From 5ec176550f7863c3d1e7f03e3591b23512ebbe9b Mon Sep 17 00:00:00 2001 From: clawbot <35+clawbot@noreply.example.org> Date: Thu, 22 Oct 2026 18:36:38 +0000 Subject: [PATCH] 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 | 79 +- TODO.md | 12 + bin/clip-keyword-vectors | 25 + bin/clip-keyword-vectors.py | 158 +++ src/backup.ts | 20 +- src/clip-keyword-vectors.ts | 1836 +++++++++++++++++++++++++++++++++++ src/clip-keywords.ts | 59 ++ src/metadata-backup.ts | 14 +- 8 files changed, 2169 insertions(+), 34 deletions(-) create mode 100755 bin/clip-keyword-vectors create mode 100644 bin/clip-keyword-vectors.py create mode 100644 src/clip-keyword-vectors.ts create mode 100644 src/clip-keywords.ts diff --git a/README.md b/README.md index 06e77ea..a0372fe 100644 --- a/README.md +++ b/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 ` writes `account.json` and, for each collection, a directory `collections/-/` holding `_collection.json` and a -`.json` for each of its files. The collection's name has unsafe -characters replaced and is cut short between two characters so that the +`.json` for each of its files, with the file's ML data and keywords as +`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..json the file's basic metadata fields quak keeps, its update time, its private and - public magic metadata, its ML data, and, - for an image (for a live photo, its - image), its original's EXIF and XMP, with - dimensions for a JPEG only; none of these - three for a video + public magic metadata, its ML data and + keywords, and, for an image (for a live + photo, its image), its original's EXIF + and XMP, with dimensions for a JPEG + only; none of these three for a video YYYY-MM-DD..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 -`mlData`, the same payload `backup-metadata` writes; a file Ente has no ML data -for has no `mlData`. 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 Ente's ML data for it as `mlData`, such as the faces Ente +found, without its CLIP embedding. In place of the embedding it holds +`keywords`, the titles of the categories of the Discover section of Ente's +photos app that the file falls in, highest score first. The categories are +Identity, Screenshots, QR Codes, Receipts, Notes, Memes, Visiting Cards, Babies, +Pets, Selfies, Wallpapers, Food, Celebrations, Sunset, Hills and Greenery. As in +Ente's app, 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 minimum score, and a video stays out of Identity, Screenshots, QR +Codes, Visiting Cards and Wallpapers. A file with no ML data has neither field, +and one whose ML data holds no usable CLIP embedding has no `keywords`. +`backup-metadata` writes both the same way. + +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 - photo, its image), its original's EXIF and XMP, with its dimensions for a JPEG - only; a video's JSON holds none of these three. A fetched original is written - straight to its save path and not into the cache, which then counts it as - present; one the cache already held is copied from there. `BackupOptions`: + ledger. Each file's JSON holds its ML data and keywords and, for an image (for + a live photo, its image), its original's EXIF and XMP, with its dimensions for + a JPEG only; a video's JSON holds none of these three. A fetched original is + written straight to its save path and not into the cache, which then counts it + 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 diff --git a/TODO.md b/TODO.md index f1de6ec..99ae803 100644 --- a/TODO.md +++ b/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, diff --git a/bin/clip-keyword-vectors b/bin/clip-keyword-vectors new file mode 100755 index 0000000..7df4c71 --- /dev/null +++ b/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 diff --git a/bin/clip-keyword-vectors.py b/bin/clip-keyword-vectors.py new file mode 100644 index 0000000..af888fa --- /dev/null +++ b/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() diff --git a/src/backup.ts b/src/backup.ts index 37e5116..659275c 100644 --- a/src/backup.ts +++ b/src/backup.ts @@ -14,9 +14,9 @@ // YYYY/YYYY-MM/YYYY-MM-DD/ // YYYY-MM-DD.. the decrypted bytes (the save path) // YYYY-MM-DD..json per-file metadata sidecar, with -// the file's ML data and its -// original's EXIF, XMP and -// dimensions +// the file's ML data, its +// keywords, and its original's +// EXIF, XMP and dimensions // collections// 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 -// reason the ML data is missing, and its image metadata. +// The file's JSON: its basic fields, its magic metadata, its ML data without +// 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) { diff --git a/src/clip-keyword-vectors.ts b/src/clip-keyword-vectors.ts new file mode 100644 index 0000000..9dd3543 --- /dev/null +++ b/src/clip-keyword-vectors.ts @@ -0,0 +1,1836 @@ +// Written by bin/clip-keyword-vectors; do not edit by hand. +// +// The Discover categories of Ente's photos app, in their order, from +// mobile/apps/photos/assets/discover.json in https://github.com/ente-io/ente +// at commit f5ad70a860ed25d0c8057b0efb4bd6eaefe4b0c5. Each has the text +// embedding Ente's CLIP text model (mobileclip_s2_text_opset18_quant.onnx) +// gives its query, scaled to length 1. `showVideo` is false for a category +// that leaves out videos. + +export const DISCOVER_CATEGORIES = [ + { + title: "Identity", + query: "identity documents and cards", + minScore: 0.2, + showVideo: false, + textEmbedding: [ + 0.03607934, -0.015425337, -0.0043316674, 0.009292002, -0.025645005, + 0.014152668, -0.0076436675, 0.014114335, -0.041070342, 0.007275668, + 0.015916003, 0.017763669, 0.033603005, 0.009023668, -0.03352634, + 0.5298818, 0.0017940004, -0.0076360013, -0.042412005, -0.0015103335, + 0.025008671, -0.023552004, -0.024357004, 0.008088335, -0.013907337, + -0.0021313336, -0.019741671, 0.017365003, 0.037566673, -0.018898336, + 0.023245338, -0.008395001, 0.018032003, 0.041430674, -0.007728001, + 0.019948669, -0.010189001, 0.03670034, -0.022923337, -0.04807001, + -0.060191013, 0.01716567, -0.013171336, -0.011308336, 0.011837335, + -0.015893003, -0.018377002, 0.026296671, 0.027684337, 0.043914672, + -0.038801007, -0.024265004, 0.010127668, -0.043102007, + 0.00044466674, -0.02084567, -0.003664667, -0.008786001, + -0.020301336, -0.026036004, 0.019933337, -0.033258006, 0.014275335, + -0.039866675, -0.11761435, 0.02468667, -0.007950335, -0.024817005, + -0.0035036672, 0.0030590005, -0.038517337, 0.035282005, + -0.059524007, -0.029716006, 0.0070456676, -0.024610003, + -0.042941008, -0.015663004, 0.016935669, -0.00097366684, + -0.00071300013, 0.016859002, 0.02664167, -0.0052976673, + -0.005075334, 0.013930335, -0.0065626674, -0.031096006, 0.019120669, + 0.010434335, 0.008287668, -0.006401668, 0.006647001, 0.006340334, + 0.0077510015, 0.02337567, 0.099183686, 0.011914002, -0.011776002, + -0.0072296676, 0.00088166684, 0.029363338, 0.0118986685, + -0.030551674, -0.04252701, -0.012105669, -0.05200301, 0.007666668, + 0.0038180007, -0.011132002, -0.009713668, 0.0006516668, 0.2377357, + -0.031441007, -0.028305337, 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b/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); +}; diff --git a/src/metadata-backup.ts b/src/metadata-backup.ts index 35e13e2..fb07840 100644 --- a/src/metadata-backup.ts +++ b/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;