v0.3.0: Local embeddings via Transformers.js

- Add EmbedSearch: semantic vector search using @xenova/transformers
- Four model options (BGE Micro v2 default, MiniLM, multilingual variants)
- Per-note .ajson cache in .memex-chat/embeddings/ with incremental writes
- Model download progress + embedding speed (N/s) and ETA display
- Settings: toggle embeddings on/off, model selector
- activeSearch getter: uses EmbedSearch when enabled, falls back to VaultSearch
- esbuild: target es2020 (BigInt), externalize onnxruntime-node

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

esbuild.config.mjs

@@ -21,10 +21,14 @@ const context = await esbuild.context({
     "@lezer/common",
     "@lezer/highlight",
     "@lezer/lr",
+    // Native modules used by onnxruntime — not bundleable, loaded by Node.js at runtime
+    "onnxruntime-node",
+    "sharp",
+    "canvas",
     ...builtins,
   ],
   format: "cjs",
-  target: "es2018",
+  target: "es2020", // raised from es2018 to support BigInt used by @xenova/transformers
   logLevel: "info",
   sourcemap: prod ? false : "inline",
   treeShaking: true,

manifest.json

@@ -1,7 +1,7 @@
 {
   "id": "memex-chat",
   "name": "Memex Chat",
-  "version": "0.2.3",
+  "version": "0.3.0",
   "minAppVersion": "1.4.0",
   "description": "Chat with your Obsidian vault using Claude AI — semantic context retrieval, @ mentions, thread history.",
   "author": "Sven",

package.json

@@ -1,6 +1,6 @@
 {
   "name": "memex-chat",
-  "version": "0.2.3",
+  "version": "0.3.0",
   "description": "Obsidian plugin: Chat with your vault using Claude AI",
   "main": "main.js",
   "scripts": {
@@ -9,6 +9,7 @@
   },
   "devDependencies": {
     "@types/node": "^20.0.0",
+    "@xenova/transformers": "^2.17.2",
     "builtin-modules": "^3.3.0",
     "esbuild": "^0.20.0",
     "obsidian": "latest",

src/ChatView.ts

@@ -120,7 +120,8 @@ export class ChatView extends ItemView {
       rebuildBtn.disabled = true;
       this.setStatus("Indiziere Vault…");
       await this.plugin.rebuildIndex();
-      this.setStatus(`✓ ${this.plugin.search.isIndexed() ? "Index bereit" : ""}`);
+      const ready = this.plugin.activeSearch.isIndexed();
+      this.setStatus(ready ? "✓ Index bereit" : "");
       setTimeout(() => this.setStatus(""), 2000);
       rebuildBtn.disabled = false;
     };
@@ -301,20 +302,45 @@ export class ChatView extends ItemView {
     this.setStatus("Suche relevante Notizen…");
     this.isLoading = true;
     try {
-      if (!this.plugin.search.isIndexed()) {
+      const engine = this.plugin.activeSearch;
-        this.setStatus("Indiziere Vault…");
+      if (!engine.isIndexed()) {
-        await this.plugin.search.buildIndex();
+        this.setStatus(this.plugin.settings.useEmbeddings ? "Lade Embedding-Modell…" : "Indiziere Vault…");
+        this.attachEmbedProgress();
+        await engine.buildIndex();
+        this.detachEmbedProgress();
       }
-      this.pendingContext = await this.plugin.search.search(query, this.plugin.settings.maxContextNotes);
+      this.pendingContext = await engine.search(query, this.plugin.settings.maxContextNotes);
       this.explicitContext = mentions;
       this.renderContextPreview();
       this.setStatus("Kontext bereit — Senden bestätigen oder anpassen");
     } catch (e) {
-      this.setStatus("Fehler bei Kontextsuche: " + e.message);
+      this.detachEmbedProgress();
+      this.setStatus("Fehler bei Kontextsuche: " + (e as Error).message);
     }
     this.isLoading = false;
   }
 
+  /** Wire up EmbedSearch progress callbacks to the status bar */
+  private attachEmbedProgress(): void {
+    const es = this.plugin.embedSearch;
+    if (!es) return;
+    es.onModelStatus = (s) => this.setStatus(s);
+    es.onProgress = (done, total, speed) => {
+      const speedStr = speed > 0 ? ` • ${speed.toFixed(1)} N/s` : "";
+      const eta = speed > 0 && done < total
+        ? ` • ~${Math.ceil((total - done) / speed)}s`
+        : "";
+      this.setStatus(`Embedding ${done}/${total}${speedStr}${eta}`);
+    };
+  }
+
+  private detachEmbedProgress(): void {
+    const es = this.plugin.embedSearch;
+    if (!es) return;
+    es.onModelStatus = undefined;
+    es.onProgress = undefined;
+  }
+
   private async sendMessage(query: string, additionalFiles: TFile[] = []): Promise<void> {
     this.isLoading = true;
     this.sendBtn.disabled = true;
@@ -536,13 +562,19 @@ export class ChatView extends ItemView {
     const query = this.inputEl.value.trim() || lastUserMsg;
     this.setStatus("Suche Notizen…");
     try {
-      if (!this.plugin.search.isIndexed()) await this.plugin.search.buildIndex();
+      const engine = this.plugin.activeSearch;
-      const results = await this.plugin.search.search(query, this.plugin.settings.maxContextNotes);
+      if (!engine.isIndexed()) {
+        this.attachEmbedProgress();
+        await engine.buildIndex();
+        this.detachEmbedProgress();
+      }
+      const results = await engine.search(query, this.plugin.settings.maxContextNotes);
       this.pendingContext = results;
       this.renderContextPreview();
       this.setStatus("");
     } catch (e) {
-      this.setStatus("Fehler: " + e.message);
+      this.detachEmbedProgress();
+      this.setStatus("Fehler: " + (e as Error).message);
     }
   }
 

src/EmbedSearch.ts

@@ -0,0 +1,299 @@
+import { App, TFile } from "obsidian";
+import { promises as fsp } from "fs";
+import { join, relative, dirname } from "path";
+import type { SearchResult } from "./VaultSearch";
+
+export const EMBEDDING_MODELS = [
+  { id: "TaylorAI/bge-micro-v2", name: "BGE Micro v2 (schnell, 384-dim, empfohlen)" },
+  { id: "Xenova/all-MiniLM-L6-v2", name: "MiniLM L6 v2 (384-dim)" },
+  { id: "Xenova/multilingual-e5-small", name: "Multilingual E5 Small (mehrsprachig, DE/EN)" },
+  { id: "Xenova/paraphrase-multilingual-MiniLM-L12-v2", name: "Multilingual MiniLM L12 (mehrsprachig)" },
+];
+
+interface EmbedCacheEntry { mtime: number; vec: number[] }
+interface Manifest { model: string; version: number }
+
+/**
+ * Semantic search engine using Transformers.js for local embeddings.
+ *
+ * Cache layout (one .ajson file per note, incremental writes):
+ *   <vault>/.memex-chat/embeddings/.manifest.json   — model name + version
+ *   <vault>/.memex-chat/embeddings/some/note.ajson  — { mtime, vec }
+ *
+ * Models are downloaded from HuggingFace on first use and cached by the browser.
+ * WASM runtime is loaded from CDN (cdn.jsdelivr.net) on first use.
+ */
+export class EmbedSearch {
+  private app: App;
+  private modelId: string;
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  private pipe: ((text: string, opts: object) => Promise<{ data: Float32Array }>) | null = null;
+  private cache: Map<string, EmbedCacheEntry> = new Map(); // vaultPath → entry
+  private vecs: Map<string, { vec: number[]; file: TFile }> = new Map();
+  private indexed = false;
+  private indexing = false;
+  /** Called every ~5 notes during indexing. speed = newly embedded notes/sec (cached notes excluded). */
+  onProgress?: (done: number, total: number, speed: number) => void;
+  /** Called during model/WASM download with a human-readable status string. */
+  onModelStatus?: (status: string) => void;
+
+  constructor(app: App, modelId: string) {
+    this.app = app;
+    this.modelId = modelId;
+  }
+
+  isIndexed(): boolean { return this.indexed; }
+
+  // ─── Paths ───────────────────────────────────────────────────────────────
+
+  private get vaultRoot(): string {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    return (this.app.vault.adapter as any).basePath as string;
+  }
+
+  private get embedDir(): string {
+    return join(this.vaultRoot, ".memex-chat", "embeddings");
+  }
+
+  private get manifestPath(): string {
+    return join(this.embedDir, ".manifest.json");
+  }
+
+  /** Disk path for the embedding of a vault-relative note path (e.g. "folder/note.md") */
+  private noteEmbedPath(vaultPath: string): string {
+    return join(this.embedDir, vaultPath.replace(/\.md$/, ".ajson"));
+  }
+
+  // ─── Pipeline ────────────────────────────────────────────────────────────
+
+  private async loadPipeline(): Promise<void> {
+    if (this.pipe) return;
+
+    // Dynamic import — bundled by esbuild, WASM loaded from CDN at runtime
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const mod = await import("@xenova/transformers") as any;
+    const { pipeline, env } = mod;
+
+    env.backends.onnx.wasm.wasmPaths =
+      "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.14.0/dist/";
+    env.backends.onnx.wasm.proxy = false;
+    env.allowLocalModels = false;
+    env.useBrowserCache = true;
+
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const progress_callback = (p: any) => {
+      if (!this.onModelStatus) return;
+      if (p.status === "initiate") {
+        this.onModelStatus(`Lade Modell: ${p.name ?? p.file ?? ""}…`);
+      } else if (p.status === "download") {
+        const pct = p.progress != null ? ` ${Math.round(p.progress)}%` : "";
+        const mb = p.total ? ` (${(p.total / 1e6).toFixed(1)} MB)` : "";
+        this.onModelStatus(`Download${pct}${mb}: ${p.file ?? ""}`);
+      } else if (p.status === "ready") {
+        this.onModelStatus("Modell bereit");
+      }
+    };
+
+    this.pipe = await pipeline("feature-extraction", this.modelId, {
+      quantized: true,
+      progress_callback,
+    });
+  }
+
+  private async embed(text: string): Promise<number[]> {
+    await this.loadPipeline();
+    const result = await this.pipe!(text.slice(0, 512), { pooling: "mean", normalize: true });
+    return Array.from(result.data);
+  }
+
+  private cosine(a: number[], b: number[]): number {
+    let dot = 0;
+    for (let i = 0; i < a.length; i++) dot += a[i] * b[i];
+    return dot;
+  }
+
+  // ─── Index ────────────────────────────────────────────────────────────────
+
+  async buildIndex(): Promise<void> {
+    if (this.indexing) return;
+    this.indexing = true;
+    this.indexed = false;
+    this.vecs.clear();
+
+    const changed: string[] = []; // vault paths newly embedded this run
+
+    try {
+      await this.loadCache();
+
+      const files = this.app.vault.getMarkdownFiles();
+      const total = files.length;
+      let done = 0;
+      let windowStart = Date.now();
+      let windowEmbedded = 0;
+      let speed = 0;
+
+      for (const file of files) {
+        const mtime = file.stat.mtime;
+        const cached = this.cache.get(file.path);
+
+        if (cached && cached.mtime === mtime) {
+          this.vecs.set(file.path, { vec: cached.vec, file });
+        } else {
+          try {
+            const raw = await this.app.vault.cachedRead(file);
+            const text = this.preprocess(raw).slice(0, 800) + " " + file.basename;
+            const vec = await this.embed(text);
+            this.cache.set(file.path, { mtime, vec });
+            this.vecs.set(file.path, { vec, file });
+            changed.push(file.path);
+            windowEmbedded++;
+          } catch {
+            // skip unembeddable files
+          }
+        }
+
+        done++;
+        if (this.onProgress && done % 5 === 0) {
+          const elapsed = (Date.now() - windowStart) / 1000;
+          if (elapsed > 0 && windowEmbedded > 0) {
+            speed = windowEmbedded / elapsed;
+            if (windowEmbedded >= 25) { windowStart = Date.now(); windowEmbedded = 0; }
+          }
+          this.onProgress(done, total, speed);
+        }
+      }
+
+      const allPaths = new Set(files.map((f) => f.path));
+      await this.saveCache(changed, allPaths);
+      this.indexed = true;
+      if (this.onProgress) this.onProgress(total, total, speed);
+    } finally {
+      this.indexing = false;
+    }
+  }
+
+  async search(query: string, topK = 8): Promise<SearchResult[]> {
+    if (!this.indexed) await this.buildIndex();
+
+    const qvec = await this.embed(query);
+    const scores: Array<[string, number]> = [];
+
+    for (const [path, { vec }] of this.vecs) {
+      const s = this.cosine(qvec, vec);
+      if (s > 0.2) scores.push([path, s]);
+    }
+
+    scores.sort((a, b) => b[1] - a[1]);
+    return scores.slice(0, topK).map(([path, score]) => {
+      const { file } = this.vecs.get(path)!;
+      return { file, score, excerpt: "", title: file.basename };
+    });
+  }
+
+  // ─── Cache I/O ───────────────────────────────────────────────────────────
+
+  /**
+   * Load all existing .ajson files from embedDir into this.cache.
+   * If the manifest model doesn't match, skip loading (full rebuild).
+   */
+  private async loadCache(): Promise<void> {
+    this.cache.clear();
+    try {
+      const manifestRaw = await fsp.readFile(this.manifestPath, "utf8");
+      const manifest: Manifest = JSON.parse(manifestRaw);
+      if (manifest.model !== this.modelId) return; // model changed — rebuild all
+    } catch {
+      return; // no manifest yet — start fresh
+    }
+    await this.loadCacheDir(this.embedDir);
+  }
+
+  private async loadCacheDir(dir: string): Promise<void> {
+    let entries;
+    try { entries = await fsp.readdir(dir, { withFileTypes: true }); }
+    catch { return; }
+
+    for (const entry of entries) {
+      if (entry.name.startsWith(".")) continue; // skip .manifest.json
+      const fullPath = join(dir, entry.name);
+      if (entry.isDirectory()) {
+        await this.loadCacheDir(fullPath);
+      } else if (entry.name.endsWith(".ajson")) {
+        try {
+          const raw = await fsp.readFile(fullPath, "utf8");
+          const { mtime, vec }: EmbedCacheEntry = JSON.parse(raw);
+          // Reconstruct vault path: relative path inside embedDir, swap .ajson → .md
+          const rel = relative(this.embedDir, fullPath).replace(/\.ajson$/, ".md");
+          // Normalise to forward slashes (vault paths always use /)
+          const vaultPath = rel.split("\\").join("/");
+          this.cache.set(vaultPath, { mtime, vec });
+        } catch {
+          // skip corrupt file
+        }
+      }
+    }
+  }
+
+  /**
+   * Write .ajson for each newly embedded note; delete .ajson for removed notes;
+   * write/update the manifest.
+   */
+  private async saveCache(changed: string[], allVaultPaths: Set<string>): Promise<void> {
+    try {
+      await fsp.mkdir(this.embedDir, { recursive: true });
+
+      // Manifest
+      const manifest: Manifest = { model: this.modelId, version: 1 };
+      await fsp.writeFile(this.manifestPath, JSON.stringify(manifest), "utf8");
+
+      // Write only the newly embedded notes
+      for (const vaultPath of changed) {
+        const entry = this.cache.get(vaultPath);
+        if (!entry) continue;
+        const filePath = this.noteEmbedPath(vaultPath);
+        await fsp.mkdir(dirname(filePath), { recursive: true });
+        await fsp.writeFile(filePath, JSON.stringify({ mtime: entry.mtime, vec: entry.vec }), "utf8");
+      }
+
+      // Prune .ajson files whose notes no longer exist
+      await this.pruneStale(this.embedDir, allVaultPaths);
+    } catch (e) {
+      console.error("[Memex] Embedding-Cache konnte nicht gespeichert werden:", e);
+    }
+  }
+
+  private async pruneStale(dir: string, allVaultPaths: Set<string>): Promise<void> {
+    let entries;
+    try { entries = await fsp.readdir(dir, { withFileTypes: true }); }
+    catch { return; }
+
+    for (const entry of entries) {
+      if (entry.name.startsWith(".")) continue;
+      const fullPath = join(dir, entry.name);
+      if (entry.isDirectory()) {
+        await this.pruneStale(fullPath, allVaultPaths);
+      } else if (entry.name.endsWith(".ajson")) {
+        const rel = relative(this.embedDir, fullPath).replace(/\.ajson$/, ".md");
+        const vaultPath = rel.split("\\").join("/");
+        if (!allVaultPaths.has(vaultPath)) {
+          await fsp.unlink(fullPath).catch(() => {});
+        }
+      }
+    }
+  }
+
+  // ─── Text preprocessing ──────────────────────────────────────────────────
+
+  private preprocess(raw: string): string {
+    let c = raw;
+    if (c.startsWith("---")) {
+      const end = c.indexOf("\n---", 3);
+      if (end > 0) c = c.slice(end + 4);
+    }
+    c = c.replace(/\[\[([^\]|]+)(?:\|([^\]]+))?\]\]/g, (_, t, a) => a || t);
+    c = c.replace(/!\[.*?\]\(.*?\)/g, "");
+    c = c.replace(/\[([^\]]+)\]\(.*?\)/g, "$1");
+    c = c.replace(/^#{1,6}\s+/gm, "");
+    return c;
+  }
+}

src/SettingsTab.ts

@@ -1,5 +1,6 @@
 import { App, PluginSettingTab, Setting } from "obsidian";
 import type MemexChatPlugin from "./main";
+import { EMBEDDING_MODELS } from "./EmbedSearch";
 
 export interface PromptButton {
   label: string;
@@ -24,6 +25,8 @@ export interface MemexChatSettings {
   contextProperties: string[];
   promptButtons: PromptButton[];
   systemContextFile: string; // optional vault path for extended system context
+  useEmbeddings: boolean;   // use local embedding model instead of TF-IDF
+  embeddingModel: string;   // HuggingFace model ID
 }
 
 export const DEFAULT_SETTINGS: MemexChatSettings = {
@@ -47,6 +50,8 @@ Wenn du Fragen beantwortest:
   sendOnEnter: false,
   contextProperties: ["collection", "related", "up", "tags"],
   systemContextFile: "",
+  useEmbeddings: false,
+  embeddingModel: "TaylorAI/bge-micro-v2",
   promptButtons: [
     {
       label: "Draft Check",
@@ -133,6 +138,36 @@ export class MemexChatSettingsTab extends PluginSettingTab {
         })
       );
 
+    // --- Semantic Search ---
+    containerEl.createEl("h3", { text: "Semantische Suche (Embeddings)" });
+    containerEl.createEl("p", {
+      text: "Ersetzt TF-IDF durch ein lokales KI-Modell (Transformers.js). Das Modell wird beim ersten Einsatz von HuggingFace heruntergeladen und dann lokal gecacht. WASM-Laufzeit wird einmalig vom CDN geladen.",
+      cls: "setting-item-description",
+    });
+
+    new Setting(containerEl)
+      .setName("Semantische Suche aktivieren")
+      .setDesc("Nutzt lokale Embeddings für kontextbasierte Ähnlichkeitssuche statt TF-IDF")
+      .addToggle((toggle) =>
+        toggle.setValue(this.plugin.settings.useEmbeddings).onChange(async (value) => {
+          this.plugin.settings.useEmbeddings = value;
+          await this.plugin.saveSettings();
+          await this.plugin.initEmbedSearch();
+        })
+      );
+
+    new Setting(containerEl)
+      .setName("Embedding-Modell")
+      .setDesc("Welches Modell für die semantische Suche verwenden? Kleiner = schneller, größer = besser.")
+      .addDropdown((drop) => {
+        for (const m of EMBEDDING_MODELS) drop.addOption(m.id, m.name);
+        drop.setValue(this.plugin.settings.embeddingModel).onChange(async (value) => {
+          this.plugin.settings.embeddingModel = value;
+          await this.plugin.saveSettings();
+          await this.plugin.initEmbedSearch();
+        });
+      });
+
     // --- Context ---
     containerEl.createEl("h3", { text: "Kontext-Einstellungen" });
 
@@ -428,7 +463,7 @@ export class MemexChatSettingsTab extends PluginSettingTab {
 
     new Setting(containerEl)
       .setName("Index neu aufbauen")
-      .setDesc("Vault-Index für die Suche neu aufbauen (dauert je nach Vault-Größe einige Sekunden)")
+      .setDesc("Vault-Index neu aufbauen (TF-IDF oder Embedding-Index, je nach Einstellung)")
       .addButton((btn) =>
         btn
           .setButtonText("Index neu aufbauen")

src/main.ts

@@ -1,6 +1,7 @@
 import { Plugin, WorkspaceLeaf } from "obsidian";
 import { ChatView, VIEW_TYPE_MEMEX_CHAT } from "./ChatView";
 import { VaultSearch } from "./VaultSearch";
+import { EmbedSearch } from "./EmbedSearch";
 import { ClaudeClient } from "./ClaudeClient";
 import { MemexChatSettingsTab, MemexChatSettings, DEFAULT_SETTINGS } from "./SettingsTab";
 
@@ -12,9 +13,15 @@ interface PluginData {
 export default class MemexChatPlugin extends Plugin {
   settings!: MemexChatSettings;
   search!: VaultSearch;
+  embedSearch: EmbedSearch | null = null;
   claude!: ClaudeClient;
   data!: PluginData;
 
+  /** Returns the active search engine: EmbedSearch when enabled, else VaultSearch */
+  get activeSearch(): VaultSearch | EmbedSearch {
+    return this.embedSearch ?? this.search;
+  }
+
   async onload(): Promise<void> {
     // Load data
     const loaded = (await this.loadData()) as PluginData | null;
@@ -85,6 +92,9 @@ export default class MemexChatPlugin extends Plugin {
         this.search.priorityProperties = this.settings.contextProperties;
         this.search.buildIndex().catch(console.error);
       }
+      if (this.settings.useEmbeddings) {
+        this.initEmbedSearch().catch(console.error);
+      }
     });
 
     console.log("[Memex Chat] Plugin geladen");
@@ -107,19 +117,49 @@ export default class MemexChatPlugin extends Plugin {
     this.app.workspace.revealLeaf(leaf);
   }
 
+  /** Create or recreate the EmbedSearch instance (called when settings change) */
+  async initEmbedSearch(): Promise<void> {
+    if (!this.settings.useEmbeddings) {
+      this.embedSearch = null;
+      return;
+    }
+    this.embedSearch = new EmbedSearch(this.app, this.settings.embeddingModel);
+    // Don't build immediately — build on first search or explicit rebuild
+  }
+
   async rebuildIndex(): Promise<void> {
     const leaves = this.app.workspace.getLeavesOfType(VIEW_TYPE_MEMEX_CHAT);
     const view = leaves[0]?.view as ChatView | undefined;
 
-    this.search.priorityProperties = this.settings.contextProperties;
+    if (this.settings.useEmbeddings && this.embedSearch) {
-    this.search.onProgress = (done, total) => {
+      // Rebuild semantic (embedding) index
-      if (view && done % 200 === 0) {
+      this.embedSearch.onModelStatus = (status) => {
-        view.setStatus(`Indiziere… ${done}/${total}`);
+        if (view) view.setStatus(status);
-      }
+      };
-    };
+      this.embedSearch.onProgress = (done, total, speed) => {
+        if (view) {
+          const speedStr = speed > 0 ? ` • ${speed.toFixed(1)} N/s` : "";
+          const eta = speed > 0 && done < total
+            ? ` • noch ~${Math.ceil((total - done) / speed)}s`
+            : "";
+          view.setStatus(`Embedding ${done}/${total}${speedStr}${eta}`);
+        }
+      };
+      await this.embedSearch.buildIndex();
+      this.embedSearch.onProgress = undefined;
+      this.embedSearch.onModelStatus = undefined;
+    } else {
+      // Rebuild TF-IDF index
+      this.search.priorityProperties = this.settings.contextProperties;
+      this.search.onProgress = (done, total) => {
+        if (view && done % 200 === 0) {
+          view.setStatus(`Indiziere… ${done}/${total}`);
+        }
+      };
+      await this.search.buildIndex();
+      this.search.onProgress = undefined;
+    }
 
-    await this.search.buildIndex();
-    this.search.onProgress = undefined;
     if (view) {
       view.setStatus(`✓ ${this.app.vault.getMarkdownFiles().length} Notizen indiziert`);
       setTimeout(() => view.setStatus(""), 3000);