Revert ML search (#50) to restore a green next (build fix pending)

Reverts the #50 merge (224bd101): mlsearch.ts failed tsc in the CI build (make check does not run make build, so it slipped past review). next restored to green; #50 to be redone with make build in its gate.

Model: opus-4-8

src/library/index.ts

@@ -41,7 +41,6 @@ import {
     type PhotosAPI,
     type TimelineAPI,
 } from "./read.js";
-import { makeMLDataAPI, type MLDataAPI } from "./mlsearch.js";
 
 export {
     Album,
@@ -53,7 +52,6 @@ export {
     type TimelineGroup,
     type GroupBy,
 } from "./read.js";
-export { type MLDataAPI, type SimilarResult } from "./mlsearch.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";
@@ -140,10 +138,6 @@ export class Library {
     readonly albums: AlbumsAPI;
     readonly photos: PhotosAPI;
     readonly timeline: TimelineAPI;
-    // The content-similarity search surface over the CLIP index (issue #50).
-    // Present whether or not ML fetching is enabled; with no ML store it
-    // returns empty results.
-    readonly mldata: MLDataAPI;
 
     private readonly client: LibraryClient;
     private readonly store: MetadataStore;
@@ -152,7 +146,7 @@ export class Library {
     private readonly onProgress?: RefreshProgressCallback;
     private readonly pools: RequestPools;
     // The ML-data cache, present only when the client can fetch ML data.
-    private readonly mlStore?: MLDataStore;
+    private readonly mldata?: MLDataStore;
 
     private timer?: ReturnType<typeof setTimeout>;
     private refreshing = false;
@@ -194,7 +188,7 @@ export class Library {
         this.intervalMs = args.intervalMs;
         this.onProgress = args.onProgress;
         this.pools = args.pools;
-        this.mlStore = args.mldata;
+        this.mldata = args.mldata;
         this.lastRecords = this.deriveNow();
 
         // The read namespaces derive fresh from the store on each call, so they
@@ -203,8 +197,6 @@ export class Library {
         this.albums = makeAlbumsAPI(derive);
         this.photos = makePhotosAPI(derive);
         this.timeline = makeTimelineAPI(derive);
-        // Reads the ML store live so results grow as ML data is fetched.
-        this.mldata = makeMLDataAPI(() => this.mlStore);
     }
 
     // Load the cache and start the refresh loop. With an empty cache the first
@@ -301,7 +293,7 @@ export class Library {
         for (const c of collections) {
             files += this.store.listFiles(c.id).length;
         }
-        const ml = this.mlStore?.stats();
+        const ml = this.mldata?.stats();
         return {
             userID: this.store.userID,
             collections: collections.length,
@@ -456,7 +448,7 @@ export class Library {
     // 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.mlStore;
+        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);

src/library/mlsearch.ts

@@ -1,120 +0,0 @@
-// The content-similarity search surface over the CLIP index (issue #50).
-//
-// This is `lib.mldata`. It answers three questions against the ML-data cache
-// (#49) without touching the network:
-//
-//   - `forFile` returns the whole stored payload (face boxes, landmarks,
-//     embedding) for a file, read from disk on demand — the only method here
-//     that touches the disk, and the only one that is async.
-//   - `similar` and `searchByEmbedding` rank fileIDs by cosine similarity over
-//     the packed `Float32Array` index alone. That index (~50k×512) already
-//     lives in RAM, so each query is a plain loop over it and nothing else.
-//
-// quak bundles no text encoder (owner-deferred), so `searchByEmbedding` takes
-// the query vector the caller has produced elsewhere; `similar` uses the
-// query file's own indexed embedding.
-
-import type { MLData } from "../mldata-fetch.js";
-import type { MLDataStore, MLIndex } from "./mldata.js";
-
-// How many nearest files a query returns when the caller names no limit.
-const DEFAULT_LIMIT = 20;
-
-// One ranked result: a fileID and its cosine similarity to the query, in
-// [-1, 1]. Callers wanting only the ids read `.fileID`.
-export interface SimilarResult {
-    fileID: number;
-    score: number;
-}
-
-export interface MLDataAPI {
-    // The whole stored ML payload for a file, or undefined when it is not
-    // cached. Reads the payload from disk, so it is async.
-    forFile(args: { fileID: number }): Promise<MLData | undefined>;
-    // The files nearest the given file by cosine over their CLIP embeddings,
-    // most similar first, excluding the file itself. Empty when the file has
-    // no indexed embedding.
-    similar(args: { fileID: number; limit?: number }): SimilarResult[];
-    // The files nearest a caller-supplied query embedding by cosine, most
-    // similar first. Empty when the query is the wrong length for the index,
-    // has zero magnitude, or the index is empty.
-    searchByEmbedding(args: {
-        embedding: ArrayLike<number>;
-        limit?: number;
-    }): SimilarResult[];
-}
-
-// Rank the packed index by cosine similarity to `query`, most similar first,
-// and return the top `limit`. `skip` (a query file's own id) is left out. Both
-// each row's magnitude and the query's are computed here rather than cached:
-// the index mutates as ML data is fetched, and one plain pass over ~50k×512
-// floats is fast enough that a norm cache would only add a staleness bug. A
-// zero-magnitude vector has no direction, so it is dropped rather than divided
-// by zero.
-const topByCosine = (
-    index: MLIndex,
-    query: ArrayLike<number>,
-    limit: number,
-    skip?: number,
-): SimilarResult[] => {
-    const { fileIDs, embeddingLength, embeddings } = index;
-    if (embeddingLength === 0 || query.length !== embeddingLength) return [];
-
-    let queryNorm = 0;
-    for (let k = 0; k < embeddingLength; k++) queryNorm += query[k] * query[k];
-    queryNorm = Math.sqrt(queryNorm);
-    if (queryNorm === 0) return [];
-
-    const results: SimilarResult[] = [];
-    for (let i = 0; i < fileIDs.length; i++) {
-        const id = fileIDs[i];
-        if (id === skip) continue;
-        const base = i * embeddingLength;
-        let dot = 0;
-        let norm = 0;
-        for (let k = 0; k < embeddingLength; k++) {
-            const v = embeddings[base + k];
-            dot += query[k] * v;
-            norm += v * v;
-        }
-        if (norm === 0) continue;
-        results.push({
-            fileID: id,
-            score: dot / (queryNorm * Math.sqrt(norm)),
-        });
-    }
-
-    // Descending score, ties broken by ascending fileID for a stable order.
-    results.sort((a, b) => b.score - a.score || a.fileID - b.fileID);
-    return results.slice(0, Math.max(0, Math.trunc(limit)));
-};
-
-// Build the search surface over a store the library supplies lazily (the store
-// is absent when the client cannot fetch ML data). Reading it per call keeps
-// the surface current as the index grows.
-export const makeMLDataAPI = (
-    store: () => MLDataStore | undefined,
-): MLDataAPI => ({
-    forFile: ({ fileID }): Promise<MLData | undefined> => {
-        const s = store();
-        return s ? s.readPayload(fileID) : Promise.resolve(undefined);
-    },
-    similar: ({ fileID, limit }): SimilarResult[] => {
-        const s = store();
-        if (!s) return [];
-        const index = s.getIndex();
-        const pos = index.fileIDs.indexOf(fileID);
-        if (pos < 0) return [];
-        const base = pos * index.embeddingLength;
-        const query = index.embeddings.subarray(
-            base,
-            base + index.embeddingLength,
-        );
-        return topByCosine(index, query, limit ?? DEFAULT_LIMIT, fileID);
-    },
-    searchByEmbedding: ({ embedding, limit }): SimilarResult[] => {
-        const s = store();
-        if (!s) return [];
-        return topByCosine(s.getIndex(), embedding, limit ?? DEFAULT_LIMIT);
-    },
-});