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

The **[Google Generative AI provider](https://ai-sdk.dev/providers/ai-sdk-providers/google-generative-ai)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the [Google Generative AI](https://ai.google/discover/generativeai/)

{"version":3,"sources":["../src/google-provider.ts","../src/version.ts","../src/google-generative-ai-embedding-model.ts","../src/google-error.ts","../src/google-generative-ai-embedding-options.ts","../src/google-generative-ai-language-model.ts","../src/convert-google-generative-ai-usage.ts","../src/convert-json-schema-to-openapi-schema.ts","../src/convert-to-google-generative-ai-messages.ts","../src/get-model-path.ts","../src/google-generative-ai-options.ts","../src/google-prepare-tools.ts","../src/google-json-accumulator.ts","../src/map-google-generative-ai-finish-reason.ts","../src/tool/code-execution.ts","../src/tool/enterprise-web-search.ts","../src/tool/file-search.ts","../src/tool/google-maps.ts","../src/tool/google-search.ts","../src/tool/url-context.ts","../src/tool/vertex-rag-store.ts","../src/google-tools.ts","../src/google-generative-ai-image-model.ts","../src/google-generative-ai-video-model.ts","../src/interactions/google-interactions-language-model.ts","../src/interactions/convert-google-interactions-usage.ts","../src/interactions/extract-google-interactions-sources.ts","../src/interactions/map-google-interactions-finish-reason.ts","../src/interactions/build-google-interactions-stream-transform.ts","../src/interactions/convert-to-google-interactions-input.ts","../src/interactions/google-interactions-api.ts","../src/interactions/google-interactions-language-model-options.ts","../src/interactions/parse-google-interactions-outputs.ts","../src/interactions/poll-google-interactions.ts","../src/interactions/cancel-google-interaction.ts","../src/interactions/prepare-google-interactions-tools.ts","../src/interactions/stream-google-interactions.ts","../src/interactions/synthesize-google-interactions-agent-stream.ts"],"sourcesContent":["import type {\n  EmbeddingModelV3,\n  Experimental_VideoModelV3,\n  ImageModelV3,\n  LanguageModelV3,\n  ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n  generateId,\n  loadApiKey,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n  type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleGenerativeAIEmbeddingModel } from './google-generative-ai-embedding-model';\nimport type { GoogleGenerativeAIEmbeddingModelId } from './google-generative-ai-embedding-options';\nimport { GoogleGenerativeAILanguageModel } from './google-generative-ai-language-model';\nimport type { GoogleGenerativeAIModelId } from './google-generative-ai-options';\nimport { googleTools } from './google-tools';\n\nimport type {\n  GoogleGenerativeAIImageSettings,\n  GoogleGenerativeAIImageModelId,\n} from './google-generative-ai-image-settings';\nimport { GoogleGenerativeAIImageModel } from './google-generative-ai-image-model';\nimport { GoogleGenerativeAIVideoModel } from './google-generative-ai-video-model';\nimport type { GoogleGenerativeAIVideoModelId } from './google-generative-ai-video-settings';\nimport {\n  GoogleInteractionsLanguageModel,\n  type GoogleInteractionsModelInput,\n} from './interactions/google-interactions-language-model';\nimport type { GoogleInteractionsModelId } from './interactions/google-interactions-language-model-options';\nimport type { GoogleInteractionsAgentName } from './interactions/google-interactions-agent';\n\nexport interface GoogleGenerativeAIProvider extends ProviderV3 {\n  (modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  languageModel(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  chat(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  /**\n   * Creates a model for image generation.\n   */\n  image(\n    modelId: GoogleGenerativeAIImageModelId,\n    settings?: GoogleGenerativeAIImageSettings,\n  ): ImageModelV3;\n\n  /**\n   * @deprecated Use `chat()` instead.\n   */\n  generativeAI(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  /**\n   * Creates a model for text embeddings.\n   */\n  embedding(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\n   * Creates a model for text embeddings.\n   */\n  embeddingModel(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\n   * @deprecated Use `embedding` instead.\n   */\n  textEmbedding(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(\n    modelId: GoogleGenerativeAIEmbeddingModelId,\n  ): EmbeddingModelV3;\n\n  /**\n   * Creates a model for video generation.\n   */\n  video(modelId: GoogleGenerativeAIVideoModelId): Experimental_VideoModelV3;\n\n  /**\n   * Creates a model for video generation.\n   */\n  videoModel(\n    modelId: GoogleGenerativeAIVideoModelId,\n  ): Experimental_VideoModelV3;\n\n  /**\n   * Creates a language model targeting the Gemini Interactions API\n   * (`POST /v1beta/interactions`). Pass:\n   *   - a model ID (string),\n   *   - `{ agent: <name> }` to use a known Gemini agent preset, or\n   *   - `{ managedAgent: <name> }` to use a user-defined agent created via\n   *     the `/v1beta/agents` endpoint.\n   */\n  interactions(\n    modelIdOrAgent:\n      | GoogleInteractionsModelId\n      | { agent: GoogleInteractionsAgentName }\n      | { managedAgent: string },\n  ): LanguageModelV3;\n\n  tools: typeof googleTools;\n}\n\nexport interface GoogleGenerativeAIProviderSettings {\n  /**\n   * Use a different URL prefix for API calls, e.g. to use proxy servers.\n   * The default prefix is `https://generativelanguage.googleapis.com/v1beta`.\n   */\n  baseURL?: string;\n\n  /**\n   * API key that is being send using the `x-goog-api-key` header.\n   * It defaults to the `GOOGLE_GENERATIVE_AI_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\n   * Custom headers to include in the requests.\n   */\n  headers?: Record<string, string | undefined>;\n\n  /**\n   * Custom fetch implementation. You can use it as a middleware to intercept requests,\n   * or to provide a custom fetch implementation for e.g. testing.\n   */\n  fetch?: FetchFunction;\n\n  /**\n   * Optional function to generate a unique ID for each request.\n   */\n  generateId?: () => string;\n\n  /**\n   * Custom provider name\n   * Defaults to 'google.generative-ai'.\n   */\n  name?: string;\n}\n\n/**\n * Create a Google Generative AI provider instance.\n */\nexport function createGoogleGenerativeAI(\n  options: GoogleGenerativeAIProviderSettings = {},\n): GoogleGenerativeAIProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ??\n    'https://generativelanguage.googleapis.com/v1beta';\n\n  const providerName = options.name ?? 'google.generative-ai';\n\n  const getHeaders = () =>\n    withUserAgentSuffix(\n      {\n        'x-goog-api-key': loadApiKey({\n          apiKey: options.apiKey,\n          environmentVariableName: 'GOOGLE_GENERATIVE_AI_API_KEY',\n          description: 'Google Generative AI',\n        }),\n        ...options.headers,\n      },\n      `ai-sdk/google/${VERSION}`,\n    );\n\n  const createChatModel = (modelId: GoogleGenerativeAIModelId) =>\n    new GoogleGenerativeAILanguageModel(modelId, {\n      provider: providerName,\n      baseURL,\n      headers: getHeaders,\n      generateId: options.generateId ?? generateId,\n      supportedUrls: () => ({\n        '*': [\n          // Google Generative Language \"files\" endpoint\n          // e.g. https://generativelanguage.googleapis.com/v1beta/files/...\n          new RegExp(`^${baseURL}/files/.*$`),\n          // YouTube URLs (public or unlisted videos)\n          new RegExp(\n            `^https://(?:www\\\\.)?youtube\\\\.com/watch\\\\?v=[\\\\w-]+(?:&[\\\\w=&.-]*)?$`,\n          ),\n          new RegExp(`^https://youtu\\\\.be/[\\\\w-]+(?:\\\\?[\\\\w=&.-]*)?$`),\n        ],\n      }),\n      fetch: options.fetch,\n    });\n\n  const createEmbeddingModel = (modelId: GoogleGenerativeAIEmbeddingModelId) =>\n    new GoogleGenerativeAIEmbeddingModel(modelId, {\n      provider: providerName,\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createImageModel = (\n    modelId: GoogleGenerativeAIImageModelId,\n    settings: GoogleGenerativeAIImageSettings = {},\n  ) =>\n    new GoogleGenerativeAIImageModel(modelId, settings, {\n      provider: providerName,\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createVideoModel = (modelId: GoogleGenerativeAIVideoModelId) =>\n    new GoogleGenerativeAIVideoModel(modelId, {\n      provider: providerName,\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n      generateId: options.generateId ?? generateId,\n    });\n\n  const createInteractionsModel = (\n    modelIdOrAgent:\n      | GoogleInteractionsModelId\n      | { agent: GoogleInteractionsAgentName }\n      | { managedAgent: string },\n  ) =>\n    new GoogleInteractionsLanguageModel(\n      modelIdOrAgent as GoogleInteractionsModelInput,\n      {\n        provider: `${providerName}.interactions`,\n        baseURL,\n        headers: getHeaders,\n        generateId: options.generateId ?? generateId,\n        fetch: options.fetch,\n      },\n    );\n\n  const provider = function (modelId: GoogleGenerativeAIModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Google Generative 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.chat = createChatModel;\n  provider.generativeAI = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.embeddingModel = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n  provider.video = createVideoModel;\n  provider.videoModel = createVideoModel;\n  provider.interactions = createInteractionsModel;\n  provider.tools = googleTools;\n\n  return provider as GoogleGenerativeAIProvider;\n}\n\n/**\n * Default Google Generative AI provider instance.\n */\nexport const google = createGoogleGenerativeAI();\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  TooManyEmbeddingValuesForCallError,\n  type EmbeddingModelV3,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  lazySchema,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n  zodSchema,\n  type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleFailedResponseHandler } from './google-error';\nimport {\n  googleEmbeddingModelOptions,\n  type GoogleGenerativeAIEmbeddingModelId,\n} from './google-generative-ai-embedding-options';\n\ntype GoogleGenerativeAIEmbeddingConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class GoogleGenerativeAIEmbeddingModel implements EmbeddingModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly modelId: GoogleGenerativeAIEmbeddingModelId;\n  readonly maxEmbeddingsPerCall = 2048;\n  readonly supportsParallelCalls = true;\n\n  private readonly config: GoogleGenerativeAIEmbeddingConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n  constructor(\n    modelId: GoogleGenerativeAIEmbeddingModelId,\n    config: GoogleGenerativeAIEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    headers,\n    abortSignal,\n    providerOptions,\n  }: Parameters<EmbeddingModelV3['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV3['doEmbed']>>\n  > {\n    // Parse provider options\n    const googleOptions = await parseProviderOptions({\n      provider: 'google',\n      providerOptions,\n      schema: googleEmbeddingModelOptions,\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 multimodalContent = googleOptions?.content;\n\n    if (\n      multimodalContent != null &&\n      multimodalContent.length !== values.length\n    ) {\n      throw new Error(\n        `The number of multimodal content entries (${multimodalContent.length}) must match the number of values (${values.length}).`,\n      );\n    }\n\n    // For single embeddings, use the single endpoint\n    if (values.length === 1) {\n      const valueParts = multimodalContent?.[0];\n      const textPart = values[0] ? [{ text: values[0] }] : [];\n      const parts =\n        valueParts != null\n          ? [...textPart, ...valueParts]\n          : [{ text: values[0] }];\n\n      const {\n        responseHeaders,\n        value: response,\n        rawValue,\n      } = await postJsonToApi({\n        url: `${this.config.baseURL}/models/${this.modelId}:embedContent`,\n        headers: mergedHeaders,\n        body: {\n          model: `models/${this.modelId}`,\n          content: {\n            parts,\n          },\n          outputDimensionality: googleOptions?.outputDimensionality,\n          taskType: googleOptions?.taskType,\n        },\n        failedResponseHandler: googleFailedResponseHandler,\n        successfulResponseHandler: createJsonResponseHandler(\n          googleGenerativeAISingleEmbeddingResponseSchema,\n        ),\n        abortSignal,\n        fetch: this.config.fetch,\n      });\n\n      return {\n        warnings: [],\n        embeddings: [response.embedding.values],\n        usage: undefined,\n        response: { headers: responseHeaders, body: rawValue },\n      };\n    }\n\n    // For multiple values, use the batch endpoint\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/models/${this.modelId}:batchEmbedContents`,\n      headers: mergedHeaders,\n      body: {\n        requests: values.map((value, index) => {\n          const valueParts = multimodalContent?.[index];\n          const textPart = value ? [{ text: value }] : [];\n          return {\n            model: `models/${this.modelId}`,\n            content: {\n              role: 'user',\n              parts:\n                valueParts != null\n                  ? [...textPart, ...valueParts]\n                  : [{ text: value }],\n            },\n            outputDimensionality: googleOptions?.outputDimensionality,\n            taskType: googleOptions?.taskType,\n          };\n        }),\n      },\n      failedResponseHandler: googleFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        googleGenerativeAITextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      warnings: [],\n      embeddings: response.embeddings.map(item => item.values),\n      usage: undefined,\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 googleGenerativeAITextEmbeddingResponseSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      embeddings: z.array(z.object({ values: z.array(z.number()) })),\n    }),\n  ),\n);\n\n// Schema for single embedding response\nconst googleGenerativeAISingleEmbeddingResponseSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      embedding: z.object({ values: z.array(z.number()) }),\n    }),\n  ),\n);\n","import {\n  createJsonErrorResponseHandler,\n  type InferSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleErrorDataSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      error: z.object({\n        code: z.number().nullable(),\n        message: z.string(),\n        status: z.string(),\n      }),\n    }),\n  ),\n);\n\nexport type GoogleErrorData = InferSchema<typeof googleErrorDataSchema>;\n\nexport const googleFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: googleErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","import {\n  type InferSchema,\n  lazySchema,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type GoogleGenerativeAIEmbeddingModelId =\n  | 'gemini-embedding-001'\n  | 'gemini-embedding-2-preview'\n  | (string & {});\n\nconst googleEmbeddingContentPartSchema = z.union([\n  z.object({ text: z.string() }),\n  z.object({\n    inlineData: z.object({\n      mimeType: z.string(),\n      data: z.string(),\n    }),\n  }),\n  z.object({\n    fileData: z.object({\n      fileUri: z.string(),\n      mimeType: z.string(),\n    }),\n  }),\n]);\n\nexport const googleEmbeddingModelOptions = lazySchema(() =>\n  zodSchema(\n    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. Per-value multimodal content parts for embedding non-text\n       * content (images, video, PDF, audio). Each entry corresponds to the\n       * embedding value at the same index and its parts are merged with the\n       * text value in the request. Use `null` for entries that are text-only.\n       *\n       * The array length must match the number of values being embedded. In\n       * the case of a single embedding, the array length must be 1.\n       */\n      content: z\n        .array(z.array(googleEmbeddingContentPartSchema).min(1).nullable())\n        .optional(),\n    }),\n  ),\n);\n\nexport type GoogleEmbeddingModelOptions = InferSchema<\n  typeof googleEmbeddingModelOptions\n>;\n","import type {\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3Source,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  JSONObject,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  lazySchema,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n  zodSchema,\n  type FetchFunction,\n  type InferSchema,\n  type ParseResult,\n  type Resolvable,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  convertGoogleGenerativeAIUsage,\n  type GoogleGenerativeAIUsageMetadata,\n} from './convert-google-generative-ai-usage';\nimport { convertJSONSchemaToOpenAPISchema } from './convert-json-schema-to-openapi-schema';\nimport { convertToGoogleGenerativeAIMessages } from './convert-to-google-generative-ai-messages';\nimport { getModelPath } from './get-model-path';\nimport { googleFailedResponseHandler } from './google-error';\nimport {\n  googleLanguageModelOptions,\n  type GoogleGenerativeAIModelId,\n} from './google-generative-ai-options';\nimport type {\n  GoogleGenerativeAIContentPart,\n  GoogleGenerativeAIProviderMetadata,\n} from './google-generative-ai-prompt';\nimport { prepareTools } from './google-prepare-tools';\nimport {\n  GoogleJSONAccumulator,\n  type PartialArg,\n} from './google-json-accumulator';\nimport { mapGoogleGenerativeAIFinishReason } from './map-google-generative-ai-finish-reason';\n\ntype GoogleGenerativeAIConfig = {\n  provider: string;\n  baseURL: string;\n  headers: Resolvable<Record<string, string | undefined>>;\n  fetch?: FetchFunction;\n  generateId: () => string;\n\n  /**\n   * The supported URLs for the model.\n   */\n  supportedUrls?: () => LanguageModelV3['supportedUrls'];\n};\n\nexport class GoogleGenerativeAILanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\n\n  readonly modelId: GoogleGenerativeAIModelId;\n\n  private readonly config: GoogleGenerativeAIConfig;\n  private readonly generateId: () => string;\n\n  constructor(\n    modelId: GoogleGenerativeAIModelId,\n    config: GoogleGenerativeAIConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n    this.generateId = config.generateId ?? generateId;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get supportedUrls() {\n    return this.config.supportedUrls?.() ?? {};\n  }\n\n  private async getArgs(\n    {\n      prompt,\n      maxOutputTokens,\n      temperature,\n      topP,\n      topK,\n      frequencyPenalty,\n      presencePenalty,\n      stopSequences,\n      responseFormat,\n      seed,\n      tools,\n      toolChoice,\n      providerOptions,\n    }: LanguageModelV3CallOptions,\n    { isStreaming = false }: { isStreaming?: boolean } = {},\n  ) {\n    const warnings: SharedV3Warning[] = [];\n\n    const providerOptionsName = this.config.provider.includes('vertex')\n      ? 'vertex'\n      : 'google';\n    let googleOptions = await parseProviderOptions({\n      provider: providerOptionsName,\n      providerOptions,\n      schema: googleLanguageModelOptions,\n    });\n\n    if (googleOptions == null && providerOptionsName !== 'google') {\n      googleOptions = await parseProviderOptions({\n        provider: 'google',\n        providerOptions,\n        schema: googleLanguageModelOptions,\n      });\n    }\n\n    // Add warning if Vertex rag tools are used with a non-Vertex Google provider\n    const isVertexProvider = this.config.provider.startsWith('google.vertex.');\n\n    if (\n      tools?.some(\n        tool =>\n          tool.type === 'provider' && tool.id === 'google.vertex_rag_store',\n      ) &&\n      !isVertexProvider\n    ) {\n      warnings.push({\n        type: 'other',\n        message:\n          \"The 'vertex_rag_store' tool is only supported with the Google Vertex provider \" +\n          'and might not be supported or could behave unexpectedly with the current Google provider ' +\n          `(${this.config.provider}).`,\n      });\n    }\n\n    if (googleOptions?.streamFunctionCallArguments && !isVertexProvider) {\n      warnings.push({\n        type: 'other',\n        message:\n          \"'streamFunctionCallArguments' is only supported on the Vertex AI API \" +\n          'and will be ignored with the current Google provider ' +\n          `(${this.config.provider}). See https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling#streaming-fc`,\n      });\n    }\n\n    if (googleOptions?.serviceTier && isVertexProvider) {\n      warnings.push({\n        type: 'other',\n        message:\n          \"'serviceTier' is a Gemini API option and is not supported on Vertex AI. \" +\n          \"Use 'sharedRequestType' (and optionally 'requestType') instead. See \" +\n          'https://docs.cloud.google.com/vertex-ai/generative-ai/docs/priority-paygo',\n      });\n    }\n    if (\n      (googleOptions?.sharedRequestType || googleOptions?.requestType) &&\n      !isVertexProvider\n    ) {\n      warnings.push({\n        type: 'other',\n        message:\n          \"'sharedRequestType' and 'requestType' are Vertex AI options and \" +\n          `are ignored with the current Google provider (${this.config.provider}).`,\n      });\n    }\n\n    const vertexPaygoHeaders: Record<string, string> | undefined =\n      isVertexProvider &&\n      (googleOptions?.sharedRequestType || googleOptions?.requestType)\n        ? {\n            ...(googleOptions.sharedRequestType && {\n              'X-Vertex-AI-LLM-Shared-Request-Type':\n                googleOptions.sharedRequestType,\n            }),\n            ...(googleOptions.requestType && {\n              'X-Vertex-AI-LLM-Request-Type': googleOptions.requestType,\n            }),\n          }\n        : undefined;\n    const bodyServiceTier = isVertexProvider\n      ? undefined\n      : googleOptions?.serviceTier;\n\n    const isGemmaModel = this.modelId.toLowerCase().startsWith('gemma-');\n    const supportsFunctionResponseParts = this.modelId.startsWith('gemini-3');\n\n    const { contents, systemInstruction } = convertToGoogleGenerativeAIMessages(\n      prompt,\n      {\n        isGemmaModel,\n        providerOptionsName,\n        supportsFunctionResponseParts,\n      },\n    );\n\n    const {\n      tools: googleTools,\n      toolConfig: googleToolConfig,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n      modelId: this.modelId,\n      isVertexProvider,\n    });\n\n    const streamFunctionCallArguments =\n      isStreaming && isVertexProvider\n        ? (googleOptions?.streamFunctionCallArguments ?? false)\n        : undefined;\n\n    const toolConfig =\n      googleToolConfig ||\n      streamFunctionCallArguments ||\n      googleOptions?.retrievalConfig\n        ? {\n            ...googleToolConfig,\n            ...(streamFunctionCallArguments && {\n              functionCallingConfig: {\n                ...googleToolConfig?.functionCallingConfig,\n                streamFunctionCallArguments: true as const,\n              },\n            }),\n            ...(googleOptions?.retrievalConfig && {\n              retrievalConfig: googleOptions.retrievalConfig,\n            }),\n          }\n        : undefined;\n\n    return {\n      args: {\n        generationConfig: {\n          // standardized settings:\n          maxOutputTokens,\n          temperature,\n          topK,\n          topP,\n          frequencyPenalty,\n          presencePenalty,\n          stopSequences,\n          seed,\n\n          // response format:\n          responseMimeType:\n            responseFormat?.type === 'json' ? 'application/json' : undefined,\n          responseSchema:\n            responseFormat?.type === 'json' &&\n            responseFormat.schema != null &&\n            // Google GenAI does not support all OpenAPI Schema features,\n            // so this is needed as an escape hatch:\n            // TODO convert into provider option\n            (googleOptions?.structuredOutputs ?? true)\n              ? convertJSONSchemaToOpenAPISchema(responseFormat.schema)\n              : undefined,\n          ...(googleOptions?.audioTimestamp && {\n            audioTimestamp: googleOptions.audioTimestamp,\n          }),\n\n          // provider options:\n          responseModalities: googleOptions?.responseModalities,\n          thinkingConfig: googleOptions?.thinkingConfig,\n          ...(googleOptions?.mediaResolution && {\n            mediaResolution: googleOptions.mediaResolution,\n          }),\n          ...(googleOptions?.imageConfig && {\n            imageConfig: googleOptions.imageConfig,\n          }),\n        },\n        contents,\n        systemInstruction: isGemmaModel ? undefined : systemInstruction,\n        safetySettings: googleOptions?.safetySettings,\n        tools: googleTools,\n        toolConfig,\n        cachedContent: googleOptions?.cachedContent,\n        labels: googleOptions?.labels,\n        serviceTier: bodyServiceTier,\n      },\n      warnings: [...warnings, ...toolWarnings],\n      providerOptionsName,\n      extraHeaders: vertexPaygoHeaders,\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args, warnings, providerOptionsName, extraHeaders } =\n      await this.getArgs(options);\n\n    const mergedHeaders = combineHeaders(\n      await resolve(this.config.headers),\n      options.headers,\n      extraHeaders,\n    );\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/${getModelPath(\n        this.modelId,\n      )}:generateContent`,\n      headers: mergedHeaders,\n      body: args,\n      failedResponseHandler: googleFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(responseSchema),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const candidate = response.candidates[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // map ordered parts to content:\n    const parts = candidate.content?.parts ?? [];\n\n    const usageMetadata = response.usageMetadata;\n\n    // Associates a code execution result with its preceding call.\n    let lastCodeExecutionToolCallId: string | undefined;\n    // Associates a server-side tool response with its preceding call (tool combination).\n    let lastServerToolCallId: string | undefined;\n\n    // Build content array from all parts\n    for (const part of parts) {\n      if ('executableCode' in part && part.executableCode?.code) {\n        const toolCallId = this.config.generateId();\n        lastCodeExecutionToolCallId = toolCallId;\n\n        content.push({\n          type: 'tool-call',\n          toolCallId,\n          toolName: 'code_execution',\n          input: JSON.stringify(part.executableCode),\n          providerExecuted: true,\n        });\n      } else if ('codeExecutionResult' in part && part.codeExecutionResult) {\n        content.push({\n          type: 'tool-result',\n          // Assumes a result directly follows its corresponding call part.\n          toolCallId: lastCodeExecutionToolCallId!,\n          toolName: 'code_execution',\n          result: {\n            outcome: part.codeExecutionResult.outcome,\n            output: part.codeExecutionResult.output ?? '',\n          },\n        });\n        // Clear the ID after use to avoid accidental reuse.\n        lastCodeExecutionToolCallId = undefined;\n      } else if ('text' in part && part.text != null) {\n        const thoughtSignatureMetadata = part.thoughtSignature\n          ? {\n              [providerOptionsName]: {\n                thoughtSignature: part.thoughtSignature,\n              },\n            }\n          : undefined;\n\n        if (part.text.length === 0) {\n          if (thoughtSignatureMetadata != null && content.length > 0) {\n            const lastContent = content[content.length - 1];\n            lastContent.providerMetadata = thoughtSignatureMetadata;\n          }\n        } else {\n          content.push({\n            type: part.thought === true ? 'reasoning' : 'text',\n            text: part.text,\n            providerMetadata: thoughtSignatureMetadata,\n          });\n        }\n      } else if ('functionCall' in part && part.functionCall.name != null) {\n        content.push({\n          type: 'tool-call' as const,\n          toolCallId: part.functionCall.id ?? this.config.generateId(),\n          toolName: part.functionCall.name,\n          input: JSON.stringify(part.functionCall.args ?? {}),\n          providerMetadata: part.thoughtSignature\n            ? {\n                [providerOptionsName]: {\n                  thoughtSignature: part.thoughtSignature,\n                },\n              }\n            : undefined,\n        });\n      } else if ('inlineData' in part) {\n        const hasThought = part.thought === true;\n        const hasThoughtSignature = !!part.thoughtSignature;\n        content.push({\n          type: 'file' as const,\n          data: part.inlineData.data,\n          mediaType: part.inlineData.mimeType,\n          providerMetadata:\n            hasThought || hasThoughtSignature\n              ? {\n                  [providerOptionsName]: {\n                    ...(hasThought ? { thought: true } : {}),\n                    ...(hasThoughtSignature\n                      ? { thoughtSignature: part.thoughtSignature }\n                      : {}),\n                  },\n                }\n              : undefined,\n        });\n      } else if ('toolCall' in part && part.toolCall) {\n        const toolCallId = part.toolCall.id ?? this.config.generateId();\n        lastServerToolCallId = toolCallId;\n        content.push({\n          type: 'tool-call',\n          toolCallId,\n          toolName: `server:${part.toolCall.toolType}`,\n          input: JSON.stringify(part.toolCall.args ?? {}),\n          providerExecuted: true,\n          dynamic: true,\n          providerMetadata: part.thoughtSignature\n            ? {\n                [providerOptionsName]: {\n                  thoughtSignature: part.thoughtSignature,\n                  serverToolCallId: toolCallId,\n                  serverToolType: part.toolCall.toolType,\n                },\n              }\n            : {\n                [providerOptionsName]: {\n                  serverToolCallId: toolCallId,\n                  serverToolType: part.toolCall.toolType,\n                },\n              },\n        });\n      } else if ('toolResponse' in part && part.toolResponse) {\n        const responseToolCallId =\n          lastServerToolCallId ??\n          part.toolResponse.id ??\n          this.config.generateId();\n        content.push({\n          type: 'tool-result',\n          toolCallId: responseToolCallId,\n          toolName: `server:${part.toolResponse.toolType}`,\n          result: (part.toolResponse.response ?? {}) as JSONObject,\n          providerMetadata: part.thoughtSignature\n            ? {\n                [providerOptionsName]: {\n                  thoughtSignature: part.thoughtSignature,\n                  serverToolCallId: responseToolCallId,\n                  serverToolType: part.toolResponse.toolType,\n                },\n              }\n            : {\n                [providerOptionsName]: {\n                  serverToolCallId: responseToolCallId,\n                  serverToolType: part.toolResponse.toolType,\n                },\n              },\n        });\n        lastServerToolCallId = undefined;\n      }\n    }\n\n    const sources =\n      extractSources({\n        groundingMetadata: candidate.groundingMetadata,\n        generateId: this.config.generateId,\n      }) ?? [];\n    for (const source of sources) {\n      content.push(source);\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapGoogleGenerativeAIFinishReason({\n          finishReason: candidate.finishReason,\n          // Only count client-executed tool calls for finish reason determination.\n          hasToolCalls: content.some(\n            part => part.type === 'tool-call' && !part.providerExecuted,\n          ),\n        }),\n        raw: candidate.finishReason ?? undefined,\n      },\n      usage: convertGoogleGenerativeAIUsage(usageMetadata),\n      warnings,\n      providerMetadata: {\n        [providerOptionsName]: {\n          promptFeedback: response.promptFeedback ?? null,\n          groundingMetadata: candidate.groundingMetadata ?? null,\n          urlContextMetadata: candidate.urlContextMetadata ?? null,\n          safetyRatings: candidate.safetyRatings ?? null,\n          usageMetadata: usageMetadata ?? null,\n          finishMessage: candidate.finishMessage ?? null,\n          serviceTier: usageMetadata?.serviceTier ?? null,\n        } satisfies GoogleGenerativeAIProviderMetadata,\n      },\n      request: { body: args },\n      response: {\n        // TODO timestamp, model id, id\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const { args, warnings, providerOptionsName, extraHeaders } =\n      await this.getArgs(options, { isStreaming: true });\n\n    const headers = combineHeaders(\n      await resolve(this.config.headers),\n      options.headers,\n      extraHeaders,\n    );\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/${getModelPath(\n        this.modelId,\n      )}:streamGenerateContent?alt=sse`,\n      headers,\n      body: args,\n      failedResponseHandler: googleFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(chunkSchema),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage: GoogleGenerativeAIUsageMetadata | undefined = undefined;\n    let providerMetadata: SharedV3ProviderMetadata | undefined = undefined;\n    let lastGroundingMetadata: GroundingMetadataSchema | null = null;\n    let lastUrlContextMetadata: UrlContextMetadataSchema | null = null;\n\n    const generateId = this.config.generateId;\n    let hasToolCalls = false;\n\n    // Track active blocks to group consecutive parts of same type\n    let currentTextBlockId: string | null = null;\n    let currentReasoningBlockId: string | null = null;\n    let blockCounter = 0;\n\n    // Track emitted sources to prevent duplicates\n    const emittedSourceUrls = new Set<string>();\n    // Associates a code execution result with its preceding call.\n    let lastCodeExecutionToolCallId: string | undefined;\n    // Associates a server-side tool response with its preceding call (tool combination).\n    let lastServerToolCallId: string | undefined;\n\n    const activeStreamingToolCalls: Array<{\n      toolCallId: string;\n      toolName: string;\n      accumulator: GoogleJSONAccumulator;\n      providerMetadata?: SharedV3ProviderMetadata;\n    }> = [];\n\n    const finishActiveStreamingToolCall = (\n      controller: TransformStreamDefaultController<LanguageModelV3StreamPart>,\n    ) => {\n      const active = activeStreamingToolCalls.pop();\n      if (active == null) {\n        return;\n      }\n\n      const { finalJSON, closingDelta } = active.accumulator.finalize();\n\n      if (closingDelta.length > 0) {\n        controller.enqueue({\n          type: 'tool-input-delta',\n          id: active.toolCallId,\n          delta: closingDelta,\n          providerMetadata: active.providerMetadata,\n        });\n      }\n\n      controller.enqueue({\n        type: 'tool-input-end',\n        id: active.toolCallId,\n        providerMetadata: active.providerMetadata,\n      });\n\n      controller.enqueue({\n        type: 'tool-call',\n        toolCallId: active.toolCallId,\n        toolName: active.toolName,\n        input: finalJSON,\n        providerMetadata: active.providerMetadata,\n      });\n\n      hasToolCalls = true;\n    };\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<ChunkSchema>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            const usageMetadata = value.usageMetadata;\n\n            if (usageMetadata != null) {\n              usage = usageMetadata;\n            }\n\n            const candidate = value.candidates?.[0];\n\n            // sometimes the API returns an empty candidates array\n            if (candidate == null) {\n              return;\n            }\n\n            const content = candidate.content;\n\n            if (candidate.groundingMetadata != null) {\n              lastGroundingMetadata = candidate.groundingMetadata;\n            }\n            if (candidate.urlContextMetadata != null) {\n              lastUrlContextMetadata = candidate.urlContextMetadata;\n            }\n\n            const sources = extractSources({\n              groundingMetadata: candidate.groundingMetadata,\n              generateId,\n            });\n            if (sources != null) {\n              for (const source of sources) {\n                if (\n                  source.sourceType === 'url' &&\n                  !emittedSourceUrls.has(source.url)\n                ) {\n                  emittedSourceUrls.add(source.url);\n                  controller.enqueue(source);\n                }\n              }\n            }\n\n            // Process tool call's parts before determining finishReason to ensure hasToolCalls is properly set\n            if (content != null) {\n              // Process all parts in a single loop to preserve original order\n              const parts = content.parts ?? [];\n              for (const part of parts) {\n                if ('executableCode' in part && part.executableCode?.code) {\n                  const toolCallId = generateId();\n                  lastCodeExecutionToolCallId = toolCallId;\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId,\n                    toolName: 'code_execution',\n                    input: JSON.stringify(part.executableCode),\n                    providerExecuted: true,\n                  });\n                } else if (\n                  'codeExecutionResult' in part &&\n                  part.codeExecutionResult\n                ) {\n                  // Assumes a result directly follows its corresponding call part.\n                  const toolCallId = lastCodeExecutionToolCallId;\n\n                  if (toolCallId) {\n                    controller.enqueue({\n                      type: 'tool-result',\n                      toolCallId,\n                      toolName: 'code_execution',\n                      result: {\n                        outcome: part.codeExecutionResult.outcome,\n                        output: part.codeExecutionResult.output ?? '',\n                      },\n                    });\n                    // Clear the ID after use.\n                    lastCodeExecutionToolCallId = undefined;\n                  }\n                } else if ('text' in part && part.text != null) {\n                  const thoughtSignatureMetadata = part.thoughtSignature\n                    ? {\n                        [providerOptionsName]: {\n                          thoughtSignature: part.thoughtSignature,\n                        },\n                      }\n                    : undefined;\n\n                  if (part.text.length === 0) {\n                    if (\n                      thoughtSignatureMetadata != null &&\n                      currentTextBlockId !== null\n                    ) {\n                      controller.enqueue({\n                        type: 'text-delta',\n                        id: currentTextBlockId,\n                        delta: '',\n                        providerMetadata: thoughtSignatureMetadata,\n                      });\n                    }\n                  } else if (part.thought === true) {\n                    // End any active text block before starting reasoning\n                    if (currentTextBlockId !== null) {\n                      controller.enqueue({\n                        type: 'text-end',\n                        id: currentTextBlockId,\n                      });\n                      currentTextBlockId = null;\n                    }\n\n                    // Start new reasoning block if not already active\n                    if (currentReasoningBlockId === null) {\n                      currentReasoningBlockId = String(blockCounter++);\n                      controller.enqueue({\n                        type: 'reasoning-start',\n                        id: currentReasoningBlockId,\n                        providerMetadata: thoughtSignatureMetadata,\n                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'reasoning-delta',\n                      id: currentReasoningBlockId,\n                      delta: part.text,\n                      providerMetadata: thoughtSignatureMetadata,\n                    });\n                  } else {\n                    if (currentReasoningBlockId !== null) {\n                      controller.enqueue({\n                        type: 'reasoning-end',\n                        id: currentReasoningBlockId,\n                      });\n                      currentReasoningBlockId = null;\n                    }\n\n                    if (currentTextBlockId === null) {\n                      currentTextBlockId = String(blockCounter++);\n                      controller.enqueue({\n                        type: 'text-start',\n                        id: currentTextBlockId,\n                        providerMetadata: thoughtSignatureMetadata,\n                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'text-delta',\n                      id: currentTextBlockId,\n                      delta: part.text,\n                      providerMetadata: thoughtSignatureMetadata,\n                    });\n                  }\n                } else if ('inlineData' in part) {\n                  // End any active text or reasoning block before starting file output.\n                  // Relevant for multimodal output models.\n                  if (currentTextBlockId !== null) {\n                    controller.enqueue({\n                      type: 'text-end',\n                      id: currentTextBlockId,\n                    });\n                    currentTextBlockId = null;\n                  }\n                  if (currentReasoningBlockId !== null) {\n                    controller.enqueue({\n                      type: 'reasoning-end',\n                      id: currentReasoningBlockId,\n                    });\n                    currentReasoningBlockId = null;\n                  }\n\n                  const hasThought = part.thought === true;\n                  const hasThoughtSignature = !!part.thoughtSignature;\n                  const fileMeta =\n                    hasThought || hasThoughtSignature\n                      ? {\n                          [providerOptionsName]: {\n                            ...(hasThought ? { thought: true } : {}),\n                            ...(hasThoughtSignature\n                              ? { thoughtSignature: part.thoughtSignature }\n                              : {}),\n                          },\n                        }\n                      : undefined;\n                  controller.enqueue({\n                    type: 'file',\n                    mediaType: part.inlineData.mimeType,\n                    data: part.inlineData.data,\n                    providerMetadata: fileMeta,\n                  });\n                } else if ('toolCall' in part && part.toolCall) {\n                  const toolCallId = part.toolCall.id ?? generateId();\n                  lastServerToolCallId = toolCallId;\n                  const serverMeta = {\n                    [providerOptionsName]: {\n                      ...(part.thoughtSignature\n                        ? { thoughtSignature: part.thoughtSignature }\n                        : {}),\n                      serverToolCallId: toolCallId,\n                      serverToolType: part.toolCall.toolType,\n                    },\n                  };\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId,\n                    toolName: `server:${part.toolCall.toolType}`,\n                    input: JSON.stringify(part.toolCall.args ?? {}),\n                    providerExecuted: true,\n                    dynamic: true,\n                    providerMetadata: serverMeta,\n                  });\n                } else if ('toolResponse' in part && part.toolResponse) {\n                  const responseToolCallId =\n                    lastServerToolCallId ??\n                    part.toolResponse.id ??\n                    generateId();\n                  const serverMeta = {\n                    [providerOptionsName]: {\n                      ...(part.thoughtSignature\n                        ? { thoughtSignature: part.thoughtSignature }\n                        : {}),\n                      serverToolCallId: responseToolCallId,\n                      serverToolType: part.toolResponse.toolType,\n                    },\n                  };\n\n                  controller.enqueue({\n                    type: 'tool-result',\n                    toolCallId: responseToolCallId,\n                    toolName: `server:${part.toolResponse.toolType}`,\n                    result: (part.toolResponse.response ?? {}) as JSONObject,\n                    providerMe