Fetch, store, and index per-file ML data (closes #49)

Adds the machine-learning (magic) data layer: fetches per-file ML payloads (face detections + CLIP embeddings) via the existing metadata-backup fetch through the metadata pool after each refresh, decrypts and gunzips them, and stores one mldata/<fileID>.json per file by rename (present-means-complete). A derived index (mldata/clip.f32 + clip.json) loads in one read and is rebuilt whenever it disagrees with the payloads on disk in either direction, so an interrupted backfill self-heals. Never in metadata.json; incremental on later refreshes; progress via onProgress/status.

Model: opus-4-8

src/client.ts

@@ -7,6 +7,7 @@ import {
 } from "./auth/login.js";
 import { unwrapAuth } from "./auth/unwrap.js";
 import { init, fromBase64, toBase64 } from "./crypto/index.js";
+import { fetchMLDataBatch, type MLData } from "./mldata-fetch.js";
 import { decryptCollection, decryptFile } from "./model/index.js";
 import {
     downloadFile as dlFile,
@@ -273,6 +274,18 @@ export class Client {
         return files;
     }
 
+    // Fetch machine-learning data (face detections + CLIP embeddings) for up
+    // to a batch of files, each decrypted with its own key. One request; the
+    // library batches at `MLDATA_BATCH_SIZE` and schedules each batch through
+    // its metadata request pool.
+    async fetchMLData(args: {
+        fileIDs: number[];
+        fileKeys: Map<number, Uint8Array>;
+    }): Promise<Map<number, MLData>> {
+        this.assertLoggedIn();
+        return fetchMLDataBatch(this.api, args.fileIDs, args.fileKeys);
+    }
+
     async downloadFile(
         file: EnteFile,
         outPath?: string,