@ai-sdk/google-vertex/dist/xai/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/xai/edge/google-vertex-xai-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/xai/google-vertex-xai-provider.ts"],"sourcesContent":["import { resolve, type FetchFunction } from '@ai-sdk/provider-utils';\nimport {\n  generateAuthToken,\n  type GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n  createGoogleVertexXai as createGoogleVertexXaiOriginal,\n  type GoogleVertexXaiProvider,\n  type GoogleVertexXaiProviderSettings as GoogleVertexXaiProviderSettingsOriginal,\n} from '../google-vertex-xai-provider';\nexport type { GoogleVertexXaiProvider };\n\nexport interface GoogleVertexXaiProviderSettings extends GoogleVertexXaiProviderSettingsOriginal {\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\n/**\n * Create a Google Vertex AI xAI provider instance for Edge runtimes.\n * Uses the OpenAI-compatible Chat Completions API for Grok partner models.\n * Automatically handles Google Cloud authentication.\n *\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/grok\n */\nexport function createGoogleVertexXai(\n  options: GoogleVertexXaiProviderSettings = {},\n): GoogleVertexXaiProvider {\n  const customFetch: FetchFunction = async (url, init) => {\n    const token = await generateAuthToken(options.googleCredentials);\n    const resolvedHeaders = await resolve(options.headers);\n    const authHeaders = {\n      ...resolvedHeaders,\n      Authorization: `Bearer ${token}`,\n    };\n\n    const fetchInit = {\n      ...init,\n      headers: {\n        ...init?.headers,\n        ...authHeaders,\n      },\n    };\n\n    return (options.fetch ?? fetch)(url, fetchInit);\n  };\n\n  return createGoogleVertexXaiOriginal({\n    ...options,\n    fetch: customFetch,\n    headers: undefined,\n  });\n}\n\n/**\n * Default Google Vertex AI xAI provider instance for Edge runtimes.\n */\nexport const googleVertexXai = createGoogleVertexXai();\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: ${error.message}`);\n  }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n  return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n  const pemHeader = '-----BEGIN PRIVATE KEY-----';\n  const pemFooter = '-----END PRIVATE KEY-----';\n\n  // Remove header, footer, and any whitespace/newlines\n  const pemContents = pemKey\n    .replace(pemHeader, '')\n    .replace(pemFooter, '')\n    .replace(/\\s/g, '');\n\n  // Decode base64 to binary\n  const binaryString = atob(pemContents);\n\n  // Convert binary string to Uint8Array\n  const binaryData = new Uint8Array(binaryString.length);\n  for (let i = 0; i < binaryString.length; i++) {\n    binaryData[i] = binaryString.charCodeAt(i);\n  }\n\n  return await crypto.subtle.importKey(\n    'pkcs8',\n    binaryData,\n    { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n    true,\n    ['sign'],\n  );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n  const now = Math.floor(Date.now() / 1000);\n\n  // Only include kid in header if privateKeyId is provided\n  const header: { alg: string; typ: string; kid?: string } = {\n    alg: 'RS256',\n    typ: 'JWT',\n  };\n\n  if (credentials.privateKeyId) {\n    header.kid = credentials.privateKeyId;\n  }\n\n  const payload = {\n    iss: credentials.clientEmail,\n    scope: 'https://www.googleapis.com/auth/cloud-platform',\n    aud: 'https://oauth2.googleapis.com/token',\n    exp: now + 3600,\n    iat: now,\n  };\n\n  const privateKey = await importPrivateKey(credentials.privateKey);\n\n  const signingInput = `${base64url(JSON.stringify(header))}.${base64url(\n    JSON.stringify(payload),\n  )}`;\n  const encoder = new TextEncoder();\n  const data = encoder.encode(signingInput);\n\n  const signature = await crypto.subtle.sign(\n    'RSASSA-PKCS1-v1_5',\n    privateKey,\n    data,\n  );\n\n  const signatureBase64 = base64url(\n    String.fromCharCode(...new Uint8Array(signature)),\n  );\n\n  return `${base64url(JSON.stringify(header))}.${base64url(\n    JSON.stringify(payload),\n  )}.${signatureBase64}`;\n};\n\n/**\n * Generate an authentication token for Google Vertex AI in a manner compatible\n * with the Edge runtime.\n */\nexport async function generateAuthToken(credentials?: GoogleCredentials) {\n  try {\n    const creds = credentials || (await loadCredentials());\n    const jwt = await buildJwt(creds);\n\n    const response = await fetch('https://oauth2.googleapis.com/token', {\n      method: 'POST',\n      headers: withUserAgentSuffix(\n        { 'Content-Type': 'application/x-www-form-urlencoded' },\n        `ai-sdk/google-vertex/${VERSION}`,\n        getRuntimeEnvironmentUserAgent(),\n      ),\n      body: new URLSearchParams({\n        grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',\n        assertion: jwt,\n      }),\n    });\n\n    if (!response.ok) {\n      throw new Error(`Token request failed: ${response.statusText}`);\n    }\n\n    const data = await response.json();\n    return data.access_token;\n  } catch (error) {\n    throw error;\n  }\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  NoSuchModelError,\n  type LanguageModelV3,\n  type LanguageModelV3Usage,\n  type ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n  createOpenAICompatible,\n  type OpenAICompatibleProvider,\n} from '@ai-sdk/openai-compatible';\nimport {\n  loadOptionalSetting,\n  loadSetting,\n  withoutTrailingSlash,\n  type FetchFunction,\n  type Resolvable,\n} from '@ai-sdk/provider-utils';\nimport type { GoogleVertexXaiModelId } from './google-vertex-xai-options';\n\nexport interface GoogleVertexXaiProvider extends ProviderV3 {\n  /**\n   * Creates a model for text generation.\n   */\n  (modelId: GoogleVertexXaiModelId): LanguageModelV3;\n\n  /**\n   * Creates a model for text generation.\n   */\n  languageModel(modelId: GoogleVertexXaiModelId): LanguageModelV3;\n\n  /**\n   * Creates a chat model for text generation.\n   */\n  chatModel(modelId: GoogleVertexXaiModelId): LanguageModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(modelId: string): never;\n}\n\nexport interface GoogleVertexXaiProviderSettings {\n  /**\n   * Google Cloud project ID. Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n   */\n  project?: string;\n\n  /**\n   * Google Cloud location/region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n   * Use 'global' for the global endpoint.\n   */\n  location?: string;\n\n  /**\n   * Base URL for the API calls. If not provided, will be constructed from project and location.\n   */\n  baseURL?: 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  /**\n   * Custom fetch implementation. You can use it as a middleware to intercept requests,\n   * or to provide a custom fetch implementation for e.g. testing.\n   */\n  fetch?: FetchFunction;\n}\n\ntype GoogleVertexXaiUsage =\n  | {\n      prompt_tokens?: number | null;\n      completion_tokens?: number | null;\n      prompt_tokens_details?: {\n        cached_tokens?: number | null;\n      } | null;\n      completion_tokens_details?: {\n        reasoning_tokens?: number | null;\n      } | null;\n    }\n  | undefined\n  | null;\n\nfunction convertGoogleVertexXaiUsage(\n  usage: GoogleVertexXaiUsage,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.prompt_tokens ?? 0;\n  const completionTokens = usage.completion_tokens ?? 0;\n  const cacheReadTokens = usage.prompt_tokens_details?.cached_tokens ?? 0;\n  const reasoningTokens =\n    usage.completion_tokens_details?.reasoning_tokens ?? 0;\n\n  return {\n    inputTokens: {\n      total: promptTokens,\n      noCache: promptTokens - cacheReadTokens,\n      cacheRead: cacheReadTokens,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: completionTokens + reasoningTokens,\n      text: completionTokens,\n      reasoning: reasoningTokens,\n    },\n    raw: usage,\n  };\n}\n\nfunction transformGoogleVertexXaiRequestBody(args: Record<string, any>) {\n  const { reasoning_effort: _reasoningEffort, ...rest } = args;\n  return rest;\n}\n\n/**\n * Create a Google Vertex AI xAI provider instance.\n * Uses the OpenAI-compatible Chat Completions API for Grok partner models.\n *\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/grok\n */\nexport function createGoogleVertexXai(\n  options: GoogleVertexXaiProviderSettings = {},\n): GoogleVertexXaiProvider {\n  const loadLocation = () =>\n    loadOptionalSetting({\n      settingValue: options.location,\n      environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n    });\n\n  const loadProject = () =>\n    loadSetting({\n      settingValue: options.project,\n      settingName: 'project',\n      environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n      description: 'Google Vertex project',\n    });\n\n  const constructBaseURL = () => {\n    const projectId = loadProject();\n    const location = loadLocation() ?? 'global';\n\n    return `https://aiplatform.googleapis.com/v1/projects/${projectId}/locations/${location}/endpoints/openapi`;\n  };\n\n  const loadBaseURL = () =>\n    withoutTrailingSlash(options.baseURL ?? '') || constructBaseURL();\n\n  let cachedProvider:\n    | OpenAICompatibleProvider<GoogleVertexXaiModelId, string, string, string>\n    | undefined;\n  const getProvider = () =>\n    (cachedProvider ??= createOpenAICompatible({\n      name: 'googleVertex.xai',\n      baseURL: loadBaseURL(),\n      fetch: options.fetch,\n      includeUsage: true,\n      supportsStructuredOutputs: true,\n      supportedUrls: () => ({\n        'image/*': [/^https?:\\/\\/.*$/],\n      }),\n      transformRequestBody: transformGoogleVertexXaiRequestBody,\n      convertUsage: convertGoogleVertexXaiUsage,\n    }));\n\n  const createChatModel = (modelId: GoogleVertexXaiModelId) =>\n    getProvider().languageModel(modelId);\n\n  const provider = function (modelId: GoogleVertexXaiModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Google Vertex xAI 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.chatModel = (modelId: GoogleVertexXaiModelId) =>\n    getProvider().chatModel(modelId);\n  provider.embeddingModel = (modelId: string): never => {\n    throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n  };\n  provider.textEmbeddingModel = provider.embeddingModel;\n  provider.imageModel = (modelId: string): never => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n\n  return 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