{"version":3,"sources":["../src/language-model.ts","../src/stream-ai.ts","../src/embedding-model.ts","../src/chromeai.ts","../src/polyfill/session.ts"],"sourcesContent":["import {\n LanguageModelV1,\n LanguageModelV1CallOptions,\n LanguageModelV1CallWarning,\n LanguageModelV1FinishReason,\n LanguageModelV1FunctionToolCall,\n LanguageModelV1ImagePart,\n LanguageModelV1LogProbs,\n LanguageModelV1Prompt,\n LanguageModelV1StreamPart,\n LanguageModelV1TextPart,\n LanguageModelV1ToolCallPart,\n LanguageModelV1ToolResultPart,\n LoadSettingError,\n UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport type {\n ChromeAIAssistant,\n ChromeAIAssistantCreateOptions,\n} from './global';\nimport { ChromeAICapabilityAvailability } from './enum';\nimport createDebug from 'debug';\nimport { objectStartSequence, objectStopSequence, StreamAI } from './stream-ai';\n\nconst debug = createDebug('chromeai');\n\nexport type ChromeAIChatModelId = 'text';\n\nexport interface ChromeAIChatSettings extends ChromeAIAssistantCreateOptions {}\n\nfunction getStringContent(\n content:\n | string\n | (LanguageModelV1TextPart | LanguageModelV1ImagePart)[]\n | (LanguageModelV1TextPart | LanguageModelV1ToolCallPart)[]\n | LanguageModelV1ToolResultPart[]\n): string {\n if (typeof content === 'string') {\n return content.trim();\n } else if (Array.isArray(content) && content.length > 0) {\n const [first] = content;\n if (first.type !== 'text') {\n throw new UnsupportedFunctionalityError({ functionality: 'toolCall' });\n }\n return first.text.trim();\n } else {\n return '';\n }\n}\n\nexport class ChromeAIChatLanguageModel implements LanguageModelV1 {\n readonly specificationVersion = 'v1';\n readonly defaultObjectGenerationMode = 'json';\n readonly modelId: ChromeAIChatModelId = 'text';\n readonly provider = 'gemini-nano';\n readonly supportsImageUrls = false;\n readonly supportsStructuredOutputs = false;\n\n options: ChromeAIChatSettings;\n\n constructor(\n modelId: ChromeAIChatModelId,\n options: ChromeAIChatSettings = {}\n ) {\n this.modelId = modelId;\n this.options = options;\n debug('init:', this.modelId);\n }\n\n private session!: ChromeAIAssistant;\n private getSession = async (\n options?: ChromeAIAssistantCreateOptions\n ): Promise => {\n if (!globalThis.ai?.assistant) {\n throw new LoadSettingError({ message: 'Browser not support' });\n }\n if (this.session) return this.session;\n\n const cap = await ai.assistant.capabilities();\n\n if (cap.available !== ChromeAICapabilityAvailability.READILY) {\n throw new LoadSettingError({ message: 'Built-in model not ready' });\n }\n\n this.options = {\n temperature: cap.defaultTemperature,\n topK: cap.defaultTopK,\n ...this.options,\n ...options,\n };\n\n this.session = await ai.assistant.create(this.options);\n\n debug('session created:', this.session, this.options);\n return this.session;\n };\n\n private formatMessages = (options: LanguageModelV1CallOptions): string => {\n let prompt: LanguageModelV1Prompt = options.prompt;\n debug('before format prompt:', prompt);\n\n let result = '';\n\n if (\n // When the user supplied a prompt input, we don't transform it\n options.inputFormat === 'prompt' &&\n prompt.length === 1 &&\n prompt[0].role === 'user' &&\n prompt[0].content.length === 1 &&\n prompt[0].content[0].type === 'text'\n ) {\n result += prompt[0].content[0].text;\n } else {\n // Use magic prompt for object-json mode\n if (options.mode.type === 'object-json') {\n prompt.unshift({\n role: 'system',\n content: `Throughout our conversation, always start your responses with \"{\" and end with \"}\", ensuring the output is a concise JSON object and strictly avoid including any comments, notes, explanations, or examples in your output.\\nFor instance, if the JSON schema is {\"type\":\"object\",\"properties\":{\"someKey\":{\"type\":\"string\"}},\"required\":[\"someKey\"],\"additionalProperties\":false,\"$schema\":\"http://json-schema.org/draft-07/schema#\"}, your response should immediately begin with \"{\" and strictly end with \"}\", following the format: {\"someKey\": \"someValue\"}.\\nAdhere to this format for all queries moving forward.`,\n });\n }\n\n for (let index = 0; index < prompt.length; index += 1) {\n const { role, content } = prompt[index];\n const contentString = getStringContent(content);\n\n switch (role) {\n case 'system':\n result += `${contentString}\\n`;\n break;\n case 'assistant':\n case 'tool':\n result += `model\\n${contentString}\\n`;\n break;\n case 'user':\n default:\n result += `user\\n${contentString}\\n`;\n break;\n }\n }\n result += `model\\n`;\n }\n\n debug('formated message:', result);\n return result;\n };\n\n public doGenerate = async (\n options: LanguageModelV1CallOptions\n ): Promise<{\n text?: string;\n toolCalls?: LanguageModelV1FunctionToolCall[];\n finishReason: LanguageModelV1FinishReason;\n usage: { promptTokens: number; completionTokens: number };\n rawCall: { rawPrompt: unknown; rawSettings: Record };\n rawResponse?: { headers?: Record };\n warnings?: LanguageModelV1CallWarning[];\n logprobs?: LanguageModelV1LogProbs;\n }> => {\n debug('generate options:', options);\n\n if (['regular', 'object-json'].indexOf(options.mode.type) < 0) {\n throw new UnsupportedFunctionalityError({\n functionality: `${options.mode.type} mode`,\n });\n }\n\n const session = await this.getSession();\n const message = this.formatMessages(options);\n let text = await session.prompt(message);\n\n if (options.mode.type === 'object-json') {\n text = text.replace(new RegExp('^' + objectStartSequence, 'ig'), '');\n text = text.replace(new RegExp(objectStopSequence + '$', 'ig'), '');\n }\n\n debug('generate result:', text);\n\n return {\n text,\n finishReason: 'stop',\n usage: { promptTokens: 0, completionTokens: 0 },\n rawCall: { rawPrompt: options.prompt, rawSettings: this.options },\n };\n };\n\n public doStream = async (\n options: LanguageModelV1CallOptions\n ): Promise<{\n stream: ReadableStream;\n rawCall: { rawPrompt: unknown; rawSettings: Record };\n rawResponse?: { headers?: Record };\n warnings?: LanguageModelV1CallWarning[];\n }> => {\n debug('stream options:', options);\n\n if (['regular', 'object-json'].indexOf(options.mode.type) < 0) {\n throw new UnsupportedFunctionalityError({\n functionality: `${options.mode.type} mode`,\n });\n }\n\n const session = await this.getSession();\n const message = this.formatMessages(options);\n const promptStream = session.promptStreaming(message);\n const transformStream = new StreamAI(options);\n const stream = promptStream.pipeThrough(transformStream);\n\n return {\n stream,\n rawCall: { rawPrompt: options.prompt, rawSettings: this.options },\n };\n };\n}\n","import {\n LanguageModelV1CallOptions,\n LanguageModelV1StreamPart,\n} from '@ai-sdk/provider';\nimport createDebug from 'debug';\n\nconst debug = createDebug('chromeai');\n\nexport const objectStartSequence = ' ```json\\n';\nexport const objectStopSequence = '\\n```';\n\nexport class StreamAI extends TransformStream<\n string,\n LanguageModelV1StreamPart\n> {\n public constructor(options: LanguageModelV1CallOptions) {\n let buffer = '';\n let transforming = false;\n\n const reset = () => {\n buffer = '';\n transforming = false;\n };\n\n super({\n start: (controller) => {\n reset();\n if (!options.abortSignal) return;\n options.abortSignal.addEventListener('abort', () => {\n debug('streamText terminate by abortSignal');\n controller.terminate();\n });\n },\n transform: (chunk, controller) => {\n if (options.mode.type === 'object-json') {\n transforming =\n chunk.startsWith(objectStartSequence) &&\n !chunk.endsWith(objectStopSequence);\n chunk = chunk.replace(\n new RegExp('^' + objectStartSequence, 'ig'),\n ''\n );\n chunk = chunk.replace(new RegExp(objectStopSequence + '$', 'ig'), '');\n } else {\n transforming = true;\n }\n const textDelta = chunk.replace(buffer, ''); // See: https://github.com/jeasonstudio/chrome-ai/issues/11\n if (transforming) controller.enqueue({ type: 'text-delta', textDelta });\n buffer = chunk;\n },\n flush: (controller) => {\n controller.enqueue({\n type: 'finish',\n finishReason: 'stop',\n usage: { completionTokens: 0, promptTokens: 0 },\n });\n debug('stream result:', buffer);\n },\n });\n }\n}\n","import { EmbeddingModelV1, EmbeddingModelV1Embedding } from '@ai-sdk/provider';\nimport { TextEmbedder } from '@mediapipe/tasks-text';\n\nexport interface ChromeAIEmbeddingModelSettings {\n /**\n * An optional base path to specify the directory the Wasm files should be loaded from.\n * @default 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/text_wasm_internal.js'\n */\n wasmLoaderPath?: string;\n /**\n * It's about 6mb before gzip.\n * @default 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/text_wasm_internal.wasm'\n */\n wasmBinaryPath?: string;\n /**\n * The model path to the model asset file.\n * It's about 6.1mb before gzip.\n * @default 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/universal_sentence_encoder.tflite'\n */\n modelAssetPath?: string;\n /**\n * Whether to normalize the returned feature vector with L2 norm. Use this\n * option only if the model does not already contain a native L2_NORMALIZATION\n * TF Lite Op. In most cases, this is already the case and L2 norm is thus\n * achieved through TF Lite inference.\n * @default false\n */\n l2Normalize?: boolean;\n /**\n * Whether the returned embedding should be quantized to bytes via scalar\n * quantization. Embeddings are implicitly assumed to be unit-norm and\n * therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use\n * the l2_normalize option if this is not the case.\n * @default false\n */\n quantize?: boolean;\n /**\n * Overrides the default backend to use for the provided model.\n */\n delegate?: 'CPU' | 'GPU';\n}\n\n// See more:\n// - https://github.com/google-ai-edge/mediapipe\n// - https://ai.google.dev/edge/mediapipe/solutions/text/text_embedder/web_js\nexport class ChromeAIEmbeddingModel implements EmbeddingModelV1 {\n readonly specificationVersion = 'v1';\n readonly provider = 'google-mediapipe';\n readonly modelId: string = 'embedding';\n readonly supportsParallelCalls = true;\n readonly maxEmbeddingsPerCall = undefined;\n\n private settings: ChromeAIEmbeddingModelSettings = {\n wasmLoaderPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/text_wasm_internal.js',\n wasmBinaryPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/text_wasm_internal.wasm',\n modelAssetPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/universal_sentence_encoder.tflite',\n l2Normalize: false,\n quantize: false,\n };\n private modelAssetBuffer!: Promise;\n private textEmbedder!: Promise;\n\n public constructor(settings: ChromeAIEmbeddingModelSettings = {}) {\n this.settings = { ...this.settings, ...settings };\n this.modelAssetBuffer = fetch(this.settings.modelAssetPath!).then(\n (response) => response.body!.getReader()\n )!;\n this.textEmbedder = this.getTextEmbedder();\n }\n\n protected getTextEmbedder = async (): Promise => {\n return TextEmbedder.createFromOptions(\n {\n wasmBinaryPath: this.settings.wasmBinaryPath!,\n wasmLoaderPath: this.settings.wasmLoaderPath!,\n },\n {\n baseOptions: {\n modelAssetBuffer: await this.modelAssetBuffer,\n delegate: this.settings.delegate,\n },\n l2Normalize: this.settings.l2Normalize,\n quantize: this.settings.quantize,\n }\n );\n };\n\n public doEmbed = async (options: {\n values: string[];\n abortSignal?: AbortSignal;\n }): Promise<{\n embeddings: Array;\n rawResponse?: Record;\n }> => {\n // if (options.abortSignal) console.warn('abortSignal is not supported');\n const embedder = await this.textEmbedder;\n const embeddings = options.values.map((text) => {\n const embedderResult = embedder.embed(text);\n const [embedding] = embedderResult.embeddings;\n return embedding?.floatEmbedding ?? [];\n });\n return { embeddings };\n };\n}\n","import {\n ChromeAIEmbeddingModel,\n ChromeAIEmbeddingModelSettings,\n} from './embedding-model';\nimport {\n ChromeAIChatLanguageModel,\n ChromeAIChatModelId,\n ChromeAIChatSettings,\n} from './language-model';\nimport createDebug from 'debug';\n\nconst debug = createDebug('chromeai');\n\n/**\n * Create a new ChromeAI model/embedding instance.\n * @param modelId 'text' | 'embedding'\n * @param settings Options for the model\n */\nexport function chromeai(\n modelId?: ChromeAIChatModelId,\n settings?: ChromeAIChatSettings\n): ChromeAIChatLanguageModel;\nexport function chromeai(\n modelId: 'embedding',\n settings?: ChromeAIEmbeddingModelSettings\n): ChromeAIEmbeddingModel;\nexport function chromeai(modelId: unknown = 'text', settings: unknown = {}) {\n debug('create instance', modelId, settings);\n if (modelId === 'embedding') {\n return new ChromeAIEmbeddingModel(\n settings as ChromeAIEmbeddingModelSettings\n );\n }\n return new ChromeAIChatLanguageModel(\n modelId as ChromeAIChatModelId,\n settings as ChromeAIChatSettings\n );\n}\n\n/** @deprecated use `chromeai('embedding'[, options])` */\nchromeai.embedding = (settings: ChromeAIEmbeddingModelSettings = {}) =>\n new ChromeAIEmbeddingModel(settings);\n","import { LlmInference, ProgressListener } from '@mediapipe/tasks-genai';\nimport type {\n ChromeAIAssistant,\n ChromeAIAssistantFactory,\n ChromeAIAssistantCapabilities,\n ChromeAIAssistantCreateOptions,\n PolyfillChromeAIOptions,\n} from '../global';\nimport { ChromeAICapabilityAvailability } from '../enum';\nimport createDebug from 'debug';\n\nconst debug = createDebug('chromeai:polyfill');\n\nclass PolyfillChromeAIAssistant implements ChromeAIAssistant {\n public constructor(private llm: LlmInference) {\n debug('PolyfillChromeAIAssistant created', llm);\n }\n\n public prompt = async (prompt: string): Promise => {\n const response = await this.llm.generateResponse(prompt);\n debug('prompt', prompt, response);\n return response;\n };\n\n public promptStreaming = (prompt: string): ReadableStream => {\n debug('promptStreaming', prompt);\n const stream = new ReadableStream({\n start: (controller) => {\n const listener: ProgressListener = (\n partialResult: string,\n done: boolean\n ) => {\n controller.enqueue(partialResult);\n if (done) {\n controller.close();\n }\n };\n this.llm.generateResponse(prompt, listener);\n },\n cancel: (reason) => {\n console.warn('stream text canceled', reason);\n },\n });\n debug('promptStreaming', prompt);\n return stream;\n };\n\n public destroy = async () => this.llm.close();\n}\n\n/**\n * Model: https://huggingface.co/oongaboongahacker/Gemini-Nano\n */\nexport class PolyfillChromeAIAssistantFactory\n implements ChromeAIAssistantFactory\n{\n private aiOptions: PolyfillChromeAIOptions = {\n wasmBinaryPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/genai_wasm_internal.wasm',\n wasmLoaderPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/genai_wasm_internal.js',\n // About 1.78GB, should cache by browser\n modelAssetPath:\n 'https://pub-ddcfe353995744e89b8002f16bf98575.r2.dev/gemini-nano-it-chrome-128.bin',\n };\n\n public constructor(aiOptions: Partial = {}) {\n this.aiOptions = Object.assign(this.aiOptions, aiOptions);\n debug('PolyfillChromeAI created', this.aiOptions);\n this.modelAssetBuffer = fetch(this.aiOptions.modelAssetPath).then(\n (response) => response.body!.getReader()\n )!;\n }\n\n private modelAssetBuffer: Promise;\n\n public capabilities = async (): Promise => {\n const defaultOptions = {\n defaultTemperature: 0.8,\n defaultTopK: 3,\n maxTopK: 128,\n };\n\n // If browser do not support WebAssembly/WebGPU, return 'no';\n if (typeof WebAssembly.instantiate !== 'function')\n return {\n ...defaultOptions,\n available: ChromeAICapabilityAvailability.NO,\n };\n if (!(navigator).gpu)\n return {\n ...defaultOptions,\n available: ChromeAICapabilityAvailability.NO,\n };\n\n // Check if modelAssetBuffer is downloaded, if not, return 'after-download';\n const isModelAssetBufferReady = await Promise.race([\n this.modelAssetBuffer,\n Promise.resolve('sentinel'),\n ])\n .then((value) => value === 'sentinel')\n .catch(() => true);\n\n return {\n ...defaultOptions,\n available: isModelAssetBufferReady\n ? ChromeAICapabilityAvailability.READILY\n : ChromeAICapabilityAvailability.AFTER_DOWNLOAD,\n };\n };\n\n public create = async (\n options?: ChromeAIAssistantCreateOptions\n ): Promise => {\n const defaultParams = await this.capabilities();\n const argv = Object.assign(\n {\n temperature: defaultParams.defaultTemperature,\n topK: defaultParams.defaultTopK,\n },\n options\n );\n const llm = await LlmInference.createFromOptions(\n {\n wasmLoaderPath: this.aiOptions.wasmLoaderPath!,\n wasmBinaryPath: this.aiOptions.wasmBinaryPath!,\n },\n {\n baseOptions: {\n modelAssetBuffer: await this.modelAssetBuffer,\n },\n temperature: argv.temperature,\n topK: argv.topK,\n }\n );\n const session = new PolyfillChromeAIAssistant(llm);\n debug('createSession', options, session);\n return session;\n };\n}\n\nexport const polyfillChromeAI = (\n options?: Partial\n) => {\n const ai = {\n assistant: new PolyfillChromeAIAssistantFactory(options),\n };\n globalThis.ai = globalThis.ai || ai;\n globalThis.model = globalThis.model || 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