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

Ente's "magic" search data (face detections + CLIP embeddings) is now
fetched, decrypted, and cached under cacheDirectory/mldata/, never in
metadata.json.

A new mldata-fetch module holds the /files/data/fetch (type mldata)
decrypt+gunzip, reused by both the metadata backup and the library.
MLDataStore writes one payload file per fileID by rename (present means
complete) and maintains a derived index: clip.json (fileIDs in order +
embedding length) and clip.f32 (embeddings packed as one Float32Array,
loaded in a single read). The index is rebuilt from the payloads when
missing or structurally inconsistent with the files present, and appended
to as payloads arrive; fetched.json records each file's fetch-time
updationTime for refetch decisions.

After each refresh the library fetches, through the #45 metadata pool, the
ML data for every known file absent from mldata/ or whose updationTime
advanced, reporting via onProgress (operation fetchMLData) and status().
RAM holds only the id list and Float32Array; payloads are read on demand.

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,

src/library/index.ts

@@ -23,6 +23,8 @@ import { join } from "node:path";
 import envPaths from "env-paths";
 
 import { MetadataStore } from "./store.js";
+import { MLDataStore } from "./mldata.js";
+import { RequestPools } from "./pools.js";
 import {
     deriveRecords,
     snapshotFrom,
@@ -51,6 +53,7 @@ export {
     type GroupBy,
 } from "./read.js";
 import type { CollectionsPage, FilesPage } from "../client.js";
+import { MLDATA_BATCH_SIZE, type MLData } from "../mldata-fetch.js";
 import type { Collection, EnteFile } from "../model/types.js";
 
 export const DEFAULT_REFRESH_INTERVAL_SECONDS = 3;
@@ -66,14 +69,24 @@ export interface LibraryClient {
         collectionKey: Uint8Array;
         sinceTime: number;
     }): Promise<FilesPage>;
+    // Fetch ML data (face detections + CLIP embeddings) for up to a batch of
+    // files. Optional: a client without it simply disables ML fetching, leaving
+    // the metadata refresh untouched.
+    fetchMLData?(args: {
+        fileIDs: number[];
+        fileKeys: Map<number, Uint8Array>;
+    }): Promise<Map<number, MLData>>;
 }
 
-// A single refresh cycle's progress. "started" fires before the network work,
-// then exactly one of "done" or "failed"; "failed" carries the error message.
+// A progress event for one unit of background work. A metadata "refresh" or an
+// ML "fetchMLData" pass each fire "started" before their network work and then
+// exactly one of "done" or "failed"; "failed" carries the error message and
+// an ML "done" reports how many payloads it stored.
 export interface RefreshEvent {
-    operation: "refresh";
+    operation: "refresh" | "fetchMLData";
     status: "started" | "done" | "failed";
     error?: string;
+    fetched?: number;
 }
 
 export type RefreshProgressCallback = (event: RefreshEvent) => void;
@@ -88,6 +101,9 @@ export interface LibraryOptions {
     downloadDirectory?: string;
     refreshIntervalSeconds?: number;
     onProgress?: RefreshProgressCallback;
+    // The bounded request pools (issue #45). ML data is fetched through the
+    // metadata pool. Defaults to a fresh set at the design's caps.
+    pools?: RequestPools;
 }
 
 export interface LibraryStatus {
@@ -100,6 +116,15 @@ export interface LibraryStatus {
     // The message from the most recent refresh, set only while that refresh
     // failed; cleared by the next success.
     lastError?: string;
+    // Wall-clock ms of the last ML fetch pass that succeeded, or undefined if
+    // none has yet (or ML fetching is disabled).
+    lastMLFetchAt?: number;
+    // The most recent ML fetch pass's error, set only while it failed.
+    lastMLError?: string;
+    // ML payloads stored on disk and CLIP embeddings in the index; undefined
+    // when ML fetching is disabled.
+    mlStored?: number;
+    mlIndexed?: number;
     closed: boolean;
 }
 
@@ -119,12 +144,20 @@ export class Library {
     private readonly userID: number;
     private readonly intervalMs: number;
     private readonly onProgress?: RefreshProgressCallback;
+    private readonly pools: RequestPools;
+    // The ML-data cache, present only when the client can fetch ML data.
+    private readonly mldata?: MLDataStore;
 
     private timer?: ReturnType<typeof setTimeout>;
     private refreshing = false;
+    // Guards the ML fetch pass so a slow backfill never runs twice at once; a
+    // refresh whose pass is still running kicks nothing new.
+    private mlFetching = false;
     private closed = false;
     private lastRefreshAt?: number;
     private lastError?: string;
+    private lastMLFetchAt?: number;
+    private lastMLError?: string;
     // The plain-record projection as of the last refresh, and the GUI change
     // subscribers. A refresh that alters the projection notifies each with the
     // delta; `lastRecords` is kept current every refresh so a subscriber that
@@ -144,6 +177,8 @@ export class Library {
         downloadDirectory?: string;
         intervalMs: number;
         onProgress?: RefreshProgressCallback;
+        pools: RequestPools;
+        mldata?: MLDataStore;
     }) {
         this.client = args.client;
         this.store = args.store;
@@ -152,6 +187,8 @@ export class Library {
         this.downloadDirectory = args.downloadDirectory;
         this.intervalMs = args.intervalMs;
         this.onProgress = args.onProgress;
+        this.pools = args.pools;
+        this.mldata = args.mldata;
         this.lastRecords = this.deriveNow();
 
         // The read namespaces derive fresh from the store on each call, so they
@@ -181,6 +218,12 @@ export class Library {
             (opts.refreshIntervalSeconds ?? DEFAULT_REFRESH_INTERVAL_SECONDS) *
             1000;
 
+        // The ML cache only earns its keep when the client can fetch ML data;
+        // a client without that capability opens no `mldata/` directory.
+        const mldata = opts.client.fetchMLData
+            ? await MLDataStore.open(join(cacheDirectory, "mldata"))
+            : undefined;
+
         const lib = new Library({
             client: opts.client,
             store,
@@ -189,6 +232,8 @@ export class Library {
             downloadDirectory: opts.downloadDirectory,
             intervalMs,
             onProgress: opts.onProgress,
+            pools: opts.pools ?? new RequestPools(),
+            mldata,
         });
 
         if (store.loadedFromDisk) {
@@ -248,12 +293,17 @@ export class Library {
         for (const c of collections) {
             files += this.store.listFiles(c.id).length;
         }
+        const ml = this.mldata?.stats();
         return {
             userID: this.store.userID,
             collections: collections.length,
             files,
             lastRefreshAt: this.lastRefreshAt,
             lastError: this.lastError,
+            lastMLFetchAt: this.lastMLFetchAt,
+            lastMLError: this.lastMLError,
+            mlStored: ml?.stored,
+            mlIndexed: ml?.indexed,
             closed: this.closed,
         };
     }
@@ -288,6 +338,11 @@ export class Library {
             this.lastRefreshAt = Date.now();
             this.lastError = undefined;
             this.emit({ operation: "refresh", status: "done" });
+            // Backfill ML data for the files this refresh knows about. It runs
+            // outside the refresh's success/failure so a fetch or disk problem
+            // there never marks the metadata refresh failed, and it is not
+            // awaited so it never stalls the refresh interval.
+            void this.runMLFetch();
         } catch (err) {
             const error = err instanceof Error ? err.message : String(err);
             this.lastError = error;
@@ -388,6 +443,85 @@ export class Library {
         }
     }
 
+    // One ML fetch pass: fetch, decrypt and store the ML data for every file
+    // the store knows about that is not cached (or whose `updationTime` has
+    // advanced), through the metadata pool, and update the CLIP index. Guarded
+    // so passes never overlap; a failure is reported, not thrown.
+    private async runMLFetch(): Promise<void> {
+        const mldata = this.mldata;
+        // Bind so the call keeps the client as its receiver when invoked
+        // through the pool below.
+        const fetchMLData = this.client.fetchMLData?.bind(this.client);
+        if (!mldata || !fetchMLData || this.closed || this.mlFetching) return;
+
+        const files = this.uniqueFiles();
+        const needed = mldata.neededFor(files);
+        if (needed.length === 0) return;
+
+        this.mlFetching = true;
+        this.emit({ operation: "fetchMLData", status: "started" });
+        try {
+            const fileKeys = new Map<number, Uint8Array>();
+            const updation = new Map<number, number>();
+            for (const f of files) {
+                fileKeys.set(f.id, f.key);
+                updation.set(f.id, f.updationTime);
+            }
+
+            let stored = 0;
+            for (let i = 0; i < needed.length; i += MLDATA_BATCH_SIZE) {
+                if (this.closed) break;
+                const batch = needed.slice(i, i + MLDATA_BATCH_SIZE);
+                const payloads = await this.pools.metadata.run(
+                    () => fetchMLData({ fileIDs: batch, fileKeys }),
+                    { priority: "background" },
+                );
+                stored += (await mldata.storeFetched(payloads, updation))
+                    .stored;
+            }
+
+            this.lastMLFetchAt = Date.now();
+            this.lastMLError = undefined;
+            this.emit({
+                operation: "fetchMLData",
+                status: "done",
+                fetched: stored,
+            });
+        } catch (err) {
+            const error = err instanceof Error ? err.message : String(err);
+            this.lastMLError = error;
+            this.emit({ operation: "fetchMLData", status: "failed", error });
+        } finally {
+            this.mlFetching = false;
+        }
+    }
+
+    // The distinct files the store holds, one entry per fileID (a file in
+    // several collections shares its ML data), each carrying the key and the
+    // newest `updationTime` seen across its memberships.
+    private uniqueFiles(): {
+        id: number;
+        key: Uint8Array;
+        updationTime: number;
+    }[] {
+        const byID = new Map<
+            number,
+            { id: number; key: Uint8Array; updationTime: number }
+        >();
+        for (const collection of this.store.listCollections()) {
+            for (const f of this.store.listFiles(collection.id)) {
+                const seen = byID.get(f.id);
+                if (seen === undefined || f.updationTime > seen.updationTime)
+                    byID.set(f.id, {
+                        id: f.id,
+                        key: f.key,
+                        updationTime: f.updationTime,
+                    });
+            }
+        }
+        return [...byID.values()];
+    }
+
     // Gather every file membership and project the store into by-id records.
     private deriveNow(): DerivedRecords {
         const collections = this.store.listCollections();

src/library/mldata.ts

@@ -0,0 +1,361 @@
+// The on-disk cache of Ente's per-file machine-learning data and the CLIP
+// index derived from it (issue #49).
+//
+// Under `<cacheDirectory>/mldata/` this keeps:
+//
+//   - `<fileID>.json` — one decrypted, gunzipped payload per file, written by
+//     rename. Its presence means it is complete: a torn write never leaves a
+//     half-file, so the set of these files is the source of truth for what is
+//     cached. The full payload (face boxes, landmarks, embeddings) is read back
+//     from here on demand and never held in RAM.
+//
+//   - `clip.f32` + `clip.json` — the derived index the content search runs on.
+//     `clip.json` lists the indexed fileIDs in order plus the embedding length;
+//     `clip.f32` is those CLIP embeddings packed as one `Float32Array`, so the
+//     index loads in a single read with no per-vector parse. The index is
+//     rebuilt from the payloads whenever it is missing or structurally
+//     disagrees with the files present, and appended to as new payloads arrive.
+//
+//   - `fetched.json` — a small map of fileID to the `updationTime` it was
+//     fetched at. This is best-effort bookkeeping for refetch decisions (a file
+//     whose `updationTime` later advances is refetched); the payloads, not this
+//     file, remain the record of what is cached, so losing it only forgoes
+//     update-driven refetch until the next fetch rewrites it.
+//
+// In RAM this holds only the id list and the packed `Float32Array`.
+
+import { mkdir, readFile, readdir } from "node:fs/promises";
+import { join } from "node:path";
+
+import { writeAtomic } from "../download/index.js";
+import type { MLData } from "../mldata-fetch.js";
+
+const CLIP_VECTORS = "clip.f32";
+const CLIP_INDEX = "clip.json";
+const FETCHED = "fetched.json";
+// A payload file is named for its fileID alone; the derived files above are
+// not, so this pattern picks out payloads and nothing else.
+const PAYLOAD_RE = /^(\d+)\.json$/;
+const BYTES_PER_FLOAT = 4;
+
+// The on-disk form of `clip.json`.
+interface ClipIndexFile {
+    fileIDs: number[];
+    embeddingLength: number;
+}
+
+// A file the model knows about, for deciding what to fetch.
+export interface MLDataFile {
+    id: number;
+    updationTime: number;
+}
+
+// The RAM index the search reads: `fileIDs[i]` owns the `embeddingLength`
+// floats of `embeddings` starting at `i * embeddingLength`.
+export interface MLIndex {
+    fileIDs: number[];
+    embeddingLength: number;
+    embeddings: Float32Array;
+}
+
+// Pull the CLIP embedding out of a payload, or undefined when it is absent or
+// misshapen. Kept strict so a bad payload is skipped rather than corrupting the
+// packed index.
+const clipEmbedding = (payload: MLData): number[] | undefined => {
+    const clip = payload.clip;
+    if (typeof clip !== "object" || clip === null) return undefined;
+    const embedding = (clip as { embedding?: unknown }).embedding;
+    if (!Array.isArray(embedding)) return undefined;
+    if (embedding.some((v) => typeof v !== "number" || !Number.isFinite(v)))
+        return undefined;
+    return embedding as number[];
+};
+
+export class MLDataStore {
+    readonly dir: string;
+
+    // fileIDs whose payload JSON is present on disk (present means complete).
+    private readonly present = new Set<number>();
+    // fileID -> updationTime it was fetched at.
+    private readonly fetched = new Map<number, number>();
+
+    // The packed index and where each id sits in it.
+    private ids: number[] = [];
+    private embeddingLength = 0;
+    private embeddings = new Float32Array(0);
+    private readonly pos = new Map<number, number>();
+
+    private constructor(dir: string) {
+        this.dir = dir;
+    }
+
+    // Open (creating the directory) and load the id list and packed index into
+    // RAM, rebuilding the index from the payloads when it is missing or does
+    // not match the files present.
+    static async open(dir: string): Promise<MLDataStore> {
+        const store = new MLDataStore(dir);
+        await mkdir(dir, { recursive: true });
+        await store.loadPresent();
+        await store.loadFetched();
+        if (!(await store.tryLoadIndex())) await store.rebuildIndex();
+        return store;
+    }
+
+    // The fileIDs among `files` that must be fetched: every file with no
+    // payload yet (first run, then new files), plus any whose `updationTime`
+    // has advanced past the one its cached payload was fetched at. Returned
+    // sorted and unique.
+    neededFor(files: MLDataFile[]): number[] {
+        const latest = new Map<number, number>();
+        for (const f of files) {
+            const seen = latest.get(f.id);
+            if (seen === undefined || f.updationTime > seen)
+                latest.set(f.id, f.updationTime);
+        }
+        const needed: number[] = [];
+        for (const [id, updationTime] of latest) {
+            if (!this.present.has(id)) {
+                needed.push(id);
+                continue;
+            }
+            const at = this.fetched.get(id);
+            if (at !== undefined && updationTime > at) needed.push(id);
+        }
+        return needed.sort((a, b) => a - b);
+    }
+
+    // Store a batch of fetched payloads: write one file per id, fold their CLIP
+    // embeddings into the packed index (in place for a refetch, appended for a
+    // new file), and persist the derived files. Returns how many payloads were
+    // stored and how many ids the index now holds.
+    async storeFetched(
+        payloads: Map<number, MLData>,
+        updation: Map<number, number>,
+    ): Promise<{ stored: number; indexed: number }> {
+        if (payloads.size === 0) return { stored: 0, indexed: this.ids.length };
+
+        for (const [id, payload] of payloads) {
+            await this.writePayload(id, payload);
+            this.present.add(id);
+            const at = updation.get(id);
+            if (at !== undefined) this.fetched.set(id, at);
+        }
+
+        const updates: { at: number; vector: number[] }[] = [];
+        const appends: { id: number; vector: number[] }[] = [];
+        for (const [id, payload] of payloads) {
+            const vector = clipEmbedding(payload);
+            if (!vector) continue;
+            if (this.embeddingLength === 0 && this.ids.length === 0)
+                this.embeddingLength = vector.length;
+            // The index is fixed-width; a vector of another length (never seen
+            // from Ente's CLIP model) is stored but left out of the index.
+            if (vector.length !== this.embeddingLength) continue;
+            const at = this.pos.get(id);
+            if (at !== undefined) updates.push({ at, vector });
+            else appends.push({ id, vector });
+        }
+
+        for (const { at, vector } of updates)
+            this.embeddings.set(vector, at * this.embeddingLength);
+
+        if (appends.length > 0) {
+            const length = this.embeddingLength;
+            const grown = new Float32Array(
+                this.embeddings.length + appends.length * length,
+            );
+            grown.set(this.embeddings);
+            let offset = this.embeddings.length;
+            for (const { id, vector } of appends) {
+                grown.set(vector, offset);
+                this.pos.set(id, this.ids.length);
+                this.ids.push(id);
+                offset += length;
+            }
+            this.embeddings = grown;
+        }
+
+        await this.persistIndex();
+        await this.persistFetched();
+        return { stored: payloads.size, indexed: this.ids.length };
+    }
+
+    // The packed index the search runs on. The id list is copied so callers
+    // cannot disturb the store's own order; the embeddings are the live buffer.
+    getIndex(): MLIndex {
+        return {
+            fileIDs: [...this.ids],
+            embeddingLength: this.embeddingLength,
+            embeddings: this.embeddings,
+        };
+    }
+
+    // The full payload for a file, read from disk, or undefined when it is not
+    // cached or does not parse.
+    async readPayload(fileID: number): Promise<MLData | undefined> {
+        if (!this.present.has(fileID)) return undefined;
+        let raw: string;
+        try {
+            raw = await readFile(this.payloadPath(fileID), "utf-8");
+        } catch {
+            return undefined;
+        }
+        try {
+            return JSON.parse(raw) as MLData;
+        } catch {
+            return undefined;
+        }
+    }
+
+    stats(): { stored: number; indexed: number } {
+        return { stored: this.present.size, indexed: this.ids.length };
+    }
+
+    private payloadPath(id: number): string {
+        return join(this.dir, `${id}.json`);
+    }
+
+    private async writePayload(id: number, payload: MLData): Promise<void> {
+        await writeAtomic(
+            this.payloadPath(id),
+            new TextEncoder().encode(JSON.stringify(payload)),
+        );
+    }
+
+    private async loadPresent(): Promise<void> {
+        let names: string[];
+        try {
+            names = await readdir(this.dir);
+        } catch {
+            return;
+        }
+        for (const name of names) {
+            const match = PAYLOAD_RE.exec(name);
+            if (match) this.present.add(Number(match[1]));
+        }
+    }
+
+    private async loadFetched(): Promise<void> {
+        let raw: string;
+        try {
+            raw = await readFile(join(this.dir, FETCHED), "utf-8");
+        } catch {
+            return;
+        }
+        try {
+            const parsed = JSON.parse(raw) as Record<string, unknown>;
+            for (const [key, value] of Object.entries(parsed)) {
+                const id = Number(key);
+                if (
+                    Number.isInteger(id) &&
+                    typeof value === "number" &&
+                    this.present.has(id)
+                )
+                    this.fetched.set(id, value);
+            }
+        } catch {
+            // Corrupt bookkeeping degrades refetch decisions, never fails open.
+        }
+    }
+
+    // Load the packed index if it is present and consistent with the payloads:
+    // its ids must all still be present and its vector file must be exactly the
+    // size the id count and embedding length imply. Returns whether it loaded.
+    private async tryLoadIndex(): Promise<boolean> {
+        let metaRaw: string;
+        try {
+            metaRaw = await readFile(join(this.dir, CLIP_INDEX), "utf-8");
+        } catch {
+            return false;
+        }
+        let meta: ClipIndexFile;
+        try {
+            meta = JSON.parse(metaRaw) as ClipIndexFile;
+        } catch {
+            return false;
+        }
+        if (
+            !Array.isArray(meta.fileIDs) ||
+            typeof meta.embeddingLength !== "number"
+        )
+            return false;
+        if (meta.fileIDs.some((id) => !this.present.has(id))) return false;
+
+        let bytes: Buffer;
+        try {
+            bytes = await readFile(join(this.dir, CLIP_VECTORS));
+        } catch {
+            return false;
+        }
+        const expected =
+            meta.fileIDs.length * meta.embeddingLength * BYTES_PER_FLOAT;
+        if (bytes.byteLength !== expected) return false;
+
+        // One read, no parse: copy into an aligned buffer and view it as
+        // floats. The copy is needed because a Buffer from the pool can start
+        // at an offset a Float32Array cannot be laid over.
+        const aligned = new Uint8Array(bytes.byteLength);
+        aligned.set(bytes);
+        this.embeddings = new Float32Array(aligned.buffer);
+        this.embeddingLength = meta.embeddingLength;
+        this.ids = [...meta.fileIDs];
+        this.pos.clear();
+        this.ids.forEach((id, i) => this.pos.set(id, i));
+        return true;
+    }
+
+    // Rebuild the packed index by reading every payload present, then persist
+    // it. Payloads without a CLIP embedding (or of an unexpected length) are
+    // simply not indexed.
+    private async rebuildIndex(): Promise<void> {
+        this.ids = [];
+        this.pos.clear();
+        this.embeddingLength = 0;
+        const vectors: number[][] = [];
+        for (const id of [...this.present].sort((a, b) => a - b)) {
+            const payload = await this.readPayload(id);
+            if (!payload) continue;
+            const vector = clipEmbedding(payload);
+            if (!vector) continue;
+            if (this.embeddingLength === 0)
+                this.embeddingLength = vector.length;
+            if (vector.length !== this.embeddingLength) continue;
+            this.pos.set(id, this.ids.length);
+            this.ids.push(id);
+            vectors.push(vector);
+        }
+        const length = this.embeddingLength;
+        const packed = new Float32Array(this.ids.length * length);
+        vectors.forEach((vector, i) => packed.set(vector, i * length));
+        this.embeddings = packed;
+        await this.persistIndex();
+    }
+
+    private async persistIndex(): Promise<void> {
+        const meta: ClipIndexFile = {
+            fileIDs: this.ids,
+            embeddingLength: this.embeddingLength,
+        };
+        await writeAtomic(
+            join(this.dir, CLIP_INDEX),
+            new TextEncoder().encode(JSON.stringify(meta)),
+        );
+        await writeAtomic(
+            join(this.dir, CLIP_VECTORS),
+            new Uint8Array(
+                this.embeddings.buffer,
+                this.embeddings.byteOffset,
+                this.embeddings.byteLength,
+            ),
+        );
+    }
+
+    private async persistFetched(): Promise<void> {
+        const record: Record<string, number> = {};
+        for (const [id, at] of this.fetched) record[id] = at;
+        await writeAtomic(
+            join(this.dir, FETCHED),
+            new TextEncoder().encode(JSON.stringify(record)),
+        );
+    }
+}

src/metadata-backup.ts

@@ -1,4 +1,3 @@
-import { gunzipSync } from "node:zlib";
 import {
     mkdirSync,
     mkdtempSync,
@@ -11,7 +10,7 @@ import { tmpdir } from "node:os";
 import * as jpeg from "jpeg-js";
 import exifReader from "exif-reader";
 import type { Client } from "./client.js";
-import { decryptBlob, fromBase64 } from "./crypto/index.js";
+import { fetchMLData } from "./mldata-fetch.js";
 import type { EnteFile } from "./model/types.js";
 
 export type ProgressCallback = (message: string) => void;
@@ -24,50 +23,6 @@ export interface MetadataBackupOptions {
 const sanitizePath = (name: string): string =>
     name.replace(/[/\\:*?"<>|]/g, "_").replace(/^\.+/, "_");
 
-interface RawRemoteFileData {
-    fileID: number;
-    encryptedData: string;
-    decryptionHeader: string;
-    updatedAt?: number;
-}
-
-const fetchMLDataForFiles = async (
-    client: Client,
-    fileIDs: number[],
-    fileKeys: Map<number, Uint8Array>,
-): Promise<Map<number, Record<string, unknown>>> => {
-    const api = client.getApiClient();
-    const result = new Map<number, Record<string, unknown>>();
-    const batchSize = 200;
-
-    for (let i = 0; i < fileIDs.length; i += batchSize) {
-        const batch = fileIDs.slice(i, i + batchSize);
-        const { data } = await api.postJSON<{ data: RawRemoteFileData[] }>(
-            "/files/data/fetch",
-            { type: "mldata", fileIDs: batch },
-        );
-
-        for (const entry of data ?? []) {
-            const key = fileKeys.get(entry.fileID);
-            if (!key) continue;
-            try {
-                const decrypted = decryptBlob(
-                    fromBase64(entry.encryptedData),
-                    fromBase64(entry.decryptionHeader),
-                    key,
-                );
-                const jsonStr = gunzipSync(Buffer.from(decrypted)).toString(
-                    "utf-8",
-                );
-                result.set(entry.fileID, JSON.parse(jsonStr));
-            } catch {
-                // Corrupted ML data for this file; skip it
-            }
-        }
-    }
-    return result;
-};
-
 // Extract the raw EXIF APP1 segment from JPEG bytes. Returns the EXIF
 // data buffer (starting after the APP1 length field, at the "Exif\0\0"
 // header) or undefined if no APP1 marker is found.
@@ -228,8 +183,8 @@ export const runMetadataBackup = async (
     }
 
     log("Fetching ML data (face detections, CLIP embeddings)...");
-    const mlDataMap = await fetchMLDataForFiles(
-        client,
+    const mlDataMap = await fetchMLData(
+        client.getApiClient(),
         [...fileKeys.keys()],
         fileKeys,
     );

src/mldata-fetch.ts

@@ -0,0 +1,93 @@
+// Fetch and decrypt Ente's per-file machine-learning data ("magic" search
+// data: face detections + CLIP embeddings).
+//
+// The data lives behind `/files/data/fetch` with `type: "mldata"`. Each entry
+// 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 `fetchMLData` batches for callers that want many at once while
+// `fetchMLDataBatch` is the single-request unit the library submits to its
+// request pool.
+
+import { gunzipSync } from "node:zlib";
+
+import type { ApiClient } from "./api/client.js";
+import { decryptBlob, fromBase64 } from "./crypto/index.js";
+
+// The most ids one `/files/data/fetch` request may carry.
+export const MLDATA_BATCH_SIZE = 200;
+
+// The decrypted, gunzipped per-file payload. Its concrete shape is Ente's; the
+// store keeps the whole object verbatim and each consumer reads the fields it
+// needs, so it stays an open record rather than a fixed interface.
+export type MLData = Record<string, unknown>;
+
+interface RawRemoteFileData {
+    fileID: number;
+    encryptedData: string;
+    decryptionHeader: string;
+    updatedAt?: number;
+}
+
+// Decrypt one entry with its file key and gunzip the JSON payload. Returns
+// undefined when the key is unknown or the entry does not decrypt/parse, so one
+// corrupt file never fails a whole batch.
+const decodeEntry = (
+    entry: RawRemoteFileData,
+    key: Uint8Array | undefined,
+): MLData | undefined => {
+    if (!key) return undefined;
+    try {
+        const decrypted = decryptBlob(
+            fromBase64(entry.encryptedData),
+            fromBase64(entry.decryptionHeader),
+            key,
+        );
+        const json = gunzipSync(Buffer.from(decrypted)).toString("utf-8");
+        return JSON.parse(json) as MLData;
+    } catch {
+        return undefined;
+    }
+};
+
+// Fetch ML data for up to `MLDATA_BATCH_SIZE` ids in a single request. This is
+// the unit the request pools schedule; callers with more ids split them into
+// batches and submit each batch to the pool.
+export const fetchMLDataBatch = async (
+    api: ApiClient,
+    fileIDs: number[],
+    fileKeys: Map<number, Uint8Array>,
+): Promise<Map<number, MLData>> => {
+    const { data } = await api.postJSON<{ data: RawRemoteFileData[] }>(
+        "/files/data/fetch",
+        { type: "mldata", fileIDs },
+    );
+    const result = new Map<number, MLData>();
+    for (const entry of data ?? []) {
+        const payload = decodeEntry(entry, fileKeys.get(entry.fileID));
+        if (payload) result.set(entry.fileID, payload);
+    }
+    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;
+};