A file's JSON holds keywords in place of its CLIP embedding (closes #218)

quak backup and quak backup-metadata write each file's ML data without its
CLIP embedding, and keywords in its place: the titles of the Discover
categories of Ente's photos app that the file falls in, by the app's rule.
src/clip-keywords.ts applies the rule. src/clip-keyword-vectors.ts holds the
16 categories from Ente's discover.json with the text embedding Ente's CLIP
text model gives each query. bin/clip-keyword-vectors writes that file and the
test fixture in a digest-pinned Python image, with every download pinned by
sha256; it runs by hand only. quak gains no dependency.

Model: opus-5-5

README.md

@@ -371,6 +371,11 @@ quak/
                         backup-metadata: the metadata quak keeps, as JSON
         exif.ts         EXIF read from an image's bytes with exifreader
         mldata-fetch.ts fetch + decrypt per-file ML data
+        clip-keywords.ts
+                        a file's keywords from its CLIP embedding
+        clip-keyword-vectors.ts
+                        Ente's Discover categories with their text
+                        embeddings, written by bin/clip-keyword-vectors
         filename.ts     safe file names from server metadata
         errors.ts       error types shared across layers, and how an error
                         is described
@@ -387,6 +392,9 @@ quak/
         index.ts        public library exports
     bin/
         quak.ts         CLI entrypoint (commander.js)
+        clip-keyword-vectors, clip-keyword-vectors.py
+                        write src/clip-keyword-vectors.ts and a test
+                        fixture (see Source attribution)
     examples/
         download-albums.ts
                         download every album's photos and metadata
@@ -783,8 +791,9 @@ as it was and goes into `failures.json` (see "Backup layout").
 
 `backup-metadata <dir>` writes `account.json` and, for each collection, a
 directory `collections/<id>-<name>/` holding `_collection.json` and a
-`<fileID>.json` for each of its files. The collection's name has unsafe
+`<fileID>.json` for each of its files, with the file's ML data and keywords as
-characters replaced and is cut short between two characters so that the
+`quak backup` writes them (see "Backup layout"). The collection's name has
+unsafe characters replaced and is cut short between two characters so that the
 directory's name fits in 255 bytes; the ID keeps the directories apart.
 
 `backup-metadata` fetches ML data in requests of up to 200 files. When a request
@@ -818,11 +827,11 @@ the smallest does not.
                                     below)
         YYYY-MM-DD.<fileID>.json    the file's basic metadata fields quak
                                     keeps, its update time, its private and
-                                    public magic metadata, its ML data, and,
+                                    public magic metadata, its ML data and
-                                    for an image (for a live photo, its
+                                    keywords, and, for an image (for a live
-                                    image), its original's EXIF and XMP, with
+                                    photo, its image), its original's EXIF
-                                    dimensions for a JPEG only; none of these
+                                    and XMP, with dimensions for a JPEG
-                                    three for a video
+                                    only; none of these three for a video
         YYYY-MM-DD.<fileID>.livephoto.json
                                     which of a live photo's two files is which
     collections/
@@ -853,20 +862,30 @@ JSON holds its `id`, `collectionID`, `ownerID`, `metadata` and `updationTime`,
 and its `magicMetadata` and `pubMagicMetadata` when it has them. Update times
 are in microseconds, as Ente records them.
 
-A file's JSON holds Ente's ML data for it (its faces and its CLIP embedding) as
+A file's JSON holds Ente's ML data for it as `mlData`, such as the faces Ente
-`mlData`, the same payload `backup-metadata` writes; a file Ente has no ML data
+found, without its CLIP embedding. In place of the embedding it holds
-for has no `mlData`. The backup waits for the library's ML data fetch to finish
+`keywords`, the titles of the categories of the Discover section of Ente's
-before it writes the JSON files. If that fetch fails, each file whose ML data is
+photos app that the file falls in, highest score first. The categories are
-not in the cache gets the reason in `mlDataError` instead and counts as failed,
+Identity, Screenshots, QR Codes, Receipts, Notes, Memes, Visiting Cards, Babies,
-and the next run fetches it again. The same goes for a file with no ML data in
+Pets, Selfies, Wallpapers, Food, Celebrations, Sunset, Hills and Greenery. As in
-the cache whose ML data the server sent but quak could not decrypt, gunzip or
+Ente's app, a file falls in a category when the cosine similarity of its CLIP
-parse. Its `mlDataError` says why, and `failures.json` records it with an error
+embedding and the text embedding of the category's query is at least the
-starting `ML data: `. quak keeps that reason in memory only and caches nothing
+category's minimum score, and a video stays out of Identity, Screenshots, QR
-for the file, so the next run asks for its ML data again. An ML data fetch that
+Codes, Visiting Cards and Wallpapers. A file with no ML data has neither field,
-fails, such as one the library's refresh starts while the backup writes the JSON
+and one whose ML data holds no usable CLIP embedding has no `keywords`.
-files, leaves the reasons as the last fetch that succeeded left them. The JSON
+`backup-metadata` writes both the same way.
-files are rewritten on every run, so ML data that arrived since the last run
+
-appears.
+The backup waits for the library's ML data fetch to finish before it writes the
+JSON files. If that fetch fails, each file whose ML data is not in the cache
+gets the reason in `mlDataError` instead and counts as failed, and the next run
+fetches it again. The same goes for a file with no ML data in the cache whose ML
+data the server sent but quak could not decrypt, gunzip or parse. Its
+`mlDataError` says why, and `failures.json` records it with an error starting
+`ML data: `. quak keeps that reason in memory only and caches nothing for the
+file, so the next run asks for its ML data again. An ML data fetch that fails,
+such as one the library's refresh starts while the backup writes the JSON files,
+leaves the reasons as the last fetch that succeeded left them. The JSON files
+are rewritten on every run, so ML data that arrived since the last run appears.
 
 A file's JSON holds, as `imageMetadata`, what `backup-metadata --exif` records
 from its original, or from a live photo's image: `format`, `width` and `height`
@@ -1270,11 +1289,11 @@ photos newest first). `lib.subscribe({ onChange })` delivers a `LibraryChange`
   not already at its save path there as `photo.download()` does (and, with
   `includeThumbnails`, fetches thumbnails) through the content cache, waits for
   an ML data fetch, and rebuilds the on-disk backup tree with a durable failure
-  ledger. Each file's JSON holds its ML data and, for an image (for a live
+  ledger. Each file's JSON holds its ML data and keywords and, for an image (for
-  photo, its image), its original's EXIF and XMP, with its dimensions for a JPEG
+  a live photo, its image), its original's EXIF and XMP, with its dimensions for
-  only; a video's JSON holds none of these three. A fetched original is written
+  a JPEG only; a video's JSON holds none of these three. A fetched original is
-  straight to its save path and not into the cache, which then counts it as
+  written straight to its save path and not into the cache, which then counts it
-  present; one the cache already held is copied from there. `BackupOptions`:
+  as present; one the cache already held is copied from there. `BackupOptions`:
   `downloadDirectory` (falls back to the library's), `includeOriginals` (default
   `true`), `includeThumbnails` (default `false`), `onlyAlbumNames`, `verify`
   (default `false`), `onProgress`, and `lock`. See Backup layout above for the
@@ -1364,6 +1383,14 @@ is rewritten in TypeScript in this repository. Protocol fidelity is verified
 against the upstream implementations in `web/packages/base/`,
 `mobile/apps/photos/lib/`, and `cli/`.
 
+The keywords come from Ente too. `src/clip-keyword-vectors.ts` holds the
+Discover categories of Ente's photos app, from
+`mobile/apps/photos/assets/discover.json`, each with the text embedding Ente's
+CLIP text model gives its query. `bin/clip-keyword-vectors` writes that file,
+and the CLIP embedding of a public-domain photo that the tests use, from Ente's
+list and models; it needs docker, pins every download by sha256, and runs only
+by hand, followed by `make fmt`.
+
 ## For LLMs
 
 If you are an LLM agent working on this repository, read and follow these

TODO.md

@@ -26,6 +26,18 @@ declares one.
 
 # Completed Steps
 
+- 2026-10-22: The JSON `quak backup` and `quak backup-metadata` write for a file
+  holds `keywords` in place of the CLIP embedding in its ML data
+  (https://git.eeqj.de/sneak/quak/issues/218). The keywords are the titles of
+  the Discover categories of Ente's photos app that the file falls in, by the
+  app's rule: the cosine similarity of the file's CLIP embedding and the text
+  embedding of a category's query is at least the category's minimum score, and
+  videos stay out of the categories that leave them out. `src/clip-keywords.ts`
+  applies the rule. `src/clip-keyword-vectors.ts` holds the 16 categories with
+  their text embeddings. `bin/clip-keyword-vectors` writes it with Ente's CLIP
+  text model, and writes the CLIP embedding of a public-domain photo of a sunset
+  that the tests use.
+
 - 2026-10-18: `-v`/`--verbose` raises the CLI's log level once for each repeat,
   and `-q`/`--quiet` prints only fatal errors
   (https://git.eeqj.de/sneak/quak/issues/212). The levels are fatal, error,

bin/clip-keyword-vectors

@@ -0,0 +1,25 @@
+#!/bin/sh
+# Writes src/clip-keyword-vectors.ts and test/clip-keywords-sunset.json by
+# running bin/clip-keyword-vectors.py in a Python image pinned by digest, with
+# its packages pinned by hash. Run it by hand from a checkout, then `make fmt`;
+# no gate runs it. It downloads about 215 MB, most of it Ente's two CLIP models.
+set -eu
+
+cd "$(dirname "$0")/.."
+
+# python:3.13-slim (Python 3.13.17 on Debian 13.7), 2026-10-21. The wheels
+# below are the ones for this image on linux/amd64.
+image=python@sha256:57b29a7c050c4e679e4bea572e14b3965cf4b5839de2da49d55e49ffc4024f39
+
+docker run --rm -i --platform linux/amd64 \
+    --user "$(id -u):$(id -g)" -e HOME=/tmp \
+    -v "$PWD:/repo" -w /repo "$image" sh -c '
+        python -m venv /tmp/venv &&
+        /tmp/venv/bin/pip install --quiet --disable-pip-version-check \
+            --no-cache-dir --no-deps --require-hashes -r /dev/stdin &&
+        /tmp/venv/bin/python bin/clip-keyword-vectors.py' <<'EOF'
+numpy==2.5.3 --hash=sha256:a5fa86b80fd24bcd1aff83ad23be44ea323de3f787be8f8b15d4a65621e25321
+onnxruntime==1.30.0 --hash=sha256:86f940afc801ea9681a4da8af84fbe95e1d9ea7d80903952cc1bfad54faad38f
+pillow==12.3.0 --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3
+tokenizers==0.23.2 --hash=sha256:41c2f84d172449b4dadb9cdc508e3e364076613c35b16e76ecfe47a60d1e3305
+EOF

bin/clip-keyword-vectors.py

@@ -0,0 +1,158 @@
+"""Write src/clip-keyword-vectors.ts and test/clip-keywords-sunset.json.
+
+bin/clip-keyword-vectors runs this in a pinned Python image. It downloads
+Ente's Discover list, Ente's CLIP text and image models, the CLIP tokenizer
+and a public-domain photo of a sunset, checks each against its sha256, and
+writes:
+
+- src/clip-keyword-vectors.ts: each Discover category of Ente's photos app,
+  with the text embedding Ente's CLIP text model gives its query;
+- test/clip-keywords-sunset.json: the CLIP embedding Ente's image model gives
+  the photo, prepared as Ente's apps prepare a photo.
+
+Both embeddings are scaled to length 1, as Ente's apps scale them.
+"""
+
+import hashlib
+import io
+import json
+import urllib.request
+
+import numpy as np
+import onnxruntime
+from PIL import Image, ImageOps
+from tokenizers import Tokenizer
+
+ENTE_COMMIT = "f5ad70a860ed25d0c8057b0efb4bd6eaefe4b0c5"
+DISCOVER_PATH = "mobile/apps/photos/assets/discover.json"
+DISCOVER_URL = (
+    f"https://raw.githubusercontent.com/ente-io/ente/{ENTE_COMMIT}/{DISCOVER_PATH}"
+)
+DISCOVER_SHA256 = "cf70ac7116b592f7921ca4cf67cdbb421a8e6b287fbe44b2be678b8d198e5c24"
+
+TEXT_MODEL = "mobileclip_s2_text_opset18_quant.onnx"
+TEXT_MODEL_SHA256 = "d92f33dfcff83077fc2e0d3414250710efbb51795dfd89767bdbefb5fdc47322"
+IMAGE_MODEL = "mobileclip_s2_image_gelu_opset20.onnx"
+IMAGE_MODEL_SHA256 = "205a430af825e501c5138e5bb9abea942482a7a4fd4a680e98e47cf0830dce7e"
+
+# The CLIP tokenizer Hugging Face publishes for OpenAI's CLIP. Its vocabulary
+# and merges are those of bpe_simple_vocab_16e6.txt, the vocabulary Ente's
+# apps tokenize with.
+TOKENIZER_URL = (
+    "https://huggingface.co/openai/clip-vit-base-patch32/resolve/"
+    "3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268/tokenizer.json"
+)
+TOKENIZER_SHA256 = "b556ac8c99757ffb677208af34bc8c6721572114111a6e0aaf5fa69ff0b8d842"
+
+# "Sunset off the Massachusetts coast", a NOAA photo in the public domain:
+# https://commons.wikimedia.org/wiki/File:Sunset_off_the_Massachusetts_coast_-_NOAA.jpg
+PHOTO_URL = (
+    "https://upload.wikimedia.org/wikipedia/commons/d/db/"
+    "Sunset_off_the_Massachusetts_coast_-_NOAA.jpg"
+)
+PHOTO_SHA256 = "be62f3289c9bf7b75af68cd6978021d54e8a531d5d4cc493f6e9fbf5e1ab74d3"
+
+# Ente's text model takes 77 token ids, padded with 0; its image model takes
+# a 256x256 RGB image.
+TOKEN_COUNT = 77
+IMAGE_SIZE = 256
+
+
+def download(url, sha256):
+    # Wikimedia refuses requests without a User-Agent.
+    request = urllib.request.Request(url, headers={"User-Agent": "quak"})
+    with urllib.request.urlopen(request) as response:
+        data = response.read()
+    digest = hashlib.sha256(data).hexdigest()
+    if digest != sha256:
+        raise SystemExit(f"{url} has sha256 {digest}, not {sha256}")
+    return data
+
+
+def ente_model(name, sha256):
+    return onnxruntime.InferenceSession(
+        download(f"https://models.ente.com/{name}", sha256),
+        providers=["CPUExecutionProvider"],
+    )
+
+
+def run(session, tensor):
+    (output,) = session.run(None, {session.get_inputs()[0].name: tensor})
+    embedding = output[0]
+    return embedding / np.linalg.norm(embedding)
+
+
+def text_embedding(session, tokenizer, query):
+    ids = tokenizer.encode(query).ids
+    ids += [0] * (TOKEN_COUNT - len(ids))
+    return run(session, np.array([ids], dtype=np.int32))
+
+
+def image_embedding(session, photo):
+    image = ImageOps.exif_transpose(Image.open(io.BytesIO(photo))).convert("RGB")
+    # Scale to cover IMAGE_SIZE x IMAGE_SIZE and crop the centre.
+    square = ImageOps.fit(image, (IMAGE_SIZE, IMAGE_SIZE), Image.Resampling.BILINEAR)
+    pixels = np.asarray(square, dtype=np.float32) / 255
+    return run(session, pixels.transpose(2, 0, 1)[np.newaxis])
+
+
+def numbers(embedding):
+    return ", ".join(str(value) for value in embedding)
+
+
+def write_categories(prompts, embeddings):
+    lines = [
+        "// Written by bin/clip-keyword-vectors; do not edit by hand.",
+        "//",
+        "// The Discover categories of Ente's photos app, in their order, from",
+        f"// {DISCOVER_PATH} in https://github.com/ente-io/ente",
+        f"// at commit {ENTE_COMMIT}. Each has the text",
+        f"// embedding Ente's CLIP text model ({TEXT_MODEL})",
+        "// gives its query, scaled to length 1. `showVideo` is false for a category",
+        "// that leaves out videos.",
+        "",
+        "export const DISCOVER_CATEGORIES = [",
+    ]
+    for prompt, embedding in zip(prompts, embeddings):
+        lines += [
+            "    {",
+            f"        title: {json.dumps(prompt['title'])},",
+            f"        query: {json.dumps(prompt['query'])},",
+            f"        minScore: {json.dumps(prompt['minScore'])},",
+            f"        showVideo: {json.dumps(prompt.get('showVideo', True))},",
+            f"        textEmbedding: [{numbers(embedding)}],",
+            "    },",
+        ]
+    lines.append("];")
+    with open("src/clip-keyword-vectors.ts", "w", encoding="utf-8") as file:
+        file.write("\n".join(lines) + "\n")
+
+
+def write_fixture(embedding):
+    with open("test/clip-keywords-sunset.json", "w", encoding="utf-8") as file:
+        file.write(
+            "{\n"
+            f'    "photo": {json.dumps(PHOTO_URL)},\n'
+            f'    "sha256": {json.dumps(PHOTO_SHA256)},\n'
+            f'    "embedding": [{numbers(embedding)}]\n'
+            "}\n"
+        )
+
+
+def main():
+    prompts = json.loads(download(DISCOVER_URL, DISCOVER_SHA256))["prompts"]
+    tokenizer = Tokenizer.from_str(
+        download(TOKENIZER_URL, TOKENIZER_SHA256).decode("utf-8")
+    )
+    text_model = ente_model(TEXT_MODEL, TEXT_MODEL_SHA256)
+    embeddings = [
+        text_embedding(text_model, tokenizer, prompt["query"]) for prompt in prompts
+    ]
+    write_categories(prompts, embeddings)
+
+    image_model = ente_model(IMAGE_MODEL, IMAGE_MODEL_SHA256)
+    write_fixture(image_embedding(image_model, download(PHOTO_URL, PHOTO_SHA256)))
+
+
+if __name__ == "__main__":
+    main()

src/backup.ts

@@ -14,9 +14,9 @@
 //         YYYY/YYYY-MM/YYYY-MM-DD/
 //             YYYY-MM-DD.<fileID>.<ext>    the decrypted bytes (the save path)
 //             YYYY-MM-DD.<fileID>.json     per-file metadata sidecar, with
-//                                          the file's ML data and its
+//                                          the file's ML data, its
-//                                          original's EXIF, XMP and
+//                                          keywords, and its original's
-//                                          dimensions
+//                                          EXIF, XMP and dimensions
 //         collections/<name>/<title>       symlink to the original
 //         collections/<name>.json          per-collection metadata
 //         account.json                     the account's email and user ID
@@ -77,6 +77,7 @@ import { readFile } from "node:fs/promises";
 import { dirname, extname, join, relative, resolve } from "node:path";
 import lockfile from "proper-lockfile";
 
+import { clipKeywords } from "./clip-keywords.js";
 import { removeLeftoverTempFiles } from "./download/index.js";
 import { describeError } from "./errors.js";
 import {
@@ -497,8 +498,9 @@ const imageMetadataFor = async (
     }
 };
 
-// The file's JSON: its basic fields, its magic metadata, its ML data or the
+// The file's JSON: its basic fields, its magic metadata, its ML data without
-// reason the ML data is missing, and its image metadata.
+// the CLIP embedding and the keywords that embedding gives, or the reason the
+// ML data is missing, and its image metadata.
 const writeSidecar = (
     path: string,
     file: EnteFile,
@@ -514,7 +516,13 @@ const writeSidecar = (
     };
     if (file.magicMetadata) meta.magicMetadata = file.magicMetadata;
     if (file.pubMagicMetadata) meta.pubMagicMetadata = file.pubMagicMetadata;
-    if (ml.mlData) meta.mlData = ml.mlData;
+    if (ml.mlData) {
+        const { clip: _clip, ...mlDataWithoutClip } = ml.mlData;
+        meta.mlData = mlDataWithoutClip;
+        const isVideo = file.metadata.fileType === "video";
+        const keywords = clipKeywords(ml.mlData, isVideo);
+        if (keywords) meta.keywords = keywords;
+    }
     if (ml.mlDataError) meta.mlDataError = ml.mlDataError;
     if (image.imageMetadata) meta.imageMetadata = image.imageMetadata;
     if (image.imageMetadataError) {

src/clip-keywords.ts

@@ -0,0 +1,59 @@
+// Keywords for a file from its CLIP embedding.
+//
+// Ente stores no keywords. A file's ML data holds a CLIP embedding, the 512
+// numbers Ente's image model gave the photo, and Ente's photos app sorts files
+// into the categories of its Discover section with it: a file falls in a
+// category when the cosine similarity of its CLIP embedding and the text
+// embedding of the category's query is at least the category's `minScore`,
+// unless the file is a video and the category leaves out videos. A file's
+// keywords are the titles of the categories it falls in. The categories and
+// their text embeddings are in `clip-keyword-vectors.ts`.
+
+import { DISCOVER_CATEGORIES } from "./clip-keyword-vectors.js";
+import type { MLData } from "./mldata-fetch.js";
+
+// How many numbers a CLIP embedding from Ente's image model has.
+const EMBEDDING_LENGTH = 512;
+
+const length = (vector: number[]): number => Math.hypot(...vector);
+
+const cosineSimilarity = (a: number[], b: number[]): number => {
+    let dot = 0;
+    a.forEach((value, i) => {
+        dot += value * (b[i] ?? 0);
+    });
+    return dot / (length(a) * length(b));
+};
+
+// The titles of the Discover categories a file falls in, highest score first,
+// or undefined when its ML data has no CLIP embedding of 512 finite numbers
+// that are not all zero. Tests pass their own `categories`.
+export const clipKeywords = (
+    mlData: MLData,
+    isVideo: boolean,
+    categories = DISCOVER_CATEGORIES,
+): string[] | undefined => {
+    const clip = mlData.clip;
+    if (typeof clip !== "object" || clip === null) return undefined;
+    const embedding = (clip as { embedding?: unknown }).embedding;
+    if (
+        !Array.isArray(embedding) ||
+        embedding.length !== EMBEDDING_LENGTH ||
+        !embedding.every((v) => typeof v === "number" && Number.isFinite(v)) ||
+        length(embedding) === 0
+    ) {
+        return undefined;
+    }
+
+    const matches: { title: string; score: number }[] = [];
+    for (const category of categories) {
+        if (isVideo && !category.showVideo) continue;
+        const score = cosineSimilarity(embedding, category.textEmbedding);
+        if (score >= category.minScore) {
+            matches.push({ title: category.title, score });
+        }
+    }
+    return matches
+        .sort((a, b) => b.score - a.score)
+        .map((match) => match.title);
+};

src/metadata-backup.ts

@@ -1,6 +1,7 @@
 import { mkdirSync, readFileSync, writeFileSync } from "node:fs";
 import { join } from "node:path";
 import * as jpeg from "jpeg-js";
+import { clipKeywords } from "./clip-keywords.js";
 import type { Client } from "./client.js";
 import { describeError } from "./errors.js";
 import { readExifTags } from "./exif.js";
@@ -97,7 +98,8 @@ const extractExif = async (
 
 // Dump every decrypted metadata layer the account holds into a directory tree
 // of plain JSON: account, per-collection, and per-file records including the
-// private and public magic metadata and (by default) the ML data. Collections
+// private and public magic metadata and the ML data, with the keywords of its
+// CLIP embedding in place of the embedding (see clip-keywords.ts). Collections
 // and files are enumerated from the library's cache, which the caller refreshes
 // first. Returns how many files have no ML data because their ML data request
 // failed or their ML data did not decrypt, gunzip or parse; they are still
@@ -230,8 +232,16 @@ export const runMetadataBackup = async (
         if (file.pubMagicMetadata)
             fileMeta.pubMagicMetadata = file.pubMagicMetadata;
 
+        // The ML data without the CLIP embedding, and the keywords that
+        // embedding gives.
         const ml = mlDataMap.get(file.id);
-        if (ml) fileMeta.mlData = ml;
+        if (ml) {
+            const { clip: _clip, ...mlDataWithoutClip } = ml;
+            fileMeta.mlData = mlDataWithoutClip;
+            const isVideo = file.metadata.fileType === "video";
+            const keywords = clipKeywords(ml, isVideo);
+            if (keywords) fileMeta.keywords = keywords;
+        }
         const mlError = mlDataErrors.get(file.id);
         if (mlError) fileMeta.mlDataError = mlError;
 

test/cli/backup-end-to-end.test.ts

@@ -86,10 +86,17 @@ const DAY = "2024/2024-03/2024-03-01";
 const TOAST = new TextEncoder().encode("a few seconds of the wedding toast");
 
 // Ente's ML data for beach.jpg: a face it found, and the photo's CLIP
-// embedding.
+// embedding, here that of a photo of a sunset (see clip-keywords.test.ts).
 const ML_DATA = {
     face: { faces: [{ faceID: "100_1", score: 0.98 }] },
-    clip: { embedding: [0.5, 0.6, 0.7] },
+    clip: {
+        embedding: JSON.parse(
+            readFileSync(
+                new URL("../clip-keywords-sunset.json", import.meta.url),
+                "utf-8",
+            ),
+        ).embedding,
+    },
 };
 
 // Each file's ID, its owner, its original as uploaded, and the metadata its
@@ -486,7 +493,10 @@ describe("the first backup of the account", () => {
             },
             updationTime: TAKEN,
         });
-        expect(beach.mlData).toEqual(ML_DATA);
+        // The JSON holds the ML data without its CLIP embedding, and the
+        // keywords the embedding gives in its place.
+        expect(beach.mlData).toEqual({ face: ML_DATA.face });
+        expect(beach.keywords).toEqual(["Sunset"]);
         expect(beach.imageMetadata.exif.Make.description).toBe("Canon");
         expect(beach.imageMetadata.exif.Model.description).toBe("EOS R5");
 
@@ -497,6 +507,7 @@ describe("the first backup of the account", () => {
             metadata: { title: "toast.mp4", fileType: "video" },
         });
         expect(toast).not.toHaveProperty("mlData");
+        expect(toast).not.toHaveProperty("keywords");
         expect(toast).not.toHaveProperty("imageMetadata");
         expect(toast).not.toHaveProperty("imageMetadataError");
 

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.
+// Also serves ML data: file 100 has `ML_PAYLOAD`, whose CLIP embedding is that
-// While `mlError` is set, every ML data request fails with it.
+// of a photo of a sunset (see clip-keywords.test.ts), and the other two have
-const ML_PAYLOAD = { face: { faces: [] }, clip: { embedding: [0.5, 0.25] } };
+// 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");
         }

test/cli/metadata-backup.test.ts

@@ -66,6 +66,15 @@ vi.mock("../../src/mldata-fetch.js", async (importOriginal) => ({
 const TEST_EMAIL = "metabackup@example.com";
 const TEST_PASSWORD = "metapass";
 
+// The CLIP embedding of a photo of a sunset (see clip-keywords.test.ts), which
+// the ML data of beach.jpg holds.
+const SUNSET_EMBEDDING: number[] = JSON.parse(
+    readFileSync(
+        new URL("../clip-keywords-sunset.json", import.meta.url),
+        "utf-8",
+    ),
+).embedding;
+
 let mock: MetaMockState;
 let testDir: string;
 
@@ -230,7 +239,7 @@ const buildMetaMock = async (): Promise<MetaMockState> => {
         clip: {
             version: 1,
             client: "test",
-            embedding: [0.5, 0.6, 0.7],
+            embedding: SUNSET_EMBEDDING,
         },
     });
     const gzipped = gzipSync(Buffer.from(mlPayload));
@@ -432,7 +441,9 @@ describe("quak backup-metadata", () => {
         expect(account.email).toBe(TEST_EMAIL);
     });
 
-    it("fetches and decrypts ML data by default", async () => {
+    // The CLIP embedding is left out of the ML data, and the keywords it gives
+    // are written in its place.
+    it("fetches and decrypts ML data by default, with keywords for the CLIP embedding", async () => {
         const outDir = join(testDir, "ml-data");
         await runBackup(outDir);
 
@@ -452,8 +463,8 @@ describe("quak backup-metadata", () => {
         expect(fileMeta.mlData.face.faces[0].faceID).toBe("face-abc");
         expect(fileMeta.mlData.face.faces[0].score).toBeCloseTo(0.98);
         expect(fileMeta.mlData.face.faces[0].detection.box.x).toBeCloseTo(0.1);
-        expect(fileMeta.mlData.clip).toBeDefined();
+        expect(fileMeta.mlData).not.toHaveProperty("clip");
-        expect(fileMeta.mlData.clip.embedding).toEqual([0.5, 0.6, 0.7]);
+        expect(fileMeta.keywords).toEqual(["Sunset"]);
     });
 
     it("extracts EXIF from downloaded files when --exif is set", async () => {
@@ -541,7 +552,7 @@ describe("quak backup-metadata when a file's ML data cannot be read", () => {
                 "utf-8",
             ),
         );
-        expect(ok.mlData.clip.embedding).toEqual([0.5, 0.6, 0.7]);
+        expect(ok.keywords).toEqual(["Sunset"]);
         expect(ok.mlDataError).toBeUndefined();
 
         const failed = JSON.parse(
@@ -599,7 +610,7 @@ describe("quak backup-metadata when a file's ML data cannot be read", () => {
                 "utf-8",
             ),
         );
-        expect(ok.mlData.clip.embedding).toEqual([0.5, 0.6, 0.7]);
+        expect(ok.keywords).toEqual(["Sunset"]);
     });
 
     it("exits 0 when every ML data request succeeds", async () => {

test/clip-keywords-sunset.json

@@ -0,0 +1,109 @@
+{
+    "photo": "https://upload.wikimedia.org/wikipedia/commons/d/db/Sunset_off_the_Massachusetts_coast_-_NOAA.jpg",
+    "sha256": "be62f3289c9bf7b75af68cd6978021d54e8a531d5d4cc493f6e9fbf5e1ab74d3",
+    "embedding": [
+        -0.0021123015, -0.04437615, -0.062354308, 0.006249992, 0.022141185,
+        0.011159019, 0.01227392, -0.00030532264, 0.03186437, 0.09196922,
+        0.039111234, -0.037084445, -0.032962628, -0.019820206, -0.013141825,
+        0.0047183544, 0.017137334, 0.046581306, 0.0047622235, 0.04982521,
+        0.023128154, 0.032080866, 0.037709303, 0.019241406, 0.0032100384,
+        -0.014275843, 0.049337693, 0.025953151, 0.014263995, -0.0062328726,
+        -0.040504098, -0.015083303, -0.008123875, -0.006233639, -0.05352683,
+        -0.0048083956, 0.0763486, -0.013155067, 0.025114346, 0.022016674,
+        0.0001588023, -0.010341572, -0.0037518279, -0.019181008, 0.04549337,
+        -0.0010725886, 0.004636866, 0.017177915, -0.0117317885, 0.0026097358,
+        0.030738998, 0.013158572, -0.08856403, -0.0121391015, 0.011477229,
+        -0.0033104012, 0.035524756, -0.018607879, -0.0071739843, -0.031906743,
+        0.0056713386, -0.0044195866, 0.00292471, 0.00061686034, -0.043075223,
+        0.09040427, 0.001522537, -0.011445652, 0.009609886, -0.0053201183,
+        -0.0057452377, 0.00972853, -0.0024397348, -0.021255083, 0.015893811,
+        -0.006020548, -0.09428023, 0.014277583, -0.017975299, -0.011878315,
+        0.011825521, 0.00014203975, -0.0020437292, -0.05305343, -0.024840152,
+        -0.011336872, -0.002796541, -0.00036171873, 0.004530499, 0.044486605,
+        0.021602718, -0.01292857, 0.024530916, 0.019559948, -0.039928272,
+        -0.0681174, 0.049101, 0.02461532, 0.0365531, -0.04070358, -0.011011939,
+        -0.0027188526, -0.020370876, 0.012904856, 0.043157287, -0.051658224,
+        -0.3726012, 0.019549074, -0.0499252, -0.04800559, -0.011493859,
+        0.02893256, 0.060341954, 0.0013929688, -0.051283866, -0.024286803,
+        0.044124287, -0.005995806, -0.062817656, 0.007369614, -0.021470634,
+        -0.028147545, 0.00411659, 0.04234773, 0.03787647, 0.008579682,
+        -0.038568925, -0.027498025, -0.028416378, 0.0022012168, 0.010906191,
+        -0.013745787, 0.018267838, 0.013531097, -0.061559845, 0.004682517,
+        -0.005993303, 0.0028113858, -0.0028286583, 0.0026029332, -0.06693403,
+        -0.0072381413, -0.02419584, 0.0037009937, -0.0011296495, -0.030310456,
+        0.0005999609, 0.009945354, 0.024915686, 0.038672045, 0.023009432,
+        -0.05093051, -0.09101409, -0.06686167, -0.06211481, -0.022600474,
+        0.0024902367, -0.0067171026, -0.016769778, -0.0032803628, 0.008623083,
+        -0.0016110836, 0.014925073, 0.033811048, 0.012829449, -0.052575592,
+        0.03728281, -0.0051201633, 0.021238064, -0.031086605, -0.0043402985,
+        0.25054494, -0.028396713, -0.010725523, -0.023445267, 0.021751683,
+        0.003130971, 0.012270548, -0.014836475, 0.014466885, 0.0021478906,
+        -0.088153616, 0.015356315, 0.039639276, -0.040283255, 0.014923924,
+        -0.029303031, -0.004475201, 0.047299225, -0.06726505, -0.012491676,
+        -0.002080582, 0.03506341, 0.04146718, 0.0074114986, -0.009582633,
+        0.006623749, -0.05454898, -0.0034387247, -0.014780821, 0.03698328,
+        -0.034441486, 0.22032379, -0.021792699, 0.008001907, 0.0341019,
+        0.0025784625, -0.0071689896, -0.014151225, -0.050547987, -0.030372301,
+        -0.03288388, -0.042987447, -0.0033656165, 0.021502906, -0.0030374376,
+        -0.024413, -0.03506603, 0.030891743, 0.0069994032, 0.004887366,
+        0.030707076, 0.0065348223, 0.0008240555, 0.016809713, -0.024151647,
+        -0.0040458064, 0.044567548, 0.03908212, -0.061782435, 0.0026002831,
+        0.02003242, -0.0115451, -0.045135964, -0.0663542, 0.005240975,
+        0.0010777109, -0.009276002, -0.011919081, 0.027654119, -0.013634551,
+        0.0129154315, -0.013195512, 0.040571272, -0.099290505, -0.04656693,
+        -0.20979854, 0.04181004, -0.06769166, -0.04121946, 0.018260444,
+        0.005617506, -0.15694222, 0.032472793, 0.014189598, 0.008345103,
+        -0.041118417, 0.0074527003, -0.011494104, 0.004906474, -0.0027100458,
+        0.0009994594, -0.041061398, -0.045274835, 0.015187793, 0.034275036,
+        -0.02813289, 0.02098019, -0.015043198, 0.03716667, 0.013542642,
+        -0.008104835, 0.014965486, -0.027978558, 0.021163663, -0.006942737,
+        -0.034969814, -0.038961593, 0.016802894, -0.012318175, -0.047821593,
+        -0.01933216, 0.0064432374, 0.0322358, -0.020437784, 0.02659447,
+        -0.03022022, -0.019443465, -0.094178624, -0.017009107, 0.03860671,
+        -0.00023695534, -0.033089854, -0.014677462, 0.012356399, 0.018040499,
+        -0.19360113, 0.011710817, -0.019871691, 0.020124774, 0.024948105,
+        -0.024573864, -0.028349072, -0.041731454, 0.032646254, 0.015098165,
+        0.0018447012, -0.01129152, -0.02481687, 0.036048, 0.20797811,
+        0.04317757, -0.017239755, 0.033431605, -0.011384422, 0.0099174455,
+        -0.19739223, -0.008901062, -0.056426458, 0.014591864, 0.0044474266,
+        0.008209326, -0.012241819, 0.042553645, -0.021552453, 0.008611734,
+        0.048944127, -0.013872049, 0.019179989, 0.008183264, -0.053223096,
+        -0.027327595, -0.0033542586, -0.012353858, -0.010941209, 0.010212546,
+        0.029714134, -0.011385593, -0.0013529479, 0.014505385, 0.01148663,
+        0.012613636, -0.0074430504, -0.014008101, -0.028146038, -0.042778917,
+        0.02143165, 0.016812252, -0.06286215, 0.02085532, -0.035128277,
+        0.0241282, 0.01397908, -0.034992814, 0.013153766, 0.0068236147,
+        0.013594184, -0.06859393, 0.051849406, 0.029675119, -0.0037699796,
+        -0.00013216575, 0.02916647, 0.031859048, -0.015006051, 0.0001439966,
+        0.0026584554, -0.0046948595, 0.039754897, 0.034873154, 0.0012870439,
+        -0.011516082, -0.074089095, 0.019241294, 0.00076195505, 0.0027334464,
+        -0.036206458, -0.0011109262, -0.048838835, -0.00204685, 0.01179991,
+        0.0068136845, 0.009852495, 0.017308988, 0.03159545, 0.058803353,
+        0.038116634, 0.008836143, -0.010460637, -0.010994099, -0.0012926942,
+        -0.048370842, 0.037696566, -0.08093239, -0.0041242186, -0.005866373,
+        0.02048607, -0.12549639, 0.011705739, -0.0021425558, 0.018078592,
+        -0.038228087, 0.004245971, -0.11560507, 0.01381203, -0.02734893,
+        0.029595908, 0.101082794, -0.042842254, -0.08397301, -0.1546237,
+        -0.037624564, 0.0069606556, -0.008020479, 0.0043268185, 0.016504597,
+        0.044554908, -0.05240209, 0.020519335, -0.050994243, -0.019861093,
+        0.022066997, -0.0188911, 0.012919696, -0.022872692, -0.012961374,
+        -0.020071112, 0.0074553066, 0.06402014, 0.04065211, 0.014139608,
+        0.06221546, -0.02574805, 0.010114545, 0.020619567, 0.032264773,
+        -0.009148828, 0.022849955, -0.016593235, -0.032456633, -0.006737303,
+        -0.0037493357, -0.065912865, -0.0024181998, -0.0037048983, -0.019304488,
+        0.009085037, 0.057529982, -0.0378338, 0.01792042, 0.01885012,
+        -0.0043600523, 0.017382368, -0.027792772, 0.006749118, -0.0053297766,
+        -0.011628587, 0.015736576, 0.024801968, 0.011543913, -0.01159873,
+        -0.000759976, 0.042183932, 0.05764386, -0.06422416, 0.010577374,
+        -0.02261249, 0.02282793, -0.05141075, -0.11287078, -0.020670144,
+        -0.020953743, 0.030885734, -0.012597732, -0.121472664, 0.028447151,
+        -0.0415664, -0.010061708, 0.06441338, 0.0271207, -0.0016693748,
+        -0.0045694243, 0.020342957, 0.030528478, 0.007129517, -0.0029600926,
+        -0.007061397, 0.06036486, -0.004736018, -0.0090786135, -0.07601116,
+        -0.039556272, -0.051269546, -0.019473685, 0.029858151, 0.028464556,
+        0.012424476, 0.013742254, 0.008742927, 0.006065751, -0.009892663,
+        0.07699999, -0.003443197, 0.0027863425, 0.029197995, 0.0211013,
+        0.01098379, 0.016684106, -0.028917195, 0.008122037, 0.0265241,
+        -0.04461223
+    ]
+}

test/clip-keywords.test.ts

@@ -0,0 +1,129 @@
+// `clipKeywords`: the keywords quak writes for a file in place of its CLIP
+// embedding.
+//
+// Ente's ML data for a file holds a CLIP embedding, 512 numbers its image
+// model gave the photo. Ente's photos app puts a file in a category of its
+// Discover section when the cosine similarity of that embedding and the text
+// embedding of the category's query is at least the category's minimum score,
+// and keeps videos out of some categories. `clipKeywords` applies that rule
+// and returns the titles of the categories a file falls in, highest score
+// first; `quak backup` and `quak backup-metadata` write them as `keywords`.
+
+import { readFileSync } from "node:fs";
+import { describe, expect, it } from "vitest";
+
+import { DISCOVER_CATEGORIES } from "../src/clip-keyword-vectors.js";
+import { clipKeywords } from "../src/clip-keywords.js";
+
+// 512 numbers: the ones given, then zeros.
+const vector = (...start: number[]): number[] => [
+    ...start,
+    ...new Array<number>(512 - start.length).fill(0),
+];
+
+const withEmbedding = (embedding: unknown) => ({
+    face: { faces: [] },
+    clip: { embedding },
+});
+
+// Two made-up categories, each with a text embedding along one axis. The
+// embedding [3, 4] has a cosine similarity of 3/5 with "Across" and 4/5 with
+// "Up". Videos are kept out of "Across".
+const ACROSS = {
+    title: "Across",
+    query: "across",
+    minScore: 0.6,
+    showVideo: false,
+    textEmbedding: vector(1),
+};
+const UP = {
+    title: "Up",
+    query: "up",
+    minScore: 0.5,
+    showVideo: true,
+    textEmbedding: vector(0, 1),
+};
+
+describe("clipKeywords", () => {
+    it("gives each category whose minimum score the similarity reaches, highest score first", () => {
+        expect(
+            clipKeywords(withEmbedding(vector(3, 4)), false, [ACROSS, UP]),
+        ).toEqual(["Up", "Across"]);
+    });
+
+    it("leaves out a category when the similarity is just below its minimum score", () => {
+        const stricter = { ...ACROSS, minScore: 0.6 + 1e-9 };
+
+        expect(
+            clipKeywords(withEmbedding(vector(3, 4)), false, [stricter, UP]),
+        ).toEqual(["Up"]);
+    });
+
+    it("keeps a video out of a category that leaves out videos", () => {
+        expect(
+            clipKeywords(withEmbedding(vector(3, 4)), true, [ACROSS, UP]),
+        ).toEqual(["Up"]);
+    });
+
+    it("gives an empty list for an embedding that falls in no category", () => {
+        expect(
+            clipKeywords(withEmbedding(vector(0, 0, 1)), false, [ACROSS, UP]),
+        ).toEqual([]);
+    });
+
+    it.each([
+        ["no CLIP embedding", { face: { faces: [] } }],
+        ["a CLIP part that is not an object", { clip: null }],
+        ["a CLIP part without an embedding", { clip: {} }],
+        ["an embedding of 511 numbers", withEmbedding(vector(3, 4).slice(1))],
+        ["an embedding with NaN in it", withEmbedding(vector(3, NaN))],
+        ["an embedding with Infinity in it", withEmbedding(vector(Infinity))],
+        ["an embedding of strings", withEmbedding(vector(3, 4).map(String))],
+        ["an embedding of zeros", withEmbedding(vector())],
+    ])("gives no keywords for ML data with %s", (_, mlData) => {
+        expect(clipKeywords(mlData, false, [ACROSS, UP])).toBeUndefined();
+    });
+});
+
+// The CLIP embedding Ente's image model gives a public-domain NOAA photo of a
+// sunset, made by bin/clip-keyword-vectors from the photo at `photo`.
+const SUNSET: { photo: string; sha256: string; embedding: number[] } =
+    JSON.parse(
+        readFileSync(
+            new URL("clip-keywords-sunset.json", import.meta.url),
+            "utf-8",
+        ),
+    );
+
+describe("Ente's Discover categories", () => {
+    it("are the 16 of Ente's photos app, each with a text embedding of length 1", () => {
+        expect(DISCOVER_CATEGORIES.map((c) => c.title)).toEqual([
+            "Identity",
+            "Screenshots",
+            "QR Codes",
+            "Receipts",
+            "Notes",
+            "Memes",
+            "Visiting Cards",
+            "Babies",
+            "Pets",
+            "Selfies",
+            "Wallpapers",
+            "Food",
+            "Celebrations",
+            "Sunset",
+            "Hills",
+            "Greenery",
+        ]);
+        for (const category of DISCOVER_CATEGORIES) {
+            expect(category.textEmbedding).toHaveLength(512);
+            expect(Math.hypot(...category.textEmbedding)).toBeCloseTo(1, 5);
+        }
+    });
+
+    it("give a photo of a sunset the keyword Sunset and no other", () => {
+        expect(
+            clipKeywords({ clip: { embedding: SUNSET.embedding } }, false),
+        ).toEqual(["Sunset"]);
+    });
+});