@ai-sdk/google-vertex/dist/edge/index.mjs.map

The **[Google Vertex provider](https://ai-sdk.dev/providers/ai-sdk-providers/google-vertex)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the [Google Vertex AI](https://cloud.google.com/vertex-ai) APIs.

{"version":3,"sources":["../../src/edge/google-vertex-provider-edge.ts","../../src/google-vertex-provider.ts","../../src/version.ts","../../src/google-vertex-embedding-model.ts","../../src/google-vertex-error.ts","../../src/google-vertex-embedding-options.ts","../../src/google-vertex-image-model.ts","../../src/google-vertex-tools.ts","../../src/edge/google-vertex-auth-edge.ts"],"sourcesContent":["import { loadOptionalSetting, resolve } from '@ai-sdk/provider-utils';\nimport {\n  createVertex as createVertexOriginal,\n  GoogleVertexProvider,\n  GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from '../google-vertex-provider';\nimport {\n  generateAuthToken,\n  GoogleCredentials,\n} from './google-vertex-auth-edge';\n\nexport type { GoogleVertexProvider };\n\nexport interface GoogleVertexProviderSettings\n  extends GoogleVertexProviderSettingsOriginal {\n  /**\n   * Optional. The Google credentials for the Google Cloud service account. If\n   * not provided, the Google Vertex provider will use environment variables to\n   * load the credentials.\n   */\n  googleCredentials?: GoogleCredentials;\n}\n\nexport function createVertex(\n  options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n  const apiKey = loadOptionalSetting({\n    settingValue: options.apiKey,\n    environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n  });\n\n  if (apiKey) {\n    return createVertexOriginal(options);\n  }\n\n  return createVertexOriginal({\n    ...options,\n    headers: async () => ({\n      Authorization: `Bearer ${await generateAuthToken(\n        options.googleCredentials,\n      )}`,\n      ...(await resolve(options.headers)),\n    }),\n  });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV3, LanguageModelV3, ProviderV3 } from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadOptionalSetting,\n  loadSetting,\n  normalizeHeaders,\n  resolve,\n  Resolvable,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleVertexConfig } from './google-vertex-config';\nimport { GoogleVertexEmbeddingModel } from './google-vertex-embedding-model';\nimport { GoogleVertexEmbeddingModelId } from './google-vertex-embedding-options';\nimport { GoogleVertexImageModel } from './google-vertex-image-model';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\nimport { GoogleVertexModelId } from './google-vertex-options';\nimport { googleVertexTools } from './google-vertex-tools';\n\nconst EXPRESS_MODE_BASE_URL =\n  'https://aiplatform.googleapis.com/v1/publishers/google';\n\n// set `x-goog-api-key` header to API key for express mode\nfunction createExpressModeFetch(\n  apiKey: string,\n  customFetch?: FetchFunction,\n): FetchFunction {\n  return async (url, init) => {\n    const modifiedInit: RequestInit = {\n      ...init,\n      headers: {\n        ...(init?.headers ? normalizeHeaders(init.headers) : {}),\n        'x-goog-api-key': apiKey,\n      },\n    };\n    return (customFetch ?? fetch)(url.toString(), modifiedInit);\n  };\n}\n\nexport interface GoogleVertexProvider extends ProviderV3 {\n  /**\nCreates a model for text generation.\n   */\n  (modelId: GoogleVertexModelId): LanguageModelV3;\n\n  languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;\n\n  /**\n   * Creates a model for image generation.\n   */\n  image(modelId: GoogleVertexImageModelId): ImageModelV3;\n\n  /**\nCreates a model for image generation.\n   */\n  imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;\n\n  tools: typeof googleVertexTools;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(\n    modelId: GoogleVertexEmbeddingModelId,\n  ): GoogleVertexEmbeddingModel;\n}\n\nexport interface GoogleVertexProviderSettings {\n  /**\n   * Optional. The API key for the Google Cloud project. If provided, the\n   * provider will use express mode with API key authentication. Defaults to\n   * the value of the `GOOGLE_VERTEX_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\nYour Google Vertex location. Defaults to the environment variable `GOOGLE_VERTEX_LOCATION`.\n   */\n  location?: string;\n\n  /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.\n  */\n  project?: string;\n\n  /**\n   * Headers to use for requests. Can be:\n   * - A headers object\n   * - A Promise that resolves to a headers object\n   * - A function that returns a headers object\n   * - A function that returns a Promise of a headers object\n   */\n  headers?: Resolvable<Record<string, string | undefined>>;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n\n  // for testing\n  generateId?: () => string;\n\n  /**\nBase URL for the Google Vertex API calls.\n     */\n  baseURL?: string;\n}\n\n/**\nCreate a Google Vertex AI provider instance.\n */\nexport function createVertex(\n  options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n  const apiKey = loadOptionalSetting({\n    settingValue: options.apiKey,\n    environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n  });\n\n  const loadVertexProject = () =>\n    loadSetting({\n      settingValue: options.project,\n      settingName: 'project',\n      environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n      description: 'Google Vertex project',\n    });\n\n  const loadVertexLocation = () =>\n    loadSetting({\n      settingValue: options.location,\n      settingName: 'location',\n      environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n      description: 'Google Vertex location',\n    });\n\n  const loadBaseURL = () => {\n    if (apiKey) {\n      return withoutTrailingSlash(options.baseURL) ?? EXPRESS_MODE_BASE_URL;\n    }\n\n    const region = loadVertexLocation();\n    const project = loadVertexProject();\n\n    // For global region, use aiplatform.googleapis.com directly\n    // For other regions, use region-aiplatform.googleapis.com\n    const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n    return (\n      withoutTrailingSlash(options.baseURL) ??\n      `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`\n    );\n  };\n\n  const createConfig = (name: string): GoogleVertexConfig => {\n    const getHeaders = async () => {\n      const originalHeaders = await resolve(options.headers ?? {});\n      return withUserAgentSuffix(\n        originalHeaders,\n        `ai-sdk/google-vertex/${VERSION}`,\n      );\n    };\n\n    return {\n      provider: `google.vertex.${name}`,\n      headers: getHeaders,\n      fetch: apiKey\n        ? createExpressModeFetch(apiKey, options.fetch)\n        : options.fetch,\n      baseURL: loadBaseURL(),\n    };\n  };\n\n  const createChatModel = (modelId: GoogleVertexModelId) => {\n    return new GoogleGenerativeAILanguageModel(modelId, {\n      ...createConfig('chat'),\n      generateId: options.generateId ?? generateId,\n      supportedUrls: () => ({\n        '*': [\n          // HTTP URLs:\n          /^https?:\\/\\/.*$/,\n          // Google Cloud Storage URLs:\n          /^gs:\\/\\/.*$/,\n        ],\n      }),\n    });\n  };\n\n  const createEmbeddingModel = (modelId: GoogleVertexEmbeddingModelId) =>\n    new GoogleVertexEmbeddingModel(modelId, createConfig('embedding'));\n\n  const createImageModel = (modelId: GoogleVertexImageModelId) =>\n    new GoogleVertexImageModel(modelId, createConfig('image'));\n\n  const provider = function (modelId: GoogleVertexModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Google Vertex AI model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(modelId);\n  };\n\n  provider.specificationVersion = 'v3' as const;\n  provider.languageModel = createChatModel;\n  provider.embeddingModel = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n  provider.tools = googleVertexTools;\n\n  return provider;\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n  typeof __PACKAGE_VERSION__ !== 'undefined'\n    ? __PACKAGE_VERSION__\n    : '0.0.0-test';\n","import {\n  EmbeddingModelV3,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  postJsonToApi,\n  resolve,\n  parseProviderOptions,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport {\n  GoogleVertexEmbeddingModelId,\n  googleVertexEmbeddingProviderOptions,\n} from './google-vertex-embedding-options';\nimport { GoogleVertexConfig } from './google-vertex-config';\n\nexport class GoogleVertexEmbeddingModel implements EmbeddingModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly modelId: GoogleVertexEmbeddingModelId;\n  readonly maxEmbeddingsPerCall = 2048;\n  readonly supportsParallelCalls = true;\n\n  private readonly config: GoogleVertexConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    modelId: GoogleVertexEmbeddingModelId,\n    config: GoogleVertexConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    headers,\n    abortSignal,\n    providerOptions,\n  }: Parameters<EmbeddingModelV3['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV3['doEmbed']>>\n  > {\n    let googleOptions = await parseProviderOptions({\n      provider: 'vertex',\n      providerOptions,\n      schema: googleVertexEmbeddingProviderOptions,\n    });\n\n    if (googleOptions == null) {\n      googleOptions = await parseProviderOptions({\n        provider: 'google',\n        providerOptions,\n        schema: googleVertexEmbeddingProviderOptions,\n      });\n    }\n\n    googleOptions = googleOptions ?? {};\n\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values,\n      });\n    }\n\n    const mergedHeaders = combineHeaders(\n      await resolve(this.config.headers),\n      headers,\n    );\n\n    const url = `${this.config.baseURL}/models/${this.modelId}:predict`;\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url,\n      headers: mergedHeaders,\n      body: {\n        instances: values.map(value => ({\n          content: value,\n          task_type: googleOptions.taskType,\n          title: googleOptions.title,\n        })),\n        parameters: {\n          outputDimensionality: googleOptions.outputDimensionality,\n          autoTruncate: googleOptions.autoTruncate,\n        },\n      },\n      failedResponseHandler: googleVertexFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        googleVertexTextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      warnings: [],\n      embeddings: response.predictions.map(\n        prediction => prediction.embeddings.values,\n      ),\n      usage: {\n        tokens: response.predictions.reduce(\n          (tokenCount, prediction) =>\n            tokenCount + prediction.embeddings.statistics.token_count,\n          0,\n        ),\n      },\n      response: { headers: responseHeaders, body: rawValue },\n    };\n  }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleVertexTextEmbeddingResponseSchema = z.object({\n  predictions: z.array(\n    z.object({\n      embeddings: z.object({\n        values: z.array(z.number()),\n        statistics: z.object({\n          token_count: z.number(),\n        }),\n      }),\n    }),\n  ),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleVertexErrorDataSchema = z.object({\n  error: z.object({\n    code: z.number().nullable(),\n    message: z.string(),\n    status: z.string(),\n  }),\n});\n\nexport type GoogleVertexErrorData = z.infer<typeof googleVertexErrorDataSchema>;\n\nexport const googleVertexFailedResponseHandler = createJsonErrorResponseHandler(\n  {\n    errorSchema: googleVertexErrorDataSchema,\n    errorToMessage: data => data.error.message,\n  },\n);\n","import { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api\nexport type GoogleVertexEmbeddingModelId =\n  | 'textembedding-gecko'\n  | 'textembedding-gecko@001'\n  | 'textembedding-gecko@003'\n  | 'textembedding-gecko-multilingual'\n  | 'textembedding-gecko-multilingual@001'\n  | 'text-multilingual-embedding-002'\n  | 'text-embedding-004'\n  | 'text-embedding-005'\n  | (string & {});\n\nexport const googleVertexEmbeddingProviderOptions = z.object({\n  /**\n   * Optional. Optional reduced dimension for the output embedding.\n   * If set, excessive values in the output embedding are truncated from the end.\n   */\n  outputDimensionality: z.number().optional(),\n\n  /**\n   * Optional. Specifies the task type for generating embeddings.\n   * Supported task types:\n   * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n   * - CLASSIFICATION: Optimized for text classification.\n   * - CLUSTERING: Optimized for clustering texts based on similarity.\n   * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n   * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n   * - QUESTION_ANSWERING: Optimized for answering questions.\n   * - FACT_VERIFICATION: Optimized for verifying factual information.\n   * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n   */\n  taskType: z\n    .enum([\n      'SEMANTIC_SIMILARITY',\n      'CLASSIFICATION',\n      'CLUSTERING',\n      'RETRIEVAL_DOCUMENT',\n      'RETRIEVAL_QUERY',\n      'QUESTION_ANSWERING',\n      'FACT_VERIFICATION',\n      'CODE_RETRIEVAL_QUERY',\n    ])\n    .optional(),\n\n  /**\n   * Optional. The title of the document being embedded.\n   * Only valid when task_type is set to 'RETRIEVAL_DOCUMENT'.\n   * Helps the model produce better embeddings by providing additional context.\n   */\n  title: z.string().optional(),\n\n  /**\n   * Optional. When set to true, input text will be truncated. When set to false,\n   * an error is returned if the input text is longer than the maximum length supported by the model. Defaults to true.\n   */\n  autoTruncate: z.boolean().optional(),\n});\n\nexport type GoogleVertexEmbeddingProviderOptions = z.infer<\n  typeof googleVertexEmbeddingProviderOptions\n>;\n","import {\n  ImageModelV3,\n  ImageModelV3File,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  Resolvable,\n  combineHeaders,\n  convertUint8ArrayToBase64,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleVertexFailedResponseHandler } from './google-vertex-error';\nimport { GoogleVertexImageModelId } from './google-vertex-image-settings';\n\ninterface GoogleVertexImageModelConfig {\n  provider: string;\n  baseURL: string;\n  headers?: Resolvable<Record<string, string | undefined>>;\n  fetch?: typeof fetch;\n  _internal?: {\n    currentDate?: () => Date;\n  };\n}\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images\nexport class GoogleVertexImageModel implements ImageModelV3 {\n  readonly specificationVersion = 'v3';\n  // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list\n  readonly maxImagesPerCall = 4;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    readonly modelId: GoogleVertexImageModelId,\n    private config: GoogleVertexImageModelConfig,\n  ) {}\n\n  async doGenerate({\n    prompt,\n    n,\n    size,\n    aspectRatio,\n    seed,\n    providerOptions,\n    headers,\n    abortSignal,\n    files,\n    mask,\n  }: Parameters<ImageModelV3['doGenerate']>[0]): Promise<\n    Awaited<ReturnType<ImageModelV3['doGenerate']>>\n  > {\n    const warnings: Array<SharedV3Warning> = [];\n\n    if (size != null) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'size',\n        details:\n          'This model does not support the `size` option. Use `aspectRatio` instead.',\n      });\n    }\n\n    const vertexImageOptions = await parseProviderOptions({\n      provider: 'vertex',\n      providerOptions,\n      schema: vertexImageProviderOptionsSchema,\n    });\n\n    // Extract edit-specific options from provider options\n    const { edit, ...otherOptions } = vertexImageOptions ?? {};\n    const { mode: editMode, baseSteps, maskMode, maskDilation } = edit ?? {};\n\n    // Build the request body based on whether we're editing or generating\n    const isEditMode = files != null && files.length > 0;\n\n    let body: Record<string, unknown>;\n\n    if (isEditMode) {\n      // Build reference images for editing\n      const referenceImages: Array<Record<string, unknown>> = [];\n\n      // Add the source image(s)\n      for (let i = 0; i < files.length; i++) {\n        const file = files[i];\n        referenceImages.push({\n          referenceType: 'REFERENCE_TYPE_RAW',\n          referenceId: i + 1,\n          referenceImage: {\n            bytesBase64Encoded: getBase64Data(file),\n          },\n        });\n      }\n\n      // Add mask if provided\n      if (mask != null) {\n        referenceImages.push({\n          referenceType: 'REFERENCE_TYPE_MASK',\n          referenceId: files.length + 1,\n          referenceImage: {\n            bytesBase64Encoded: getBase64Data(mask),\n          },\n          maskImageConfig: {\n            maskMode: maskMode ?? 'MASK_MODE_USER_PROVIDED',\n            ...(maskDilation != null ? { dilation: maskDilation } : {}),\n          },\n        });\n      }\n\n      body = {\n        instances: [\n          {\n            prompt,\n            referenceImages,\n          },\n        ],\n        parameters: {\n          sampleCount: n,\n          ...(aspectRatio != null ? { aspectRatio } : {}),\n          ...(seed != null ? { seed } : {}),\n          editMode: editMode ?? 'EDIT_MODE_INPAINT_INSERTION',\n          ...(baseSteps != null ? { editConfig: { baseSteps } } : {}),\n          ...otherOptions,\n        },\n      };\n    } else {\n      // Standard image generation\n      body = {\n        instances: [{ prompt }],\n        parameters: {\n          sampleCount: n,\n          ...(aspectRatio != null ? { aspectRatio } : {}),\n          ...(seed != null ? { seed } : {}),\n          ...otherOptions,\n        },\n      };\n    }\n\n    const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n    const { value: response, responseHeaders } = await postJsonToApi({\n      url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n      headers: combineHeaders(await resolve(this.config.headers), headers),\n      body,\n      failedResponseHandler: googleVertexFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        vertexImageResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      images:\n        response.predictions?.map(\n          ({ bytesBase64Encoded }) => bytesBase64Encoded,\n        ) ?? [],\n      warnings,\n      response: {\n        timestamp: currentDate,\n        modelId: this.modelId,\n        headers: responseHeaders,\n      },\n      providerMetadata: {\n        vertex: {\n          images:\n            response.predictions?.map(prediction => {\n              const {\n                // normalize revised prompt property\n                prompt: revisedPrompt,\n              } = prediction;\n\n              return { ...(revisedPrompt != null && { revisedPrompt }) };\n            }) ?? [],\n        },\n      },\n    };\n  }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst vertexImageResponseSchema = z.object({\n  predictions: z\n    .array(\n      z.object({\n        bytesBase64Encoded: z.string(),\n        mimeType: z.string(),\n        prompt: z.string().nullish(),\n      }),\n    )\n    .nullish(),\n});\n\nconst vertexImageProviderOptionsSchema = z.object({\n  negativePrompt: z.string().nullish(),\n  personGeneration: z\n    .enum(['dont_allow', 'allow_adult', 'allow_all'])\n    .nullish(),\n  safetySetting: z\n    .enum([\n      'block_low_and_above',\n      'block_medium_and_above',\n      'block_only_high',\n      'block_none',\n    ])\n    .nullish(),\n  addWatermark: z.boolean().nullish(),\n  storageUri: z.string().nullish(),\n  sampleImageSize: z.enum(['1K', '2K']).nullish(),\n  /**\n   * Configuration for image editing operations\n   */\n  edit: z\n    .object({\n      /**\n       * An integer that represents the number of sampling steps.\n       * A higher value offers better image quality, a lower value offers better latency.\n       * Try 35 steps to start. If the quality doesn't meet your requirements,\n       * increase the value towards an upper limit of 75.\n       */\n      baseSteps: z.number().nullish(),\n\n      // Edit mode options\n      // https://cloud.google.com/vertex-ai/generative-ai/docs/image/edit-insert-objects\n      mode: z\n        .enum([\n          'EDIT_MODE_INPAINT_INSERTION',\n          'EDIT_MODE_INPAINT_REMOVAL',\n          'EDIT_MODE_OUTPAINT',\n          'EDIT_MODE_CONTROLLED_EDITING',\n          'EDIT_MODE_PRODUCT_IMAGE',\n          'EDIT_MODE_BGSWAP',\n        ])\n        .nullish(),\n\n      /**\n       * The mask mode to use.\n       * - `MASK_MODE_DEFAULT` - Default value for mask mode.\n       * - `MASK_MODE_USER_PROVIDED` - User provided mask. No segmentation needed.\n       * - `MASK_MODE_DETECTION_BOX` - Mask from detected bounding boxes.\n       * - `MASK_MODE_CLOTHING_AREA` - Masks from segmenting the clothing area with open-vocab segmentation.\n       * - `MASK_MODE_PARSED_PERSON` - Masks from segmenting the person body and clothing using the person-parsing model.\n       */\n      maskMode: z\n        .enum([\n          'MASK_MODE_DEFAULT',\n          'MASK_MODE_USER_PROVIDED',\n          'MASK_MODE_DETECTION_BOX',\n          'MASK_MODE_CLOTHING_AREA',\n          'MASK_MODE_PARSED_PERSON',\n        ])\n        .nullish(),\n\n      /**\n       * Optional. A float value between 0 and 1, inclusive, that represents the\n       * percentage of the image width to grow the mask by. Using dilation helps\n       * compensate for imprecise masks. We recommend a value of 0.01.\n       */\n      maskDilation: z.number().nullish(),\n    })\n    .nullish(),\n});\nexport type GoogleVertexImageProviderOptions = z.infer<\n  typeof vertexImageProviderOptionsSchema\n>;\n\n/**\n * Helper to convert ImageModelV3File data to base64 string\n */\nfunction getBase64Data(file: ImageModelV3File): string {\n  if (file.type === 'url') {\n    throw new Error(\n      'URL-based images are not supported for Google Vertex image editing. Please provide the image data directly.',\n    );\n  }\n\n  if (typeof file.data === 'string') {\n    return file.data;\n  }\n\n  // Convert Uint8Array to base64\n  return convertUint8ArrayToBase64(file.data);\n}\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n  googleSearch: googleTools.googleSearch,\n  enterpriseWebSearch: googleTools.enterpriseWebSearch,\n  googleMaps: googleTools.googleMaps,\n  urlContext: googleTools.urlContext,\n  fileSearch: googleTools.fileSearch,\n  codeExecution: googleTools.codeExecution,\n  vertexRagStore: googleTools.vertexRagStore,\n};\n","import {\n  loadOptionalSetting,\n  loadSetting,\n  withUserAgentSuffix,\n  getRuntimeEnvironmentUserAgent,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from '../version';\n\nexport interface GoogleCredentials {\n  /**\n   * The client email for the Google Cloud service account. Defaults to the\n   * value of the `GOOGLE_CLIENT_EMAIL` environment variable.\n   */\n  clientEmail: string;\n\n  /**\n   * The private key for the Google Cloud service account. Defaults to the\n   * value of the `GOOGLE_PRIVATE_KEY` environment variable.\n   */\n  privateKey: string;\n\n  /**\n   * Optional. The private key ID for the Google Cloud service account. Defaults\n   * to the value of the `GOOGLE_PRIVATE_KEY_ID` environment variable.\n   */\n  privateKeyId?: string;\n}\n\nconst loadCredentials = async (): Promise<GoogleCredentials> => {\n  try {\n    return {\n      clientEmail: loadSetting({\n        settingValue: undefined,\n        settingName: 'clientEmail',\n        environmentVariableName: 'GOOGLE_CLIENT_EMAIL',\n        description: 'Google client email',\n      }),\n      privateKey: loadSetting({\n        settingValue: undefined,\n        settingName: 'privateKey',\n        environmentVariableName: 'GOOGLE_PRIVATE_KEY',\n        description: 'Google private key',\n      }),\n      privateKeyId: loadOptionalSetting({\n        settingValue: undefined,\n        environmentVariableName: 'GOOGLE_PRIVATE_KEY_ID',\n      }),\n    };\n  } catch (error: any) {\n    throw new Error(`Failed to load Google credentials: ${error.message}`);\n  }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n  return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n  const pemHeader = '-----BEGIN PRIVATE KEY-----';\n  const pemFooter = '-----END PRIVATE KEY-----';\n\n  // Remove header, footer, and any whitespace/newlines\n  const pemContents = pemKey\n    .replace(pemHeader, '')\n    .replace(pemFooter, '')\n    .replace(/\\s/g, '');\n\n  // Decode base64 to binary\n  const binaryString = atob(pemContents);\n\n  // Convert binary string to Uint8Array\n  const binaryData = new Uint8Array(binaryString.length);\n  for (let i = 0; i < binaryString.length; i++) {\n    binaryData[i] = binaryString.charCodeAt(i);\n  }\n\n  return await crypto.subtle.importKey(\n    'pkcs8',\n    binaryData,\n    { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n    true,\n    ['sign'],\n  );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n  const now = Math.floor(Date.now() / 1000);\n\n  // Only include kid in header if 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