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documenso/patches/@ai-sdk__google-vertex@3.0.81.patch
Lucas Smith 960217c78d build: migrate from npm to pnpm with workspace catalogs 
- Switch package manager to pnpm 10 via corepack
- Add pnpm-workspace.yaml with 54+ shared dependency catalogs
- Convert all workspace packages to catalog: and workspace:* protocols
- Upgrade Turborepo from 1.x to 2.8.12 (pipeline -> tasks)
- Upgrade apps/openpage-api from Next 15 to Next 16
- Update Docker, CI workflows, and GitHub Actions for pnpm
- Convert patch file to pnpm native format
- Replace deprecated next lint with standalone eslint
- Update all documentation references from npm to pnpm
- Fix stale dependabot config and documentation paths
2026-03-06 14:28:12 +11:00

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diff --git a/dist/anthropic/edge/index.d.mts b/dist/anthropic/edge/index.d.mts
index b4a9a4c..382b922 100644
--- a/dist/anthropic/edge/index.d.mts
+++ b/dist/anthropic/edge/index.d.mts
@@ -1,4 +1,4 @@
-import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
import { anthropicTools } from '@ai-sdk/anthropic/internal';
@@ -20,17 +20,17 @@ interface GoogleCredentials {
privateKeyId?: string;
}
-type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
-interface GoogleVertexAnthropicProvider extends ProviderV2 {
+interface GoogleVertexAnthropicProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Creates a model for text generation.
*/
- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Anthropic-specific computer use tool.
*/
diff --git a/dist/anthropic/edge/index.d.ts b/dist/anthropic/edge/index.d.ts
index b4a9a4c..382b922 100644
--- a/dist/anthropic/edge/index.d.ts
+++ b/dist/anthropic/edge/index.d.ts
@@ -1,4 +1,4 @@
-import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
import { anthropicTools } from '@ai-sdk/anthropic/internal';
@@ -20,17 +20,17 @@ interface GoogleCredentials {
privateKeyId?: string;
}
-type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
-interface GoogleVertexAnthropicProvider extends ProviderV2 {
+interface GoogleVertexAnthropicProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Creates a model for text generation.
*/
- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Anthropic-specific computer use tool.
*/
diff --git a/dist/anthropic/edge/index.js b/dist/anthropic/edge/index.js
index f23e9a5..eee11de 100644
--- a/dist/anthropic/edge/index.js
+++ b/dist/anthropic/edge/index.js
@@ -32,7 +32,7 @@ var import_provider_utils3 = require("@ai-sdk/provider-utils");
var import_provider_utils = require("@ai-sdk/provider-utils");
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/edge/google-vertex-auth-edge.ts
var loadCredentials = async () => {
@@ -181,6 +181,7 @@ function createVertexAnthropic(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.chat = createChatModel;
provider.messages = createChatModel;
diff --git a/dist/anthropic/edge/index.js.map b/dist/anthropic/edge/index.js.map
index cb63175..ed91fdc 100644
--- a/dist/anthropic/edge/index.js.map
+++ b/dist/anthropic/edge/index.js.map
@@ -1 +1 @@
-{"version":3,"sources":["../../../src/anthropic/edge/index.ts","../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n createVertexAnthropic,\n vertexAnthropic,\n} from './google-vertex-anthropic-provider-edge';\nexport type {\n GoogleVertexAnthropicProviderSettings,\n GoogleVertexAnthropicProvider,\n} from './google-vertex-anthropic-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\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 createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\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 LanguageModelV2,\n NoSuchModelError,\n ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. 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\ No newline at end of file
+{"version":3,"sources":["../../../src/anthropic/edge/index.ts","../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n createVertexAnthropic,\n vertexAnthropic,\n} from './google-vertex-anthropic-provider-edge';\nexport type {\n GoogleVertexAnthropicProviderSettings,\n GoogleVertexAnthropicProvider,\n} from './google-vertex-anthropic-provider-edge';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\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 createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\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 LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\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 region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\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\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic 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.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;;;AAAA;AAAA;AAAA,+BAAAA;AAAA,EAAA;AAAA;AAAA;;;ACAA,IAAAC,yBAAwB;;;ACAxB,4BAKO;;;ACHA,IAAM,UACX,OACI,kBACA;;;ADuBN,IAAM,kBAAkB,YAAwC;AAC9D,MAAI;AACF,WAAO;AAAA,MACL,iBAAa,mCAAY;AAAA,QACvB,cAAc;AAAA,QACd,aAAa;AAAA,QACb,yBAAyB;AAAA,QACzB,aAAa;AAAA,MACf,CAAC;AAAA,MACD,gBAAY,mCAAY;AAAA,QACtB,cAAc;AAAA,QACd,aAAa;AAAA,QACb,yBAAyB;AAAA,QACzB,aAAa;AAAA,MACf,CAAC;AAAA,MACD,kBAAc,2CAAoB;AAAA,QAChC,cAAc;AAAA,QACd,yBAAyB;AAAA,MAC3B,CAAC;AAAA,IACH;AAAA,EACF,SAAS,OAAY;AACnB,UAAM,IAAI,MAAM,sCAAsC,MAAM,OAAO,EAAE;AAAA,EACvE;AACF;AAGA,IAAM,YAAY,CAAC,QAAgB;AACjC,SAAO,KAAK,GAAG,EAAE,QAAQ,OAAO,GAAG,EAAE,QAAQ,OAAO,GAAG,EAAE,QAAQ,MAAM,EAAE;AAC3E;AACA,IAAM,mBAAmB,OAAO,WAAmB;AACjD,QAAM,YAAY;AAClB,QAAM,YAAY;AAGlB,QAAM,cAAc,OACjB,QAAQ,WAAW,EAAE,EACrB,QAAQ,WAAW,EAAE,EACrB,QAAQ,OAAO,EAAE;AAGpB,QAAM,eAAe,KAAK,WAAW;AAGrC,QAAM,aAAa,IAAI,WAAW,aAAa,MAAM;AACrD,WAAS,IAAI,GAAG,IAAI,aAAa,QAAQ,KAAK;AAC5C,eAAW,CAAC,IAAI,aAAa,WAAW,CAAC;AAAA,EAC3C;AAEA,SAAO,MAAM,OAAO,OAAO;AAAA,IACzB;AAAA,IACA;AAAA,IACA,EAAE,MAAM,qBAAqB,MAAM,UAAU;AAAA,IAC7C;AAAA,IACA,CAAC,MAAM;AAAA,EACT;AACF;AAEA,IAAM,WAAW,OAAO,gBAAmC;AACzD,QAAM,MAAM,KAAK,MAAM,KAAK,IAAI,IAAI,GAAI;AAGxC,QAAM,SAAqD;AAAA,IACzD,KAAK;AAAA,IACL,KAAK;AAAA,EACP;AAEA,MAAI,YAAY,cAAc;AAC5B,WAAO,MAAM,YAAY;AAAA,EAC3B;AAEA,QAAM,UAAU;AAAA,IACd,KAAK,YAAY;AAAA,IACjB,OAAO;AAAA,IACP,KAAK;AAAA,IACL,KAAK,MAAM;AAAA,IACX,KAAK;AAAA,EACP;AAEA,QAAM,aAAa,MAAM,iBAAiB,YAAY,UAAU;AAEhE,QAAM,eAAe,GAAG,UAAU,KAAK,UAAU,MAAM,CAAC,CAAC,IAAI;AAAA,IAC3D,KAAK,UAAU,OAAO;AAAA,EACxB,CAAC;AACD,QAAM,UAAU,IAAI,YAAY;AAChC,QAAM,OAAO,QAAQ,OAAO,YAAY;AAExC,QAAM,YAAY,MAAM,OAAO,OAAO;AAAA,IACpC;AAAA,IACA;AAAA,IACA;AAAA,EACF;AAEA,QAAM,kBAAkB;AAAA,IACtB,OAAO,aAAa,GAAG,IAAI,WAAW,SAAS,CAAC;AAAA,EAClD;AAEA,SAAO,GAAG,UAAU,KAAK,UAAU,MAAM,CAAC,CAAC,IAAI;AAAA,IAC7C,KAAK,UAAU,OAAO;AAAA,EACxB,CAAC,IAAI,eAAe;AACtB;AAMA,eAAsB,kBAAkB,aAAiC;AACvE,MAAI;AACF,UAAM,QAAQ,eAAgB,MAAM,gBAAgB;AACpD,UAAM,MAAM,MAAM,SAAS,KAAK;AAEhC,UAAM,WAAW,MAAM,MAAM,uCAAuC;AAAA,MAClE,QAAQ;AAAA,MACR,aAAS;AAAA,QACP,EAAE,gBAAgB,oCAAoC;AAAA,QACtD,wBAAwB,OAAO;AAAA,YAC/B,sDAA+B;AAAA,MACjC;AAAA,MACA,MAAM,IAAI,gBAAgB;AAAA,QACxB,YAAY;AAAA,QACZ,WAAW;AAAA,MACb,CAAC;AAAA,IACH,CAAC;AAED,QAAI,CAAC,SAAS,IAAI;AAChB,YAAM,IAAI,MAAM,yBAAyB,SAAS,UAAU,EAAE;AAAA,IAChE;AAEA,UAAM,OAAO,MAAM,SAAS,KAAK;AACjC,WAAO,KAAK;AAAA,EACd,SAAS,OAAO;AACd,UAAM;AAAA,EACR;AACF;;;AEhKA,sBAIO;AACP,IAAAC,yBAKO;AACP,sBAGO;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,eAAW,4CAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,cAAU,4CAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,sDAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAC;AAkFI,eAAI,+CAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AH1GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,UAAM,gCAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["createVertexAnthropic","import_provider_utils","import_provider_utils","_a","baseURL","createVertexAnthropic"]}
\ No newline at end of file
diff --git a/dist/anthropic/edge/index.mjs b/dist/anthropic/edge/index.mjs
index 3719f5f..726fb5d 100644
--- a/dist/anthropic/edge/index.mjs
+++ b/dist/anthropic/edge/index.mjs
@@ -10,7 +10,7 @@ import {
} from "@ai-sdk/provider-utils";
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/edge/google-vertex-auth-edge.ts
var loadCredentials = async () => {
@@ -167,6 +167,7 @@ function createVertexAnthropic(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.chat = createChatModel;
provider.messages = createChatModel;
diff --git a/dist/anthropic/edge/index.mjs.map b/dist/anthropic/edge/index.mjs.map
index 9200347..ba38267 100644
--- a/dist/anthropic/edge/index.mjs.map
+++ b/dist/anthropic/edge/index.mjs.map
@@ -1 +1 @@
-{"version":3,"sources":["../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\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 createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleCredentials,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\n * Default Google Vertex AI Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\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 LanguageModelV2,\n NoSuchModelError,\n ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\n /**\n * Google Cloud project ID. 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+{"version":3,"sources":["../../../src/anthropic/edge/google-vertex-anthropic-provider-edge.ts","../../../src/edge/google-vertex-auth-edge.ts","../../../src/version.ts","../../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport {\n generateAuthToken,\n GoogleCredentials,\n} from '../../edge/google-vertex-auth-edge';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from '../google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n * Optional. The Google credentials for the Google Cloud service account. 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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 LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\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 region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\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\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic 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.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";AAAA,SAAS,eAAe;;;ACAxB;AAAA,EACE;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,OACK;;;ACHA,IAAM,UACX,OACI,kBACA;;;ADuBN,IAAM,kBAAkB,YAAwC;AAC9D,MAAI;AACF,WAAO;AAAA,MACL,aAAa,YAAY;AAAA,QACvB,cAAc;AAAA,QACd,aAAa;AAAA,QACb,yBAAyB;AAAA,QACzB,aAAa;AAAA,MACf,CAAC;AAAA,MACD,YAAY,YAAY;AAAA,QACtB,cAAc;AAAA,QACd,aAAa;AAAA,QACb,yBAAyB;AAAA,QACzB,aAAa;AAAA,MACf,CAAC;AAAA,MACD,cAAc,oBAAoB;AAAA,QAChC,cAAc;AAAA,QACd,yBAAyB;AAAA,MAC3B,CAAC;AAAA,IACH;AAAA,EACF,SAAS,OAAY;AACnB,UAAM,IAAI,MAAM,sCAAsC,MAAM,OAAO,EAAE;AAAA,EACvE;AACF;AAGA,IAAM,YAAY,CAAC,QAAgB;AACjC,SAAO,KAAK,GAAG,EAAE,QAAQ,OAAO,GAAG,EAAE,QAAQ,OAAO,GAAG,EAAE,QAAQ,MAAM,EAAE;AAC3E;AACA,IAAM,mBAAmB,OAAO,WAAmB;AACjD,QAAM,YAAY;AAClB,QAAM,YAAY;AAGlB,QAAM,cAAc,OACjB,QAAQ,WAAW,EAAE,EACrB,QAAQ,WAAW,EAAE,EACrB,QAAQ,OAAO,EAAE;AAGpB,QAAM,eAAe,KAAK,WAAW;AAGrC,QAAM,aAAa,IAAI,WAAW,aAAa,MAAM;AACrD,WAAS,IAAI,GAAG,IAAI,aAAa,QAAQ,KAAK;AAC5C,eAAW,CAAC,IAAI,aAAa,WAAW,CAAC;AAAA,EAC3C;AAEA,SAAO,MAAM,OAAO,OAAO;AAAA,IACzB;AAAA,IACA;AAAA,IACA,EAAE,MAAM,qBAAqB,MAAM,UAAU;AAAA,IAC7C;AAAA,IACA,CAAC,MAAM;AAAA,EACT;AACF;AAEA,IAAM,WAAW,OAAO,gBAAmC;AACzD,QAAM,MAAM,KAAK,MAAM,KAAK,IAAI,IAAI,GAAI;AAGxC,QAAM,SAAqD;AAAA,IACzD,KAAK;AAAA,IACL,KAAK;AAAA,EACP;AAEA,MAAI,YAAY,cAAc;AAC5B,WAAO,MAAM,YAAY;AAAA,EAC3B;AAEA,QAAM,UAAU;AAAA,IACd,KAAK,YAAY;AAAA,IACjB,OAAO;AAAA,IACP,KAAK;AAAA,IACL,KAAK,MAAM;AAAA,IACX,KAAK;AAAA,EACP;AAEA,QAAM,aAAa,MAAM,iBAAiB,YAAY,UAAU;AAEhE,QAAM,eAAe,GAAG,UAAU,KAAK,UAAU,MAAM,CAAC,CAAC,IAAI;AAAA,IAC3D,KAAK,UAAU,OAAO;AAAA,EACxB,CAAC;AACD,QAAM,UAAU,IAAI,YAAY;AAChC,QAAM,OAAO,QAAQ,OAAO,YAAY;AAExC,QAAM,YAAY,MAAM,OAAO,OAAO;AAAA,IACpC;AAAA,IACA;AAAA,IACA;AAAA,EACF;AAEA,QAAM,kBAAkB;AAAA,IACtB,OAAO,aAAa,GAAG,IAAI,WAAW,SAAS,CAAC;AAAA,EAClD;AAEA,SAAO,GAAG,UAAU,KAAK,UAAU,MAAM,CAAC,CAAC,IAAI;AAAA,IAC7C,KAAK,UAAU,OAAO;AAAA,EACxB,CAAC,IAAI,eAAe;AACtB;AAMA,eAAsB,kBAAkB,aAAiC;AACvE,MAAI;AACF,UAAM,QAAQ,eAAgB,MAAM,gBAAgB;AACpD,UAAM,MAAM,MAAM,SAAS,KAAK;AAEhC,UAAM,WAAW,MAAM,MAAM,uCAAuC;AAAA,MAClE,QAAQ;AAAA,MACR,SAAS;AAAA,QACP,EAAE,gBAAgB,oCAAoC;AAAA,QACtD,wBAAwB,OAAO;AAAA,QAC/B,+BAA+B;AAAA,MACjC;AAAA,MACA,MAAM,IAAI,gBAAgB;AAAA,QACxB,YAAY;AAAA,QACZ,WAAW;AAAA,MACb,CAAC;AAAA,IACH,CAAC;AAED,QAAI,CAAC,SAAS,IAAI;AAChB,YAAM,IAAI,MAAM,yBAAyB,SAAS,UAAU,EAAE;AAAA,IAChE;AAEA,UAAM,OAAO,MAAM,SAAS,KAAK;AACjC,WAAO,KAAK;AAAA,EACd,SAAS,OAAO;AACd,UAAM;AAAA,EACR;AACF;;;AEhKA;AAAA,EAEE;AAAA,OAEK;AACP;AAAA,EAGE,uBAAAA;AAAA,EACA;AAAA,OACK;AACP;AAAA,EACE;AAAA,EACA;AAAA,OACK;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,WAAWA,qBAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,UAAUA,qBAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,0BAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAC;AAkFI,eAAI,+BAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AH1GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,MAAM,QAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["loadOptionalSetting","_a","baseURL","createVertexAnthropic"]}
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diff --git a/dist/anthropic/index.d.mts b/dist/anthropic/index.d.mts
index 0650bf2..948aa4e 100644
--- a/dist/anthropic/index.d.mts
+++ b/dist/anthropic/index.d.mts
@@ -1,19 +1,19 @@
import { GoogleAuthOptions } from 'google-auth-library';
-import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
import { anthropicTools } from '@ai-sdk/anthropic/internal';
-type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
-interface GoogleVertexAnthropicProvider extends ProviderV2 {
+interface GoogleVertexAnthropicProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Creates a model for text generation.
*/
- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Anthropic-specific computer use tool.
*/
diff --git a/dist/anthropic/index.d.ts b/dist/anthropic/index.d.ts
index 0650bf2..948aa4e 100644
--- a/dist/anthropic/index.d.ts
+++ b/dist/anthropic/index.d.ts
@@ -1,19 +1,19 @@
import { GoogleAuthOptions } from 'google-auth-library';
-import { ProviderV2, LanguageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3 } from '@ai-sdk/provider';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
import { anthropicTools } from '@ai-sdk/anthropic/internal';
-type GoogleVertexAnthropicMessagesModelId = 'claude-opus-4-5@20251101' | 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
+type GoogleVertexAnthropicMessagesModelId = 'claude-sonnet-4-5@20250929' | 'claude-opus-4-1@20250805' | 'claude-opus-4@20250514' | 'claude-sonnet-4@20250514' | 'claude-3-7-sonnet@20250219' | 'claude-3-5-sonnet-v2@20241022' | 'claude-3-5-haiku@20241022' | 'claude-3-5-sonnet@20240620' | 'claude-3-haiku@20240307' | 'claude-3-sonnet@20240229' | 'claude-3-opus@20240229' | (string & {});
-interface GoogleVertexAnthropicProvider extends ProviderV2 {
+interface GoogleVertexAnthropicProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Creates a model for text generation.
*/
- languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV2;
+ languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;
/**
Anthropic-specific computer use tool.
*/
diff --git a/dist/anthropic/index.js b/dist/anthropic/index.js
index 2f15ff7..e4811ee 100644
--- a/dist/anthropic/index.js
+++ b/dist/anthropic/index.js
@@ -91,6 +91,7 @@ function createVertexAnthropic(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.chat = createChatModel;
provider.messages = createChatModel;
diff --git a/dist/anthropic/index.js.map b/dist/anthropic/index.js.map
index c93b564..01468bb 100644
--- a/dist/anthropic/index.js.map
+++ b/dist/anthropic/index.js.map
@@ -1 +1 @@
-{"version":3,"sources":["../../src/anthropic/index.ts","../../src/anthropic/google-vertex-anthropic-provider-node.ts","../../src/google-vertex-auth-google-auth-library.ts","../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n vertexAnthropic,\n createVertexAnthropic,\n} from './google-vertex-anthropic-provider-node';\nexport type {\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings,\n} from './google-vertex-anthropic-provider-node';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from '../google-vertex-auth-google-auth-library';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from './google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n Optional. 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Defaults to the value of the `GOOGLE_VERTEX_PROJECT` environment variable.\n */\n project?: string;\n\n /**\n * Google Cloud region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Resolvable<Record<string, string | undefined>>;\n\n /**\nCustom fetch implementation. 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+{"version":3,"sources":["../../src/anthropic/index.ts","../../src/anthropic/google-vertex-anthropic-provider-node.ts","../../src/google-vertex-auth-google-auth-library.ts","../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["export {\n vertexAnthropic,\n createVertexAnthropic,\n} from './google-vertex-anthropic-provider-node';\nexport type {\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings,\n} from './google-vertex-anthropic-provider-node';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from '../google-vertex-auth-google-auth-library';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from './google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\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 region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\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\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic 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.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;;;AAAA;AAAA;AAAA,+BAAAA;AAAA,EAAA;AAAA;AAAA;;;ACAA,IAAAC,yBAAwB;;;ACAxB,iCAA8C;AAE9C,IAAI,eAAkC;AACtC,IAAI,cAAwC;AAE5C,SAAS,QAAQ,SAA4B;AAC3C,MAAI,CAAC,gBAAgB,YAAY,aAAa;AAC5C,mBAAe,IAAI,sCAAW;AAAA,MAC5B,QAAQ,CAAC,gDAAgD;AAAA,MACzD,GAAG;AAAA,IACL,CAAC;AACD,kBAAc;AAAA,EAChB;AACA,SAAO;AACT;AAEA,eAAsB,kBAAkB,SAA6B;AACnE,QAAM,OAAO,QAAQ,WAAW,CAAC,CAAC;AAClC,QAAM,SAAS,MAAM,KAAK,UAAU;AACpC,QAAM,QAAQ,MAAM,OAAO,eAAe;AAC1C,UAAO,+BAAO,UAAS;AACzB;;;ACrBA,sBAIO;AACP,4BAKO;AACP,sBAGO;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,eAAW,2CAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,cAAU,2CAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,qDAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAC;AAkFI,eAAI,+CAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iCAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AF3GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,UAAM,gCAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["createVertexAnthropic","import_provider_utils","_a","baseURL","createVertexAnthropic"]}
\ No newline at end of file
diff --git a/dist/anthropic/index.mjs b/dist/anthropic/index.mjs
index d3bb8e6..ac0adf7 100644
--- a/dist/anthropic/index.mjs
+++ b/dist/anthropic/index.mjs
@@ -72,6 +72,7 @@ function createVertexAnthropic(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.chat = createChatModel;
provider.messages = createChatModel;
diff --git a/dist/anthropic/index.mjs.map b/dist/anthropic/index.mjs.map
index 16f8ed3..0876a26 100644
--- a/dist/anthropic/index.mjs.map
+++ b/dist/anthropic/index.mjs.map
@@ -1 +1 @@
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\ No newline at end of file
+{"version":3,"sources":["../../src/anthropic/google-vertex-anthropic-provider-node.ts","../../src/google-vertex-auth-google-auth-library.ts","../../src/anthropic/google-vertex-anthropic-provider.ts"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from '../google-vertex-auth-google-auth-library';\nimport {\n createVertexAnthropic as createVertexAnthropicOriginal,\n GoogleVertexAnthropicProvider,\n GoogleVertexAnthropicProviderSettings as GoogleVertexAnthropicProviderSettingsOriginal,\n} from './google-vertex-anthropic-provider';\n\nexport type { GoogleVertexAnthropicProvider };\n\nexport interface GoogleVertexAnthropicProviderSettings\n extends GoogleVertexAnthropicProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n return createVertexAnthropicOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex Anthropic provider instance.\n */\nexport const vertexAnthropic = createVertexAnthropic();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import {\n LanguageModelV3,\n NoSuchModelError,\n ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n FetchFunction,\n Resolvable,\n loadOptionalSetting,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport {\n anthropicTools,\n AnthropicMessagesLanguageModel,\n} from '@ai-sdk/anthropic/internal';\nimport { GoogleVertexAnthropicMessagesModelId } from './google-vertex-anthropic-messages-options';\nexport interface GoogleVertexAnthropicProvider extends ProviderV3 {\n /**\nCreates a model for text generation.\n*/\n (modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nCreates a model for text generation.\n*/\n languageModel(modelId: GoogleVertexAnthropicMessagesModelId): LanguageModelV3;\n\n /**\nAnthropic-specific computer use tool.\n */\n tools: typeof anthropicTools;\n}\n\nexport interface GoogleVertexAnthropicProviderSettings {\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 region. Defaults to the value of the `GOOGLE_VERTEX_LOCATION` environment variable.\n */\n location?: string;\n\n /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n */\n baseURL?: string;\n\n /**\nCustom headers to include in the requests.\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\n/**\nCreate a Google Vertex Anthropic provider instance.\n */\nexport function createVertexAnthropic(\n options: GoogleVertexAnthropicProviderSettings = {},\n): GoogleVertexAnthropicProvider {\n const location = loadOptionalSetting({\n settingValue: options.location,\n environmentVariableName: 'GOOGLE_VERTEX_LOCATION',\n });\n const project = loadOptionalSetting({\n settingValue: options.project,\n environmentVariableName: 'GOOGLE_VERTEX_PROJECT',\n });\n\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n `https://${location === 'global' ? '' : location + '-'}aiplatform.googleapis.com/v1/projects/${project}/locations/${location}/publishers/anthropic/models`;\n\n const createChatModel = (modelId: GoogleVertexAnthropicMessagesModelId) =>\n new AnthropicMessagesLanguageModel(modelId, {\n provider: 'vertex.anthropic.messages',\n baseURL,\n headers: options.headers ?? {},\n fetch: options.fetch,\n\n buildRequestUrl: (baseURL, isStreaming) =>\n `${baseURL}/${modelId}:${\n isStreaming ? 'streamRawPredict' : 'rawPredict'\n }`,\n transformRequestBody: args => {\n // Remove model from args and add anthropic version\n const { model, ...rest } = args;\n return {\n ...rest,\n anthropic_version: 'vertex-2023-10-16',\n };\n },\n // Google Vertex Anthropic doesn't support URL sources, force download and base64 conversion\n supportedUrls: () => ({}),\n });\n\n const provider = function (modelId: GoogleVertexAnthropicMessagesModelId) {\n if (new.target) {\n throw new Error(\n 'The Anthropic 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.chat = createChatModel;\n provider.messages = createChatModel;\n\n provider.textEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' });\n };\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n provider.tools = anthropicTools;\n\n return provider;\n}\n"],"mappings":";AAAA,SAAS,eAAe;;;ACAxB,SAAS,kBAAqC;AAE9C,IAAI,eAAkC;AACtC,IAAI,cAAwC;AAE5C,SAAS,QAAQ,SAA4B;AAC3C,MAAI,CAAC,gBAAgB,YAAY,aAAa;AAC5C,mBAAe,IAAI,WAAW;AAAA,MAC5B,QAAQ,CAAC,gDAAgD;AAAA,MACzD,GAAG;AAAA,IACL,CAAC;AACD,kBAAc;AAAA,EAChB;AACA,SAAO;AACT;AAEA,eAAsB,kBAAkB,SAA6B;AACnE,QAAM,OAAO,QAAQ,WAAW,CAAC,CAAC;AAClC,QAAM,SAAS,MAAM,KAAK,UAAU;AACpC,QAAM,QAAQ,MAAM,OAAO,eAAe;AAC1C,UAAO,+BAAO,UAAS;AACzB;;;ACrBA;AAAA,EAEE;AAAA,OAEK;AACP;AAAA,EAGE;AAAA,EACA;AAAA,OACK;AACP;AAAA,EACE;AAAA,EACA;AAAA,OACK;AAmDA,SAAS,sBACd,UAAiD,CAAC,GACnB;AAnEjC;AAoEE,QAAM,WAAW,oBAAoB;AAAA,IACnC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AACD,QAAM,UAAU,oBAAoB;AAAA,IAClC,cAAc,QAAQ;AAAA,IACtB,yBAAyB;AAAA,EAC3B,CAAC;AAED,QAAM,WACJ,0BAAqB,QAAQ,OAAO,MAApC,YACA,WAAW,aAAa,WAAW,KAAK,WAAW,GAAG,yCAAyC,OAAO,cAAc,QAAQ;AAE9H,QAAM,kBAAkB,CAAC,YAA+C;AAjF1E,QAAAA;AAkFI,eAAI,+BAA+B,SAAS;AAAA,MAC1C,UAAU;AAAA,MACV;AAAA,MACA,UAASA,MAAA,QAAQ,YAAR,OAAAA,MAAmB,CAAC;AAAA,MAC7B,OAAO,QAAQ;AAAA,MAEf,iBAAiB,CAACC,UAAS,gBACzB,GAAGA,QAAO,IAAI,OAAO,IACnB,cAAc,qBAAqB,YACrC;AAAA,MACF,sBAAsB,UAAQ;AAE5B,cAAM,EAAE,OAAO,GAAG,KAAK,IAAI;AAC3B,eAAO;AAAA,UACL,GAAG;AAAA,UACH,mBAAmB;AAAA,QACrB;AAAA,MACF;AAAA;AAAA,MAEA,eAAe,OAAO,CAAC;AAAA,IACzB,CAAC;AAAA;AAEH,QAAM,WAAW,SAAU,SAA+C;AACxE,QAAI,YAAY;AACd,YAAM,IAAI;AAAA,QACR;AAAA,MACF;AAAA,IACF;AAEA,WAAO,gBAAgB,OAAO;AAAA,EAChC;AAEA,WAAS,uBAAuB;AAChC,WAAS,gBAAgB;AACzB,WAAS,OAAO;AAChB,WAAS,WAAW;AAEpB,WAAS,qBAAqB,CAAC,YAAoB;AACjD,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,qBAAqB,CAAC;AAAA,EACzE;AACA,WAAS,aAAa,CAAC,YAAoB;AACzC,UAAM,IAAI,iBAAiB,EAAE,SAAS,WAAW,aAAa,CAAC;AAAA,EACjE;AAEA,WAAS,QAAQ;AAEjB,SAAO;AACT;;;AF3GO,SAASC,uBACd,UAAiD,CAAC,GACnB;AAC/B,SAAO,sBAA8B;AAAA,IACnC,GAAG;AAAA,IACH,SAAS,aAAa;AAAA,MACpB,eAAe,UAAU,MAAM;AAAA,QAC7B,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,GAAI,MAAM,QAAQ,QAAQ,OAAO;AAAA,IACnC;AAAA,EACF,CAAC;AACH;AAKO,IAAM,kBAAkBA,uBAAsB;","names":["_a","baseURL","createVertexAnthropic"]}
\ No newline at end of file
diff --git a/dist/edge/index.d.mts b/dist/edge/index.d.mts
index d82459e..020874d 100644
--- a/dist/edge/index.d.mts
+++ b/dist/edge/index.d.mts
@@ -1,10 +1,10 @@
-import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider';
import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
type GoogleVertexImageModelId = 'imagen-3.0-generate-001' | 'imagen-3.0-generate-002' | 'imagen-3.0-fast-generate-001' | 'imagen-4.0-generate-preview-06-06' | 'imagen-4.0-fast-generate-preview-06-06' | 'imagen-4.0-ultra-generate-preview-06-06' | (string & {});
-type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
declare const googleVertexTools: {
googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, {
@@ -31,20 +31,20 @@ declare const googleVertexTools: {
}>;
};
-interface GoogleVertexProvider extends ProviderV2 {
+interface GoogleVertexProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexModelId): LanguageModelV2;
- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;
+ (modelId: GoogleVertexModelId): LanguageModelV3;
+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;
/**
* Creates a model for image generation.
*/
- image(modelId: GoogleVertexImageModelId): ImageModelV2;
+ image(modelId: GoogleVertexImageModelId): ImageModelV3;
/**
Creates a model for image generation.
*/
- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;
+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;
tools: typeof googleVertexTools;
}
interface GoogleVertexProviderSettings$1 {
@@ -56,6 +56,12 @@ interface GoogleVertexProviderSettings$1 {
Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.
*/
project?: string;
+ /**
+ * API key for Express Mode authentication. When provided, uses simplified
+ * authentication with `x-goog-api-key` header instead of OAuth.
+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.
+ */
+ apiKey?: string;
/**
* Headers to use for requests. Can be:
* - A headers object
diff --git a/dist/edge/index.d.ts b/dist/edge/index.d.ts
index d82459e..020874d 100644
--- a/dist/edge/index.d.ts
+++ b/dist/edge/index.d.ts
@@ -1,10 +1,10 @@
-import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider';
import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
type GoogleVertexImageModelId = 'imagen-3.0-generate-001' | 'imagen-3.0-generate-002' | 'imagen-3.0-fast-generate-001' | 'imagen-4.0-generate-preview-06-06' | 'imagen-4.0-fast-generate-preview-06-06' | 'imagen-4.0-ultra-generate-preview-06-06' | (string & {});
-type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
declare const googleVertexTools: {
googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, {
@@ -31,20 +31,20 @@ declare const googleVertexTools: {
}>;
};
-interface GoogleVertexProvider extends ProviderV2 {
+interface GoogleVertexProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexModelId): LanguageModelV2;
- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;
+ (modelId: GoogleVertexModelId): LanguageModelV3;
+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;
/**
* Creates a model for image generation.
*/
- image(modelId: GoogleVertexImageModelId): ImageModelV2;
+ image(modelId: GoogleVertexImageModelId): ImageModelV3;
/**
Creates a model for image generation.
*/
- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;
+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;
tools: typeof googleVertexTools;
}
interface GoogleVertexProviderSettings$1 {
@@ -56,6 +56,12 @@ interface GoogleVertexProviderSettings$1 {
Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.
*/
project?: string;
+ /**
+ * API key for Express Mode authentication. When provided, uses simplified
+ * authentication with `x-goog-api-key` header instead of OAuth.
+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.
+ */
+ apiKey?: string;
/**
* Headers to use for requests. Can be:
* - A headers object
diff --git a/dist/edge/index.js b/dist/edge/index.js
index 5061210..e47d6de 100644
--- a/dist/edge/index.js
+++ b/dist/edge/index.js
@@ -33,7 +33,7 @@ var import_internal2 = require("@ai-sdk/google/internal");
var import_provider_utils4 = require("@ai-sdk/provider-utils");
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/google-vertex-embedding-model.ts
var import_provider = require("@ai-sdk/provider");
@@ -103,7 +103,7 @@ var googleVertexEmbeddingProviderOptions = import_v42.z.object({
// src/google-vertex-embedding-model.ts
var GoogleVertexEmbeddingModel = class {
constructor(modelId, config) {
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
this.maxEmbeddingsPerCall = 2048;
this.supportsParallelCalls = true;
this.modelId = modelId;
@@ -196,7 +196,7 @@ var GoogleVertexImageModel = class {
constructor(modelId, config) {
this.modelId = modelId;
this.config = config;
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list
this.maxImagesPerCall = 4;
}
@@ -318,12 +318,28 @@ function createVertex(options = {}) {
environmentVariableName: "GOOGLE_VERTEX_LOCATION",
description: "Google Vertex location"
});
+ const loadApiKey = () => (0, import_provider_utils4.loadSetting)({
+ settingValue: options.apiKey,
+ settingName: "apiKey",
+ environmentVariableName: "GOOGLE_VERTEX_API_KEY",
+ description: "Google Vertex API key"
+ });
+ const isExpressMode = () => {
+ try {
+ const apiKey = loadApiKey();
+ return apiKey != null && apiKey !== "";
+ } catch (e) {
+ return false;
+ }
+ };
const loadBaseURL = () => {
- var _a;
const region = loadVertexLocation();
const project = loadVertexProject();
+ if (options.baseURL) {
+ return (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL);
+ }
const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`;
- return (_a = (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
};
const createConfig = (name) => {
const getHeaders = async () => {
@@ -366,6 +382,7 @@ function createVertex(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.textEmbeddingModel = createEmbeddingModel;
provider.image = createImageModel;
@@ -484,12 +501,21 @@ async function generateAuthToken(credentials) {
function createVertex2(options = {}) {
return createVertex({
...options,
- headers: async () => ({
- Authorization: `Bearer ${await generateAuthToken(
- options.googleCredentials
- )}`,
- ...await (0, import_provider_utils6.resolve)(options.headers)
- })
+ headers: async () => {
+ const resolvedHeaders = await (0, import_provider_utils6.resolve)(options.headers);
+ if (options.apiKey) {
+ return {
+ "x-goog-api-key": options.apiKey,
+ ...resolvedHeaders
+ };
+ }
+ return {
+ Authorization: `Bearer ${await generateAuthToken(
+ options.googleCredentials
+ )}`,
+ ...resolvedHeaders
+ };
+ }
});
}
var vertex = createVertex2();
diff --git a/dist/edge/index.js.map b/dist/edge/index.js.map
index 7162bdc..dbdd593 100644
--- a/dist/edge/index.js.map
+++ b/dist/edge/index.js.map
@@ -1 +1 @@
-{"version":3,"sources":["../../src/edge/index.ts","../../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":["export { createVertex, vertex } from './google-vertex-provider-edge';\nexport type {\n GoogleVertexProviderSettings,\n GoogleVertexProvider,\n} from './google-vertex-provider-edge';\n","import { 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 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 { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\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\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\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 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 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/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.languageModel = createChatModel;\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 EmbeddingModelV2,\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 EmbeddingModelV2<string> {\n readonly specificationVersion = 'v2';\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<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 ImageModelV2 {\n readonly specificationVersion = 'v2';\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 }: Parameters<ImageModelV2['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV2['doGenerate']>>\n > {\n const warnings: Array<ImageModelV2CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\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 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+{"version":3,"sources":["../../src/edge/index.ts","../../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":["export { createVertex, vertex } from './google-vertex-provider-edge';\nexport type {\n GoogleVertexProviderSettings,\n GoogleVertexProvider,\n} from './google-vertex-provider-edge';\n","import { 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. 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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 * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: 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 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 loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.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<string> {\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<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV3<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 }: Parameters<ImageModelV3['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV3['doGenerate']>>\n > {\n const warnings: Array<ImageModelV3CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\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: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n 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\ No newline at end of file
diff --git a/dist/edge/index.mjs b/dist/edge/index.mjs
index 98f21bd..861ab39 100644
--- a/dist/edge/index.mjs
+++ b/dist/edge/index.mjs
@@ -12,7 +12,7 @@ import {
} from "@ai-sdk/provider-utils";
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/google-vertex-embedding-model.ts
import {
@@ -90,7 +90,7 @@ var googleVertexEmbeddingProviderOptions = z2.object({
// src/google-vertex-embedding-model.ts
var GoogleVertexEmbeddingModel = class {
constructor(modelId, config) {
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
this.maxEmbeddingsPerCall = 2048;
this.supportsParallelCalls = true;
this.modelId = modelId;
@@ -189,7 +189,7 @@ var GoogleVertexImageModel = class {
constructor(modelId, config) {
this.modelId = modelId;
this.config = config;
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list
this.maxImagesPerCall = 4;
}
@@ -311,12 +311,28 @@ function createVertex(options = {}) {
environmentVariableName: "GOOGLE_VERTEX_LOCATION",
description: "Google Vertex location"
});
+ const loadApiKey = () => loadSetting({
+ settingValue: options.apiKey,
+ settingName: "apiKey",
+ environmentVariableName: "GOOGLE_VERTEX_API_KEY",
+ description: "Google Vertex API key"
+ });
+ const isExpressMode = () => {
+ try {
+ const apiKey = loadApiKey();
+ return apiKey != null && apiKey !== "";
+ } catch (e) {
+ return false;
+ }
+ };
const loadBaseURL = () => {
- var _a;
const region = loadVertexLocation();
const project = loadVertexProject();
+ if (options.baseURL) {
+ return withoutTrailingSlash(options.baseURL);
+ }
const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`;
- return (_a = withoutTrailingSlash(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
};
const createConfig = (name) => {
const getHeaders = async () => {
@@ -359,6 +375,7 @@ function createVertex(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.textEmbeddingModel = createEmbeddingModel;
provider.image = createImageModel;
@@ -482,12 +499,21 @@ async function generateAuthToken(credentials) {
function createVertex2(options = {}) {
return createVertex({
...options,
- headers: async () => ({
- Authorization: `Bearer ${await generateAuthToken(
- options.googleCredentials
- )}`,
- ...await resolve4(options.headers)
- })
+ headers: async () => {
+ const resolvedHeaders = await resolve4(options.headers);
+ if (options.apiKey) {
+ return {
+ "x-goog-api-key": options.apiKey,
+ ...resolvedHeaders
+ };
+ }
+ return {
+ Authorization: `Bearer ${await generateAuthToken(
+ options.googleCredentials
+ )}`,
+ ...resolvedHeaders
+ };
+ }
});
}
var vertex = createVertex2();
diff --git a/dist/edge/index.mjs.map b/dist/edge/index.mjs.map
index 75d535e..d072547 100644
--- a/dist/edge/index.mjs.map
+++ b/dist/edge/index.mjs.map
@@ -1 +1 @@
-{"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 { 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 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 { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\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\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\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 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 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/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.languageModel = createChatModel;\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 EmbeddingModelV2,\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 EmbeddingModelV2<string> {\n readonly specificationVersion = 'v2';\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<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 ImageModelV2 {\n readonly specificationVersion = 'v2';\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 }: Parameters<ImageModelV2['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV2['doGenerate']>>\n > {\n const warnings: Array<ImageModelV2CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\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 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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 * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: 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 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 loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.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<string> {\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<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV3<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 }: Parameters<ImageModelV3['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV3['doGenerate']>>\n > {\n const warnings: Array<ImageModelV3CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\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: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.access_token;\n } catch (error) {\n throw error;\n 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\ No newline at end of file
diff --git a/dist/index.d.mts b/dist/index.d.mts
index 8d77cd2..6714ed9 100644
--- a/dist/index.d.mts
+++ b/dist/index.d.mts
@@ -1,6 +1,6 @@
import { z } from 'zod/v4';
import { GoogleAuthOptions } from 'google-auth-library';
-import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider';
import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
@@ -24,7 +24,7 @@ declare const vertexImageProviderOptionsSchema: z.ZodObject<{
}, z.core.$strip>;
type GoogleVertexImageProviderOptions = z.infer<typeof vertexImageProviderOptionsSchema>;
-type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
declare const googleVertexTools: {
googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, {
@@ -51,20 +51,20 @@ declare const googleVertexTools: {
}>;
};
-interface GoogleVertexProvider extends ProviderV2 {
+interface GoogleVertexProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexModelId): LanguageModelV2;
- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;
+ (modelId: GoogleVertexModelId): LanguageModelV3;
+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;
/**
* Creates a model for image generation.
*/
- image(modelId: GoogleVertexImageModelId): ImageModelV2;
+ image(modelId: GoogleVertexImageModelId): ImageModelV3;
/**
Creates a model for image generation.
*/
- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;
+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;
tools: typeof googleVertexTools;
}
interface GoogleVertexProviderSettings$1 {
@@ -76,6 +76,12 @@ interface GoogleVertexProviderSettings$1 {
Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.
*/
project?: string;
+ /**
+ * API key for Express Mode authentication. When provided, uses simplified
+ * authentication with `x-goog-api-key` header instead of OAuth.
+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.
+ */
+ apiKey?: string;
/**
* Headers to use for requests. Can be:
* - A headers object
diff --git a/dist/index.d.ts b/dist/index.d.ts
index 8d77cd2..6714ed9 100644
--- a/dist/index.d.ts
+++ b/dist/index.d.ts
@@ -1,6 +1,6 @@
import { z } from 'zod/v4';
import { GoogleAuthOptions } from 'google-auth-library';
-import { ProviderV2, LanguageModelV2, ImageModelV2 } from '@ai-sdk/provider';
+import { ProviderV3, LanguageModelV3, ImageModelV3 } from '@ai-sdk/provider';
import * as _ai_sdk_provider_utils from '@ai-sdk/provider-utils';
import { Resolvable, FetchFunction } from '@ai-sdk/provider-utils';
@@ -24,7 +24,7 @@ declare const vertexImageProviderOptionsSchema: z.ZodObject<{
}, z.core.$strip>;
type GoogleVertexImageProviderOptions = z.infer<typeof vertexImageProviderOptionsSchema>;
-type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-3-pro-preview' | 'gemini-3-pro-image-preview' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
+type GoogleVertexModelId = 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' | 'gemini-2.0-flash-lite' | 'gemini-2.0-flash' | 'gemini-2.0-flash-001' | 'gemini-1.5-flash' | 'gemini-1.5-flash-001' | 'gemini-1.5-flash-002' | 'gemini-1.5-pro' | 'gemini-1.5-pro-001' | 'gemini-1.5-pro-002' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-vision-001' | 'gemini-1.0-pro' | 'gemini-1.0-pro-001' | 'gemini-1.0-pro-002' | 'gemini-2.0-flash-lite-preview-02-05' | 'gemini-2.5-flash-lite-preview-09-2025' | 'gemini-2.5-flash-preview-09-2025' | 'gemini-2.0-pro-exp-02-05' | 'gemini-2.0-flash-exp' | (string & {});
declare const googleVertexTools: {
googleSearch: _ai_sdk_provider_utils.ProviderDefinedToolFactory<{}, {
@@ -51,20 +51,20 @@ declare const googleVertexTools: {
}>;
};
-interface GoogleVertexProvider extends ProviderV2 {
+interface GoogleVertexProvider extends ProviderV3 {
/**
Creates a model for text generation.
*/
- (modelId: GoogleVertexModelId): LanguageModelV2;
- languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;
+ (modelId: GoogleVertexModelId): LanguageModelV3;
+ languageModel: (modelId: GoogleVertexModelId) => LanguageModelV3;
/**
* Creates a model for image generation.
*/
- image(modelId: GoogleVertexImageModelId): ImageModelV2;
+ image(modelId: GoogleVertexImageModelId): ImageModelV3;
/**
Creates a model for image generation.
*/
- imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;
+ imageModel(modelId: GoogleVertexImageModelId): ImageModelV3;
tools: typeof googleVertexTools;
}
interface GoogleVertexProviderSettings$1 {
@@ -76,6 +76,12 @@ interface GoogleVertexProviderSettings$1 {
Your Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT`.
*/
project?: string;
+ /**
+ * API key for Express Mode authentication. When provided, uses simplified
+ * authentication with `x-goog-api-key` header instead of OAuth.
+ * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.
+ */
+ apiKey?: string;
/**
* Headers to use for requests. Can be:
* - A headers object
diff --git a/dist/index.js b/dist/index.js
index 7079679..773069e 100644
--- a/dist/index.js
+++ b/dist/index.js
@@ -55,7 +55,7 @@ var import_internal2 = require("@ai-sdk/google/internal");
var import_provider_utils4 = require("@ai-sdk/provider-utils");
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/google-vertex-embedding-model.ts
var import_provider = require("@ai-sdk/provider");
@@ -125,7 +125,7 @@ var googleVertexEmbeddingProviderOptions = import_v42.z.object({
// src/google-vertex-embedding-model.ts
var GoogleVertexEmbeddingModel = class {
constructor(modelId, config) {
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
this.maxEmbeddingsPerCall = 2048;
this.supportsParallelCalls = true;
this.modelId = modelId;
@@ -218,7 +218,7 @@ var GoogleVertexImageModel = class {
constructor(modelId, config) {
this.modelId = modelId;
this.config = config;
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list
this.maxImagesPerCall = 4;
}
@@ -340,12 +340,28 @@ function createVertex(options = {}) {
environmentVariableName: "GOOGLE_VERTEX_LOCATION",
description: "Google Vertex location"
});
+ const loadApiKey = () => (0, import_provider_utils4.loadSetting)({
+ settingValue: options.apiKey,
+ settingName: "apiKey",
+ environmentVariableName: "GOOGLE_VERTEX_API_KEY",
+ description: "Google Vertex API key"
+ });
+ const isExpressMode = () => {
+ try {
+ const apiKey = loadApiKey();
+ return apiKey != null && apiKey !== "";
+ } catch (e) {
+ return false;
+ }
+ };
const loadBaseURL = () => {
- var _a;
const region = loadVertexLocation();
const project = loadVertexProject();
+ if (options.baseURL) {
+ return (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL);
+ }
const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`;
- return (_a = (0, import_provider_utils4.withoutTrailingSlash)(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
};
const createConfig = (name) => {
const getHeaders = async () => {
@@ -388,6 +404,7 @@ function createVertex(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.textEmbeddingModel = createEmbeddingModel;
provider.image = createImageModel;
@@ -400,12 +417,21 @@ function createVertex(options = {}) {
function createVertex2(options = {}) {
return createVertex({
...options,
- headers: async () => ({
- Authorization: `Bearer ${await generateAuthToken(
- options.googleAuthOptions
- )}`,
- ...await (0, import_provider_utils5.resolve)(options.headers)
- })
+ headers: async () => {
+ const resolvedHeaders = await (0, import_provider_utils5.resolve)(options.headers);
+ if (options.apiKey) {
+ return {
+ "x-goog-api-key": options.apiKey,
+ ...resolvedHeaders
+ };
+ }
+ return {
+ Authorization: `Bearer ${await generateAuthToken(
+ options.googleAuthOptions
+ )}`,
+ ...resolvedHeaders
+ };
+ }
});
}
var vertex = createVertex2();
diff --git a/dist/index.js.map b/dist/index.js.map
index 7e93232..474dc37 100644
--- a/dist/index.js.map
+++ b/dist/index.js.map
@@ -1 +1 @@
-{"version":3,"sources":["../src/index.ts","../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.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"],"sourcesContent":["export type { GoogleVertexImageProviderOptions } from './google-vertex-image-model';\nexport { createVertex, vertex } from './google-vertex-provider-node';\nexport type {\n GoogleVertexProvider,\n GoogleVertexProviderSettings,\n} from './google-vertex-provider-node';\nexport { VERSION } from './version';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from './google-vertex-provider';\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport type { GoogleVertexProvider };\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\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\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\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 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 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/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.languageModel = createChatModel;\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 EmbeddingModelV2,\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 EmbeddingModelV2<string> {\n readonly specificationVersion = 'v2';\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<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 ImageModelV2 {\n readonly specificationVersion = 'v2';\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 }: Parameters<ImageModelV2['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV2['doGenerate']>>\n > {\n const warnings: Array<ImageModelV2CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = {\n googleSearch: googleTools.googleSearch,\n urlContext: googleTools.urlContext,\n fileSearch: googleTools.fileSearch,\n codeExecution: googleTools.codeExecution,\n vertexRagStore: 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+{"version":3,"sources":["../src/index.ts","../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.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"],"sourcesContent":["export type { GoogleVertexImageProviderOptions } from './google-vertex-image-model';\nexport { createVertex, vertex } from './google-vertex-provider-node';\nexport type {\n GoogleVertexProvider,\n GoogleVertexProviderSettings,\n} from './google-vertex-provider-node';\nexport { VERSION } from './version';\n","import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from './google-vertex-provider';\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport type { GoogleVertexProvider };\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => {\n const resolvedHeaders = await resolve(options.headers);\n\n // Express Mode - Use API key authentication\n if (options.apiKey) {\n return {\n 'x-goog-api-key': options.apiKey,\n ...resolvedHeaders,\n };\n }\n\n // OAuth Mode - Use Bearer token authentication\n return {\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...resolvedHeaders,\n };\n },\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV3, LanguageModelV3, ProviderV3 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\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\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\nexport interface GoogleVertexProviderSettings {\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 * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: 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. 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'' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\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: 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.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<string> {\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<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV3<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 }: Parameters<ImageModelV3['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV3['doGenerate']>>\n > {\n const warnings: Array<ImageModelV3CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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\ No newline at end of file
diff --git a/dist/index.mjs b/dist/index.mjs
index e95580d..b788754 100644
--- a/dist/index.mjs
+++ b/dist/index.mjs
@@ -33,7 +33,7 @@ import {
} from "@ai-sdk/provider-utils";
// src/version.ts
-var VERSION = true ? "3.0.81" : "0.0.0-test";
+var VERSION = true ? "4.0.0-beta.73" : "0.0.0-test";
// src/google-vertex-embedding-model.ts
import {
@@ -111,7 +111,7 @@ var googleVertexEmbeddingProviderOptions = z2.object({
// src/google-vertex-embedding-model.ts
var GoogleVertexEmbeddingModel = class {
constructor(modelId, config) {
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
this.maxEmbeddingsPerCall = 2048;
this.supportsParallelCalls = true;
this.modelId = modelId;
@@ -210,7 +210,7 @@ var GoogleVertexImageModel = class {
constructor(modelId, config) {
this.modelId = modelId;
this.config = config;
- this.specificationVersion = "v2";
+ this.specificationVersion = "v3";
// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/imagen-api#parameter_list
this.maxImagesPerCall = 4;
}
@@ -332,12 +332,28 @@ function createVertex(options = {}) {
environmentVariableName: "GOOGLE_VERTEX_LOCATION",
description: "Google Vertex location"
});
+ const loadApiKey = () => loadSetting({
+ settingValue: options.apiKey,
+ settingName: "apiKey",
+ environmentVariableName: "GOOGLE_VERTEX_API_KEY",
+ description: "Google Vertex API key"
+ });
+ const isExpressMode = () => {
+ try {
+ const apiKey = loadApiKey();
+ return apiKey != null && apiKey !== "";
+ } catch (e) {
+ return false;
+ }
+ };
const loadBaseURL = () => {
- var _a;
const region = loadVertexLocation();
const project = loadVertexProject();
+ if (options.baseURL) {
+ return withoutTrailingSlash(options.baseURL);
+ }
const baseHost = `${region === "global" ? "" : region + "-"}aiplatform.googleapis.com`;
- return (_a = withoutTrailingSlash(options.baseURL)) != null ? _a : `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
+ return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;
};
const createConfig = (name) => {
const getHeaders = async () => {
@@ -380,6 +396,7 @@ function createVertex(options = {}) {
}
return createChatModel(modelId);
};
+ provider.specificationVersion = "v3";
provider.languageModel = createChatModel;
provider.textEmbeddingModel = createEmbeddingModel;
provider.image = createImageModel;
@@ -392,12 +409,21 @@ function createVertex(options = {}) {
function createVertex2(options = {}) {
return createVertex({
...options,
- headers: async () => ({
- Authorization: `Bearer ${await generateAuthToken(
- options.googleAuthOptions
- )}`,
- ...await resolve4(options.headers)
- })
+ headers: async () => {
+ const resolvedHeaders = await resolve4(options.headers);
+ if (options.apiKey) {
+ return {
+ "x-goog-api-key": options.apiKey,
+ ...resolvedHeaders
+ };
+ }
+ return {
+ Authorization: `Bearer ${await generateAuthToken(
+ options.googleAuthOptions
+ )}`,
+ ...resolvedHeaders
+ };
+ }
});
}
var vertex = createVertex2();
diff --git a/dist/index.mjs.map b/dist/index.mjs.map
index 19e04d6..0238ee3 100644
--- a/dist/index.mjs.map
+++ b/dist/index.mjs.map
@@ -1 +1 @@
-{"version":3,"sources":["../src/google-vertex-provider-node.ts","../src/google-vertex-auth-google-auth-library.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"],"sourcesContent":["import { resolve } from '@ai-sdk/provider-utils';\nimport { GoogleAuthOptions } from 'google-auth-library';\nimport { generateAuthToken } from './google-vertex-auth-google-auth-library';\nimport {\n createVertex as createVertexOriginal,\n GoogleVertexProvider,\n GoogleVertexProviderSettings as GoogleVertexProviderSettingsOriginal,\n} from './google-vertex-provider';\n\nexport interface GoogleVertexProviderSettings\n extends GoogleVertexProviderSettingsOriginal {\n /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n */\n googleAuthOptions?: GoogleAuthOptions;\n}\n\nexport type { GoogleVertexProvider };\n\nexport function createVertex(\n options: GoogleVertexProviderSettings = {},\n): GoogleVertexProvider {\n return createVertexOriginal({\n ...options,\n headers: async () => ({\n Authorization: `Bearer ${await generateAuthToken(\n options.googleAuthOptions,\n )}`,\n ...(await resolve(options.headers)),\n }),\n });\n}\n\n/**\nDefault Google Vertex AI provider instance.\n */\nexport const vertex = createVertex();\n","import { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\n\nlet authInstance: GoogleAuth | null = null;\nlet authOptions: GoogleAuthOptions | null = null;\n\nfunction getAuth(options: GoogleAuthOptions) {\n if (!authInstance || options !== authOptions) {\n authInstance = new GoogleAuth({\n scopes: ['https://www.googleapis.com/auth/cloud-platform'],\n ...options,\n });\n authOptions = options;\n }\n return authInstance;\n}\n\nexport async function generateAuthToken(options?: GoogleAuthOptions) {\n const auth = getAuth(options || {});\n const client = await auth.getClient();\n const token = await client.getAccessToken();\n return token?.token || null;\n}\n\n// For testing purposes only\nexport function _resetAuthInstance() {\n authInstance = null;\n}\n","import { GoogleGenerativeAILanguageModel } from '@ai-sdk/google/internal';\nimport { ImageModelV2, LanguageModelV2, ProviderV2 } from '@ai-sdk/provider';\nimport {\n FetchFunction,\n generateId,\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\nexport interface GoogleVertexProvider extends ProviderV2 {\n /**\nCreates a model for text generation.\n */\n (modelId: GoogleVertexModelId): LanguageModelV2;\n\n languageModel: (modelId: GoogleVertexModelId) => LanguageModelV2;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n /**\nCreates a model for image generation.\n */\n imageModel(modelId: GoogleVertexImageModelId): ImageModelV2;\n\n tools: typeof googleVertexTools;\n}\n\nexport interface GoogleVertexProviderSettings {\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 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 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/${region}/publishers/google`\n );\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.languageModel = createChatModel;\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 EmbeddingModelV2,\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 EmbeddingModelV2<string> {\n readonly specificationVersion = 'v2';\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<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 ImageModelV2 {\n readonly specificationVersion = 'v2';\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 }: Parameters<ImageModelV2['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV2['doGenerate']>>\n > {\n const warnings: Array<ImageModelV2CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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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 * API key for Express Mode authentication. When provided, uses simplified\n * authentication with `x-goog-api-key` header instead of OAuth.\n * Defaults to the environment variable `GOOGLE_VERTEX_API_KEY`.\n */\n apiKey?: 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 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 loadApiKey = () =>\n loadSetting({\n settingValue: options.apiKey,\n settingName: 'apiKey',\n environmentVariableName: 'GOOGLE_VERTEX_API_KEY',\n description: 'Google Vertex API key',\n });\n\n const isExpressMode = () => {\n try {\n const apiKey = loadApiKey();\n return apiKey != null && apiKey !== '';\n } catch {\n return false;\n }\n };\n\n const loadBaseURL = (): string => {\n const region = loadVertexLocation();\n const project = loadVertexProject();\n\n // If baseURL is explicitly provided, use it\n if (options.baseURL) {\n return withoutTrailingSlash(options.baseURL) as string;\n }\n\n // Express Mode uses generativelanguage.googleapis.com\n // if (isExpressMode()) {\n // return `https://generativelanguage.googleapis.com/v1beta/projects/${project}/locations/${region}/publishers/google`;\n // }\n\n // OAuth mode: For global region, use aiplatform.googleapis.com directly\n // For other regions, use region-aiplatform.googleapis.com\n const baseHost = `${region === 'global' ? '' : region + '-'}aiplatform.googleapis.com`;\n\n return `https://${baseHost}/v1beta1/projects/${project}/locations/${region}/publishers/google`;\n };\n\n const createConfig = (name: string): GoogleVertexConfig => {\n // Create a function that adds the user-agent suffix to headers\n const getHeaders = async () => {\n const originalHeaders = await resolve(options.headers ?? {});\n return withUserAgentSuffix(\n originalHeaders,\n `ai-sdk/google-vertex/${VERSION}`,\n );\n };\n\n return {\n provider: `google.vertex.${name}`,\n headers: getHeaders,\n fetch: options.fetch,\n baseURL: loadBaseURL(),\n };\n };\n\n const createChatModel = (modelId: GoogleVertexModelId) => {\n return new GoogleGenerativeAILanguageModel(modelId, {\n ...createConfig('chat'),\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // HTTP URLs:\n /^https?:\\/\\/.*$/,\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.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<string> {\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<string>['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV3<string>['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions =\n (await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleVertexEmbeddingProviderOptions,\n })) ?? {};\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 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 { ImageModelV3, ImageModelV3CallWarning } from '@ai-sdk/provider';\nimport {\n Resolvable,\n combineHeaders,\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 }: Parameters<ImageModelV3['doGenerate']>[0]): Promise<\n Awaited<ReturnType<ImageModelV3['doGenerate']>>\n > {\n const warnings: Array<ImageModelV3CallWarning> = [];\n\n if (size != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: '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 const body = {\n instances: [{ prompt }],\n parameters: {\n sampleCount: n,\n ...(aspectRatio != null ? { aspectRatio } : {}),\n ...(seed != null ? { seed } : {}),\n ...(vertexImageOptions ?? {}),\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});\nexport type GoogleVertexImageProviderOptions = z.infer<\n typeof vertexImageProviderOptionsSchema\n>;\n","import { googleTools } from '@ai-sdk/google/internal';\n\nexport const googleVertexTools = 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\ No newline at end of file
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