@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/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"],"sourcesContent":["import {\n  EmbeddingModelV3,\n  LanguageModelV3,\n  ProviderV3,\n  ImageModelV3,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadApiKey,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { VERSION } from './version';\nimport { GoogleGenerativeAIEmbeddingModel } from './google-generative-ai-embedding-model';\nimport { GoogleGenerativeAIEmbeddingModelId } from './google-generative-ai-embedding-options';\nimport { GoogleGenerativeAILanguageModel } from './google-generative-ai-language-model';\nimport { GoogleGenerativeAIModelId } from './google-generative-ai-options';\nimport { googleTools } from './google-tools';\n\nimport {\n  GoogleGenerativeAIImageSettings,\n  GoogleGenerativeAIImageModelId,\n} from './google-generative-ai-image-settings';\nimport { GoogleGenerativeAIImageModel } from './google-generative-ai-image-model';\n\nexport interface GoogleGenerativeAIProvider extends ProviderV3 {\n  (modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  languageModel(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  chat(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n  /**\nCreates 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  tools: typeof googleTools;\n}\n\nexport interface GoogleGenerativeAIProviderSettings {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://generativelanguage.googleapis.com/v1beta`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `x-goog-api-key` header.\nIt defaults to the `GOOGLE_GENERATIVE_AI_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: 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  /**\nOptional 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/**\nCreate 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 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.tools = googleTools;\n\n  return provider as GoogleGenerativeAIProvider;\n}\n\n/**\nDefault 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  EmbeddingModelV3,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  lazySchema,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleFailedResponseHandler } from './google-error';\nimport {\n  GoogleGenerativeAIEmbeddingModelId,\n  googleGenerativeAIEmbeddingProviderOptions,\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: googleGenerativeAIEmbeddingProviderOptions,\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    // For single embeddings, use the single endpoint (ratelimits, etc.)\n    if (values.length === 1) {\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: [{ text: values[0] }],\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    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 => ({\n          model: `models/${this.modelId}`,\n          content: { role: 'user', parts: [{ text: value }] },\n          outputDimensionality: googleOptions?.outputDimensionality,\n          taskType: googleOptions?.taskType,\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  | 'text-embedding-004'\n  | (string & {});\n\nexport const googleGenerativeAIEmbeddingProviderOptions = 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);\n\nexport type GoogleGenerativeAIEmbeddingProviderOptions = InferSchema<\n  typeof googleGenerativeAIEmbeddingProviderOptions\n>;\n","import {\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3Source,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  FetchFunction,\n  generateId,\n  InferSchema,\n  lazySchema,\n  parseProviderOptions,\n  ParseResult,\n  postJsonToApi,\n  Resolvable,\n  resolve,\n  zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  convertGoogleGenerativeAIUsage,\n  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  GoogleGenerativeAIModelId,\n  googleGenerativeAIProviderOptions,\n} from './google-generative-ai-options';\nimport { GoogleGenerativeAIContentPart } from './google-generative-ai-prompt';\nimport { prepareTools } from './google-prepare-tools';\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    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    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: googleGenerativeAIProviderOptions,\n    });\n\n    if (googleOptions == null && providerOptionsName !== 'google') {\n      googleOptions = await parseProviderOptions({\n        provider: 'google',\n        providerOptions,\n        schema: googleGenerativeAIProviderOptions,\n      });\n    }\n\n    // Add warning if Vertex rag tools are used with a non-Vertex Google provider\n    if (\n      tools?.some(\n        tool =>\n          tool.type === 'provider' && tool.id === 'google.vertex_rag_store',\n      ) &&\n      !this.config.provider.startsWith('google.vertex.')\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    const isGemmaModel = this.modelId.toLowerCase().startsWith('gemma-');\n\n    const { contents, systemInstruction } = convertToGoogleGenerativeAIMessages(\n      prompt,\n      { isGemmaModel, providerOptionsName },\n    );\n\n    const {\n      tools: googleTools,\n      toolConfig: googleToolConfig,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n      modelId: this.modelId,\n    });\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: googleOptions?.retrievalConfig\n          ? {\n              ...googleToolConfig,\n              retrievalConfig: googleOptions.retrievalConfig,\n            }\n          : googleToolConfig,\n        cachedContent: googleOptions?.cachedContent,\n        labels: googleOptions?.labels,\n      },\n      warnings: [...warnings, ...toolWarnings],\n      providerOptionsName,\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args, warnings, providerOptionsName } = await this.getArgs(options);\n\n    const mergedHeaders = combineHeaders(\n      await resolve(this.config.headers),\n      options.headers,\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\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 && part.text.length > 0) {\n        content.push({\n          type: part.thought === true ? 'reasoning' : 'text',\n          text: part.text,\n          providerMetadata: part.thoughtSignature\n            ? {\n                [providerOptionsName]: {\n                  thoughtSignature: part.thoughtSignature,\n                },\n              }\n            : undefined,\n        });\n      } else if ('functionCall' in part) {\n        content.push({\n          type: 'tool-call' as const,\n          toolCallId: 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        content.push({\n          type: 'file' as const,\n          data: part.inlineData.data,\n          mediaType: part.inlineData.mimeType,\n          providerMetadata: part.thoughtSignature\n            ? {\n                [providerOptionsName]: {\n                  thoughtSignature: part.thoughtSignature,\n                },\n              }\n            : undefined,\n        });\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        },\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 } = await this.getArgs(options);\n\n    const headers = combineHeaders(\n      await resolve(this.config.headers),\n      options.headers,\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\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\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            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 (\n                  'text' in part &&\n                  part.text != null &&\n                  part.text.length > 0\n                ) {\n                  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: part.thoughtSignature\n                          ? {\n                              [providerOptionsName]: {\n                                thoughtSignature: part.thoughtSignature,\n                              },\n                            }\n                          : undefined,\n                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'reasoning-delta',\n                      id: currentReasoningBlockId,\n                      delta: part.text,\n                      providerMetadata: part.thoughtSignature\n                        ? {\n                            [providerOptionsName]: {\n                              thoughtSignature: part.thoughtSignature,\n                            },\n                          }\n                        : undefined,\n                    });\n                  } else {\n                    // End any active reasoning block before starting text\n                    if (currentReasoningBlockId !== null) {\n                      controller.enqueue({\n                        type: 'reasoning-end',\n                        id: currentReasoningBlockId,\n                      });\n                      currentReasoningBlockId = null;\n                    }\n\n                    // Start new text block if not already active\n                    if (currentTextBlockId === null) {\n                      currentTextBlockId = String(blockCounter++);\n                      controller.enqueue({\n                        type: 'text-start',\n                        id: currentTextBlockId,\n                        providerMetadata: part.thoughtSignature\n                          ? {\n                              [providerOptionsName]: {\n                                thoughtSignature: part.thoughtSignature,\n                              },\n                            }\n                          : undefined,\n                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'text-delta',\n                      id: currentTextBlockId,\n                      delta: part.text,\n                      providerMetadata: part.thoughtSignature\n                        ? {\n                            [providerOptionsName]: {\n                              thoughtSignature: part.thoughtSignature,\n                            },\n                          }\n                        : undefined,\n                    });\n                  }\n                } else if ('inlineData' in part) {\n                  // Process file parts inline to preserve order with text\n                  controller.enqueue({\n                    type: 'file',\n                    mediaType: part.inlineData.mimeType,\n                    data: part.inlineData.data,\n                  });\n                }\n              }\n\n              const toolCallDeltas = getToolCallsFromParts({\n                parts: content.parts,\n                generateId,\n                providerOptionsName,\n              });\n\n              if (toolCallDeltas != null) {\n                for (const toolCall of toolCallDeltas) {\n                  controller.enqueue({\n                    type: 'tool-input-start',\n                    id: toolCall.toolCallId,\n                    toolName: toolCall.toolName,\n                    providerMetadata: toolCall.providerMetadata,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-input-delta',\n                    id: toolCall.toolCallId,\n                    delta: toolCall.args,\n                    providerMetadata: toolCall.providerMetadata,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-input-end',\n                    id: toolCall.toolCallId,\n                    providerMetadata: toolCall.providerMetadata,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId: toolCall.toolCallId,\n                    toolName: toolCall.toolName,\n                    input: toolCall.args,\n                    providerMetadata: toolCall.providerMetadata,\n                  });\n\n                  hasToolCalls = true;\n                }\n              }\n            }\n\n            if (candidate.finishReason != null) {\n              finishReason = {\n                unified: mapGoogleGenerativeAIFinishReason({\n                  finishReason: candidate.finishReason,\n                  hasToolCalls,\n                }),\n                raw: candidate.finishReason,\n              };\n\n              providerMetadata = {\n                [providerOptionsName]: {\n                  promptFeedback: value.promptFeedback ?? null,\n                  groundingMetadata: candidate.groundingMetadata ?? null,\n                  urlContextMetadata: candidate.urlContextMetadata ?? null,\n                  safetyRatings: candidate.safetyRatings ?? null,\n                },\n              };\n              if (usageMetadata != null) {\n                (\n                  providerMetadata[providerOptionsName] as Record<\n                    string,\n                    unknown\n                  >\n                ).usageMetadata = usageMetadata;\n              }\n            }\n          },\n\n          flush(controller) {\n            // Close any open blocks before finishing\n            if (currentTextBlockId !== null) {\n              controller.enqueue({\n                type: 'text-end',\n                id: currentTextBlockId,\n              });\n            }\n            if (currentReasoningBlockId !== null) {\n              controller.enqueue({\n                type: 'reasoning-end',\n                id: currentReasoningBlockId,\n              });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertGoogleGenerativeAIUsage(usage),\n              providerMetadata,\n            });\n          },\n        }),\n      ),\n      response: { headers: responseHeaders },\n      request: { body: args },\n    };\n  }\n}\n\nfunction getToolCallsFromParts({\n  parts,\n  generateId,\n  providerOptionsName,\n}: {\n  parts: ContentSchema['parts'];\n  generateId: () => string;\n  providerOptionsName: string;\n}) {\n  const functionCallParts = parts?.filter(\n    part => 'functionCall' in part,\n  ) as Array<\n    GoogleGenerativeAIContentPart & {\n      functionCall: { name: string; args: unknown };\n      thoughtSignature?: string | null;\n    }\n  >;\n\n  return functionCallParts == null || functionCallParts.length === 0\n    ? undefined\n    : functionCallParts.map(part => ({\n        type: 'tool-call' as const,\n        toolCallId: generateId(),\n        toolName: part.functionCall.name,\n        args: JSON.stringify(part.functionCall.args),\n        providerMetadata: part.thoughtSignature\n          ? {\n              [providerOptionsName]: {\n                thoughtSignature: part.thoughtSignature,\n              },\n            }\n          : undefined,\n      }));\n}\n\nfunction extractSources({\n  groundingMetadata,\n  generateId,\n}: {\n  groundingMetadata: GroundingMetadataSchema | undefined | null;\n  generateId: () => string;\n}): undefined | LanguageModelV3Source[] {\n  if (!groundingMetadata?.groundingChunks) {\n    return undefined;\n  }\n\n  const sources: LanguageModelV3Source[] = [];\n\n  for (const chunk of groundingMetadata.groundingChunks) {\n    if (chunk.web != null) {\n      // Handle web chunks as URL sources\n      sources.push({\n        type: 'source',\n        sourceType: 'url',\n        id: generateId(),\n        url: chunk.web.uri,\n        title: chunk.web.title ?? undefined,\n      });\n    } else if (chunk.retrievedContext != null) {\n      // Handle retrievedContext chunks from RAG operations\n      const uri = chunk.retrievedContext.uri;\n      const fileSearchStore = chunk.retrievedContext.fileSearchStore;\n\n      if (uri && (uri.startsWith('http://') || uri.startsWith('https://'))) {\n        // Old format: Google Search with HTTP/HTTPS URL\n        sources.push({\n          type: 'source',\n          sourceType: 'url',\n          id: generateId(),\n          url: uri,\n          title: chunk.retrievedContext.title ?? undefined,\n        });\n      } else if (uri) {\n        // Old format: Document with file path (gs://, etc.)\n        const title = chunk.retrievedContext.title ?? 'Unknown Document';\n        let mediaType = 'application/octet-stream';\n        let filename: string | undefined = undefined;\n\n        if (uri.endsWith('.pdf')) {\n          mediaType = 'application/pdf';\n          filename = uri.split('/').pop();\n        } else if (uri.endsWith('.txt')) {\n          mediaType = 'text/plain';\n          filename = uri.split('/').pop();\n        } else if (uri.endsWith('.docx')) {\n          mediaType =\n            'application/vnd.openxmlformats-officedocument.wordprocessingml.document';\n          filename = uri.split('/').pop();\n        } else if (uri.endsWith('.doc')) {\n          mediaType = 'application/msword';\n          filename = uri.split('/').pop();\n        } else if (uri.match(/\\.(md|markdown)$/)) {\n          mediaType = 'text/markdown';\n          filename = uri.split('/').pop();\n        } else {\n          filename = uri.split('/').pop();\n        }\n\n        sources.push({\n          type: 'source',\n          sourceType: 'document',\n          id: generateId(),\n          mediaType,\n          title,\n          filename,\n        });\n      } else if (fileSearchStore) {\n        // New format: File Search with fileSearchStore (no uri)\n        const title = chunk.retrievedContext.title ?? 'Unknown Document';\n        sources.push({\n          type: 'source',\n          sourceType: 'document',\n          id: generateId(),\n          mediaType: 'application/octet-stream',\n          title,\n          filename: fileSearchStore.split('/').pop(),\n        });\n      }\n    } else if (chunk.maps != null) {\n      if (chunk.maps.uri) {\n        sources.push({\n          type: 'source',\n          sourceType: 'url',\n          id: generateId(),\n          url: chunk.maps.uri,\n          title: chunk.maps.title ?? undefined,\n        });\n      }\n    }\n  }\n\n  return sources.length > 0 ? sources : undefined;\n}\n\nexport const getGroundingMetadataSchema = () =>\n  z.object({\n    webSearchQueries: z.array(z.string()).nullish(),\n    retrievalQueries: z.array(z.string()).nullish(),\n    searchEntryPoint: z.object({ renderedContent: z.string() }).nullish(),\n    groundingChunks: z\n      .array(\n        z.object({\n          web: z\n            .object({ uri: z.string(), title: z.string().nullish() })\n            .nullish(),\n          retrievedContext: z\n            .object({\n              uri: z.string().nullish(),\n              title: z.string().nullish(),\n              text: z.string().nullish(),\n              fileSearchStore: z.string().nullish(),\n            })\n            .nullish(),\n          maps: z\n            .object({\n              uri: z.string().nullish(),\n              title: z.string().nullish(),\n              text: z.string().nullish(),\n              placeId: z.string().nullish(),\n            })\n            .nullish(),\n        }),\n      )\n      .nullish(),\n    groundingSupports: z\n      .array(\n        z.object({\n          segment: z.object({\n            startIndex: z.number().nullish(),\n            endIndex: z.number().nullish(),\n            text: z.string().nullish(),\n          }),\n          segment_text: z.string().nullish(),\n          groundingChunkIndices: z.array(z.number()).nullish(),\n          supportChunkIndices: z.array(z.number()).nullish(),\n          confidenceScores: z.array(z.number()).nullish(),\n          confidenceScore: z.array(z.number()).nullish(),\n        }),\n      )\n      .nullish(),\n    retrievalMetadata: z\n      .union([\n        z.object({\n          webDynamicRetrievalScore: z.number(),\n        }),\n        z.object({}),\n      ])\n      .nullish(),\n  });\n\nconst getContentSchema = () =>\n  z.object({\n    parts: z\n      .array(\n        z.union([\n          // note: order matters since text can be fully empty\n          z.object({\n            functionCall: z.object({\n              name: z.string(),\n              args: z.unknown(),\n            }),\n            thoughtSignature: z.string().nullish(),\n          }),\n          z.object({\n            inlineData: z.object({\n              mimeType: z.string(),\n              data: z.string(),\n            }),\n            thoughtSignature: z.string().nullish(),\n          }),\n          z.object({\n            executableCode: z\n              .object({\n                language: z.string(),\n                code: z.string(),\n              })\n              .nullish(),\n            codeExecutionResult: z\n              .object({\n                outcome: z.string(),\n                output: z.string(),\n              })\n              .nullish(),\n            text: z.string().nullish(),\n            thought: z.boolean().nullish(),\n            thoughtSignature: z.string().nullish(),\n          }),\n        ]),\n      )\n      .nullish(),\n  });\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-filters\nconst getSafetyRatingSchema = () =>\n  z.object({\n    category: z.string().nullish(),\n    probability: z.string().nullish(),\n    probabilityScore: z.number().nullish(),\n    severity: z.string().nullish(),\n    severityScore: z.number().nullish(),\n    blocked: z.boolean().nullish(),\n  });\n\nconst usageSchema = z.object({\n  cachedContentTokenCount: z.number().nullish(),\n  thoughtsTokenCount: z.number().nullish(),\n  promptTokenCount: z.number().nullish(),\n  candidatesTokenCount: z.number().nullish(),\n  totalTokenCount: z.number().nullish(),\n  // https://cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/GenerateContentResponse#TrafficType\n  trafficType: z.string().nullish(),\n});\n\n// https://ai.google.dev/api/generate-content#UrlRetrievalMetadata\nexport const getUrlContextMetadataSchema = () =>\n  z.object({\n    urlMetadata: z.array(\n      z.object({\n        retrievedUrl: z.string(),\n        urlRetrievalStatus: z.string(),\n      }),\n    ),\n  });\n\nconst responseSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      candidates: z.array(\n        z.object({\n          content: getContentSchema().nullish().or(z.object({}).strict()),\n          finishReason: z.string().nullish(),\n          safetyRatings: z.array(getSafetyRatingSchema()).nullish(),\n          groundingMetadata: getGroundingMetadataSchema().nullish(),\n          urlContextMetadata: getUrlContextMetadataSchema().nullish(),\n        }),\n      ),\n      usageMetadata: usageSchema.nullish(),\n      promptFeedback: z\n        .object({\n          blockReason: z.string().nullish(),\n          safetyRatings: z.array(getSafetyRatingSchema()).nullish(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n\ntype ContentSchema = NonNullable<\n  InferSchema<typeof responseSchema>['candidates'][number]['content']\n>;\nexport type GroundingMetadataSchema = NonNullable<\n  InferSchema<typeof responseSchema>['candidates'][number]['groundingMetadata']\n>;\n\ntype GroundingChunkSchema = NonNullable<\n  GroundingMetadataSchema['groundingChunks']\n>[number];\n\nexport type UrlContextMetadataSchema = NonNullable<\n  InferSchema<typeof responseSchema>['candidates'][number]['urlContextMetadata']\n>;\n\nexport type SafetyRatingSchema = NonNullable<\n  InferSchema<typeof responseSchema>['candidates'][number]['safetyRatings']\n>[number];\n\n// limited 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 chunkSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      candidates: z\n        .array(\n          z.object({\n            content: getContentSchema().nullish(),\n            finishReason: z.string().nullish(),\n            safetyRatings: z.array(getSafetyRatingSchema()).nullish(),\n            groundingMetadata: getGroundingMetadataSchema().nullish(),\n            urlContextMetadata: getUrlContextMetadataSchema().nullish(),\n          }),\n        )\n        .nullish(),\n      usageMetadata: usageSchema.nullish(),\n      promptFeedback: z\n        .object({\n          blockReason: z.string().nullish(),\n          safetyRatings: z.array(getSafetyRatingSchema()).nullish(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n\ntype ChunkSchema = InferSchema<typeof chunkSchema>;\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport type GoogleGenerativeAIUsageMetadata = {\n  promptTokenCount?: number | null;\n  candidatesTokenCount?: number | null;\n  totalTokenCount?: number | null;\n  cachedContentTokenCount?: number | null;\n  thoughtsTokenCount?: number | null;\n  trafficType?: string | null;\n};\n\nexport function convertGoogleGenerativeAIUsage(\n  usage: GoogleGenerativeAIUsageMetadata | undefined | null,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.promptTokenCount ?? 0;\n  const candidatesTokens = usage.candidatesTokenCount ?? 0;\n  const cachedContentTokens = usage.cachedContentTokenCount ?? 0;\n  const thoughtsTokens = usage.thoughtsTokenCount ?? 0;\n\n  return {\n    inputTokens: {\n      total: promptTokens,\n      noCache: promptTokens - cachedContentTokens,\n      cacheRead: cachedContentTokens,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: candidatesTokens + thoughtsTokens,\n      text: candidatesTokens,\n      reasoning: thoughtsTokens,\n    },\n    raw: usage,\n  };\n}\n","import { JSONSchema7Definition } from '@ai-sdk/provider';\n\n/**\n * Converts JSON Schema 7 to OpenAPI Schema 3.0\n */\nexport function convertJSONSchemaToOpenAPISchema(\n  jsonSchema: JSONSchema7Definition | undefined,\n  isRoot = true,\n): unknown {\n  // Handle empty object schemas: undefined at root, preserved when nested\n  if (jsonSchema == null) {\n    return undefined;\n  }\n\n  if (isEmptyObjectSchema(jsonSchema)) {\n    if (is