ai-gateway-provider
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AI Gateway Provider for AI-SDK
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{"version":3,"sources":["../../src/providers/amazon-bedrock.ts","../../src/auth.ts","../../src/providers/anthropic.ts","../../src/providers/azure.ts","../../src/providers/cerebras.ts","../../src/providers/cohere.ts","../../src/providers/deepgram.ts","../../src/providers/deepseek.ts","../../src/providers/elevenlabs.ts","../../src/providers/fireworks.ts","../../src/providers/google.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/edge/google-vertex-provider-edge.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-provider.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/version.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-embedding-model.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-error.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-embedding-options.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-image-model.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/google-vertex-tools.ts","../../../../node_modules/.pnpm/@ai-sdk+google-vertex@4.0.1_patch_hash=271963aef1bb8d69c1fbba83342f6bdf1b39f53c660e9b34659e6ce279c0a100_zod@4.2.1/node_modules/@ai-sdk/google-vertex/src/edge/google-vertex-auth-edge.ts","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/classic/external.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/index.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/core.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/util.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/errors.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/parse.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/regexes.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/checks.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/doc.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/versions.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/core/schemas.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/index.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/ar.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/az.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/be.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/bg.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/ca.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/cs.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/da.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/de.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/en.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/eo.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/es.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/fa.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/fi.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/fr.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/fr-CA.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/he.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/hu.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/id.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/is.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/it.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/ja.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/zod/v4/locales/ka.js","../../../../node_modules/.pnpm/zod@4.2.1/node_modules/z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{ createAmazonBedrock as createAmazonBedrockOriginal } from \"@ai-sdk/amazon-bedrock\";\nimport { authWrapper } from \"../auth\";\n\nexport const createAmazonBedrock = (...args: Parameters<typeof createAmazonBedrockOriginal>) =>\n\tauthWrapper(createAmazonBedrockOriginal)(...args);\n","export const CF_TEMP_TOKEN = \"CF_TEMP_TOKEN\";\n\ntype HasApiKey = { apiKey: string };\n\nexport function authWrapper<Func extends (config?: HasApiKey) => any>(\n\tfunc: Func,\n): (config: Parameters<Func>[0]) => ReturnType<Func> {\n\treturn (config) => {\n\t\tif (!config) {\n\t\t\treturn func({ apiKey: CF_TEMP_TOKEN });\n\t\t}\n\t\tif (config.apiKey === undefined) {\n\t\t\tconfig.apiKey = CF_TEMP_TOKEN;\n\t\t}\n\t\treturn func(config);\n\t};\n}\n","import { createAnthropic as createAnthropicOriginal } from \"@ai-sdk/anthropic\";\nimport { authWrapper } from \"../auth\";\n\nexport const createAnthropic = (...args: Parameters<typeof createAnthropicOriginal>) =>\n\tauthWrapper(createAnthropicOriginal)(...args);\n","import { createAzure as createAzureOriginal } from \"@ai-sdk/azure\";\nimport { authWrapper } from \"../auth\";\n\nexport const createAzure = (...args: Parameters<typeof createAzureOriginal>) =>\n\tauthWrapper(createAzureOriginal)(...args);\n","import { createCerebras as createCerebrasOriginal } from \"@ai-sdk/cerebras\";\nimport { authWrapper } from \"../auth\";\n\nexport const createCerebras = (...args: Parameters<typeof createCerebrasOriginal>) =>\n\tauthWrapper(createCerebrasOriginal)(...args);\n","import { createCohere as createCohereOriginal } from \"@ai-sdk/cohere\";\nimport { authWrapper } from \"../auth\";\n\nexport const createCohere = (...args: Parameters<typeof createCohereOriginal>) =>\n\tauthWrapper(createCohereOriginal)(...args);\n","import { createDeepgram as createDeepgramOriginal } from \"@ai-sdk/deepgram\";\nimport { authWrapper } from \"../auth\";\n\nexport const createDeepgram = (...args: Parameters<typeof createDeepgramOriginal>) =>\n\tauthWrapper(createDeepgramOriginal)(...args);\n","import { createDeepSeek as createDeepSeekOriginal } from \"@ai-sdk/deepseek\";\nimport { authWrapper } from \"../auth\";\n\nexport const createDeepSeek = (...args: Parameters<typeof createDeepSeekOriginal>) =>\n\tauthWrapper(createDeepSeekOriginal)(...args);\n","import { createElevenLabs as createElevenLabsOriginal } from \"@ai-sdk/elevenlabs\";\nimport { authWrapper } from \"../auth\";\n\nexport const createElevenLabs = (...args: Parameters<typeof createElevenLabsOriginal>) =>\n\tauthWrapper(createElevenLabsOriginal)(...args);\n","import { createFireworks as createFireworksOriginal } from \"@ai-sdk/fireworks\";\nimport { authWrapper } from \"../auth\";\n\nexport const createFireworks = (...args: Parameters<typeof createFireworksOriginal>) =>\n\tauthWrapper(createFireworksOriginal)(...args);\n","import { createGoogleGenerativeAI as createGoogleGenerativeAIOriginal } from \"@ai-sdk/google\";\nimport { authWrapper } from \"../auth\";\n\nexport const createGoogleGenerativeAI = (\n\t...args: Parameters<typeof createGoogleGenerativeAIOriginal>\n) => authWrapper(createGoogleGenerativeAIOriginal)(...args);\n","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 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\nfunction createExpressModeFetch(\n apiKey: string,\n customFetch?: FetchFunction,\n): FetchFunction {\n return async (url, init) => {\n const urlWithKey = new URL(url.toString());\n urlWithKey.searchParams.set('key', apiKey);\n return (customFetch ?? fetch)(urlWithKey.toString(), init);\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: ${error.message}`);\n }\n};\n\n// Convert a string to base64url\nconst base64url = (str: string) => {\n return btoa(str).replace(/\\+/g, '-').replace(/\\//g, '_').replace(/=/g, '');\n};\nconst importPrivateKey = async (pemKey: string) => {\n const pemHeader = '-----BEGIN PRIVATE KEY-----';\n const pemFooter = '-----END PRIVATE KEY-----';\n\n // Remove header, footer, and any whitespace/newlines\n const pemContents = pemKey\n .replace(pemHeader, '')\n .replace(pemFooter, '')\n .replace(/\\s/g, '');\n\n // Decode base64 to binary\n const binaryString = atob(pemContents);\n\n // Convert binary string to Uint8Array\n const binaryData = new Uint8Array(binaryString.length);\n for (let i = 0; i < binaryString.length; i++) {\n binaryData[i] = binaryString.charCodeAt(i);\n }\n\n return await crypto.subtle.importKey(\n 'pkcs8',\n binaryData,\n { name: 'RSASSA-PKCS1-v1_5', hash: 'SHA-256' },\n true,\n ['sign'],\n );\n};\n\nconst buildJwt = async (credentials: GoogleCredentials) => {\n const now = Math.floor(Date.now() / 1000);\n\n // Only include kid in header if privateKeyId is provided\n const header: { alg: string; typ: string; kid?: string } = {\n alg: 'RS256',\n typ: 'JWT',\n };\n\n if (credentials.privateKeyId) {\n header.kid = credentials.privateKeyId;\n }\n\n const payload = {\n iss: credentials.clientEmail,\n scope: 'https://www.googleapis.com/auth/cloud-platform',\n aud: 'https://oauth2.googleapis.com/token',\n exp: now + 3600,\n iat: now,\n };\n\n const privateKey = await importPrivateKey(credentials.privateKey);\n\n const signingInput = `${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","export * as core from \"../core/index.js\";\nexport * from \"./schemas.js\";\nexport * from \"./checks.js\";\nexport * from \"./errors.js\";\nexport * from \"./parse.js\";\nexport * from \"./compat.js\";\n// zod-specified\nimport { config } from \"../core/index.js\";\nimport en from \"../locales/en.js\";\nconfig(en());\nexport { globalRegistry, registry, config, $output, $input, $brand, clone, regexes, treeifyError, prettifyError, formatError, flattenError, TimePrecision, util, NEVER, } from \"../core/index.js\";\nexport { toJSONSchema } from \"../core/json-schema-processors.js\";\nexport { fromJSONSchema } from \"./from-json-schema.js\";\nexport * as locales from \"../locales/index.js\";\n// iso\n// must be exported from top-level\n// https://github.com/colinhacks/zod/issues/4491\nexport { ZodISODateTime, ZodISODate, ZodISOTime, ZodISODuration } from \"./iso.js\";\nexport * as iso from \"./iso.js\";\nexport * as coerce from \"./coerce.js\";\n","export * from \"./core.js\";\nexport * from \"./parse.js\";\nexport * from \"./errors.js\";\nexport * from \"./schemas.js\";\nexport * from \"./checks.js\";\nexport * from \"./versions.js\";\nexport * as util from \"./util.js\";\nexport * as regexes from \"./regexes.js\";\nexport * as locales from \"../locales/index.js\";\nexport * from \"./registries.js\";\nexport * from \"./doc.js\";\nexport * from \"./api.js\";\nexport * from \"./to-json-schema.js\";\nexport { toJSONSchema } from \"./json-schema-processors.js\";\nexport { JSONSchemaGenerator } from \"./json-schema-generator.js\";\nexport * as JSONSchema from \"./json-schema.js\";\n","/** A special constant with type `never` */\nexport const NEVER = Object.freeze({\n status: \"aborted\",\n});\nexport /*@__NO_SIDE_EFFECTS__*/ function $constructor(name, initializer, params) {\n function init(inst, def) {\n if (!inst._zod) {\n Object.defineProperty(inst, \"_zod\", {\n value: {\n def,\n constr: _,\n traits: new Set(),\n },\n enumerable: false,\n });\n }\n if (inst._zod.traits.has(name)) {\n return;\n }\n inst._zod.traits.add(name);\n initializer(inst, def);\n // support prototype modifications\n const proto = _.prototype;\n const keys = Object.keys(proto);\n for (let i = 0; i < keys.length; i++) {\n const k = keys[i];\n if (!(k in inst)) {\n inst[k] = proto[k].bind(inst);\n }\n }\n }\n // doesn't work if Parent has a constructor with arguments\n const Parent = params?.Parent ?? Object;\n class Definition extends Parent {\n }\n Object.defineProperty(Definition, \"name\", { value: name });\n function _(def) {\n var _a;\n const inst = params?.Parent ? new Definition() : this;\n init(inst, def);\n (_a = inst._zod).deferred ?? (_a.deferred = []);\n for (const fn of inst._zod.deferred) {\n fn();\n }\n return inst;\n }\n Object.defineProperty(_, \"init\", { value: init });\n Object.defineProperty(_, Symbol.hasInstance, {\n value: (inst) => {\n if (params?.Parent && inst instanceof params.Parent)\n return true;\n return inst?._zod?.traits?.has(name);\n },\n });\n Object.defineProperty(_, \"name\", { value: name });\n return _;\n}\n////////////////////////////// UTILITIES ///////////////////////////////////////\nexport const $brand = Symbol(\"zod_brand\");\nexport class $ZodAsyncError extends Error {\n constructor() {\n super(`Encountered Promise during synchronous parse. Use .parseAsync() instead.`);\n }\n}\nexport class $ZodEncodeError extends Error {\n constructor(name) {\n super(`Encountered unidirectional transform during encode: ${name}`);\n this.name = \"ZodEncodeError\";\n }\n}\nexport const globalConfig = {};\nexport function config(newConfig) {\n if (newConfig)\n Object.assign(globalConfig, newConfig);\n return globalConfig;\n}\n","// functions\nexport function assertEqual(val) {\n return val;\n}\nexport function assertNotEqual(val) {\n return val;\n}\nexport function assertIs(_arg) { }\nexport function assertNever(_x) {\n throw new Error(\"Unexpected value in exhaustive check\");\n}\nexport function assert(_) { }\nexport function getEnumValues(entries) {\n const numericValues = Object.values(entries).filter((v) => typeof v === \"number\");\n const values = Object.entries(entries)\n .filter(([k, _]) => numericValues.indexOf(+k) === -1)\n .map(([_, v]) => v);\n return values;\n}\nexport function joinValues(array, separator = \"|\") {\n return array.map((val) => stringifyPrimitive(val)).join(separator);\n}\nexport function jsonStringifyReplacer(_, value) {\n if (typeof value === \"bigint\")\n return value.toString();\n return value;\n}\nexport function cached(getter) {\n const set = false;\n return {\n get value() {\n if (!set) {\n const value = getter();\n Object.defineProperty(this, \"value\", { value });\n return value;\n }\n throw new Error(\"cached value already set\");\n },\n };\n}\nexport function nullish(input) {\n return input === null || input === undefined;\n}\nexport function cleanRegex(source) {\n const start = source.startsWith(\"^\") ? 1 : 0;\n const end = source.endsWith(\"$\") ? source.length - 1 : source.length;\n return source.slice(start, end);\n}\nexport function floatSafeRemainder(val, step) {\n const valDecCount = (val.toString().split(\".\")[1] || \"\").length;\n const stepString = step.toString();\n let stepDecCount = (stepString.split(\".\")[1] || \"\").length;\n if (stepDecCount === 0 && /\\d?e-\\d?/.test(stepString)) {\n const match = stepString.match(/\\d?e-(\\d?)/);\n if (match?.[1]) {\n stepDecCount = Number.parseInt(match[1]);\n }\n }\n const decCount = valDecCount > stepDecCount ? valDecCount : stepDecCount;\n const valInt = Number.parseInt(val.toFixed(decCount).replace(\".\", \"\"));\n const stepInt = Number.parseInt(step.toFixed(decCount).replace(\".\", \"\"));\n return (valInt % stepInt) / 10 ** decCount;\n}\nconst EVALUATING = Symbol(\"evaluating\");\nexport function defineLazy(object, key, getter) {\n let value = undefined;\n Object.defineProperty(object, key, {\n get() {\n if (value === EVALUATING) {\n // Circular reference detected, return undefined to break the cycle\n return undefined;\n }\n if (value === undefined) {\n value = EVALUATING;\n value = getter();\n }\n return value;\n },\n set(v) {\n Object.defineProperty(object, key, {\n value: v,\n // configurable: true,\n });\n // object[key] = v;\n },\n configurable: true,\n });\n}\nexport function objectClone(obj) {\n return Object.create(Object.getPrototypeOf(obj), Object.getOwnPropertyDescriptors(obj));\n}\nexport function assignProp(target, prop, value) {\n Object.defineProperty(target, prop, {\n value,\n writable: true,\n enumerable: true,\n configurable: true,\n });\n}\nexport function mergeDefs(...defs) {\n const mergedDescriptors = {};\n for (const def of defs) {\n const descriptors = Object.getOwnPropertyDescriptors(def);\n Object.assign(mergedDescriptors, descriptors);\n }\n return Object.defineProperties({}, mergedDescriptors);\n}\nexport function cloneDef(schema) {\n return mergeDefs(schema._zod.def);\n}\nexport function getElementAtPath(obj, path) {\n if (!path)\n return obj;\n return path.reduce((acc, key) => acc?.[key], obj);\n}\nexport function promiseAllObject(promisesObj) {\n const keys = Object.keys(promisesObj);\n const promises = keys.map((key) => promisesObj[key]);\n return Promise.all(promises).then((results) => {\n const resolvedObj = {};\n for (let i = 0; i < keys.length; i++) {\n resolvedObj[keys[i]] = results[i];\n }\n return resolvedObj;\n });\n}\nexport function randomString(length = 10) {\n const chars = \"abcdefghijklmnopqrstuvwxyz\";\n let str = \"\";\n for (let i = 0; i < length; i++) {\n str += chars[Math.floor(Math.random() * chars.length)];\n }\n return str;\n}\nexport function esc(str) {\n return JSON.stringify(str);\n}\nexport function slugify(input) {\n return input\n .toLowerCase()\n .trim()\n .replace(/[^\\w\\s-]/g, \"\")\n .replace(/[\\s_-]+/g, \"-\")\n .replace(/^-+|-+$/g, \"\");\n}\nexport const captureStackTrace = (\"captureStackTrace\" in Error ? Error.captureStackTrace : (..._args) => { });\nexport function isObject(data) {\n return typeof data === \"object\" && data !== null && !Array.isArray(data);\n}\nexport const allowsEval = cached(() => {\n // @ts-ignore\n if (typeof navigator !== \"undefined\" && navigator?.userAgent?.includes(\"Cloudflare\")) {\n return false;\n }\n try {\n const F = Function;\n new F(\"\");\n return true;\n }\n catch (_) {\n return false;\n }\n});\nexport function isPlainObject(o) {\n if (isObject(o) === false)\n return false;\n // modified constructor\n const ctor = o.constructor;\n if (ctor === undefined)\n return true;\n if (typeof ctor !== \"function\")\n return true;\n // modified prototype\n const prot = ctor.prototype;\n if (isObject(prot) === false)\n return false;\n // ctor doesn't have static `isPrototypeOf`\n if (Object.prototype.hasOwnProperty.call(prot, \"isPrototypeOf\") ==