@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/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-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/google-search.ts","../src/tool/url-context.ts","../src/tool/code-execution.ts","../src/google-tools.ts","../src/google-generative-ai-image-model.ts"],"sourcesContent":["import {\n  EmbeddingModelV2,\n  LanguageModelV2,\n  ProviderV2,\n  ImageModelV2,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\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 ProviderV2 {\n  (modelId: GoogleGenerativeAIModelId): LanguageModelV2;\n\n  languageModel(modelId: GoogleGenerativeAIModelId): LanguageModelV2;\n\n  chat(modelId: GoogleGenerativeAIModelId): LanguageModelV2;\n\n  /**\nCreates a model for image generation.\n */\n  image(\n    modelId: GoogleGenerativeAIImageModelId,\n    settings?: GoogleGenerativeAIImageSettings,\n  ): ImageModelV2;\n\n  /**\n   * @deprecated Use `chat()` instead.\n   */\n  generativeAI(modelId: GoogleGenerativeAIModelId): LanguageModelV2;\n\n  /**\n@deprecated Use `textEmbedding()` instead.\n   */\n  embedding(\n    modelId: GoogleGenerativeAIEmbeddingModelId,\n  ): EmbeddingModelV2<string>;\n\n  textEmbedding(\n    modelId: GoogleGenerativeAIEmbeddingModelId,\n  ): EmbeddingModelV2<string>;\n\n  textEmbeddingModel(\n    modelId: GoogleGenerativeAIEmbeddingModelId,\n  ): EmbeddingModelV2<string>;\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/**\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 getHeaders = () => ({\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\n  const createChatModel = (modelId: GoogleGenerativeAIModelId) =>\n    new GoogleGenerativeAILanguageModel(modelId, {\n      provider: 'google.generative-ai',\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: 'google.generative-ai',\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: 'google.generative-ai',\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.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.generativeAI = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n  provider.tools = googleTools;\n  return provider as GoogleGenerativeAIProvider;\n}\n\n/**\nDefault Google Generative AI provider instance.\n */\nexport const google = createGoogleGenerativeAI();\n","import {\n  EmbeddingModelV2,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\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\n  implements EmbeddingModelV2<string>\n{\n  readonly specificationVersion = 'v2';\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<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV2<string>['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/${this.modelId}`,\n          content: {\n            parts: [{ text: values[0] }],\n          },\n          outputDimensionality: googleOptions?.outputDimensionality,\n          taskType: googleOptions?.taskType,\n        },\n        failedResponseHandler: googleFailedResponseHandler,\n        successfulResponseHandler: createJsonResponseHandler(\n          googleGenerativeAISingleEmbeddingResponseSchema,\n        ),\n        abortSignal,\n        fetch: this.config.fetch,\n      });\n\n      return {\n        embeddings: [response.embedding.values],\n        usage: undefined,\n        response: { headers: responseHeaders, body: rawValue },\n      };\n    }\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url: `${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      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 = z.object({\n  embeddings: z.array(z.object({ values: z.array(z.number()) })),\n});\n\n// Schema for single embedding response\nconst googleGenerativeAISingleEmbeddingResponseSchema = z.object({\n  embedding: z.object({ values: z.array(z.number()) }),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleErrorDataSchema = z.object({\n  error: z.object({\n    code: z.number().nullable(),\n    message: z.string(),\n    status: z.string(),\n  }),\n});\n\nexport type GoogleErrorData = z.infer<typeof googleErrorDataSchema>;\n\nexport const googleFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: googleErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","import { z } from 'zod/v4';\n\nexport type GoogleGenerativeAIEmbeddingModelId =\n  | 'gemini-embedding-001'\n  | 'text-embedding-004'\n  | (string & {});\n\nexport const googleGenerativeAIEmbeddingProviderOptions = 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\nexport type GoogleGenerativeAIEmbeddingProviderOptions = z.infer<\n  typeof googleGenerativeAIEmbeddingProviderOptions\n>;\n","import {\n  LanguageModelV2,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2Source,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n  SharedV2ProviderMetadata,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  Resolvable,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\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 { GoogleGenerativeAIContentPart } from './google-generative-ai-prompt';\nimport {\n  GoogleGenerativeAIModelId,\n  googleGenerativeAIProviderOptions,\n} from './google-generative-ai-options';\nimport { prepareTools } from './google-prepare-tools';\nimport { mapGoogleGenerativeAIFinishReason } from './map-google-generative-ai-finish-reason';\nimport {\n  groundingChunkSchema,\n  groundingMetadataSchema,\n} from './tool/google-search';\nimport { urlContextMetadataSchema } from './tool/url-context';\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?: () => LanguageModelV2['supportedUrls'];\n};\n\nexport class GoogleGenerativeAILanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\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  }: Parameters<LanguageModelV2['doGenerate']>[0]) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    const googleOptions = await parseProviderOptions({\n      provider: 'google',\n      providerOptions,\n      schema: googleGenerativeAIProviderOptions,\n    });\n\n    // Add warning if includeThoughts is used with a non-Vertex Google provider\n    if (\n      googleOptions?.thinkingConfig?.includeThoughts === true &&\n      !this.config.provider.startsWith('google.vertex.')\n    ) {\n      warnings.push({\n        type: 'other',\n        message:\n          \"The 'includeThoughts' option 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 },\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        },\n        contents,\n        systemInstruction: isGemmaModel ? undefined : systemInstruction,\n        safetySettings: googleOptions?.safetySettings,\n        tools: googleTools,\n        toolConfig: googleToolConfig,\n        cachedContent: googleOptions?.cachedContent,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const { args, warnings } = await this.getArgs(options);\n    const body = JSON.stringify(args);\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<LanguageModelV2Content> = [];\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          providerExecuted: true,\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        if (part.thought === true) {\n          content.push({ type: 'reasoning', text: part.text });\n        } else {\n          content.push({ type: 'text', text: part.text });\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        });\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        });\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: mapGoogleGenerativeAIFinishReason({\n        finishReason: candidate.finishReason,\n        hasToolCalls: content.some(part => part.type === 'tool-call'),\n      }),\n      usage: {\n        inputTokens: usageMetadata?.promptTokenCount ?? undefined,\n        outputTokens: usageMetadata?.candidatesTokenCount ?? undefined,\n        totalTokens: usageMetadata?.totalTokenCount ?? undefined,\n        reasoningTokens: usageMetadata?.thoughtsTokenCount ?? undefined,\n        cachedInputTokens: usageMetadata?.cachedContentTokenCount ?? undefined,\n      },\n      warnings,\n      providerMetadata: {\n        google: {\n          groundingMetadata: candidate.groundingMetadata ?? null,\n          urlContextMetadata: candidate.urlContextMetadata ?? null,\n          safetyRatings: candidate.safetyRatings ?? null,\n          usageMetadata: usageMetadata ?? null,\n        },\n      },\n      request: { body },\n      response: {\n        // TODO timestamp, model id, id\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const body = JSON.stringify(args);\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: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    let providerMetadata: SharedV2ProviderMetadata | 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<z.infer<typeof chunkSchema>>,\n          LanguageModelV2StreamPart\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.inputTokens = usageMetadata.promptTokenCount ?? undefined;\n              usage.outputTokens =\n                usageMetadata.candidatesTokenCount ?? undefined;\n              usage.totalTokens = usageMetadata.totalTokenCount ?? undefined;\n              usage.reasoningTokens =\n                usageMetadata.thoughtsTokenCount ?? undefined;\n              usage.cachedInputTokens =\n                usageMetadata.cachedContentTokenCount ?? undefined;\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 text parts individually to handle reasoning parts\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\n                  hasToolCalls = true;\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                      providerExecuted: true,\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                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'reasoning-delta',\n                      id: currentReasoningBlockId,\n                      delta: part.text,\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                      });\n                    }\n\n                    controller.enqueue({\n                      type: 'text-delta',\n                      id: currentTextBlockId,\n                      delta: part.text,\n                    });\n                  }\n                }\n              }\n\n              const inlineDataParts = getInlineDataParts(content.parts);\n              if (inlineDataParts != null) {\n                for (const part of inlineDataParts) {\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              });\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                  });\n\n                  controller.enqueue({\n                    type: 'tool-input-delta',\n                    id: toolCall.toolCallId,\n                    delta: toolCall.args,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-input-end',\n                    id: toolCall.toolCallId,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId: toolCall.toolCallId,\n                    toolName: toolCall.toolName,\n                    input: toolCall.args,\n                  });\n\n                  hasToolCalls = true;\n                }\n              }\n            }\n\n            if (candidate.finishReason != null) {\n              finishReason = mapGoogleGenerativeAIFinishReason({\n                finishReason: candidate.finishReason,\n                hasToolCalls,\n              });\n\n              providerMetadata = {\n                google: {\n                  groundingMetadata: candidate.groundingMetadata ?? null,\n                  urlContextMetadata: candidate.urlContextMetadata ?? null,\n                  safetyRatings: candidate.safetyRatings ?? null,\n                },\n              };\n              if (usageMetadata != null) {\n                providerMetadata.google.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,\n              providerMetadata,\n            });\n          },\n        }),\n      ),\n      response: { headers: responseHeaders },\n      request: { body },\n    };\n  }\n}\n\nfunction getToolCallsFromParts({\n  parts,\n  generateId,\n}: {\n  parts: z.infer<typeof contentSchema>['parts'];\n  generateId: () => string;\n}) {\n  const functionCallParts = parts?.filter(\n    part => 'functionCall' in part,\n  ) as Array<\n    GoogleGenerativeAIContentPart & {\n      functionCall: { name: string; args: unknown };\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      }));\n}\n\nfunction getTextFromParts(parts: z.infer<typeof contentSchema>['parts']) {\n  const textParts = parts?.filter(part => 'text' in part) as Array<\n    GoogleGenerativeAIContentPart & { text: string }\n  >;\n\n  return textParts == null || textParts.length === 0\n    ? undefined\n    : textParts.map(part => part.text).join('');\n}\n\nfunction getInlineDataParts(parts: z.infer<typeof contentSchema>['parts']) {\n  return parts?.filter(\n    (\n      part,\n    ): part is {\n      inlineData: { mimeType: string; data: string };\n    } => 'inlineData' in part,\n  );\n}\n\nfunction extractSources({\n  groundingMetadata,\n  generateId,\n}: {\n  groundingMetadata: z.infer<typeof groundingMetadataSchema> | undefined | null;\n  generateId: () => string;\n}): undefined | LanguageModelV2Source[] {\n  return groundingMetadata?.groundingChunks\n    ?.filter(\n      (\n        chunk,\n      ): chunk is z.infer<typeof groundingChunkSchema> & {\n        web: { uri: string; title?: string };\n      } => chunk.web != null,\n    )\n    .map(chunk => ({\n      type: 'source',\n      sourceType: 'url',\n      id: generateId(),\n      url: chunk.web.uri,\n      title: chunk.web.title,\n    }));\n}\n\nconst contentSchema = 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        }),\n        z.object({\n          inlineData: z.object({\n            mimeType: z.string(),\n            data: z.string(),\n          }),\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        }),\n      ]),\n    )\n    .nullish(),\n});\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-filters\nexport const safetyRatingSchema = 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});\n\nconst responseSchema = z.object({\n  candidates: z.array(\n    z.object({\n      content: contentSchema.nullish().or(z.object({}).strict()),\n      finishReason: z.string().nullish(),\n      safetyRatings: z.array(safetyRatingSchema).nullish(),\n      groundingMetadata: groundingMetadataSchema.nullish(),\n      urlContextMetadata: urlContextMetadataSchema.nullish(),\n    }),\n  ),\n  usageMetadata: usageSchema.nullish(),\n});\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 = z.object({\n  candidates: z\n    .array(\n      z.object({\n        content: contentSchema.nullish(),\n        finishReason: z.string().nullish(),\n        safetyRatings: z.array(safetyRatingSchema).nullish(),\n        groundingMetadata: groundingMetadataSchema.nullish(),\n        urlContextMetadata: urlContextMetadataSchema.nullish(),\n      }),\n    )\n    .nullish(),\n  usageMetadata: usageSchema.nullish(),\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): unknown {\n  // parameters need to be undefined if they are empty objects:\n  if (jsonSchema == null || isEmptyObjectSchema(jsonSchema)) {\n    return undefined;\n  }\n\n  if (typeof jsonSchema === 'boolean') {\n    return { type: 'boolean', properties: {} };\n  }\n\n  const {\n    type,\n    description,\n    required,\n    properties,\n    items,\n    allOf,\n    anyOf,\n    oneOf,\n    format,\n    const: constValue,\n    minLength,\n    enum: enumValues,\n  } = jsonSchema;\n\n  const result: Record<string, unknown> = {};\n\n  if (description) result.description = description;\n  if (required) result.required = required;\n  if (format) result.format = format;\n\n  if (constValue !== undefined) {\n    result.enum = [constValue];\n  }\n\n  // Handle type\n  if (type) {\n    if (Array.isArray(type)) {\n      if (type.includes('null')) {\n        result.type = type.filter(t => t !== 'null')[0];\n        result.nullable = true;\n      } else {\n        result.type = type;\n      }\n    } else if (type === 'null') {\n      result.type = 'null';\n    } else {\n      result.type = type;\n    }\n  }\n\n  // Handle enum\n  if (enumValues !== undefined) {\n    result.enum = enumValues;\n  }\n\n  if (properties != null) {\n    result.properties = Object.entries(properties).reduce(\n      (acc, [key, value]) => {\n        acc[key] = convertJSONSchemaToOpenAPISchema(value);\n        return acc;\n      },\n      {} as Record<string, unknown>,\n    );\n  }\n\n  if (items) {\n    result.items = Array.isArray(items)\n      ? items.map(convertJSONSchemaToOpenAPISchema)\n      : convertJSONSchemaToOpenAPISchema(items);\n  }\n\n  if (allOf) {\n    result.allOf = allOf.map(convertJSONSchemaToOpenAPISchema);\n  }\n  if (anyOf) {\n    // Handle cases where anyOf includes a null type\n    if (\n      anyOf.some(\n        schema => typeof schema === 'object' && schema?.type === 'null',\n      )\n    ) {\n      const nonNullSchemas = anyOf.filter(\n        schema => !(typeof schema === 'object' && schema?.type === 'null'),\n      );\n\n      if (nonNullSchemas.length === 1) {\n        // If there's only one non-null schema, convert it and make it nullable\n        const converted = convertJSONSchemaToOpenAPISchema(nonNullSchemas[0]);\n        if (typeof converted === 'object') {\n          result.nullable = true;\n          Object.assign(result, converted);\n        }\n      } else {\n        // If there are multiple non-null schemas, keep them in anyOf\n        result.anyOf = nonNullSchemas.map(convertJSONSchemaToOpenAPISchema);\n        result.nullable = true;\n      }\n    } else {\n      result.anyOf = anyOf.map(convertJSONSchemaToOpenAPISchema);\n    }\n  }\n  if (oneOf) {\n    result.oneOf = oneOf.map(convertJSONSchemaToOpenAPISchema);\n  }\n\n  if (minLength !== undefined) {\n    result.minLength = minLength;\n  }\n\n  return result;\n}\n\nfunction isEmptyObjectSchema(jsonSchema: JSONSchema7Definition): boolean {\n  return (\n    jsonSchema != null &&\n    typeof jsonSchema === 'object' &&\n    jsonSchema.type === 'object' &&\n    (jsonSchema.properties == null ||\n      Object.keys(jsonSchema.properties).length === 0) &&\n    !jsonSchema.additionalProperties\n  );\n}\n","import {\n  LanguageModelV2Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  GoogleGenerativeAIContent,\n  GoogleGenerativeAIContentPart,\n  GoogleGenerativeAIPrompt,\n} from './google-generative-ai-prompt';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\n\nexport function convertToGoogleGenerativeAIMessages(\n  prompt: LanguageModelV2Prompt,\n  options?: { isGemmaModel?: boolean },\n): GoogleGenerativeAIPrompt {\n  const systemInstructionParts: Array<{ text: string }> = [];\n  const contents: Array<GoogleGenerativeAIContent> = [];\n  let systemMessagesAllowed = true;\n  const isGemmaModel = options?.isGemmaModel ?? false;\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        if (!systemMessagesAllowed) {\n          throw new UnsupportedFunctionalityError({\n            functionality:\n              'system messages are only supported at the beginning of the conversation',\n          });\n        }\n\n        systemInstructionParts.push({ text: content });\n        break;\n      }\n\n      case 'user': {\n        systemMessagesAllowed = false;\n\n        const parts: GoogleGenerativeAIContentPart[] = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              parts.push({ text: part.text });\n              break;\n            }\n\n            case 'file': {\n              // default to image/jpeg for unknown image/* types\n              const mediaType =\n                part.mediaType === 'image/*' ? 'image/jpeg' : part.mediaType;\n\n              parts.push(\n                part.data instanceof URL\n                  ? {\n                      fileData: {\n                        mimeType: mediaType,\n                        fileUri: part.data.toString(),\n                      },\n                    }\n                  : {\n                      inlineData: {\n                        mimeType: mediaType,\n                        data: convertToBase64(part.data),\n                      },\n                    },\n              );\n\n              break;\n            }\n          }\n        }\n\n        contents.push({ role: 'user', parts });\n        break;\n      }\n\n      case 'assistant': {\n        systemMessagesAllowed = false;\n\n        contents.push({\n          role: 'model',\n          parts: content\n            .map(part => {\n              switch (part.type) {\n                case 'text': {\n                  return part.text.length === 0\n                    ? undefined\n                    : { text: part.text };\n                }\n\n                case 'file': {\n                  if (part.mediaType !== 'image/png') {\n                    throw new UnsupportedFunctionalityError({\n                      functionality:\n                        'Only PNG images are supported in assistant messages',\n                    });\n                  }\n\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality:\n                        'File data URLs in assistant messages are not supported',\n                    });\n                  }\n\n                  return {\n                    inlineData: {\n                      mimeType: part.mediaType,\n                      data: convertToBase64(part.data),\n                    },\n                  };\n                }\n\n                case 'tool-call': {\n                  return {\n                    functionCall: {\n                      name: part.toolName,\n                      args: part.input,\n                    },\n                  };\n                }\n              }\n            })\n            .filter(part => part !== undefined),\n        });\n        break;\n      }\n\n      case 'tool': {\n        systemMessagesAllowed = false;\n\n        contents.push({\n          role: 'user',\n          parts: content.map(part => ({\n            functionResponse: {\n              name: part.toolName,\n              response: {\n                name: part.toolName,\n                content: part.output.value,\n              },\n            },\n          })),\n        });\n        break;\n      }\n    }\n  }\n\n  if (\n    isGemmaModel &&\n    systemInstructionParts.length > 0 &&\n    contents.length > 0 &&\n    contents[0].role === 'user'\n  ) {\n    const systemText = systemInstructionParts\n      .map(part => part.text)\n      .join('\\n\\n');\n\n    contents[0].parts.unshift({ text: systemText + '\\n\\n' });\n  }\n\n  return {\n    systemInstruction:\n      systemInstructionParts.length > 0 && !isGemmaModel\n        ? { parts: systemInstructionParts }\n        : undefined,\n    contents,\n  };\n}\n","export function getModelPath(modelId: string): string {\n  return modelId.includes('/') ? modelId : `models/${modelId}`;\n}\n","import { z } from 'zod/v4';\n\nexport type GoogleGenerativeAIModelId =\n  // Stable models\n  // https://ai.google.dev/gemini-api/docs/models/gemini\n  | 'gemini-1.5-flash'\n  | 'gemini-1.5-flash-latest'\n  | 'gemini-1.5-flash-001'\n  | 'gemini-1.5-flash-002'\n  | 'gemini-1.5-flash-8b'\n  | 'gemini-1.5-flash-8b-latest'\n  | 'gemini-1.5-flash-8b-001'\n  | 'gemini-1.5-pro'\n  | 'gemini-1.5-pro-latest'\n  | 'gemini-1.5-pro-001'\n  | 'gemini-1.5-pro-002'\n  | 'gemini-2.0-flash'\n  | 'gemini-2.0-flash-001'\n  | 'gemini-2.0-flash-live-001'\n  | 'gemini-2.0-flash-lite'\n  | 'gemini-2.0-pro-exp-02-05'\n  | 'gemini-2.0-flash-thinking-exp-01-21'\n  | 'gemini-2.0-flash-exp'\n  | 'gemini-2.5-pro'\n  | 'gemini-2.5-flash'\n  | 'gemini-2.5-flash-lite'\n  // Experimental models\n  // https://ai.google.dev/gemini-api/docs/models/experimental-models\n  | 'gemini-2.5-pro-exp-03-25'\n  | 'gemini-2.5-flash-preview-04-17'\n  | 'gemini-exp-1206'\n  | 'gemma-3-12b-it'\n  | 'gemma-3-27b-it'\n  | (string & {});\n\nexport const googleGenerativeAIProviderOptions = z.object({\n  responseModalities: z.array(z.enum(['TEXT', 'IMAGE'])).optional(),\n\n  thinkingConfig: z\n    .object({\n      thinkingBudget: z.number().optional(),\n      includeThoughts: z.boolean().optional(),\n    })\n    .optional(),\n\n  /**\nOptional.\nThe name of the cached content used as context to serve the prediction.\nFormat: cachedContents/{cachedContent}\n   */\n  cachedContent: z.string().optional(),\n\n  /**\n   * Optional. Enable structured output. Default is true.\n   *\n   * This is useful when the JSON Schema contains elements that are\n   * not supported by the OpenAPI schema version that\n   * Google Generative AI uses. You can use this to disable\n   * structured outputs if you need to.\n   */\n  structuredOutputs: z.boolean().optional(),\n\n  /**\nOptional. A list of unique safety settings for blocking unsafe content.\n */\n  safetySettings: z\n    .array(\n      z.object({\n        category: z.enum([\n          'HARM_CATEGORY_UNSPECIFIED',\n          'HARM_CATEGORY_HATE_SPEECH',\n          'HARM_CATEGORY_DANGEROUS_CONTENT',\n          'HARM_CATEGORY_HARASSMENT',\n          'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n          'HARM_CATEGORY_CIVIC_INTEGRITY',\n        ]),\n        threshold: z.enum([\n          'HARM_BLOCK_THRESHOLD_UNSPECIFIED',\n          'BLOCK_LOW_AND_ABOVE',\n          'BLOCK_MEDIUM_AND_ABOVE',\n          'BLOCK_ONLY_HIGH',\n          'BLOCK_NONE',\n          'OFF',\n        ]),\n      }),\n    )\n    .optional(),\n\n  threshold: z\n    .enum([\n      'HARM_BLOCK_THRESHOLD_UNSPECIFIED',\n      'BLOCK_LOW_AND_ABOVE',\n      'BLOCK_MEDIUM_AND_ABOVE',\n      'BLOCK_ONLY_HIGH',\n      'BLOCK_NONE',\n      'OFF',\n    ])\n    .optional(),\n\n  /**\n   * Optional. Enables timestamp understanding for audio-only files.\n   *\n   * https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/audio-understanding\n   */\n  audioTimestamp: z.boolean().optional(),\n});\n\nexport type GoogleGenerativeAIProviderOptions = z.infer<\n  typeof googleGenerativeAIProviderOptions\n>;\n","import {\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertJSONSchemaToOpenAPISchema } from './convert-json-schema-to-openapi-schema';\nimport { GoogleGenerativeAIModelId } from './google-generative-ai-options';\n\nexport function prepareTools({\n  tools,\n  toolChoice,\n  modelId,\n}: {\n  tools: LanguageModelV2CallOptions['tools'];\n  toolChoice?: LanguageModelV2CallOptions['toolChoice'];\n  modelId: GoogleGenerativeAIModelId;\n}): {\n  tools:\n    | {\n        functionDeclarations: Array<{\n          name: string;\n          description: string;\n          parameters: unknown;\n        }>;\n      }\n    | Record<string, any>\n    | undefined;\n  toolConfig:\n    | undefined\n    | {\n        functionCallingConfig: {\n          mode: 'AUTO' | 'NONE' | 'ANY';\n          allowedFunctionNames?: str