => {
         expect(failedMeta.imageMetadataError).toEqual(expect.any(String));
     });
 });
-
-describe("quak backup-metadata when an ML data request fails", () => {
-    // Run the CLI command against the mock and return its exit code, stderr
-    // and output directory.
-    const runCommand = async (failMLDataFor?: number) => {
-        const client = await Client.login({
-            email: TEST_EMAIL,
-            password: TEST_PASSWORD,
-            apiOptions: {
-                fetch: buildMetaFetch(mock, failMLDataFor),
-                retry: { sleep: async () => {} },
-            },
-        });
-        const outDir = mkdtempSync(join(testDir, "ml-fail-"));
-        let stderr = "";
-        const code = await backupMetadataCommand(
-            {
-                stdout: { write: () => true },
-                stderr: { write: (text: string) => (stderr += text) },
-                sessionDir: testDir,
-                cacheDir: mkdtempSync(join(testDir, "cache-")),
-                loadSession: () => client,
-            },
-            outDir,
-            {},
-        );
-        return { code, stderr, outDir };
-    };
-
-    it("writes every file, marks the failed batch's files, and exits 1", async () => {
-        const { code, stderr, outDir } = await runCommand(200);
-
-        expect(code).toBe(1);
-        expect(stderr).toContain("ML data request for 1 file(s) failed");
-
-        const ok = JSON.parse(
-            readFileSync(
-                join(outDir, "collections", "10-Vacation", "100.json"),
-                "utf-8",
-            ),
-        );
-        expect(ok.mlData.clip.embedding).toEqual([0.5, 0.6, 0.7]);
-        expect(ok.mlDataError).toBeUndefined();
-
-        const failed = JSON.parse(
-            readFileSync(
-                join(outDir, "collections", "20-__Work", "200.json"),
-                "utf-8",
-            ),
-        );
-        expect(failed.metadata.title).toBe("diagram.png");
-        expect(failed.mlData).toBeUndefined();
-        expect(failed.mlDataError).toContain("500");
-    });
-
-    it("exits 0 when every ML data request succeeds", async () => {
-        const { code } = await runCommand();
-        expect(code).toBe(0);
-    });
-});