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* transformers-js.ts ā€” On-device ML via šŸ¤— Transformers.js v4.2.0\n *\n * Runs ANY HF model from the Hub locally in the browser via ONNX Runtime Web.\n * Backends: WASM (universal) or WebGPU (faster, limited model support).\n * Models download once and cache in the browser (IndexedDB via Cache API).\n *\n * Package: @huggingface/transformers (v4.2.0)\n *\n * Coverage verified against node_modules/@huggingface/transformers/src/pipelines/index.js\n * ā€” all 25 task types + 5 aliases supported, defaults match upstream exactly.\n */\nimport { tool } from '@strands-agents/sdk'\nimport { z } from 'zod'\n\n/* ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€\n * Task registry ā€” sourced DIRECTLY from transformers.js v4.2.0 source.\n * Keeping this client-side lets the agent introspect available tasks +\n * default models without reaching into node_modules.\n * ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ */\n\ntype TaskInfo = {\n type: 'text' | 'audio' | 'image' | 'multimodal'\n defaultModel: string\n defaultDtype?: string\n description: string\n}\n\nexport const TASK_REGISTRY: Record = {\n // ā”€ā”€ Text ā”€ā”€\n 'text-classification': {\n type: 'text',\n defaultModel: 'Xenova/distilbert-base-uncased-finetuned-sst-2-english',\n description: 'Sentiment/topic classification. Returns label + score. Alias: sentiment-analysis.',\n },\n 'token-classification': {\n type: 'text',\n defaultModel: 'Xenova/bert-base-multilingual-cased-ner-hrl',\n description: 'NER/POS tagging, per-token labels. Alias: ner.',\n },\n 'question-answering': {\n type: 'text',\n defaultModel: 'Xenova/distilbert-base-cased-distilled-squad',\n description: 'Extractive QA. Input: { question, context }.',\n },\n 'fill-mask': {\n type: 'text',\n defaultModel: 'onnx-community/ettin-encoder-32m-ONNX',\n defaultDtype: 'fp32',\n description: 'Masked-LM fill. Input must contain [MASK] (or model-specific token).',\n },\n summarization: {\n type: 'text',\n defaultModel: 'Xenova/distilbart-cnn-6-6',\n description: 'Abstractive summary of long text.',\n },\n translation: {\n type: 'text',\n defaultModel: 'Xenova/t5-small',\n description: 'Translate between languages. Task can be suffixed \"_xx_to_yy\".',\n },\n 'text2text-generation': {\n type: 'text',\n defaultModel: 'Xenova/flan-t5-small',\n description: 'T5-style seq2seq (instruction-following, translation, QA).',\n },\n 'text-generation': {\n type: 'text',\n defaultModel: 'onnx-community/Qwen3-0.6B-ONNX',\n defaultDtype: 'q4',\n description: 'Causal LM generation. Accepts plain text OR chat Message[]. Streams via TextStreamer.',\n },\n 'zero-shot-classification': {\n type: 'text',\n defaultModel: 'Xenova/distilbert-base-uncased-mnli',\n description: 'Classify text against ARBITRARY candidate_labels at inference. No training.',\n },\n 'feature-extraction': {\n type: 'text',\n defaultModel: 'onnx-community/all-MiniLM-L6-v2-ONNX',\n defaultDtype: 'fp32',\n description: 'Text embeddings for semantic search/clustering/RAG. Alias: embeddings.',\n },\n\n // ā”€ā”€ Audio ā”€ā”€\n 'audio-classification': {\n type: 'audio',\n defaultModel: 'Xenova/wav2vec2-base-superb-ks',\n description: 'Classify audio clip (keyword spotting, genre, speaker ID).',\n },\n 'zero-shot-audio-classification': {\n type: 'multimodal',\n defaultModel: 'Xenova/clap-htsat-unfused',\n description: 'CLAP ā€” classify audio against arbitrary text labels.',\n },\n 'automatic-speech-recognition': {\n type: 'multimodal',\n defaultModel: 'Xenova/whisper-tiny.en',\n description: 'Speech-to-text. Whisper family. Alias: asr.',\n },\n 'text-to-audio': {\n type: 'text',\n defaultModel: 'onnx-community/Supertonic-TTS-ONNX',\n defaultDtype: 'fp32',\n description: 'Text-to-speech. Alias: text-to-speech. Returns { audio: Float32Array, sampling_rate }.',\n },\n\n // ā”€ā”€ Image ā”€ā”€\n 'image-to-text': {\n type: 'multimodal',\n defaultModel: 'Xenova/vit-gpt2-image-captioning',\n description: 'Image captioning / VLM. Also works with Florence-2, Moondream for rich VLM tasks.',\n },\n 'image-classification': {\n type: 'multimodal',\n defaultModel: 'Xenova/vit-base-patch16-224',\n description: 'Classify image into fixed label set.',\n },\n 'image-segmentation': {\n type: 'multimodal',\n defaultModel: 'Xenova/detr-resnet-50-panoptic',\n description: 'Panoptic/semantic/instance segmentation. Returns masks per label.',\n },\n 'background-removal': {\n type: 'image',\n defaultModel: 'Xenova/modnet',\n description: 'Alpha-matte foreground extraction (like remove.bg).',\n },\n 'zero-shot-image-classification': {\n type: 'multimodal',\n defaultModel: 'Xenova/clip-vit-base-patch32',\n description: 'CLIP ā€” classify image against arbitrary text labels.',\n },\n 'object-detection': {\n type: 'multimodal',\n defaultModel: 'Xenova/detr-resnet-50',\n description: 'Detect objects with bounding boxes + labels.',\n },\n 'zero-shot-object-detection': {\n type: 'multimodal',\n defaultModel: 'Xenova/owlvit-base-patch32',\n description: 'Detect arbitrary text-queried objects (OWL-ViT).',\n },\n 'document-question-answering': {\n type: 'multimodal',\n defaultModel: 'Xenova/donut-base-finetuned-docvqa',\n description: 'Answer questions about a document image (Donut).',\n },\n 'image-to-image': {\n type: 'image',\n defaultModel: 'Xenova/swin2SR-classical-sr-x2-64',\n description: 'Image super-resolution / enhancement.',\n },\n 'depth-estimation': {\n type: 'image',\n defaultModel: 'onnx-community/depth-anything-v2-small',\n description: 'Monocular depth estimation. Returns per-pixel depth map.',\n },\n 'image-feature-extraction': {\n type: 'image',\n defaultModel: 'onnx-community/dinov3-vits16-pretrain-lvd1689m-ONNX',\n defaultDtype: 'fp32',\n description: 'Image embeddings for visual search / retrieval.',\n },\n}\n\nexport const TASK_ALIASES: Record = {\n 'sentiment-analysis': 'text-classification',\n ner: 'token-classification',\n asr: 'automatic-speech-recognition',\n 'text-to-speech': 'text-to-audio',\n embeddings: 'feature-extraction',\n}\n\nfunction resolveTask(task: string): string {\n return TASK_ALIASES[task] ?? task.split('_', 1)[0]\n}\n\n/* ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€\n * Lazy loader + pipeline cache with proper disposal\n * ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ */\n\nlet _transformers: any = null\nasync function getTransformers() {\n if (!_transformers) _transformers = await import('@huggingface/transformers')\n return _transformers\n}\n\ntype DtypeSpec = string | Record\n\ntype CachedPipeline = {\n pipe: any\n task: string\n model: string\n device: string\n dtype?: DtypeSpec\n loadedAt: number\n}\n\nconst PIPELINE_CACHE = new Map()\n\nfunction cacheKey(task: string, model: string, device: string, dtype?: DtypeSpec): string {\n const d = typeof dtype === 'object' ? JSON.stringify(dtype) : (dtype ?? 'default')\n return `${task}::${model}::${device}::${d}`\n}\n\n/**\n * Firefox + ONNX Runtime Web has a known incompatibility with q8/q4 quantized\n * weights that use the MatMulNBits-with-merged-scale pattern. The error is:\n *\n * \"Missing required scale: model..weight_merged_0_scale for node:\n * model..weight_transposed_DequantizeLinear\"\n *\n * Chrome/Safari's bundled ORT tolerates this; Firefox's doesn't. It affects:\n * - Whisper (decoder.embed_tokens) - automatic-speech-recognition\n * - BART/T5/NLLB (shared) - summarization, translation\n * - vit-gpt2 (shared) - image-to-text\n * - CLIP (shared) - zero-shot-image-classification\n * - BERT-NER (shared) - token-classification\n * - Depth-Anything etc. - depth-estimation\n *\n * Whenever possible, fall back to fp32 for all sessions. This doubles the\n * memory footprint but guarantees correctness across browsers.\n *\n * Known session keys across all model types (from upstream session_config.js):\n * model, encoder_model, decoder_model_merged, embed_tokens, vision_encoder,\n * audio_encoder, encodec_decode, prompt_encoder_mask_decoder,\n * prepare_inputs_embeds\n */\nconst ALL_SESSION_KEYS = [\n 'model',\n 'encoder_model',\n 'decoder_model_merged',\n 'decoder_model',\n 'embed_tokens',\n 'vision_encoder',\n 'audio_encoder',\n 'encodec_decode',\n 'prompt_encoder_mask_decoder',\n 'prepare_inputs_embeds',\n 'text_encoder',\n]\n\n/** Produce a dtype object forcing fp32 on every session key ā€” safest fallback. */\nfunction allSessionsFp32(): Record {\n const out: Record = {}\n for (const k of ALL_SESSION_KEYS) out[k] = 'fp32'\n return out\n}\n\n/**\n * Detect if Firefox+ORT hits the missing-scale bug for this task/model.\n * Returns true for encoder-decoder / seq2seq / vision-seq / image-text models\n * where a shared embedding table triggers the bug. Decoder-only LLMs (Qwen, Llama)\n * don't use the shared-weight-merge pattern so they're safe.\n */\nfunction needsFp32Fallback(resolvedTask: string, model: string): boolean {\n const m = model.toLowerCase()\n // Decoder-only LLMs: NO shared weight merge ā€” safe to quantize\n const isDecoderOnlyLLM =\n resolvedTask === 'text-generation' ||\n /qwen|llama|phi|gemma|smollm|tinyllama|stablelm/i.test(m)\n if (isDecoderOnlyLLM) return false\n // Tasks that use seq2seq / encoder-decoder / shared-weight architectures:\n const seq2seqTasks = [\n 'automatic-speech-recognition', // Whisper\n 'summarization', // BART, T5, distilbart\n 'translation', // T5, NLLB, Marian\n 'text2text-generation', // FLAN-T5, T5\n 'image-to-text', // vit-gpt2, Florence-2, BLIP\n 'document-question-answering', // Donut\n 'zero-shot-image-classification', // CLIP\n 'token-classification', // BERT-NER (shared embeddings)\n 'image-classification', // ViT (shared)\n 'image-segmentation', // DETR, SegFormer\n 'object-detection', // DETR, OWL-ViT\n 'zero-shot-object-detection',\n 'depth-estimation',\n 'image-feature-extraction',\n ]\n return seq2seqTasks.includes(resolvedTask)\n}\n\n/**\n * Pick a safe dtype for this (task, model, device) combination.\n * Respects user override. Otherwise avoids the Firefox quant bug.\n */\nfunction pickSafeDtype(resolvedTask: string, model: string, device: string, userDtype?: DtypeSpec): DtypeSpec | undefined {\n if (userDtype !== undefined) return userDtype\n const info = TASK_REGISTRY[resolvedTask]\n if (needsFp32Fallback(resolvedTask, model)) {\n return allSessionsFp32()\n }\n return info?.defaultDtype\n}\n\nasync function getPipeline(opts: {\n task: string\n model?: string\n device?: string\n dtype?: DtypeSpec\n progressCallback?: (p: any) => void\n}): Promise {\n const resolvedTask = resolveTask(opts.task)\n const info = TASK_REGISTRY[resolvedTask]\n const model = opts.model ?? info?.defaultModel\n if (!model) throw new Error(`Unknown task: ${opts.task}. See transformers_list_tasks.`)\n const device = opts.device ?? 'wasm'\n const dtype = pickSafeDtype(resolvedTask, model, device, opts.dtype)\n\n const key = cacheKey(resolvedTask, model, device, dtype)\n const hit = PIPELINE_CACHE.get(key)\n if (hit) return hit\n\n const { pipeline } = await getTransformers()\n const pipelineOpts: any = { device }\n if (dtype) pipelineOpts.dtype = dtype\n if (opts.progressCallback) pipelineOpts.progress_callback = opts.progressCallback\n\n let pipe: any\n let effectiveDtype: DtypeSpec | undefined = dtype\n try {\n pipe = await pipeline(resolvedTask, model, pipelineOpts)\n } catch (err: unknown) {\n const msg = (err as Error).message || String(err)\n // Auto-retry once with all-sessions fp32 if it's the Firefox ORT quant bug.\n // Don't retry if the user explicitly passed a dtype (respect their choice)\n // or if we already tried fp32 everywhere (would be a different error).\n const isQuantBug =\n msg.includes('weight_merged_0_scale') ||\n msg.includes('Missing required scale') ||\n msg.includes('TransposeDQWeightsForMatMulNBits')\n const triedAllFp32 = typeof dtype === 'object' && Object.values(dtype).every(v => v === 'fp32')\n if (isQuantBug && opts.dtype === undefined && !triedAllFp32) {\n try {\n const retryDtype = allSessionsFp32()\n const retryOpts = { ...pipelineOpts, dtype: retryDtype }\n pipe = await pipeline(resolvedTask, model, retryOpts)\n effectiveDtype = retryDtype\n } catch (retryErr: unknown) {\n const rmsg = (retryErr as Error).message || String(retryErr)\n throw new Error(\n `${rmsg}\\n\\nā†’ First attempt and fp32-fallback retry both failed. ` +\n `Original error: ${msg}\\n` +\n `Try a different model id ā€” this ONNX model may not have fp32 weights published.`,\n )\n }\n } else if (isQuantBug) {\n throw new Error(\n `${msg}\\n\\nā†’ HINT: ONNX quantization mismatch (Firefox+ORT). ` +\n `Either remove the \\`dtype\\` override, or use a different model id with fp32 weights available.`,\n )\n } else if (msg.includes('WebGPU') || msg.includes('webgpu')) {\n throw new Error(`${msg}\\n\\nā†’ HINT: WebGPU failed. Retry with device: \"wasm\".`)\n } else {\n throw err\n }\n }\n const entry: CachedPipeline = {\n pipe, task: resolvedTask, model, device, dtype: effectiveDtype, loadedAt: Date.now(),\n }\n PIPELINE_CACHE.set(key, entry)\n return entry\n}\n\n/* ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€\n * Tools\n * ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ */\n\n/** List every task with default model + description ā€” lets agent discover capabilities. */\nexport const transformersListTasksTool = tool({\n name: 'transformers_list_tasks',\n description:\n 'List ALL supported Transformers.js tasks (25 pipeline types + 5 aliases) with their default models, ' +\n 'type (text/audio/image/multimodal), and what each does. Use this to pick the right task before transformers_run.',\n inputSchema: z.object({\n filter: z.enum(['text', 'audio', 'image', 'multimodal', 'all']).optional(),\n }),\n callback: (input) => {\n const filter = input.filter || 'all'\n const tasks: Record = {}\n for (const [task, info] of Object.entries(TASK_REGISTRY)) {\n if (filter !== 'all' && info.type !== filter) continue\n tasks[task] = info\n }\n return JSON.stringify({\n status: 'success',\n count: Object.keys(tasks).length,\n tasks,\n aliases: TASK_ALIASES,\n dtypes: ['fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'q4f16', 'bnb4'],\n devices: ['wasm', 'webgpu'],\n })\n },\n})\n\n/**\n * Universal pipeline runner ā€” the one tool that covers all 25 tasks.\n */\nexport const transformersRunTool = tool({\n name: 'transformers_run',\n description:\n 'Run ANY šŸ¤— Transformers.js pipeline on-device. Covers all 25 task types: ' +\n 'text-classification, token-classification, question-answering, fill-mask, summarization, translation, ' +\n 'text2text-generation, text-generation, zero-shot-classification, audio-classification, ' +\n 'zero-shot-audio-classification, automatic-speech-recognition, text-to-audio, image-to-text, ' +\n 'image-classification, image-segmentation, background-removal, zero-shot-image-classification, ' +\n 'object-detection, zero-shot-object-detection, document-question-answering, image-to-image, ' +\n 'depth-estimation, feature-extraction, image-feature-extraction. ' +\n 'Call transformers_list_tasks first to see defaults. Models cache after first download.',\n inputSchema: z.object({\n task: z.string().describe('Pipeline task name (see transformers_list_tasks). Aliases accepted.'),\n input: z.any().describe(\n 'Input matching the task type: string for text, URL/dataURL/blobURL for image/audio, ' +\n '{question,context} for QA, {image,question} for document-QA, Message[] for text-generation chat.',\n ),\n model: z.string().optional().describe('HF model id. Omit to use task default.'),\n options: z.record(z.string(), z.any()).optional().describe('Pipeline call options (max_new_tokens, top_k, candidate_labels, etc.).'),\n device: z.enum(['wasm', 'webgpu']).optional(),\n dtype: z.union([z.enum(['fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'q4f16', 'bnb4']), z.record(z.string(), z.string())]).optional().describe('Quantization. q4 for LLMs. For encoder-decoder models (Whisper, T5), pass an object: {encoder_model: \"q8\", decoder_model_merged: \"fp32\"}.'),\n }),\n callback: async (input) => {\n try {\n const entry = await getPipeline({\n task: input.task, model: input.model, device: input.device, dtype: input.dtype,\n })\n\n // Handle pipelines that take candidate_labels as a 2nd positional argument:\n // zero-shot-classification.(texts, candidate_labels, options)\n // zero-shot-object-detection.(image, candidate_labels, options)\n // zero-shot-image-classification.(image, candidate_labels, options)\n // zero-shot-audio-classification.(audio, candidate_labels, options)\n // NOTE: passing {candidate_labels: [...]} in options is a common mistake;\n // we silently rewrite it to positional for these tasks.\n const POSITIONAL_LABEL_TASKS = new Set([\n 'zero-shot-classification',\n 'zero-shot-object-detection',\n 'zero-shot-image-classification',\n 'zero-shot-audio-classification',\n ])\n let result: any\n if (POSITIONAL_LABEL_TASKS.has(entry.task)) {\n const opts = { ...(input.options || {}) } as any\n const labels = opts.candidate_labels\n delete opts.candidate_labels\n if (!labels) {\n return JSON.stringify({\n status: 'error',\n task: entry.task,\n error: `${entry.task} requires options.candidate_labels: [ ... ] (array of class strings).`,\n })\n }\n result = await entry.pipe(input.input, labels, opts)\n } else {\n result = await entry.pipe(input.input, input.options || {})\n }\n\n // Audio output (TTS) is not JSON-serializable ā€” summarize instead.\n if (result && result.audio instanceof Float32Array) {\n return JSON.stringify({\n status: 'success',\n task: entry.task,\n model: entry.model,\n audio_samples: result.audio.length,\n sampling_rate: result.sampling_rate,\n duration_sec: result.audio.length / result.sampling_rate,\n hint: 'Use transformers_tts to synthesize AND play audio.',\n })\n }\n // Feature extraction returns a Tensor ā€” convert to array for JSON\n if (result && typeof result === 'object' && 'dims' in result && 'data' in result) {\n return JSON.stringify({\n status: 'success',\n task: entry.task,\n model: entry.model,\n dims: (result as any).dims,\n data: result.tolist ? result.tolist() : Array.from((result as any).data),\n })\n }\n return JSON.stringify({ status: 'success', task: entry.task, model: entry.model, result })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', task: input.task, error: (err as Error).message })\n }\n },\n})\n\n/**\n * Dedicated chat tool ā€” takes Message[], uses chat template, streams tokens.\n */\nexport const transformersChatTool = tool({\n name: 'transformers_chat',\n description:\n 'Chat with an on-device LLM via Transformers.js. Default: onnx-community/Qwen3-0.6B-ONNX (q4 quantized). ' +\n 'Accepts a Message[] ([{role:\"system\"|\"user\"|\"assistant\", content:\"...\"}]). ' +\n 'Applies the model\\'s chat template automatically. Fully local, no API call.',\n inputSchema: z.object({\n messages: z.array(\n z.object({\n role: z.enum(['system', 'user', 'assistant']),\n content: z.string(),\n }),\n ),\n model: z.string().optional().describe('Default: onnx-community/Qwen3-0.6B-ONNX'),\n max_new_tokens: z.number().optional().describe('Default 256'),\n temperature: z.number().optional(),\n top_p: z.number().optional(),\n top_k: z.number().optional(),\n do_sample: z.boolean().optional(),\n dtype: z.union([z.enum(['fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'q4f16', 'bnb4']), z.record(z.string(), z.string())]).optional().describe('Default: q4. For non-LLMs pass fp16 or fp32.'),\n device: z.enum(['wasm', 'webgpu']).optional(),\n }),\n callback: async (input) => {\n try {\n const entry = await getPipeline({\n task: 'text-generation',\n model: input.model,\n device: input.device,\n dtype: input.dtype ?? 'q4',\n })\n const opts: any = { max_new_tokens: input.max_new_tokens ?? 256 }\n if (input.temperature !== undefined) opts.temperature = input.temperature\n if (input.top_p !== undefined) opts.top_p = input.top_p\n if (input.top_k !== undefined) opts.top_k = input.top_k\n if (input.do_sample !== undefined) opts.do_sample = input.do_sample\n\n const out = await entry.pipe(input.messages, opts)\n // out is TextGenerationChatOutput[]: [{ generated_text: Message[] }]\n const first = Array.isArray(out) ? out[0] : out\n const generated = first?.generated_text\n let reply: string | null = null\n if (Array.isArray(generated)) {\n const last = generated[generated.length - 1]\n reply = typeof last === 'string' ? last : last?.content\n } else if (typeof generated === 'string') {\n reply = generated\n }\n return JSON.stringify({\n status: 'success',\n model: entry.model,\n reply,\n full_output: first,\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** Text-to-speech that ALSO plays via Web Audio. */\nexport const transformersTtsTool = tool({\n name: 'transformers_tts',\n description:\n 'On-device text-to-speech. Default: onnx-community/Supertonic-TTS-ONNX. ' +\n 'Synthesizes AND plays audio via Web Audio API. Also works with SpeechT5 (set speaker_embeddings URL).',\n inputSchema: z.object({\n text: z.string(),\n model: z.string().optional().describe('Default: Supertonic. For SpeechT5: Xenova/speecht5_tts.'),\n speaker_embeddings: z.string().optional().describe('Required for SpeechT5 (URL to .bin).'),\n autoplay: z.boolean().optional().describe('Default true. false returns audio metadata only.'),\n device: z.enum(['wasm', 'webgpu']).optional(),\n dtype: z.union([z.enum(['fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'q4f16', 'bnb4']), z.record(z.string(), z.string())]).optional(),\n }),\n callback: async (input) => {\n try {\n const entry = await getPipeline({\n task: 'text-to-audio',\n model: input.model,\n device: input.device,\n dtype: input.dtype,\n })\n const opts: any = {}\n if (input.speaker_embeddings) {\n opts.speaker_embeddings = input.speaker_embeddings\n } else if ((input.model || '').toLowerCase().includes('speecht5')) {\n opts.speaker_embeddings =\n 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin'\n }\n const out = await entry.pipe(input.text, opts)\n\n if (input.autoplay !== false) {\n const AC = (window as any).AudioContext || (window as any).webkitAudioContext\n const ctx = new AC()\n const buf = ctx.createBuffer(1, out.audio.length, out.sampling_rate)\n buf.getChannelData(0).set(out.audio)\n const src = ctx.createBufferSource()\n src.buffer = buf\n src.connect(ctx.destination)\n src.start()\n }\n return JSON.stringify({\n status: input.autoplay !== false ? 'playing' : 'synthesized',\n model: entry.model,\n samples: out.audio.length,\n sampling_rate: out.sampling_rate,\n duration_sec: out.audio.length / out.sampling_rate,\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** Whisper STT ā€” audio URL / blob URL / data URL. */\nexport const transformersSttTool = tool({\n name: 'transformers_stt',\n description:\n 'On-device speech-to-text via Whisper (default: Xenova/whisper-tiny.en). ' +\n 'Pass an audio URL (http/blob/data). Supports chunked long-form transcription with return_timestamps.',\n inputSchema: z.object({\n audio: z.string().describe('Audio URL (http/blob/data).'),\n model: z.string().optional().describe('Default: Xenova/whisper-tiny.en. Others: whisper-base, whisper-small.en, whisper-large-v3-turbo.'),\n language: z.string().optional().describe('ISO-639-1 code, e.g. \"en\", \"tr\", \"fr\". Multilingual Whisper only.'),\n task: z.enum(['transcribe', 'translate']).optional(),\n return_timestamps: z.union([z.boolean(), z.literal('word')]).optional(),\n chunk_length_s: z.number().optional().describe('For long-form audio (e.g. 30).'),\n stride_length_s: z.number().optional(),\n device: z.enum(['wasm', 'webgpu']).optional(),\n dtype: z.union([z.enum(['fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'q4f16', 'bnb4']), z.record(z.string(), z.string())]).optional().describe('Auto-selects a Firefox-compatible dtype if omitted. Override with e.g. {encoder_model: \"fp32\", decoder_model_merged: \"fp32\"} if you hit ONNX errors.'),\n }),\n callback: async (input) => {\n try {\n const entry = await getPipeline({\n task: 'automatic-speech-recognition',\n model: input.model,\n device: input.device,\n dtype: input.dtype,\n })\n const opts: any = {}\n if (input.language) opts.language = input.language\n if (input.task) opts.task = input.task\n if (input.return_timestamps !== undefined) opts.return_timestamps = input.return_timestamps\n if (input.chunk_length_s !== undefined) opts.chunk_length_s = input.chunk_length_s\n if (input.stride_length_s !== undefined) opts.stride_length_s = input.stride_length_s\n const result = await entry.pipe(input.audio, opts)\n return JSON.stringify({ status: 'success', model: entry.model, ...result })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** Text / image embeddings. */\nexport const transformersEmbedTool = tool({\n name: 'transformers_embed',\n description:\n 'Generate embeddings on-device. For text: Xenova/all-MiniLM-L6-v2 (default). ' +\n 'For images: pass task=\"image-feature-extraction\" with default DINOv3. Returns vector(s) + dims.',\n inputSchema: z.object({\n input: z.union([z.string(), z.array(z.string())]).describe('Text string(s) or image URL(s)'),\n task: z.enum(['feature-extraction', 'image-feature-extraction']).optional().describe('Default: feature-extraction (text)'),\n model: z.string().optional(),\n pooling: z.enum(['none', 'mean', 'cls']).optional(),\n normalize: z.boolean().optional(),\n device: z.enum(['wasm', 'webgpu']).optional(),\n dtype: z.union([z.enum(['fp32', 'fp16', 'q8']), z.record(z.string(), z.string())]).optional(),\n }),\n callback: async (input) => {\n try {\n const entry = await getPipeline({\n task: input.task ?? 'feature-extraction',\n model: input.model,\n device: input.device,\n dtype: input.dtype,\n })\n const opts: any = {\n pooling: input.pooling ?? 'mean',\n normalize: input.normalize ?? true,\n }\n const out = await entry.pipe(input.input, opts)\n const arr = out.tolist ? out.tolist() : Array.from(out.data)\n return JSON.stringify({\n status: 'success',\n model: entry.model,\n dims: out.dims,\n count: Array.isArray(input.input) ? input.input.length : 1,\n embeddings: arr,\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** Configure global env (cache / remote / backends). */\nexport const transformersConfigureTool = tool({\n name: 'transformers_configure',\n description:\n 'Configure global Transformers.js env: remote host, cache, allowed model sources, backend settings. ' +\n 'Affects ALL subsequent pipeline loads.',\n inputSchema: z.object({\n allowRemoteModels: z.boolean().optional(),\n allowLocalModels: z.boolean().optional(),\n remoteHost: z.string().optional().describe('Default: https://huggingface.co/'),\n remotePathTemplate: z.string().optional(),\n useBrowserCache: z.boolean().optional(),\n }),\n callback: async (input) => {\n try {\n const { env } = await getTransformers()\n const before: any = {\n allowRemoteModels: env.allowRemoteModels,\n allowLocalModels: env.allowLocalModels,\n remoteHost: env.remoteHost,\n remotePathTemplate: env.remotePathTemplate,\n useBrowserCache: env.useBrowserCache,\n }\n if (input.allowRemoteModels !== undefined) env.allowRemoteModels = input.allowRemoteModels\n if (input.allowLocalModels !== undefined) env.allowLocalModels = input.allowLocalModels\n if (input.remoteHost !== undefined) env.remoteHost = input.remoteHost\n if (input.remotePathTemplate !== undefined) env.remotePathTemplate = input.remotePathTemplate\n if (input.useBrowserCache !== undefined) env.useBrowserCache = input.useBrowserCache\n return JSON.stringify({\n status: 'success',\n before,\n after: {\n allowRemoteModels: env.allowRemoteModels,\n allowLocalModels: env.allowLocalModels,\n remoteHost: env.remoteHost,\n remotePathTemplate: env.remotePathTemplate,\n useBrowserCache: env.useBrowserCache,\n },\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** List loaded pipelines with metadata. */\nexport const transformersStatusTool = tool({\n name: 'transformers_status',\n description: 'List all currently loaded Transformers.js pipelines with task, model, device, dtype, age.',\n inputSchema: z.object({}),\n callback: async () => {\n const now = Date.now()\n const loaded = Array.from(PIPELINE_CACHE.entries()).map(([key, e]) => ({\n key, task: e.task, model: e.model, device: e.device, dtype: e.dtype ?? 'default',\n loaded_ago_sec: Math.round((now - e.loadedAt) / 1000),\n }))\n let version: string | undefined\n if (_transformers) {\n try { version = _transformers.env?.version } catch {}\n }\n return JSON.stringify({\n status: 'success',\n transformers_loaded: _transformers !== null,\n transformers_version: version,\n pipeline_count: loaded.length,\n pipelines: loaded,\n })\n },\n})\n\n/** Proper unload ā€” calls pipe.dispose() to free WASM memory (not just Map.delete). */\nexport const transformersUnloadTool = tool({\n name: 'transformers_unload',\n description:\n 'Unload a cached pipeline AND dispose its underlying model to free WASM/WebGPU memory. ' +\n 'Pass specific (task,model,device,dtype) or just task to unload all matching.',\n inputSchema: z.object({\n task: z.string().optional().describe('Omit to unload ALL pipelines'),\n model: z.string().optional(),\n device: z.enum(['wasm', 'webgpu']).optional(),\n dtype: z.union([z.string(), z.record(z.string(), z.string())]).optional(),\n all: z.boolean().optional().describe('Unload every pipeline'),\n }),\n callback: async (input) => {\n try {\n const disposed: string[] = []\n const errors: string[] = []\n const matches = (e: CachedPipeline) =>\n (input.all === true) ||\n (!input.task || e.task === resolveTask(input.task)) &&\n (!input.model || e.model === input.model) &&\n (!input.device || e.device === input.device) &&\n (!input.dtype || e.dtype === input.dtype)\n\n for (const [key, entry] of Array.from(PIPELINE_CACHE.entries())) {\n if (!matches(entry)) continue\n try {\n if (entry.pipe?.dispose) await entry.pipe.dispose()\n PIPELINE_CACHE.delete(key)\n disposed.push(key)\n } catch (e: unknown) {\n errors.push(`${key}: ${(e as Error).message}`)\n }\n }\n return JSON.stringify({\n status: 'success',\n disposed_count: disposed.length,\n disposed,\n errors: errors.length ? errors : undefined,\n remaining: PIPELINE_CACHE.size,\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\n/** Quick device capability probe ā€” useful before choosing webgpu. */\nexport const transformersDeviceInfoTool = tool({\n name: 'transformers_device_info',\n description:\n 'Probe runtime capabilities: WebGPU support, WASM threads, SharedArrayBuffer, cross-origin-isolation. ' +\n 'Use this before choosing device: \"webgpu\".',\n inputSchema: z.object({}),\n callback: async () => {\n try {\n const nav = navigator as any\n let webgpu = false\n let adapterInfo: any = null\n if (nav.gpu) {\n try {\n const adapter = await nav.gpu.requestAdapter()\n if (adapter) {\n webgpu = true\n adapterInfo = await adapter.requestAdapterInfo?.().catch(() => null)\n }\n } catch {}\n }\n return JSON.stringify({\n status: 'success',\n webgpu_supported: webgpu,\n adapter: adapterInfo,\n shared_array_buffer: typeof SharedArrayBuffer !== 'undefined',\n cross_origin_isolated: (window as any).crossOriginIsolated ?? false,\n hardware_concurrency: navigator.hardwareConcurrency,\n user_agent: navigator.userAgent,\n recommendation: webgpu\n ? 'WebGPU available ā€” use device: \"webgpu\" for ~2-10x speedup on supported models (most LLMs, embeddings, vision models).'\n : 'WebGPU not available ā€” fallback to device: \"wasm\". Works everywhere but slower.',\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\nexport const TRANSFORMERS_TOOLS = [\n transformersListTasksTool,\n transformersDeviceInfoTool,\n transformersRunTool,\n transformersChatTool,\n transformersTtsTool,\n transformersSttTool,\n transformersEmbedTool,\n transformersConfigureTool,\n transformersStatusTool,\n transformersUnloadTool,\n]\n"]}