Spaces:
Running
Running
File size: 19,509 Bytes
dd2cbeb b454fba dd2cbeb d443e89 dd2cbeb 01277b0 dd2cbeb b454fba dd2cbeb b454fba dd2cbeb 01277b0 dd2cbeb 01277b0 b454fba dd2cbeb d443e89 3985bb0 dd2cbeb d443e89 dd2cbeb 01277b0 dd2cbeb b454fba dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb 01277b0 dd2cbeb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 | import { VromCache } from './vrom-cache.js';
import type {
AgentMemoryOptions,
MountOptions,
MountStatus,
SearchOptions,
SearchResult,
FormatContextOptions,
VectorDB,
VectorDBConstructor,
WorkerOutMessage,
StorageEstimate,
VromRegistryEntry,
DownloadProgress,
} from './types.js';
export type WasmLoader = () => Promise<{ VectorDB: VectorDBConstructor }>;
const LOG_LEVELS = { silent: 0, error: 1, warn: 2, info: 3, debug: 4 } as const satisfies Record<string, number>;
type LogLevel = keyof typeof LOG_LEVELS;
/**
* AgentMemory β zero-boilerplate RAG for browser AI agents.
*
* Wraps the VecDB-WASM HNSW engine, a background ONNX embedding worker,
* and an OPFS-backed vROM cache into a single class.
*
* Lifecycle: `constructor` β {@link init} β {@link mount} β {@link search} β {@link destroy}
*
* @example
* ```ts
* const memory = new AgentMemory();
* await memory.init();
* await memory.mount('hf-transformers-docs');
* const results = await memory.search('how to use pipelines', { topK: 3, expandContext: true });
* const context = memory.formatContext(results, { maxTokens: 2000 });
* ```
*/
export class AgentMemoryCore {
#db: VectorDB | null = null;
#worker: Worker | null = null;
#pending = new Map<string, { resolve: (v: any) => void; reject: (e: Error) => void }>();
#cache: VromCache;
#VectorDB: VectorDBConstructor | null = null;
#initialized = false;
#modelReady = false;
#embeddingDim: number | null = null;
#currentModelId: string | null = null;
#currentDtype: string | null = null;
#activeVromId: string | null = null;
#activeManifest: any = null;
#logLevel: number;
#workerPath: string;
#wasmLoader: WasmLoader; // <-- Store the injected loader
/** Optional progress callback for model downloads. Set via {@link onProgress}. */
_onProgress?: (p: { file: string; loaded: number; total: number }) => void;
/**
* Create an AgentMemory instance.
*
* Does not perform any async work β call {@link init} to start the engine.
*
* @param options - Configuration options. All fields are optional with sensible defaults.
*/
constructor(wasmLoader: WasmLoader, options: AgentMemoryOptions = {}) {
this.#wasmLoader = wasmLoader;
// Remove the #wasmPkgPath fallback logic entirely!
this.#workerPath = options.workerPath ?? new URL('./embed-worker.js', import.meta.url).href;
const level = options.logLevel ?? 'warn';
this.#logLevel = LOG_LEVELS[level];
const headers = new Headers(options.headers || {});
if (options.apiKey) {
headers.set('x-api-key', options.apiKey);
}
this.#cache = new VromCache(headers, options.registryUrl);
}
// βββ Logging βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
#log(level: LogLevel, ...args: any[]) {
if (LOG_LEVELS[level] <= this.#logLevel) {
const prefix = `[AgentMemory:${level}]`;
if (level === 'error') console.error(prefix, ...args);
else if (level === 'warn') console.warn(prefix, ...args);
else console.log(prefix, ...args);
}
}
// βββ Worker ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
#setupWorker() {
this.#worker = new Worker(this.#workerPath, { type: 'module' });
this.#worker.addEventListener('message', (e: MessageEvent<WorkerOutMessage>) => {
const d = e.data;
if ((d as any).source !== 'vecdb') return;
switch (d.status) {
case 'dl-progress':
this.#log('debug', `DL: ${d.file} ${((d.loaded / d.total) * 100).toFixed(0)}%`);
this._onProgress?.(d);
break;
case 'ready':
this.#embeddingDim = d.dim;
this.#currentModelId = d.modelId;
this.#currentDtype = d.dtype;
this.#modelReady = true;
this.#log('info', `Model ready: ${d.modelId} (${d.dim}d)${d.cached ? ' [cached]' : ''}`);
this.#resolve('__load__');
break;
case 'result':
if (this.#pending.has(d.id)) {
this.#pending.get(d.id)!.resolve({ data: d.embeddings, dims: d.dims });
this.#pending.delete(d.id);
}
break;
case 'unloaded':
this.#modelReady = false;
this.#embeddingDim = null;
this.#currentModelId = null;
this.#currentDtype = null;
this.#log('info', 'Model unloaded');
this.#resolve('__unload__');
break;
case 'model-info':
this.#resolve('__model-info__', d);
break;
case 'error':
this.#log('error', d.message);
if (d.id && this.#pending.has(d.id)) {
this.#pending.get(d.id)!.reject(new Error(d.message));
this.#pending.delete(d.id);
}
this.#reject('__load__', d.message);
break;
}
});
}
#resolve(key: string, value?: any) {
if (this.#pending.has(key)) {
this.#pending.get(key)!.resolve(value);
this.#pending.delete(key);
}
}
#reject(key: string, message: string) {
if (this.#pending.has(key)) {
this.#pending.get(key)!.reject(new Error(message));
this.#pending.delete(key);
}
}
#workerRPC<T = void>(key: string, msg: any): Promise<T> {
return new Promise((resolve, reject) => {
this.#pending.set(key, { resolve, reject });
this.#worker!.postMessage(msg);
});
}
async #embed(texts: string[]): Promise<{ data: Float32Array; dims: number[] }> {
if (!this.#modelReady) throw new Error('No embedding model loaded');
const id = crypto.randomUUID();
return new Promise((resolve, reject) => {
this.#pending.set(id, { resolve, reject });
this.#worker!.postMessage({ type: 'embed', texts, id });
});
}
// βββ Public API ββββββββββββββββββββββββββββββββββββββββββββββββββββ
/**
* Initialize the WASM engine and spawn the background embedding worker.
*
* Must be called once before {@link mount}, {@link search}, or any other method.
* Calling `init()` multiple times is safe β subsequent calls are no-ops.
*
* @throws If the WASM module fails to load (invalid path, network error)
* @throws If the Web Worker fails to spawn (CSP violation, invalid path)
*
* @example
* ```ts
* const memory = new AgentMemory();
* await memory.init();
* ```
*/
async init(): Promise<void> {
if (this.#initialized) return;
this.#log('info', 'Initializing...');
const wasm = await this.#wasmLoader();
this.#VectorDB = wasm.VectorDB;
this.#setupWorker();
this.#initialized = true;
this.#log('info', 'Initialized');
}
/**
* Mount a vROM cartridge. Handles the full pipeline: registry lookup β
* OPFS cache check β CDN download β WASM index load β embedding model diffing.
*
* If the required embedding model is already loaded from a previous mount,
* model reload is skipped entirely (hot-swap).
*
* @param vromIdOrUri - vROM identifier, e.g. `'hf-transformers-docs'` or `'hub://hf-ml-training'`
* @param options - Mount options (progress callback, force download)
* @returns Current state after mounting
*
* @throws `'Call init() first'` β if {@link init} hasn't been called
* @throws `'vROM \'...\' not found in registry'` β if the ID doesn't exist
* @throws Network errors during CDN download
*
* @see {@link unmount} to free the HNSW graph
* @see {@link getMountStatus} to inspect the current state
*
* @example
* ```ts
* const status = await memory.mount('hf-transformers-docs', {
* onProgress: ({ phase, loaded, total }) => {
* if (phase === 'index' && total > 0)
* console.log(`${(loaded / total * 100).toFixed(0)}%`);
* },
* });
* console.log(`${status.vectors} vectors ready`);
* ```
*/
async mount(vromIdOrUri: string, options: MountOptions = {}): Promise<MountStatus> {
if (!this.#initialized) throw new Error('Call init() first');
const vromId = vromIdOrUri.replace(/^hub:\/\//, '');
this.#log('info', `Mounting: ${vromId}`);
const entry = await this.#cache.resolve(vromId);
if (!entry) throw new Error(`vROM '${vromId}' not found in registry`);
// OPFS cache check
const cached = !options.forceDownload && (await this.#cache.isCached(vromId));
if (!cached) {
this.#log('info', `Cache miss β downloading ${vromId} (${entry.size_mb} MB)`);
await this.#cache.pull(vromId, entry, options.onProgress);
} else {
this.#log('info', `Cache hit: ${vromId}`);
}
// Load into WASM (flush old graph)
const indexJson = await this.#cache.loadIndex(vromId);
if (!indexJson) throw new Error(`Failed to read index for '${vromId}'`);
if (this.#db) { try { this.#db.free(); } catch {} }
this.#db = this.#VectorDB!.load(indexJson);
this.#activeVromId = vromId;
this.#activeManifest = (await this.#cache.getCachedManifest(vromId)) ?? {};
this.#log('info', `Loaded: ${this.#db.len()} vectors, ${this.#db.dim()}d`);
// Model diffing
const requiredModel = this.#activeManifest.embedding_spec?.model || entry.model;
const requiredDtype = this.#activeManifest.embedding_spec?.quantization || 'q8';
if (!this.#modelReady || this.#currentModelId !== requiredModel || this.#currentDtype !== requiredDtype) {
this.#log('info', `Model diff: need ${requiredModel} (${requiredDtype})`);
await this.#workerRPC('__load__', { type: 'load', modelId: requiredModel, dtype: requiredDtype });
} else {
this.#log('info', `Model match: ${requiredModel} β skip reload`);
}
return this.getMountStatus();
}
/**
* Unmount the current vROM. Frees the HNSW graph from WASM memory
* but preserves the OPFS cache (so re-mounting is instant).
*
* The embedding model remains loaded in the worker.
* After unmounting, {@link search} will throw until a new vROM is mounted.
*
* @see {@link evict} to also remove from cache
*/
unmount(): void {
if (this.#db) { try { this.#db.free(); } catch {} }
this.#db = null;
const prev = this.#activeVromId;
this.#activeVromId = null;
this.#activeManifest = null;
this.#log('info', `Unmounted: ${prev}`);
}
/**
* Search the mounted vROM with a natural language query.
*
* The query is embedded in the background worker (~50ms), then
* HNSW approximate nearest neighbor search runs in WASM (<1ms).
*
* @param query - Natural language search query
* @param options - Search configuration (topK, context expansion, efSearch)
* @returns Results sorted by distance ascending (lower = more similar)
*
* @throws `'No vROM mounted β call mount() first'`
* @throws `'Embedding model not loaded'`
*
* @example
* ```ts
* const results = await memory.search('how to fine-tune', {
* topK: 5,
* expandContext: true,
* contextWindow: 1,
* });
* ```
*/
async search(query: string, options: SearchOptions = {}): Promise<SearchResult[]> {
if (!this.#db) throw new Error('No vROM mounted β call mount() first');
if (!this.#modelReady) throw new Error('Embedding model not loaded');
const topK = options.topK ?? 5;
const expandContext = options.expandContext ?? false;
const contextWindow = options.contextWindow ?? 1;
const output = await this.#embed([query]);
const vec = new Float32Array(output.data.slice(0, this.#embeddingDim!));
const rawJson = options.efSearch
? this.#db.search_with_ef(vec, topK, options.efSearch)
: this.#db.search(vec, topK);
const results: SearchResult[] = JSON.parse(rawJson).map((r: any) => {
const meta = r.metadata ? JSON.parse(r.metadata) : {};
return { text: meta.text ?? '', metadata: meta, distance: r.distance, id: r.id };
});
if (expandContext) {
for (const result of results) {
const before: string[] = [];
const after: string[] = [];
let pid = result.metadata.prev_chunk_id;
for (let i = 0; i < contextWindow && pid != null; i++) {
const raw = this.#db.get_metadata(pid);
if (!raw) break;
const m = JSON.parse(raw);
before.unshift(m.text ?? '');
pid = m.prev_chunk_id;
}
let nid = result.metadata.next_chunk_id;
for (let i = 0; i < contextWindow && nid != null; i++) {
const raw = this.#db.get_metadata(nid);
if (!raw) break;
const m = JSON.parse(raw);
after.push(m.text ?? '');
nid = m.next_chunk_id;
}
if (before.length || after.length) {
result.text = [...before, result.text, ...after].join('\n\n');
result.metadata._expanded = true;
result.metadata._contextChunks = before.length + 1 + after.length;
}
}
}
return results;
}
/**
* Format search results as a context string for LLM prompt injection.
*
* Concatenates result texts separated by `---` markers. Optionally includes
* source URLs and respects an approximate token budget.
*
* @param results - Search results from {@link search}
* @param options - Formatting options (sources, token budget)
* @returns Formatted context string ready for LLM system/user prompt
*
* @example
* ```ts
* const context = memory.formatContext(results, {
* maxTokens: 2000,
* includeSources: true,
* });
* ```
*/
formatContext(results: SearchResult[], options: FormatContextOptions = {}): string {
const includeSources = options.includeSources !== false;
const maxTokens = options.maxTokens ?? Infinity;
let ctx = '';
let tokens = 0;
for (const r of results) {
const t = Math.ceil(r.text.length / 4);
if (tokens + t > maxTokens) break;
ctx += r.text + '\n';
if (includeSources && r.metadata.url) ctx += `[Source: ${r.metadata.url}]\n`;
ctx += '\n---\n\n';
tokens += t;
}
return ctx.trim();
}
// βββ Queries βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
/**
* Get a snapshot of the current mount state.
*
* @returns A plain object describing the active vROM, model, and readiness.
* The returned object is not live β it reflects the state at call time.
*/
getMountStatus(): MountStatus {
return {
activeVrom: this.#activeVromId,
version: this.#activeManifest?.version ?? null,
ready: !!this.#db && this.#modelReady,
vectors: this.#db?.len() ?? 0,
dim: this.#db?.dim() ?? 0,
model: this.#currentModelId,
};
}
/**
* Whether the SDK is fully ready: initialized, vROM mounted, and model loaded.
*
* @remarks Equivalent to `getMountStatus().ready` after `init()`.
*/
get isReady(): boolean {
return this.#initialized && !!this.#db && this.#modelReady;
}
/**
* List all available vROMs from the registry.
*
* Fetches the registry from CDN on first call, then caches in OPFS for 1 hour.
*
* @returns Array of registry entries with IDs, sizes, model requirements, and CDN URLs
*/
async listVroms(): Promise<VromRegistryEntry[]> {
return this.#cache.list();
}
/**
* Check whether a vROM is cached locally in OPFS.
*
* @param vromId - vROM identifier
* @returns `true` if the index file exists in OPFS
*/
async isCached(vromId: string): Promise<boolean> {
return this.#cache.isCached(vromId);
}
/**
* Evict a vROM from the OPFS cache.
*
* Deletes all cached files (manifest + index) for the given vROM.
* Does not affect the currently mounted vROM β call {@link unmount} first
* if evicting the active one.
*
* @param vromId - vROM identifier to evict
*/
async evict(vromId: string): Promise<void> {
await this.#cache.evict(vromId);
this.#log('info', `Evicted: ${vromId}`);
}
/**
* Get the browser's storage usage estimate.
*
* @returns Used and quota bytes for the current origin
*/
async storageEstimate(): Promise<StorageEstimate> {
return this.#cache.storageEstimate();
}
/**
* Set a global progress callback for embedding model downloads.
*
* This is separate from the per-mount `onProgress` callback (which tracks
* vROM index downloads). This callback fires when the background worker
* downloads ONNX model weight files.
*
* @param fn - Progress callback, or `null` to remove
*
* @example
* ```ts
* memory.onProgress(({ file, loaded, total }) => {
* console.log(`${file}: ${(loaded / total * 100).toFixed(0)}%`);
* });
* ```
*/
onProgress(fn: ((p: { file: string; loaded: number; total: number }) => void) | null): void {
this._onProgress = fn ?? undefined;
}
/**
* Destroy the SDK instance. Frees the WASM HNSW graph and terminates
* the background embedding worker.
*
* The OPFS cache is **not** cleared β cached vROMs persist for future sessions.
* After calling `destroy()`, the instance cannot be reused.
*
* @see {@link evict} to clear specific vROMs from cache
*/
destroy(): void {
if (this.#db) { try { this.#db.free(); } catch {} }
if (this.#worker) this.#worker.terminate();
this.#db = null;
this.#worker = null;
this.#initialized = false;
this.#modelReady = false;
this.#log('info', 'Destroyed');
}
}
|