@built-in-ai/core/dist/index.mjs.map

Browser Built-in AI API provider for Vercel AI SDK v5+ (Chrome & Edge)

{"version":3,"sources":["../src/built-in-ai-language-model.ts","../src/convert-to-built-in-ai-messages.ts","../src/built-in-ai-embedding-model.ts","../src/built-in-ai-provider.ts"],"sourcesContent":["import {\n  LanguageModelV2,\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2Prompt,\n  LanguageModelV2StreamPart,\n  LoadSettingError,\n  JSONValue,\n} from \"@ai-sdk/provider\";\nimport { convertToBuiltInAIMessages } from \"./convert-to-built-in-ai-messages\";\n\nexport type BuiltInAIChatModelId = \"text\";\n\nexport interface BuiltInAIChatSettings extends LanguageModelCreateOptions {\n  /**\n   * Expected input types for the session, for multimodal inputs.\n   */\n  expectedInputs?: Array<{\n    type: \"text\" | \"image\" | \"audio\";\n    languages?: string[];\n  }>;\n}\n\n/**\n * Check if the browser supports the built-in AI API\n * @returns true if the browser supports the built-in AI API, false otherwise\n */\nexport function doesBrowserSupportBuiltInAI(): boolean {\n  return typeof LanguageModel !== \"undefined\";\n}\n\n/**\n * Check if the Prompt API is available\n * @deprecated Use `doesBrowserSupportBuiltInAI()` instead for clearer naming\n * @returns true if the browser supports the built-in AI API, false otherwise\n */\nexport function isBuiltInAIModelAvailable(): boolean {\n  return typeof LanguageModel !== \"undefined\";\n}\n\ntype BuiltInAIConfig = {\n  provider: string;\n  modelId: BuiltInAIChatModelId;\n  options: BuiltInAIChatSettings;\n};\n\n/**\n * Detect if the prompt contains multimodal content\n */\nfunction hasMultimodalContent(prompt: LanguageModelV2Prompt): boolean {\n  for (const message of prompt) {\n    if (message.role === \"user\") {\n      for (const part of message.content) {\n        if (part.type === \"file\") {\n          return true;\n        }\n      }\n    }\n  }\n  return false;\n}\n\n/**\n * Get expected inputs based on prompt content\n */\nfunction getExpectedInputs(\n  prompt: LanguageModelV2Prompt,\n): Array<{ type: \"text\" | \"image\" | \"audio\" }> {\n  const inputs = new Set<\"text\" | \"image\" | \"audio\">();\n  // Don't add text by default - it's assumed by the Prompt API\n\n  for (const message of prompt) {\n    if (message.role === \"user\") {\n      for (const part of message.content) {\n        if (part.type === \"file\") {\n          if (part.mediaType?.startsWith(\"image/\")) {\n            inputs.add(\"image\");\n          } else if (part.mediaType?.startsWith(\"audio/\")) {\n            inputs.add(\"audio\");\n          }\n        }\n      }\n    }\n  }\n\n  return Array.from(inputs).map((type) => ({ type }));\n}\n\nexport class BuiltInAIChatLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = \"v2\";\n  readonly modelId: BuiltInAIChatModelId;\n  readonly provider = \"browser-ai\";\n\n  private readonly config: BuiltInAIConfig;\n  private session!: LanguageModel;\n\n  constructor(\n    modelId: BuiltInAIChatModelId,\n    options: BuiltInAIChatSettings = {},\n  ) {\n    this.modelId = modelId;\n    this.config = {\n      provider: this.provider,\n      modelId,\n      options,\n    };\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    \"image/*\": [/^https?:\\/\\/.+$/],\n    \"audio/*\": [/^https?:\\/\\/.+$/],\n  };\n\n  private async getSession(\n    options?: LanguageModelCreateOptions,\n    expectedInputs?: Array<{ type: \"text\" | \"image\" | \"audio\" }>,\n    systemMessage?: string,\n    onDownloadProgress?: (progress: number) => void,\n  ): Promise<LanguageModel> {\n    if (typeof LanguageModel === \"undefined\") {\n      throw new LoadSettingError({\n        message:\n          \"Prompt API is not available. This library requires Chrome or Edge browser with built-in AI capabilities.\",\n      });\n    }\n\n    if (this.session) return this.session;\n\n    const availability = await LanguageModel.availability();\n\n    if (availability === \"unavailable\") {\n      throw new LoadSettingError({ message: \"Built-in model not available\" });\n    }\n\n    const mergedOptions = {\n      ...this.config.options,\n      ...options,\n    };\n\n    // Add system message to initialPrompts if provided\n    if (systemMessage) {\n      mergedOptions.initialPrompts = [\n        { role: \"system\", content: systemMessage },\n      ];\n    }\n\n    // Add expected inputs if provided\n    if (expectedInputs && expectedInputs.length > 0) {\n      mergedOptions.expectedInputs = expectedInputs;\n    }\n\n    // Add download progress monitoring if callback provided\n    if (onDownloadProgress) {\n      mergedOptions.monitor = (m: CreateMonitor) => {\n        m.addEventListener(\"downloadprogress\", (e: ProgressEvent) => {\n          onDownloadProgress(e.loaded); // e.loaded is between 0 and 1\n        });\n      };\n    }\n\n    this.session = await LanguageModel.create(mergedOptions);\n\n    return this.session;\n  }\n\n  private getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    tools,\n  }: Parameters<LanguageModelV2[\"doGenerate\"]>[0]) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    // Add warnings for unsupported settings\n    if (tools && tools.length > 0) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"tools\",\n        details: \"Tool calling is not yet supported by Prompt API\",\n      });\n    }\n\n    if (maxOutputTokens != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"maxOutputTokens\",\n        details: \"maxOutputTokens is not supported by Prompt API\",\n      });\n    }\n\n    if (stopSequences != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"stopSequences\",\n        details: \"stopSequences is not supported by Prompt API\",\n      });\n    }\n\n    if (topP != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"topP\",\n        details: \"topP is not supported by Prompt API\",\n      });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"presencePenalty\",\n        details: \"presencePenalty is not supported by Prompt API\",\n      });\n    }\n\n    if (frequencyPenalty != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"frequencyPenalty\",\n        details: \"frequencyPenalty is not supported by Prompt API\",\n      });\n    }\n\n    if (seed != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"seed\",\n        details: \"seed is not supported by Prompt API\",\n      });\n    }\n\n    // Check if this is a multimodal prompt\n    const hasMultiModalInput = hasMultimodalContent(prompt);\n\n    // Convert messages to the DOM API format\n    const { systemMessage, messages } = convertToBuiltInAIMessages(prompt);\n\n    // Handle response format for Prompt API\n    const promptOptions: LanguageModelPromptOptions &\n      LanguageModelCreateCoreOptions = {};\n    if (responseFormat?.type === \"json\") {\n      promptOptions.responseConstraint = responseFormat.schema as Record<\n        string,\n        JSONValue\n      >;\n    }\n\n    // Map supported settings\n    if (temperature !== undefined) {\n      promptOptions.temperature = temperature;\n    }\n\n    if (topK !== undefined) {\n      promptOptions.topK = topK;\n    }\n\n    return {\n      systemMessage,\n      messages,\n      warnings,\n      promptOptions,\n      hasMultiModalInput,\n      expectedInputs: hasMultiModalInput\n        ? getExpectedInputs(prompt)\n        : undefined,\n    };\n  }\n\n  /**\n   * Generates a complete text response using the browser's built-in Prompt API\n   * @param options\n   * @returns Promise resolving to the generated content with finish reason, usage stats, and any warnings\n   * @throws {LoadSettingError} When the Prompt API is not available or model needs to be downloaded\n   * @throws {UnsupportedFunctionalityError} When unsupported features like file input are used\n   */\n  public async doGenerate(options: LanguageModelV2CallOptions) {\n    const converted = this.getArgs(options);\n    const { systemMessage, messages, warnings, promptOptions, expectedInputs } =\n      converted;\n\n    const session = await this.getSession(\n      undefined,\n      expectedInputs,\n      systemMessage,\n    );\n\n    const text = await session.prompt(messages, promptOptions);\n\n    const content: LanguageModelV2Content[] = [\n      {\n        type: \"text\",\n        text,\n      },\n    ];\n\n    return {\n      content,\n      finishReason: \"stop\" as LanguageModelV2FinishReason,\n      usage: {\n        inputTokens: undefined,\n        outputTokens: undefined,\n        totalTokens: undefined,\n      },\n      request: { body: { messages, options: promptOptions } },\n      warnings,\n    };\n  }\n\n  /**\n   * Check the availability of the built-in AI model\n   * @returns Promise resolving to \"unavailable\", \"available\", or \"available-after-download\"\n   */\n  public async availability(): Promise<Availability> {\n    if (typeof LanguageModel === \"undefined\") {\n      return \"unavailable\";\n    }\n    return LanguageModel.availability();\n  }\n\n  /**\n   * Creates a session with download progress monitoring.\n   *\n   * @example\n   * ```typescript\n   * const session = await model.createSessionWithProgress(\n   *   (progress) => {\n   *     console.log(`Download progress: ${Math.round(progress * 100)}%`);\n   *   }\n   * );\n   * ```\n   *\n   * @param onDownloadProgress Optional callback receiving progress values 0-1 during model download\n   * @returns Promise resolving to a configured LanguageModel session\n   * @throws {LoadSettingError} When the Prompt API is not available or model is unavailable\n   */\n  public async createSessionWithProgress(\n    onDownloadProgress?: (progress: number) => void,\n  ): Promise<LanguageModel> {\n    return this.getSession(undefined, undefined, undefined, onDownloadProgress);\n  }\n\n  /**\n   * Generates a streaming text response using the browser's built-in Prompt API\n   * @param options\n   * @returns Promise resolving to a readable stream of text chunks and request metadata\n   * @throws {LoadSettingError} When the Prompt API is not available or model needs to be downloaded\n   * @throws {UnsupportedFunctionalityError} When unsupported features like file input are used\n   */\n  public async doStream(options: LanguageModelV2CallOptions) {\n    const converted = this.getArgs(options);\n    const {\n      systemMessage,\n      messages,\n      warnings,\n      promptOptions,\n      expectedInputs,\n      hasMultiModalInput,\n    } = converted;\n\n    const session = await this.getSession(\n      undefined,\n      expectedInputs,\n      systemMessage,\n    );\n\n    // Pass abort signal to the native streaming method\n    const streamOptions = {\n      ...promptOptions,\n      signal: options.abortSignal,\n    };\n\n    const promptStream = session.promptStreaming(messages, streamOptions);\n\n    let isFirstChunk = true;\n    const textId = \"text-0\";\n\n    const stream = promptStream.pipeThrough(\n      new TransformStream<string, LanguageModelV2StreamPart>({\n        start(controller) {\n          // Send stream start event with warnings\n          controller.enqueue({\n            type: \"stream-start\",\n            warnings,\n          });\n\n          // Handle abort signal\n          if (options.abortSignal) {\n            options.abortSignal.addEventListener(\"abort\", () => {\n              controller.terminate();\n            });\n          }\n        },\n\n        transform(chunk, controller) {\n          if (isFirstChunk) {\n            // Send text start event\n            controller.enqueue({\n              type: \"text-start\",\n              id: textId,\n            });\n            isFirstChunk = false;\n          }\n\n          // Send text delta\n          controller.enqueue({\n            type: \"text-delta\",\n            id: textId,\n            delta: chunk,\n          });\n        },\n\n        flush(controller) {\n          // Send text end event\n          controller.enqueue({\n            type: \"text-end\",\n            id: textId,\n          });\n\n          // Send finish event\n          controller.enqueue({\n            type: \"finish\",\n            finishReason: \"stop\" as LanguageModelV2FinishReason,\n            usage: {\n              inputTokens: session.inputUsage,\n              outputTokens: undefined,\n              totalTokens: undefined,\n            },\n          });\n        },\n      }),\n    );\n\n    return {\n      stream,\n      request: { body: { messages, options: promptOptions } },\n    };\n  }\n}\n","import {\n  LanguageModelV2Prompt,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\n\nexport interface ConvertedMessages {\n  systemMessage?: string;\n  messages: LanguageModelMessage[];\n}\n\n/**\n * Convert base64 string to Uint8Array for built-in AI compatibility\n * Built-in AI supports BufferSource (including Uint8Array) for image/audio data\n */\nfunction convertBase64ToUint8Array(base64: string): Uint8Array {\n  try {\n    const binaryString = atob(base64);\n    const bytes = new Uint8Array(binaryString.length);\n    for (let i = 0; i < binaryString.length; i++) {\n      bytes[i] = binaryString.charCodeAt(i);\n    }\n    return bytes;\n  } catch (error) {\n    throw new Error(`Failed to convert base64 to Uint8Array: ${error}`);\n  }\n}\n\n/**\n * Convert file data to the appropriate format for built-in AI\n * Built-in AI supports: Blob, BufferSource (Uint8Array), URLs\n */\nfunction convertFileData(data: any, mediaType: string): Uint8Array | string {\n  // Handle different data types from Vercel AI SDK\n  if (data instanceof URL) {\n    // URLs - keep as string (if supported by provider)\n    return data.toString();\n  }\n\n  if (data instanceof Uint8Array) {\n    // Already in correct format\n    return data;\n  }\n\n  if (typeof data === \"string\") {\n    // Base64 string from AI SDK - convert to Uint8Array\n    return convertBase64ToUint8Array(data);\n  }\n\n  // Fallback for other types (shouldn't happen with current AI SDK)\n  console.warn(`Unexpected data type for ${mediaType}:`, typeof data);\n  return data;\n}\n\n/**\n * Convert Vercel AI SDK prompt format to built-in AI Prompt API format\n * Returns system message (for initialPrompts) and regular messages (for prompt method)\n */\nexport function convertToBuiltInAIMessages(\n  prompt: LanguageModelV2Prompt,\n): ConvertedMessages {\n  let systemMessage: string | undefined;\n  const messages: LanguageModelMessage[] = [];\n\n  for (const message of prompt) {\n    switch (message.role) {\n      case \"system\": {\n        // There's only ever one system message from AI SDK\n        systemMessage = message.content;\n        break;\n      }\n\n      case \"user\": {\n        messages.push({\n          role: \"user\",\n          content: message.content.map((part) => {\n            switch (part.type) {\n              case \"text\": {\n                return {\n                  type: \"text\",\n                  value: part.text,\n                } as LanguageModelMessageContent;\n              }\n\n              case \"file\": {\n                const { mediaType, data, filename } = part;\n\n                if (mediaType?.startsWith(\"image/\")) {\n                  const convertedData = convertFileData(data, mediaType);\n\n                  return {\n                    type: \"image\",\n                    value: convertedData,\n                  } as LanguageModelMessageContent;\n                } else if (mediaType?.startsWith(\"audio/\")) {\n                  const convertedData = convertFileData(data, mediaType);\n\n                  return {\n                    type: \"audio\",\n                    value: convertedData,\n                  } as LanguageModelMessageContent;\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: `file type: ${mediaType}`,\n                  });\n                }\n              }\n\n              default: {\n                throw new UnsupportedFunctionalityError({\n                  functionality: `content type: ${(part as any).type}`,\n                });\n              }\n            }\n          }),\n        } as LanguageModelMessage);\n        break;\n      }\n\n      case \"assistant\": {\n        let text = \"\";\n\n        for (const part of message.content) {\n          switch (part.type) {\n            case \"text\": {\n              text += part.text;\n              break;\n            }\n            case \"tool-call\": {\n              throw new UnsupportedFunctionalityError({\n                functionality: \"tool calls\",\n              });\n            }\n          }\n        }\n\n        messages.push({\n          role: \"assistant\",\n          content: text,\n        } as LanguageModelMessage);\n        break;\n      }\n\n      case \"tool\": {\n        throw new UnsupportedFunctionalityError({\n          functionality: \"tool messages\",\n        });\n      }\n\n      default: {\n        throw new Error(`Unsupported role: ${(message as any).role}`);\n      }\n    }\n  }\n\n  return { systemMessage, messages };\n}\n","import { EmbeddingModelV2, EmbeddingModelV2Embedding } from \"@ai-sdk/provider\";\nimport { TextEmbedder } from \"@mediapipe/tasks-text\";\n\nexport interface BuiltInAIEmbeddingModelSettings {\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 BuiltInAIEmbeddingModel implements EmbeddingModelV2<string> {\n  readonly specificationVersion = \"v2\";\n  readonly provider = \"google-mediapipe\";\n  readonly modelId: string = \"embedding\";\n  readonly supportsParallelCalls = true;\n  readonly maxEmbeddingsPerCall = undefined;\n\n  private settings: BuiltInAIEmbeddingModelSettings = {\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<ReadableStreamDefaultReader>;\n  private textEmbedder!: Promise<TextEmbedder>;\n\n  public constructor(settings: BuiltInAIEmbeddingModelSettings = {}) {\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<TextEmbedder> => {\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<EmbeddingModelV2Embedding>;\n    rawResponse?: Record<PropertyKey, any>;\n  }> => {\n    // Note: abortSignal is not supported by MediaPipe TextEmbedder\n    if (options.abortSignal?.aborted) {\n      throw new Error(\"Operation was aborted\");\n    }\n\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\n    return {\n      embeddings,\n      rawResponse: {\n        model: \"universal_sentence_encoder\",\n        provider: \"google-mediapipe\",\n        processed_texts: options.values.length,\n      },\n    };\n  };\n}\n","import {\n  EmbeddingModelV2,\n  NoSuchModelError,\n  ProviderV2,\n} from \"@ai-sdk/provider\";\nimport {\n  BuiltInAIChatLanguageModel,\n  BuiltInAIChatModelId,\n  BuiltInAIChatSettings,\n} from \"./built-in-ai-language-model\";\nimport {\n  BuiltInAIEmbeddingModel,\n  BuiltInAIEmbeddingModelSettings,\n} from \"./built-in-ai-embedding-model\";\n\nexport interface BuiltInAIProvider extends ProviderV2 {\n  (\n    modelId?: BuiltInAIChatModelId,\n    settings?: BuiltInAIChatSettings,\n  ): BuiltInAIChatLanguageModel;\n\n  /**\n   * Creates a model for text generation.\n   */\n  languageModel(\n    modelId: BuiltInAIChatModelId,\n    settings?: BuiltInAIChatSettings,\n  ): BuiltInAIChatLanguageModel;\n\n  /**\n   * Creates a model for text generation.\n   */\n  chat(\n    modelId: BuiltInAIChatModelId,\n    settings?: BuiltInAIChatSettings,\n  ): BuiltInAIChatLanguageModel;\n\n  textEmbedding(\n    modelId: \"embedding\",\n    settings?: BuiltInAIEmbeddingModelSettings,\n  ): EmbeddingModelV2<string>;\n\n  textEmbeddingModel: (\n    modelId: \"embedding\",\n    settings?: BuiltInAIEmbeddingModelSettings,\n  ) => EmbeddingModelV2<string>;\n\n  // Not implemented\n  imageModel(modelId: string): never;\n  speechModel(modelId: string): never;\n  transcriptionModel(modelId: string): never;\n}\n\nexport interface BuiltInAIProviderSettings {\n  // Currently empty - provider settings are minimal for BuiltInAI\n  // Future provider-level settings can be added here\n}\n\n/**\n * Create a BuiltInAI provider instance.\n */\nexport function createBuiltInAI(\n  options: BuiltInAIProviderSettings = {},\n): BuiltInAIProvider {\n  const createChatModel = (\n    modelId: BuiltInAIChatModelId,\n    settings?: BuiltInAIChatSettings,\n  ) => {\n    return new BuiltInAIChatLanguageModel(modelId, settings);\n  };\n\n  const createEmbeddingModel = (\n    modelId: \"embedding\",\n    settings?: BuiltInAIEmbeddingModelSettings,\n  ) => {\n    return new BuiltInAIEmbeddingModel(settings);\n  };\n\n  const provider = function (\n    modelId: BuiltInAIChatModelId = \"text\",\n    settings?: BuiltInAIChatSettings,\n  ) {\n    if (new.target) {\n      throw new Error(\n        \"The BuiltInAI model function cannot be called with the new keyword.\",\n      );\n    }\n\n    return createChatModel(modelId, settings);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n\n  provider.imageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: \"imageModel\" });\n  };\n\n  provider.speechModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: \"speechModel\" });\n  };\n\n  provider.transcriptionModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: \"transcriptionModel\" });\n  };\n\n  return provider;\n}\n\n/**\n * Default BuiltInAI provider instance.\n */\nexport const builtInAI = 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