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{"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","../src/google-generative-ai-video-model.ts"],"sourcesContent":["import {\n EmbeddingModelV3,\n Experimental_VideoModelV3,\n ImageModelV3,\n LanguageModelV3,\n ProviderV3,\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';\nimport { GoogleGenerativeAIVideoModel } from './google-generative-ai-video-model';\nimport { GoogleGenerativeAIVideoModelId } from './google-generative-ai-video-settings';\n\nexport interface GoogleGenerativeAIProvider extends ProviderV3 {\n (modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n languageModel(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n chat(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n /**\n * Creates a model for image generation.\n */\n image(\n modelId: GoogleGenerativeAIImageModelId,\n settings?: GoogleGenerativeAIImageSettings,\n ): ImageModelV3;\n\n /**\n * @deprecated Use `chat()` instead.\n */\n generativeAI(modelId: GoogleGenerativeAIModelId): LanguageModelV3;\n\n /**\n * Creates a model for text embeddings.\n */\n embedding(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n /**\n * Creates a model for text embeddings.\n */\n embeddingModel(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n /**\n * @deprecated Use `embedding` instead.\n */\n textEmbedding(modelId: GoogleGenerativeAIEmbeddingModelId): EmbeddingModelV3;\n\n /**\n * @deprecated Use `embeddingModel` instead.\n */\n textEmbeddingModel(\n modelId: GoogleGenerativeAIEmbeddingModelId,\n ): EmbeddingModelV3;\n\n /**\n * Creates a model for video generation.\n */\n video(modelId: GoogleGenerativeAIVideoModelId): Experimental_VideoModelV3;\n\n /**\n * Creates a model for video generation.\n */\n videoModel(\n modelId: GoogleGenerativeAIVideoModelId,\n ): Experimental_VideoModelV3;\n\n tools: typeof googleTools;\n}\n\nexport interface GoogleGenerativeAIProviderSettings {\n /**\n * Use a different URL prefix for API calls, e.g. to use proxy servers.\n * The default prefix is `https://generativelanguage.googleapis.com/v1beta`.\n */\n baseURL?: string;\n\n /**\n * API key that is being send using the `x-goog-api-key` header.\n * It defaults to the `GOOGLE_GENERATIVE_AI_API_KEY` environment variable.\n */\n apiKey?: string;\n\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record<string, string | undefined>;\n\n /**\n * Custom fetch implementation. You can use it as a middleware to intercept requests,\n * or to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n\n /**\n * Optional function to generate a unique ID for each request.\n */\n generateId?: () => string;\n\n /**\n * Custom provider name\n * Defaults to 'google.generative-ai'.\n */\n name?: string;\n}\n\n/**\n * Create a Google Generative AI provider instance.\n */\nexport function createGoogleGenerativeAI(\n options: GoogleGenerativeAIProviderSettings = {},\n): GoogleGenerativeAIProvider {\n const baseURL =\n withoutTrailingSlash(options.baseURL) ??\n 'https://generativelanguage.googleapis.com/v1beta';\n\n const providerName = options.name ?? 'google.generative-ai';\n\n const getHeaders = () =>\n withUserAgentSuffix(\n {\n 'x-goog-api-key': loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'GOOGLE_GENERATIVE_AI_API_KEY',\n description: 'Google Generative AI',\n }),\n ...options.headers,\n },\n `ai-sdk/google/${VERSION}`,\n );\n\n const createChatModel = (modelId: GoogleGenerativeAIModelId) =>\n new GoogleGenerativeAILanguageModel(modelId, {\n provider: providerName,\n baseURL,\n headers: getHeaders,\n generateId: options.generateId ?? generateId,\n supportedUrls: () => ({\n '*': [\n // Google Generative Language \"files\" endpoint\n // e.g. https://generativelanguage.googleapis.com/v1beta/files/...\n new RegExp(`^${baseURL}/files/.*$`),\n // YouTube URLs (public or unlisted videos)\n new RegExp(\n `^https://(?:www\\\\.)?youtube\\\\.com/watch\\\\?v=[\\\\w-]+(?:&[\\\\w=&.-]*)?$`,\n ),\n new RegExp(`^https://youtu\\\\.be/[\\\\w-]+(?:\\\\?[\\\\w=&.-]*)?$`),\n ],\n }),\n fetch: options.fetch,\n });\n\n const createEmbeddingModel = (modelId: GoogleGenerativeAIEmbeddingModelId) =>\n new GoogleGenerativeAIEmbeddingModel(modelId, {\n provider: providerName,\n baseURL,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createImageModel = (\n modelId: GoogleGenerativeAIImageModelId,\n settings: GoogleGenerativeAIImageSettings = {},\n ) =>\n new GoogleGenerativeAIImageModel(modelId, settings, {\n provider: providerName,\n baseURL,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createVideoModel = (modelId: GoogleGenerativeAIVideoModelId) =>\n new GoogleGenerativeAIVideoModel(modelId, {\n provider: providerName,\n baseURL,\n headers: getHeaders,\n fetch: options.fetch,\n generateId: options.generateId ?? generateId,\n });\n\n const provider = function (modelId: GoogleGenerativeAIModelId) {\n if (new.target) {\n throw new Error(\n 'The Google Generative AI model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createChatModel;\n provider.chat = createChatModel;\n provider.generativeAI = createChatModel;\n provider.embedding = createEmbeddingModel;\n provider.embeddingModel = createEmbeddingModel;\n provider.textEmbedding = createEmbeddingModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.video = createVideoModel;\n provider.videoModel = createVideoModel;\n provider.tools = googleTools;\n\n return provider as GoogleGenerativeAIProvider;\n}\n\n/**\n * Default Google Generative AI provider instance.\n */\nexport const google = createGoogleGenerativeAI();\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n 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 googleEmbeddingModelOptions,\n} from './google-generative-ai-embedding-options';\n\ntype GoogleGenerativeAIEmbeddingConfig = {\n provider: string;\n baseURL: string;\n headers: () => Record<string, string | undefined>;\n fetch?: FetchFunction;\n};\n\nexport class GoogleGenerativeAIEmbeddingModel implements EmbeddingModelV3 {\n readonly specificationVersion = 'v3';\n readonly modelId: GoogleGenerativeAIEmbeddingModelId;\n readonly maxEmbeddingsPerCall = 2048;\n readonly supportsParallelCalls = true;\n\n private readonly config: GoogleGenerativeAIEmbeddingConfig;\n\n get provider(): string {\n return this.config.provider;\n }\n constructor(\n modelId: GoogleGenerativeAIEmbeddingModelId,\n config: GoogleGenerativeAIEmbeddingConfig,\n ) {\n this.modelId = modelId;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n providerOptions,\n }: Parameters<EmbeddingModelV3['doEmbed']>[0]): Promise<\n Awaited<ReturnType<EmbeddingModelV3['doEmbed']>>\n > {\n // Parse provider options\n const googleOptions = await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleEmbeddingModelOptions,\n });\n\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const mergedHeaders = combineHeaders(\n await resolve(this.config.headers),\n headers,\n );\n\n const multimodalContent = googleOptions?.content;\n\n if (\n multimodalContent != null &&\n multimodalContent.length !== values.length\n ) {\n throw new Error(\n `The number of multimodal content entries (${multimodalContent.length}) must match the number of values (${values.length}).`,\n );\n }\n\n // For single embeddings, use the single endpoint\n if (values.length === 1) {\n const valueParts = multimodalContent?.[0];\n const textPart = values[0] ? [{ text: values[0] }] : [];\n const parts =\n valueParts != null\n ? [...textPart, ...valueParts]\n : [{ text: values[0] }];\n\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:embedContent`,\n headers: mergedHeaders,\n body: {\n model: `models/${this.modelId}`,\n content: {\n parts,\n },\n outputDimensionality: googleOptions?.outputDimensionality,\n taskType: googleOptions?.taskType,\n },\n failedResponseHandler: googleFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleGenerativeAISingleEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n warnings: [],\n embeddings: [response.embedding.values],\n usage: undefined,\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n\n // For multiple values, use the batch endpoint\n const {\n responseHeaders,\n value: response,\n rawValue,\n } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:batchEmbedContents`,\n headers: mergedHeaders,\n body: {\n requests: values.map((value, index) => {\n const valueParts = multimodalContent?.[index];\n const textPart = value ? [{ text: value }] : [];\n return {\n model: `models/${this.modelId}`,\n content: {\n role: 'user',\n parts:\n valueParts != null\n ? [...textPart, ...valueParts]\n : [{ text: value }],\n },\n outputDimensionality: googleOptions?.outputDimensionality,\n taskType: googleOptions?.taskType,\n };\n }),\n },\n failedResponseHandler: googleFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleGenerativeAITextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n warnings: [],\n embeddings: response.embeddings.map(item => item.values),\n usage: undefined,\n response: { headers: responseHeaders, body: rawValue },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst googleGenerativeAITextEmbeddingResponseSchema = lazySchema(() =>\n zodSchema(\n z.object({\n embeddings: z.array(z.object({ values: z.array(z.number()) })),\n }),\n ),\n);\n\n// Schema for single embedding response\nconst googleGenerativeAISingleEmbeddingResponseSchema = lazySchema(() =>\n zodSchema(\n z.object({\n embedding: z.object({ values: z.array(z.number()) }),\n }),\n ),\n);\n","import {\n createJsonErrorResponseHandler,\n type InferSchema,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst googleErrorDataSchema = lazySchema(() =>\n zodSchema(\n z.object({\n error: z.object({\n code: z.number().nullable(),\n message: z.string(),\n status: z.string(),\n }),\n }),\n ),\n);\n\nexport type GoogleErrorData = InferSchema<typeof googleErrorDataSchema>;\n\nexport const googleFailedResponseHandler = createJsonErrorResponseHandler({\n errorSchema: googleErrorDataSchema,\n errorToMessage: data => data.error.message,\n});\n","import {\n type InferSchema,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type GoogleGenerativeAIEmbeddingModelId =\n | 'gemini-embedding-001'\n | 'gemini-embedding-2-preview'\n | (string & {});\n\nconst googleEmbeddingContentPartSchema = z.union([\n z.object({ text: z.string() }),\n z.object({\n inlineData: z.object({\n mimeType: z.string(),\n data: z.string(),\n }),\n }),\n]);\n\nexport const googleEmbeddingModelOptions = lazySchema(() =>\n zodSchema(\n z.object({\n /**\n * Optional. Optional reduced dimension for the output embedding.\n * If set, excessive values in the output embedding are truncated from the end.\n */\n outputDimensionality: z.number().optional(),\n\n /**\n * Optional. Specifies the task type for generating embeddings.\n * Supported task types:\n * - SEMANTIC_SIMILARITY: Optimized for text similarity.\n * - CLASSIFICATION: Optimized for text classification.\n * - CLUSTERING: Optimized for clustering texts based on similarity.\n * - RETRIEVAL_DOCUMENT: Optimized for document retrieval.\n * - RETRIEVAL_QUERY: Optimized for query-based retrieval.\n * - QUESTION_ANSWERING: Optimized for answering questions.\n * - FACT_VERIFICATION: Optimized for verifying factual information.\n * - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.\n */\n taskType: z\n .enum([\n 'SEMANTIC_SIMILARITY',\n 'CLASSIFICATION',\n 'CLUSTERING',\n 'RETRIEVAL_DOCUMENT',\n 'RETRIEVAL_QUERY',\n 'QUESTION_ANSWERING',\n 'FACT_VERIFICATION',\n 'CODE_RETRIEVAL_QUERY',\n ])\n .optional(),\n\n /**\n * Optional. Per-value multimodal content parts for embedding non-text\n * content (images, video, PDF, audio). Each entry corresponds to the\n * embedding value at the same index and its parts are merged with the\n * text value in the request. Use `null` for entries that are text-only.\n *\n * The array length must match the number of values being embedded. In\n * the case of a single embedding, the array length must be 1.\n */\n content: z\n .array(z.array(googleEmbeddingContentPartSchema).min(1).nullable())\n .optional(),\n }),\n ),\n);\n\nexport type GoogleEmbeddingModelOptions = InferSchema<\n typeof googleEmbeddingModelOptions\n>;\n","import {\n LanguageModelV3,\n LanguageModelV3CallOptions,\n LanguageModelV3Content,\n LanguageModelV3FinishReason,\n LanguageModelV3GenerateResult,\n LanguageModelV3Source,\n LanguageModelV3StreamPart,\n LanguageModelV3StreamResult,\n JSONObject,\n SharedV3ProviderMetadata,\n SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n 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 googleLanguageModelOptions,\n} from './google-generative-ai-options';\nimport {\n GoogleGenerativeAIContentPart,\n GoogleGenerativeAIProviderMetadata,\n} 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: googleLanguageModelOptions,\n });\n\n if (googleOptions == null && providerOptionsName !== 'google') {\n googleOptions = await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleLanguageModelOptions,\n });\n }\n\n // Add warning if Vertex rag tools are used with a non-Vertex Google provider\n 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 const supportsFunctionResponseParts = this.modelId.startsWith('gemini-3');\n\n const { contents, systemInstruction } = convertToGoogleGenerativeAIMessages(\n prompt,\n {\n isGemmaModel,\n providerOptionsName,\n supportsFunctionResponseParts,\n },\n );\n\n const {\n tools: googleTools,\n toolConfig: googleToolConfig,\n toolWarnings,\n } = prepareTools({\n tools,\n toolChoice,\n modelId: this.modelId,\n });\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 serviceTier: googleOptions?.serviceTier,\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 // Associates a server-side tool response with its preceding call (tool combination).\n let lastServerToolCallId: string | undefined;\n\n // Build content array from all parts\n for (const part of parts) {\n if ('executableCode' in part && part.executableCode?.code) {\n const toolCallId = this.config.generateId();\n lastCodeExecutionToolCallId = toolCallId;\n\n content.push({\n type: 'tool-call',\n toolCallId,\n toolName: 'code_execution',\n input: JSON.stringify(part.executableCode),\n providerExecuted: true,\n });\n } else if ('codeExecutionResult' in part && part.codeExecutionResult) {\n content.push({\n type: 'tool-result',\n // Assumes a result directly follows its corresponding call part.\n toolCallId: lastCodeExecutionToolCallId!,\n toolName: 'code_execution',\n result: {\n outcome: part.codeExecutionResult.outcome,\n output: part.codeExecutionResult.output ?? '',\n },\n });\n // Clear the ID after use to avoid accidental reuse.\n lastCodeExecutionToolCallId = undefined;\n } else if ('text' in part && part.text != null) {\n const thoughtSignatureMetadata = part.thoughtSignature\n ? {\n [providerOptionsName]: {\n thoughtSignature: part.thoughtSignature,\n },\n }\n : undefined;\n\n if (part.text.length === 0) {\n if (thoughtSignatureMetadata != null && content.length > 0) {\n const lastContent = content[content.length - 1];\n lastContent.providerMetadata = thoughtSignatureMetadata;\n }\n } else {\n content.push({\n type: part.thought === true ? 'reasoning' : 'text',\n text: part.text,\n providerMetadata: thoughtSignatureMetadata,\n });\n }\n } else if ('functionCall' in part) {\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 const hasThought = part.thought === true;\n const hasThoughtSignature = !!part.thoughtSignature;\n content.push({\n type: 'file' as const,\n data: part.inlineData.data,\n mediaType: part.inlineData.mimeType,\n providerMetadata:\n hasThought || hasThoughtSignature\n ? {\n [providerOptionsName]: {\n ...(hasThought ? { thought: true } : {}),\n ...(hasThoughtSignature\n ? { thoughtSignature: part.thoughtSignature }\n : {}),\n },\n }\n : undefined,\n });\n } else if ('toolCall' in part && part.toolCall) {\n const toolCallId = part.toolCall.id ?? this.config.generateId();\n lastServerToolCallId = toolCallId;\n content.push({\n type: 'tool-call',\n toolCallId,\n toolName: `server:${part.toolCall.toolType}`,\n input: JSON.stringify(part.toolCall.args ?? {}),\n providerExecuted: true,\n dynamic: true,\n providerMetadata: part.thoughtSignature\n ? {\n [providerOptionsName]: {\n thoughtSignature: part.thoughtSignature,\n serverToolCallId: toolCallId,\n serverToolType: part.toolCall.toolType,\n },\n }\n : {\n [providerOptionsName]: {\n serverToolCallId: toolCallId,\n serverToolType: part.toolCall.toolType,\n },\n },\n });\n } else if ('toolResponse' in part && part.toolResponse) {\n const responseToolCallId =\n lastServerToolCallId ??\n part.toolResponse.id ??\n this.config.generateId();\n content.push({\n type: 'tool-result',\n toolCallId: responseToolCallId,\n toolName: `server:${part.toolResponse.toolType}`,\n result: (part.toolResponse.response ?? {}) as JSONObject,\n providerMetadata: part.thoughtSignature\n ? {\n [providerOptionsName]: {\n thoughtSignature: part.thoughtSignature,\n serverToolCallId: responseToolCallId,\n serverToolType: part.toolResponse.toolType,\n },\n }\n : {\n [providerOptionsName]: {\n serverToolCallId: responseToolCallId,\n serverToolType: part.toolResponse.toolType,\n },\n },\n });\n lastServerToolCallId = undefined;\n }\n }\n\n const sources =\n extractSources({\n groundingMetadata: candidate.groundingMetadata,\n generateId: this.config.generateId,\n }) ?? [];\n for (const source of sources) {\n content.push(source);\n }\n\n return {\n content,\n finishReason: {\n unified: mapGoogleGenerativeAIFinishReason({\n finishReason: candidate.finishReason,\n // Only count client-executed tool calls for finish reason determination.\n hasToolCalls: content.some(\n part => part.type === 'tool-call' && !part.providerExecuted,\n ),\n }),\n raw: candidate.finishReason ?? undefined,\n },\n usage: convertGoogleGenerativeAIUsage(usageMetadata),\n warnings,\n providerMetadata: {\n [providerOptionsName]: {\n promptFeedback: response.promptFeedback ?? null,\n groundingMetadata: candidate.groundingMetadata ?? null,\n urlContextMetadata: candidate.urlContextMetadata ?? null,\n safetyRatings: candidate.safetyRatings ?? null,\n usageMetadata: usageMetadata ?? null,\n finishMessage: candidate.finishMessage ?? null,\n serviceTier: response.serviceTier ?? null,\n } satisfies GoogleGenerativeAIProviderMetadata,\n },\n request: { body: args },\n response: {\n // TODO timestamp, model id, id\n headers: responseHeaders,\n body: rawResponse,\n },\n };\n }\n\n async doStream(\n options: LanguageModelV3CallOptions,\n ): Promise<LanguageModelV3StreamResult> {\n const { args, warnings, providerOptionsName } = 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 let lastGroundingMetadata: GroundingMetadataSchema | null = null;\n let lastUrlContextMetadata: UrlContextMetadataSchema | null = null;\n let serviceTier: string | null = null;\n\n const generateId = this.config.generateId;\n let hasToolCalls = false;\n\n // Track active blocks to group consecutive parts of same type\n let currentTextBlockId: string | null = null;\n let currentReasoningBlockId: string | null = null;\n let blockCounter = 0;\n\n // Track emitted sources to prevent duplicates\n const emittedSourceUrls = new Set<string>();\n // Associates a code execution result with its preceding call.\n let lastCodeExecutionToolCallId: string | undefined;\n // Associates a server-side tool response with its preceding call (tool combination).\n let lastServerToolCallId: string | undefined;\n\n 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 if (value.serviceTier != null) {\n serviceTier = value.serviceTier;\n }\n\n const candidate = value.candidates?.[0];\n\n // sometimes the API returns an empty candidates array\n if (candidate == null) {\n return;\n }\n\n const content = candidate.content;\n\n if (candidate.groundingMetadata != null) {\n lastGroundingMetadata = candidate.groundingMetadata;\n }\n if (candidate.urlContextMetadata != null) {\n lastUrlContextMetadata = candidate.urlContextMetadata;\n }\n\n const sources = extractSources({\n groundingMetadata: candidate.groundingMetadata,\n generateId,\n });\n if (sources != null) {\n for (const source of sources) {\n if (\n source.sourceType === 'url' &&\n !emittedSourceUrls.has(source.url)\n ) {\n emittedSourceUrls.add(source.url);\n controller.enqueue(source);\n }\n }\n }\n\n // Process tool call's parts before determining finishReason to ensure hasToolCalls is properly set\n if (content != null) {\n // Process all parts in a single loop to preserve original order\n const parts = content.parts ?? [];\n for (const part of parts) {\n if ('executableCode' in part && part.executableCode?.code) {\n const toolCallId = generateId();\n lastCodeExecutionToolCallId = toolCallId;\n\n controller.enqueue({\n type: 'tool-call',\n toolCallId,\n toolName: 'code_execution',\n input: JSON.stringify(part.executableCode),\n providerExecuted: true,\n });\n } else if (\n 'codeExecutionResult' in part &&\n part.codeExecutionResult\n ) {\n // Assumes a result directly follows its corresponding call part.\n const toolCallId = lastCodeExecutionToolCallId;\n\n if (toolCallId) {\n controller.enqueue({\n type: 'tool-result',\n toolCallId,\n toolName: 'code_execution',\n result: {\n outcome: part.codeExecutionResult.outcome,\n output: part.codeExecutionResult.output ?? '',\n },\n });\n // Clear the ID after use.\n lastCodeExecutionToolCallId = undefined;\n }\n } else if ('text' in part && part.text != null) {\n const thoughtSignatureMetadata = part.thoughtSignature\n ? {\n [providerOptionsName]: {\n thoughtSignature: part.thoughtSignature,\n },\n }\n : undefined;\n\n if (part.text.length === 0) {\n if (\n thoughtSignatureMetadata != null &&\n currentTextBlockId !== null\n ) {\n controller.enqueue({\n type: 'text-delta',\n id: currentTextBlockId,\n delta: '',\n providerMetadata: thoughtSignatureMetadata,\n });\n }\n } else if (part.thought === true) {\n // End any active text block before starting reasoning\n if (currentTextBlockId !== null) {\n controller.enqueue({\n type: 'text-end',\n id: currentTextBlockId,\n });\n currentTextBlockId = null;\n }\n\n // Start new reasoning block if not already active\n if (currentReasoningBlockId === null) {\n currentReasoningBlockId = String(blockCounter++);\n controller.enqueue({\n type: 'reasoning-start',\n id: currentReasoningBlockId,\n providerMetadata: thoughtSignatureMetadata,\n });\n }\n\n controller.enqueue({\n type: 'reasoning-delta',\n id: currentReasoningBlockId,\n delta: part.text,\n providerMetadata: thoughtSignatureMetadata,\n });\n } else {\n if (currentReasoningBlockId !== null) {\n controller.enqueue({\n type: 'reasoning-end',\n id: currentReasoningBlockId,\n });\n currentReasoningBlockId = null;\n }\n\n if (currentTextBlockId === null) {\n currentTextBlockId = String(blockCounter++);\n controller.enqueue({\n type: 'text-start',\n id: currentTextBlockId,\n providerMetadata: thoughtSignatureMetadata,\n });\n }\n\n controller.enqueue({\n type: 'text-delta',\n id: currentTextBlockId,\n delta: part.text,\n providerMetadata: thoughtSignatureMetadata,\n });\n }\n } else if ('inlineData' in part) {\n // End any active text or reasoning block before starting file output.\n // Relevant for multimodal output models.\n if (currentTextBlockId !== null) {\n controller.enqueue({\n type: 'text-end',\n id: currentTextBlockId,\n });\n currentTextBlockId = null;\n }\n if (currentReasoningBlockId !== null) {\n controller.enqueue({\n type: 'reasoning-end',\n id: currentReasoningBlockId,\n });\n currentReasoningBlockId = null;\n }\n\n const hasThought = part.thought === true;\n const hasThoughtSignature = !!part.thoughtSignature;\n const fileMeta =\n hasThought || hasThoughtSignature\n ? {\n [providerOptionsName]: {\n ...(hasThought ? { thought: true } : {}),\n ...(hasThoughtSignature\n ? { thoughtSignature: part.thoughtSignature }\n : {}),\n },\n }\n : undefined;\n controller.enqueue({\n type: 'file',\n mediaType: part.inlineData.mimeType,\n data: part.inlineData.data,\n providerMetadata: fileMeta,\n });\n } else if ('toolCall' in part && part.toolCall) {\n const toolCallId = part.toolCall.id ?? generateId();\n lastServerToolCallId = toolCallId;\n const serverMeta = {\n [providerOptionsName]: {\n ...(part.thoughtSignature\n ? { thoughtSignature: part.thoughtSignature }\n : {}),\n serverToolCallId: toolCallId,\n serverToolType: part.toolCall.toolType,\n },\n };\n\n controller.enqueue({\n type: 'tool-call',\n toolCallId,\n toolName: `server:${part.toolCall.toolType}`,\n input: JSON.stringify(part.toolCall.args ?? {}),\n providerExecuted: true,\n dynamic: true,\n providerMetadata: serverMeta,\n });\n } else if ('toolResponse' in part && part.toolResponse) {\n const responseToolCallId =\n lastServerToolCallId ??\n part.toolResponse.id ??\n generateId();\n const serverMeta = {\n [providerOptionsName]: {\n ...(part.thoughtSignature\n ? { thoughtSignature: part.thoughtSignature }\n : {}),\n serverToolCallId: responseToolCallId,\n serverToolType: part.toolResponse.toolType,\n },\n };\n\n controller.enqueue({\n type: 'tool-result',\n toolCallId: responseToolCallId,\n toolName: `server:${part.toolResponse.toolType}`,\n result: (part.toolResponse.response ?? {}) as JSONObject,\n providerMetadata: serverMeta,\n });\n lastServerToolCallId = undefined;\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: lastGroundingMetadata,\n urlContextMetadata: lastUrlContextMetadata,\n safetyRatings: candidate.safetyRatings ?? null,\n usageMetadata: usageMetadata ?? null,\n finishMessage: candidate.finishMessage ?? null,\n serviceTier,\n } satisfies GoogleGenerativeAIProviderMetadata,\n };\n }\n },\n\n flush(controller) {\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.image != null) {\n // Handle image chunks as image sources\n sources.push({\n type: 'source',\n sourceType: 'url',\n id: generateId(),\n // Google requires attribution to the source URI, not the actual image URI.\n // TODO: add another type in v7 to allow both the image and source URL to be included separately\n url: chunk.image.sourceUri,\n title: chunk.image.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 imageSearchQueries: 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 image: z\n .object({\n sourceUri: z.string(),\n imageUri: z.string(),\n title: z.string().nullish(),\n domain: z.string().nullish(),\n })\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\n .object({\n startIndex: z.number().nullish(),\n endIndex: z.number().nullish(),\n text: z.string().nullish(),\n })\n .nullish(),\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 thought: z.boolean().nullish(),\n thoughtSignature: z.string().nullish(),\n }),\n z.object({\n toolCall: z.object({\n toolType: z.string(),\n args: z.unknown().nullish(),\n id: z.string(),\n }),\n thoughtSignature: z.string().nullish(),\n }),\n z.object({\n toolResponse: z.object({\n toolType: z.string(),\n response: z.unknown().nullish(),\n id: 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().nullish(),\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 tokenDetailsSchema = z\n .array(\n z.object({\n modality: z.string(),\n tokenCount: z.number(),\n }),\n )\n .nullish();\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 // https://ai.google.dev/api/generate-content#Modality\n promptTokensDetails: tokenDetailsSchema,\n candidatesTokensDetails: tokenDetailsSchema,\n});\n\n// https://ai.google.dev/api/generate-content#UrlRetrievalMetadata\nexport const getUrlContextMetadataSchema = () =>\n z.object({\n urlMetadata: z\n .array(\n z.object({\n retrievedUrl: z.string(),\n urlRetrievalStatus: z.string(),\n }),\n )\n .nullish(),\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 finishMessage: 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 serviceTier: z.string().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\nexport type PromptFeedbackSchema = NonNullable<\n InferSchema<typeof responseSchema>['promptFeedback']\n>;\n\nexport type UsageMetadataSchema = NonNullable<\n InferSchema<typeof responseSchema>['usageMetadata']\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 = lazySchema(() =>\n zodSchema(\n z.object({\n candidates: z\n .array(\n z.object({\n content: getContentSchema().nullish(),\n finishReason: z.string().nullish(),\n finishMessage: 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 serviceTier: z.string().nullish(),\n }),\n ),\n);\n\ntype ChunkSchema = InferSchema<typeof chunkSchema>;\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport type GoogleGenerativeAITokenDetail = {\n modality: string;\n tokenCount: number;\n};\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 promptTokensDetails?: GoogleGenerativeAITokenDetail[] | null;\n candidatesTokensDetails?: GoogleGenerativeAITokenDetail[] | 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 (isRoot) {\n return undefined;\n }\n\n if (typeof jsonSchema === 'object' && jsonSchema.description) {\n return { type: 'object', description: jsonSchema.description };\n }\n return { type: 'object' };\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 const hasNull = type.includes('null');\n const nonNullTypes = type.filter(t => t !== 'null');\n\n if (nonNullTypes.length === 0) {\n // Only null type\n result.type = 'null';\n } else {\n // One or more non-null types: always use anyOf\n result.anyOf = nonNullTypes.map(t => ({ type: t }));\n if (hasNull) {\n result.nullable = true;\n }\n }\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, false);\n return acc;\n },\n {} as Record<string, unknown>,\n );\n }\n\n if (items) {\n result.items = Array.isArray(items)\n ? items.map(item => convertJSONSchemaToOpenAPISchema(item, false))\n : convertJSONSchemaToOpenAPISchema(items, false);\n }\n\n if (allOf) {\n result.allOf = allOf.map(item =>\n convertJSONSchemaToOpenAPISchema(item, false),\n );\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(\n nonNullSchemas[0],\n false,\n );\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(item =>\n convertJSONSchemaToOpenAPISchema(item, false),\n );\n result.nullable = true;\n }\n } else {\n result.anyOf = anyOf.map(item =>\n convertJSONSchemaToOpenAPISchema(item, false),\n );\n }\n }\n if (oneOf) {\n result.oneOf = oneOf.map(item =>\n convertJSONSchemaToOpenAPISchema(item, false),\n );\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 LanguageModelV3Prompt,\n UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\nimport {\n GoogleGenerativeAIContent,\n GoogleGenerativeAIContentPart,\n GoogleGenerativeAIFunctionResponsePart,\n GoogleGenerativeAIPrompt,\n} from './google-generative-ai-prompt';\n\nconst dataUrlRegex = /^data:([^;,]+);base64,(.+)$/s;\n\nfunction parseBase64DataUrl(\n value: string,\n): { mediaType: string; data: string } | undefined {\n const match = dataUrlRegex.exec(value);\n if (match == null) {\n return undefined;\n }\n\n return {\n mediaType: match[1],\n data: match[2],\n };\n}\n\nfunction convertUrlToolResultPart(\n url: string,\n): GoogleGenerativeAIFunctionResponsePart | undefined {\n // Per https://ai.google.dev/api/caching#FunctionResponsePart, only inline data is supported.\n // https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/function-calling#functionresponsepart suggests that this\n // may be different for Vertex, but this needs to be confirmed and further tested for both APIs.\n const parsedDataUrl = parseBase64DataUrl(url);\n if (parsedDataUrl == null) {\n return undefined;\n }\n\n return {\n inlineData: {\n mimeType: parsedDataUrl.mediaType,\n data: parsedDataUrl.data,\n },\n };\n}\n\n/*\n * Appends tool result content parts to the message using the functionResponse\n * format with support for multimodal parts (e.g. inline images/files alongside\n * text). This format is supported by Gemini 3+ models.\n */\nfunction appendToolResultParts(\n parts: GoogleGenerativeAIContentPart[],\n toolName: string,\n outputValue: Array<{\n type: string;\n [key: string]: unknown;\n }>,\n): void {\n const functionResponseParts: GoogleGenerativeAIFunctionResponsePart[] = [];\n const responseTextParts: string[] = [];\n\n for (const contentPart of outputValue) {\n switch (contentPart.type) {\n case 'text': {\n responseTextParts.push(contentPart.text as string);\n break;\n }\n case 'image-data':\n case 'file-data': {\n functionResponseParts.push({\n inlineData: {\n mimeType: contentPart.mediaType as string,\n data: contentPart.data as string,\n },\n });\n break;\n }\n case 'image-url':\n case 'file-url': {\n const functionResponsePart = convertUrlToolResultPart(\n contentPart.url as string,\n );\n\n if (functionResponsePart != null) {\n functionResponseParts.push(functionResponsePart);\n } else {\n responseTextParts.push(JSON.stringify(contentPart));\n }\n break;\n }\n default: {\n responseTextParts.push(JSON.stringify(contentPart));\n break;\n }\n }\n }\n\n parts.push({\n functionResponse: {\n name: toolName,\n response: {\n name: toolName,\n content:\n responseTextParts.length > 0\n ? responseTextParts.join('\\n')\n : 'Tool executed successfully.',\n },\n ...(functionResponseParts.length > 0\n ? { parts: functionResponseParts }\n : {}),\n },\n });\n}\n\n/*\n * Appends tool result content parts using a legacy format for pre-Gemini 3\n * models that do not support multimodal parts within functionResponse. Instead,\n * non-text content like images is sent as separate top-level inlineData parts.\n */\nfunction appendLegacyToolResultParts(\n parts: GoogleGenerativeAIContentPart[],\n toolName: string,\n outputValue: Array<{\n type: string;\n [key: string]: unknown;\n }>,\n): void {\n for (const contentPart of outputValue) {\n switch (contentPart.type) {\n case 'text':\n parts.push({\n functionResponse: {\n name: toolName,\n response: {\n name: toolName,\n content: contentPart.text,\n },\n },\n });\n break;\n case 'image-data':\n parts.push(\n {\n inlineData: {\n mimeType: String(contentPart.mediaType),\n data: String(contentPart.data),\n },\n },\n {\n text: 'Tool executed successfully and returned this image as a response',\n },\n );\n break;\n default:\n parts.push({ text: JSON.stringify(contentPart) });\n break;\n }\n }\n}\n\nexport function convertToGoogleGenerativeAIMessages(\n prompt: LanguageModelV3Prompt,\n options?: {\n isGemmaModel?: boolean;\n providerOptionsName?: string;\n supportsFunctionResponseParts?: boolean;\n },\n): GoogleGenerativeAIPrompt {\n const systemInstructionParts: Array<{ text: string }> = [];\n const contents: Array<GoogleGenerativeAIContent> = [];\n let systemMessagesAllowed = true;\n const isGemmaModel = options?.isGemmaModel ?? false;\n const providerOptionsName = options?.providerOptionsName ?? 'google';\n const supportsFunctionResponseParts =\n options?.supportsFunctionResponseParts ?? true;\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 const providerOpts =\n part.providerOptions?.[providerOptionsName] ??\n (providerOptionsName !== 'google'\n ? part.providerOptions?.google\n : part.providerOptions?.vertex);\n const thoughtSignature =\n providerOpts?.thoughtSignature != null\n ? String(providerOpts.thoughtSignature)\n : undefined;\n\n switch (part.type) {\n case 'text': {\n return part.text.length === 0\n ? undefined\n : {\n text: part.text,\n thoughtSignature,\n };\n }\n\n case 'reasoning': {\n return part.text.length === 0\n ? undefined\n : {\n text: part.text,\n thought: true,\n thoughtSignature,\n };\n }\n\n case 'file': {\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 ...(providerOpts?.thought === true\n ? { thought: true }\n : {}),\n thoughtSignature,\n };\n }\n\n case 'tool-call': {\n const serverToolCallId =\n providerOpts?.serverToolCallId != null\n ? String(providerOpts.serverToolCallId)\n : undefined;\n const serverToolType =\n providerOpts?.serverToolType != null\n ? String(providerOpts.serverToolType)\n : undefined;\n\n if (serverToolCallId && serverToolType) {\n return {\n toolCall: {\n toolType: serverToolType,\n args:\n typeof part.input === 'string'\n ? JSON.parse(part.input)\n : part.input,\n id: serverToolCallId,\n },\n thoughtSignature,\n };\n }\n\n return {\n functionCall: {\n name: part.toolName,\n args: part.input,\n },\n thoughtSignature,\n };\n }\n\n case 'tool-result': {\n const serverToolCallId =\n providerOpts?.serverToolCallId != null\n ? String(providerOpts.serverToolCallId)\n : undefined;\n const serverToolType =\n providerOpts?.serverToolType != null\n ? String(providerOpts.serverToolType)\n : undefined;\n\n if (serverToolCallId && serverToolType) {\n return {\n toolResponse: {\n toolType: serverToolType,\n response:\n part.output.type === 'json' ? part.output.value : {},\n id: serverToolCallId,\n },\n thoughtSignature,\n };\n }\n\n return undefined;\n }\n }\n })\n .filter(part => part !== undefined),\n });\n\n break;\n }\n\n case 'tool': {\n systemMessagesAllowed = false;\n\n const parts: GoogleGenerativeAIContentPart[] = [];\n\n for (const part of content) {\n if (part.type === 'tool-approval-response') {\n continue;\n }\n\n const partProviderOpts =\n part.providerOptions?.[providerOptionsName] ??\n (providerOptionsName !== 'google'\n ? part.providerOptions?.google\n : part.providerOptions?.vertex);\n const serverToolCallId =\n partProviderOpts?.serverToolCallId != null\n ? String(partProviderOpts.serverToolCallId)\n : undefined;\n const serverToolType =\n partProviderOpts?.serverToolType != null\n ? String(partProviderOpts.serverToolType)\n : undefined;\n\n if (serverToolCallId && serverToolType) {\n const serverThoughtSignature =\n partProviderOpts?.thoughtSignature != null\n ? String(partProviderOpts.thoughtSignature)\n : undefined;\n\n if (contents.length > 0) {\n const lastContent = contents[contents.length - 1];\n if (lastContent.role === 'model') {\n lastContent.parts.push({\n toolResponse: {\n toolType: serverToolType,\n response:\n part.output.type === 'json' ? part.output.value : {},\n id: serverToolCallId,\n },\n thoughtSignature: serverThoughtSignature,\n });\n continue;\n }\n }\n }\n\n const output = part.output;\n\n if (output.type === 'content') {\n if (supportsFunctionResponseParts) {\n appendToolResultParts(parts, part.toolName, output.value);\n } else {\n appendLegacyToolResultParts(parts, part.toolName, output.value);\n }\n } else {\n parts.push({\n functionResponse: {\n name: part.toolName,\n response: {\n name: part.toolName,\n content:\n output.type === 'execution-denied'\n ? (output.reason ?? 'Tool execution denied.')\n : output.value,\n },\n },\n });\n }\n }\n\n contents.push({\n role: 'user',\n parts,\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 { InferSchema, lazySchema, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type GoogleGenerativeAIModelId =\n // Stable models\n // https://ai.google.dev/gemini-api/docs/models/gemini\n | 'gemini-2.0-flash'\n | 'gemini-2.0-flash-001'\n | 'gemini-2.0-flash-lite'\n | 'gemini-2.0-flash-lite-001'\n | 'gemini-2.5-pro'\n | 'gemini-2.5-flash'\n | 'gemini-2.5-flash-image'\n | 'gemini-2.5-flash-lite'\n | 'gemini-2.5-flash-lite-preview-09-2025'\n | 'gemini-2.5-flash-preview-tts'\n | 'gemini-2.5-pro-preview-tts'\n | 'gemini-2.5-flash-native-audio-latest'\n | 'gemini-2.5-flash-native-audio-preview-09-2025'\n | 'gemini-2.5-flash-native-audio-preview-12-2025'\n | 'gemini-2.5-computer-use-preview-10-2025'\n | 'gemini-3-pro-preview'\n | 'gemini-3-pro-image-preview'\n | 'gemini-3-flash-preview'\n | 'gemini-3.1-pro-preview'\n | 'gemini-3.1-pro-preview-customtools'\n | 'gemini-3.1-flash-image-preview'\n | 'gemini-3.1-flash-lite-preview'\n // latest version\n // https://ai.google.dev/gemini-api/docs/models#latest\n | 'gemini-pro-latest'\n | 'gemini-flash-latest'\n | 'gemini-flash-lite-latest'\n | 'deep-research-pro-preview-12-2025'\n | 'nano-banana-pro-preview'\n | 'aqa'\n // Experimental models\n // https://ai.google.dev/gemini-api/docs/models/experimental-models\n | 'gemini-robotics-er-1.5-preview'\n | 'gemma-3-1b-it'\n | 'gemma-3-4b-it'\n | 'gemma-3n-e4b-it'\n | 'gemma-3n-e2b-it'\n | 'gemma-3-12b-it'\n | 'gemma-3-27b-it'\n | (string & {});\n\nexport const googleLanguageModelOptions = lazySchema(() =>\n zodSchema(\n 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 // https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high#thinking_level\n thinkingLevel: z\n .enum(['minimal', 'low', 'medium', 'high'])\n .optional(),\n })\n .optional(),\n\n /**\n * Optional.\n * The name of the cached content used as context to serve the prediction.\n * Format: 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 /**\n * Optional. 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 /**\n * Optional. Defines labels used in billing reports. Available on Vertex AI only.\n *\n * https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/add-labels-to-api-calls\n */\n labels: z.record(z.string(), z.string()).optional(),\n\n /**\n * Optional. If specified, the media resolution specified will be used.\n *\n * https://ai.google.dev/api/generate-content#MediaResolution\n */\n mediaResolution: z\n .enum([\n 'MEDIA_RESOLUTION_UNSPECIFIED',\n 'MEDIA_RESOLUTION_LOW',\n 'MEDIA_RESOLUTION_MEDIUM',\n 'MEDIA_RESOLUTION_HIGH',\n ])\n .optional(),\n\n /**\n * Optional. Configures the image generation aspect ratio for Gemini models.\n *\n * https://ai.google.dev/gemini-api/docs/image-generation#aspect_ratios\n */\n imageConfig: z\n .object({\n aspectRatio: z\n .enum([\n '1:1',\n '2:3',\n '3:2',\n '3:4',\n '4:3',\n '4:5',\n '5:4',\n '9:16',\n '16:9',\n '21:9',\n '1:8',\n '8:1',\n '1:4',\n '4:1',\n ])\n .optional(),\n imageSize: z.enum(['1K', '2K', '4K', '512']).optional(),\n })\n .optional(),\n\n /**\n * Optional. Configuration for grounding retrieval.\n * Used to provide location context for Google Maps and Google Search grounding.\n *\n * https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-maps\n */\n retrievalConfig: z\n .object({\n latLng: z\n .object({\n latitude: z.number(),\n longitude: z.number(),\n })\n .optional(),\n })\n .optional(),\n\n /**\n * Optional. The service tier to use for the request.\n */\n serviceTier: z.enum(['standard', 'flex', 'priority']).optional(),\n }),\n ),\n);\n\nexport type GoogleLanguageModelOptions = InferSchema<\n typeof googleLanguageModelOptions\n>;\n","import {\n LanguageModelV3CallOptions,\n SharedV3Warning,\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: LanguageModelV3CallOptions['tools'];\n toolChoice?: LanguageModelV3CallOptions['toolChoice'];\n modelId: GoogleGenerativeAIModelId;\n}): {\n tools:\n | Array<\n | {\n functionDeclarations: Array<{\n name: string;\n description: string;\n parameters: unknown;\n }>;\n }\n | Record<string, any>\n >\n | undefined;\n toolConfig:\n | undefined\n | {\n functionCallingConfig?: {\n mode: 'AUTO' | 'NONE' | 'ANY' | 'VALIDATED';\n allowedFunctionNames?: string[];\n };\n includeServerSideToolInvocations?: boolean;\n };\n toolWarnings: SharedV3Warning[];\n} {\n // when the tools array is empty, change it to undefined to prevent errors:\n tools = tools?.length ? tools : undefined;\n\n const toolWarnings: SharedV3Warning[] = [];\n\n const isLatest = (\n [\n 'gemini-flash-latest',\n 'gemini-flash-lite-latest',\n 'gemini-pro-latest',\n ] as const satisfies GoogleGenerativeAIModelId[]\n ).some(id => id === modelId);\n const isGemini2orNewer =\n modelId.includes('gemini-2') ||\n modelId.includes('gemini-3') ||\n modelId.includes('nano-banana') ||\n isLatest;\n const isGemini3orNewer = modelId.includes('gemini-3');\n const supportsFileSearch =\n modelId.includes('gemini-2.5') || modelId.includes('gemini-3');\n\n if (tools == null) {\n return { tools: undefined, toolConfig: undefined, toolWarnings };\n }\n\n // Check for mixed tool types and add warnings\n const hasFunctionTools = tools.some(tool => tool.type === 'function');\n const hasProviderTools = tools.some(tool => tool.type === 'provider');\n\n if (hasFunctionTools && hasProviderTools && !isGemini3orNewer) {\n toolWarnings.push({\n type: 'unsupported',\n feature: `combination of function and provider-defined tools`,\n });\n }\n\n if (hasProviderTools) {\n const googleTools: any[] = [];\n\n const ProviderTools = tools.filter(tool => tool.type === 'provider');\n ProviderTools.forEach(tool => {\n switch (tool.id) {\n case 'google.google_search':\n if (isGemini2orNewer) {\n googleTools.push({ googleSearch: { ...tool.args } });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details: 'Google Search requires Gemini 2.0 or newer.',\n });\n }\n break;\n case 'google.enterprise_web_search':\n if (isGemini2orNewer) {\n googleTools.push({ enterpriseWebSearch: {} });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details: 'Enterprise Web Search requires Gemini 2.0 or newer.',\n });\n }\n break;\n case 'google.url_context':\n if (isGemini2orNewer) {\n googleTools.push({ urlContext: {} });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details:\n 'The URL context tool is not supported with other Gemini models than Gemini 2.',\n });\n }\n break;\n case 'google.code_execution':\n if (isGemini2orNewer) {\n googleTools.push({ codeExecution: {} });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details:\n 'The code execution tool is not supported with other Gemini models than Gemini 2.',\n });\n }\n break;\n case 'google.file_search':\n if (supportsFileSearch) {\n googleTools.push({ fileSearch: { ...tool.args } });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details:\n 'The file search tool is only supported with Gemini 2.5 models and Gemini 3 models.',\n });\n }\n break;\n case 'google.vertex_rag_store':\n if (isGemini2orNewer) {\n googleTools.push({\n retrieval: {\n vertex_rag_store: {\n rag_resources: {\n rag_corpus: tool.args.ragCorpus,\n },\n similarity_top_k: tool.args.topK as number | undefined,\n },\n },\n });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details:\n 'The RAG store tool is not supported with other Gemini models than Gemini 2.',\n });\n }\n break;\n case 'google.google_maps':\n if (isGemini2orNewer) {\n googleTools.push({ googleMaps: {} });\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details:\n 'The Google Maps grounding tool is not supported with Gemini models other than Gemini 2 or newer.',\n });\n }\n break;\n default:\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n });\n break;\n }\n });\n\n if (hasFunctionTools && isGemini3orNewer && googleTools.length > 0) {\n const functionDeclarations: Array<{\n name: string;\n description: string;\n parameters: unknown;\n }> = [];\n for (const tool of tools) {\n if (tool.type === 'function') {\n functionDeclarations.push({\n name: tool.name,\n description: tool.description ?? '',\n parameters: convertJSONSchemaToOpenAPISchema(tool.inputSchema),\n });\n }\n }\n\n const combinedToolConfig: {\n functionCallingConfig: {\n mode: 'VALIDATED' | 'ANY' | 'NONE';\n allowedFunctionNames?: string[];\n };\n includeServerSideToolInvocations: true;\n } = {\n functionCallingConfig: { mode: 'VALIDATED' },\n includeServerSideToolInvocations: true,\n };\n\n if (toolChoice != null) {\n switch (toolChoice.type) {\n case 'auto':\n break;\n case 'none':\n combinedToolConfig.functionCallingConfig = { mode: 'NONE' };\n break;\n case 'required':\n combinedToolConfig.functionCallingConfig = { mode: 'ANY' };\n break;\n case 'tool':\n combinedToolConfig.functionCallingConfig = {\n mode: 'ANY',\n allowedFunctionNames: [toolChoice.toolName],\n };\n break;\n }\n }\n\n return {\n tools: [...googleTools, { functionDeclarations }],\n toolConfig: combinedToolConfig,\n toolWarnings,\n };\n }\n\n return {\n tools: googleTools.length > 0 ? googleTools : undefined,\n toolConfig: undefined,\n toolWarnings,\n };\n }\n\n const functionDeclarations = [];\n let hasStrictTools = false;\n for (const tool of tools) {\n switch (tool.type) {\n case 'function':\n functionDeclarations.push({\n name: tool.name,\n description: tool.description ?? '',\n parameters: convertJSONSchemaToOpenAPISchema(tool.inputSchema),\n });\n if (tool.strict === true) {\n hasStrictTools = true;\n }\n break;\n default:\n toolWarnings.push({\n type: 'unsupported',\n feature: `function tool ${tool.name}`,\n });\n break;\n }\n }\n\n if (toolChoice == null) {\n return {\n tools: [{ functionDeclarations }],\n toolConfig: hasStrictTools\n ? { functionCallingConfig: { mode: 'VALIDATED' } }\n : undefined,\n toolWarnings,\n };\n }\n\n const type = toolChoice.type;\n\n switch (type) {\n case 'auto':\n return {\n tools: [{ functionDeclarations }],\n toolConfig: {\n functionCallingConfig: {\n mode: hasStrictTools ? 'VALIDATED' : 'AUTO',\n },\n },\n toolWarnings,\n };\n case 'none':\n return {\n tools: [{ functionDeclarations }],\n toolConfig: { functionCallingConfig: { mode: 'NONE' } },\n toolWarnings,\n };\n case 'required':\n return {\n tools: [{ functionDeclarations }],\n toolConfig: {\n functionCallingConfig: {\n mode: hasStrictTools ? 'VALIDATED' : 'ANY',\n },\n },\n toolWarnings,\n };\n case 'tool':\n return {\n tools: [{ functionDeclarations }],\n toolConfig: {\n functionCallingConfig: {\n mode: hasStrictTools ? 'VALIDATED' : 'ANY',\n allowedFunctionNames: [toolChoice.toolName],\n },\n },\n toolWarnings,\n };\n default: {\n const _exhaustiveCheck: never = type;\n throw new UnsupportedFunctionalityError({\n functionality: `tool choice type: ${_exhaustiveCheck}`,\n });\n }\n }\n}\n","import { LanguageModelV3FinishReason } from '@ai-sdk/provider';\n\nexport function mapGoogleGenerativeAIFinishReason({\n finishReason,\n hasToolCalls,\n}: {\n finishReason: string | null | undefined;\n hasToolCalls: boolean;\n}): LanguageModelV3FinishReason['unified'] {\n switch (finishReason) {\n case 'STOP':\n return hasToolCalls ? 'tool-calls' : 'stop';\n case 'MAX_TOKENS':\n return 'length';\n case 'IMAGE_SAFETY':\n case 'RECITATION':\n case 'SAFETY':\n case 'BLOCKLIST':\n case 'PROHIBITED_CONTENT':\n case 'SPII':\n return 'content-filter';\n case 'MALFORMED_FUNCTION_CALL':\n return 'error';\n case 'FINISH_REASON_UNSPECIFIED':\n case 'OTHER':\n default:\n return 'other';\n }\n}\n","import { createProviderToolFactoryWithOutputSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n/**\n * A tool that enables the model to generate and run Python code.\n *\n * @note Ensure the selected model supports Code Execution.\n * Multi-tool usage with the code execution tool is typically compatible with Gemini >=2 models.\n *\n * @see https://ai.google.dev/gemini-api/docs/code-execution (Google AI)\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/code-execution-api (Vertex AI)\n */\nexport const codeExecution = createProviderToolFactoryWithOutputSchema<\n {\n language: string;\n code: string;\n },\n {\n outcome: string;\n output: string;\n },\n {}\n>({\n id: 'google.code_execution',\n inputSchema: z.object({\n language: z.string().describe('The programming language of the code.'),\n code: z.string().describe('The code to be executed.'),\n }),\n outputSchema: z.object({\n outcome: z\n .string()\n .describe('The outcome of the execution (e.g., \"OUTCOME_OK\").'),\n output: z.string().describe('The output from the code execution.'),\n }),\n});\n","import {\n createProviderToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/web-grounding-enterprise\n\nexport const enterpriseWebSearch = createProviderToolFactory<\n {\n // Enterprise Web Search does not have any input schema\n },\n {}\n>({\n id: 'google.enterprise_web_search',\n inputSchema: lazySchema(() => zodSchema(z.object({}))),\n});\n","import {\n createProviderToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n/** Tool to retrieve knowledge from the File Search Stores. */\nconst fileSearchArgsBaseSchema = z\n .object({\n /** The names of the file_search_stores to retrieve from.\n * Example: `fileSearchStores/my-file-search-store-123`\n */\n fileSearchStoreNames: z\n .array(z.string())\n .describe(\n 'The names of the file_search_stores to retrieve from. Example: `fileSearchStores/my-file-search-store-123`',\n ),\n /** The number of file search retrieval chunks to retrieve. */\n topK: z\n .number()\n .int()\n .positive()\n .describe('The number of file search retrieval chunks to retrieve.')\n .optional(),\n\n /** Metadata filter to apply to the file search retrieval documents.\n * See https://google.aip.dev/160 for the syntax of the filter expression.\n */\n metadataFilter: z\n .string()\n .describe(\n 'Metadata filter to apply to the file search retrieval documents. See https://google.aip.dev/160 for the syntax of the filter expression.',\n )\n .optional(),\n })\n .passthrough();\n\nexport type GoogleFileSearchToolArgs = z.infer<typeof fileSearchArgsBaseSchema>;\n\nconst fileSearchArgsSchema = lazySchema(() =>\n zodSchema(fileSearchArgsBaseSchema),\n);\n\nexport const fileSearch = createProviderToolFactory<\n {},\n GoogleFileSearchToolArgs\n>({\n id: 'google.file_search',\n inputSchema: fileSearchArgsSchema,\n});\n","import {\n createProviderToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://ai.google.dev/gemini-api/docs/maps-grounding\n// https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-maps\n\nexport const googleMaps = createProviderToolFactory<{}, {}>({\n id: 'google.google_maps',\n inputSchema: lazySchema(() => zodSchema(z.object({}))),\n});\n","import {\n createProviderToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://ai.google.dev/gemini-api/docs/google-search\n// https://ai.google.dev/api/generate-content#GroundingSupport\n// https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-search\n\nconst googleSearchToolArgsBaseSchema = z\n .object({\n searchTypes: z\n .object({\n webSearch: z.object({}).optional(),\n imageSearch: z.object({}).optional(),\n })\n .optional(),\n\n timeRangeFilter: z\n .object({\n startTime: z.string(),\n endTime: z.string(),\n })\n .optional(),\n })\n .passthrough();\n\nexport type GoogleSearchToolArgs = z.infer<\n typeof googleSearchToolArgsBaseSchema\n>;\n\nconst googleSearchToolArgsSchema = lazySchema(() =>\n zodSchema(googleSearchToolArgsBaseSchema),\n);\n\nexport const googleSearch = createProviderToolFactory<{}, GoogleSearchToolArgs>(\n {\n id: 'google.google_search',\n inputSchema: googleSearchToolArgsSchema,\n },\n);\n","import {\n createProviderToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport const urlContext = createProviderToolFactory<\n {\n // Url context does not have any input schema, it will directly use the url from the prompt\n },\n {}\n>({\n id: 'google.url_context',\n inputSchema: lazySchema(() => zodSchema(z.object({}))),\n});\n","import { createProviderToolFactory } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-vertexai-search#generate-content-using-gemini-api\n// https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/rag-output-explained\n\n/**\n * A tool that enables the model to perform RAG searches against a Vertex RAG Store.\n *\n * @note Only works with Vertex Gemini models.\n */\nexport const vertexRagStore = createProviderToolFactory<\n {},\n {\n /**\n * RagCorpus resource names, eg: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}\n */\n ragCorpus: string;\n\n /**\n * The number of top contexts to retrieve.\n */\n topK?: number;\n }\n>({\n id: 'google.vertex_rag_store',\n inputSchema: z.object({\n ragCorpus: z.string(),\n topK: z.number().optional(),\n }),\n});\n","import { codeExecution } from './tool/code-execution';\nimport { enterpriseWebSearch } from './tool/enterprise-web-search';\nimport { fileSearch } from './tool/file-search';\nimport { googleMaps } from './tool/google-maps';\nimport { googleSearch } from './tool/google-search';\nimport { urlContext } from './tool/url-context';\nimport { vertexRagStore } from './tool/vertex-rag-store';\n\nexport const googleTools = {\n /**\n * Creates a Google search tool that gives Google direct access to real-time web content.\n * Must have name \"google_search\".\n */\n googleSearch,\n\n /**\n * Creates an Enterprise Web Search tool for grounding responses using a compliance-focused web index.\n * Designed for highly-regulated industries (finance, healthcare, public sector).\n * Does not log customer data and supports VPC service controls.\n * Must have name \"enterprise_web_search\".\n *\n * @note Only available on Vertex AI. Requires Gemini 2.0 or newer.\n *\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/web-grounding-enterprise\n */\n enterpriseWebSearch,\n\n /**\n * Creates a Google Maps grounding tool that gives the model access to Google Maps data.\n * Must have name \"google_maps\".\n *\n * @see https://ai.google.dev/gemini-api/docs/maps-grounding\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-maps\n */\n googleMaps,\n\n /**\n * Creates a URL context tool that gives Google direct access to real-time web content.\n * Must have name \"url_context\".\n */\n urlContext,\n\n /**\n * Enables Retrieval Augmented Generation (RAG) via the Gemini File Search tool.\n * Must have name \"file_search\".\n *\n * @param fileSearchStoreNames - Fully-qualified File Search store resource names.\n * @param metadataFilter - Optional filter expression to restrict the files that can be retrieved.\n * @param topK - Optional result limit for the number of chunks returned from File Search.\n *\n * @see https://ai.google.dev/gemini-api/docs/file-search\n */\n fileSearch,\n /**\n * A tool that enables the model to generate and run Python code.\n * Must have name \"code_execution\".\n *\n * @note Ensure the selected model supports Code Execution.\n * Multi-tool usage with the code execution tool is typically compatible with Gemini >=2 models.\n *\n * @see https://ai.google.dev/gemini-api/docs/code-execution (Google AI)\n * @see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/code-execution-api (Vertex AI)\n */\n codeExecution,\n\n /**\n * Creates a Vertex RAG Store tool that enables the model to perform RAG searches against a Vertex RAG Store.\n * Must have name \"vertex_rag_store\".\n */\n vertexRagStore,\n};\n","import {\n ImageModelV3,\n LanguageModelV3Prompt,\n SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n convertToBase64,\n createJsonResponseHandler,\n FetchFunction,\n generateId as defaultGenerateId,\n type InferSchema,\n lazySchema,\n parseProviderOptions,\n postJsonToApi,\n Resolvable,\n resolve,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleFailedResponseHandler } from './google-error';\nimport {\n GoogleGenerativeAIImageModelId,\n GoogleGenerativeAIImageSettings,\n} from './google-generative-ai-image-settings';\nimport { GoogleGenerativeAILanguageModel } from './google-generative-ai-language-model';\nimport type { GoogleLanguageModelOptions } from './google-generative-ai-options';\n\ninterface GoogleGenerativeAIImageModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable<Record<string, string | undefined>>;\n fetch?: FetchFunction;\n generateId?: () => string;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class GoogleGenerativeAIImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n\n get maxImagesPerCall(): number {\n if (this.settings.maxImagesPerCall != null) {\n return this.settings.maxImagesPerCall;\n }\n // https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash-image\n if (isGeminiModel(this.modelId)) {\n return 10;\n }\n // https://ai.google.dev/gemini-api/docs/imagen#imagen-model\n return 4;\n }\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: GoogleGenerativeAIImageModelId,\n private readonly settings: GoogleGenerativeAIImageSettings,\n private readonly config: GoogleGenerativeAIImageModelConfig,\n ) {}\n\n async doGenerate(\n options: Parameters<ImageModelV3['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<ImageModelV3['doGenerate']>>> {\n // Gemini image models use the language model API internally\n if (isGeminiModel(this.modelId)) {\n return this.doGenerateGemini(options);\n }\n return this.doGenerateImagen(options);\n }\n\n private async doGenerateImagen(\n options: Parameters<ImageModelV3['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<ImageModelV3['doGenerate']>>> {\n const {\n prompt,\n n = 1,\n size,\n aspectRatio = '1:1',\n seed,\n providerOptions,\n headers,\n abortSignal,\n files,\n mask,\n } = options;\n const warnings: Array<SharedV3Warning> = [];\n\n // Imagen API endpoints do not support image editing\n if (files != null && files.length > 0) {\n throw new Error(\n 'Google Generative AI does not support image editing with Imagen models. ' +\n 'Use Google Vertex AI (@ai-sdk/google-vertex) for image editing capabilities.',\n );\n }\n\n if (mask != null) {\n throw new Error(\n 'Google Generative AI does not support image editing with masks. ' +\n 'Use Google Vertex AI (@ai-sdk/google-vertex) for image editing capabilities.',\n );\n }\n\n if (size != null) {\n warnings.push({\n type: 'unsupported',\n feature: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n if (seed != null) {\n warnings.push({\n type: 'unsupported',\n feature: 'seed',\n details:\n 'This model does not support the `seed` option through this provider.',\n });\n }\n\n const googleOptions = await parseProviderOptions({\n provider: 'google',\n providerOptions,\n schema: googleImageModelOptionsSchema,\n });\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n\n const parameters: Record<string, unknown> = {\n sampleCount: n,\n };\n\n if (aspectRatio != null) {\n parameters.aspectRatio = aspectRatio;\n }\n\n if (googleOptions) {\n Object.assign(parameters, googleOptions);\n }\n\n const body = {\n instances: [{ prompt }],\n parameters,\n };\n\n const { responseHeaders, value: response } = await postJsonToApi<{\n predictions: Array<{ bytesBase64Encoded: string }>;\n }>({\n url: `${this.config.baseURL}/models/${this.modelId}:predict`,\n headers: combineHeaders(await resolve(this.config.headers), headers),\n body,\n failedResponseHandler: googleFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n googleImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n return {\n images: response.predictions.map(\n (p: { bytesBase64Encoded: string }) => p.bytesBase64Encoded,\n ),\n warnings,\n providerMetadata: {\n google: {\n images: response.predictions.map(() => ({\n // Add any prediction-specific metadata here\n })),\n },\n },\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n };\n }\n\n private async doGenerateGemini(\n options: Parameters<ImageModelV3['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<ImageModelV3['doGenerate']>>> {\n const {\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n files,\n mask,\n } = options;\n const warnings: Array<SharedV3Warning> = [];\n\n // Gemini does not support mask-based inpainting\n if (mask != null) {\n throw new Error(\n 'Gemini image models do not support mask-based image editing.',\n );\n }\n\n // Gemini does not support generating multiple images per call via n parameter\n if (n != null && n > 1) {\n throw new Error(\n 'Gemini image models do not support generating a set number of images per call. Use n=1 or omit the n parameter.',\n );\n }\n\n if (size != null) {\n warnings.push({\n type: 'unsupported',\n feature: 'size',\n details:\n 'This model does not support the `size` option. Use `aspectRatio` instead.',\n });\n }\n\n // Build user message content for language model\n const userContent: Array<\n | { type: 'text'; text: string }\n | { type: 'file'; data: string | Uint8Array | URL; mediaType: string }\n > = [];\n\n // Add text prompt\n if (prompt != null) {\n userContent.push({ type: 'text', text: prompt });\n }\n\n // Add input images for editing\n if (files != null && files.length > 0) {\n for (const file of files) {\n if (file.type === 'url') {\n userContent.push({\n type: 'file',\n data: new URL(file.url),\n mediaType: 'image/*',\n });\n } else {\n userContent.push({\n type: 'file',\n data:\n typeof file.data === 'string'\n ? file.data\n : new Uint8Array(file.data),\n mediaType: file.mediaType,\n });\n }\n }\n }\n\n const languageModelPrompt: LanguageModelV3Prompt = [\n { role: 'user', content: userContent },\n ];\n\n // Instantiate language model\n const languageModel = new GoogleGenerativeAILanguageModel(this.modelId, {\n provider: this.config.provider,\n baseURL: this.config.baseURL,\n headers: this.config.headers ?? {},\n fetch: this.config.fetch,\n generateId: this.config.generateId ?? defaultGenerateId,\n });\n\n // Call language model with image-only response modality\n const result = await languageModel.doGenerate({\n prompt: languageModelPrompt,\n seed,\n providerOptions: {\n google: {\n responseModalities: ['IMAGE'],\n imageConfig: aspectRatio\n ? {\n aspectRatio: aspectRatio as NonNullable<\n GoogleLanguageModelOptions['imageConfig']\n >['aspectRatio'],\n }\n : undefined,\n ...((providerOptions?.google as Omit<\n GoogleLanguageModelOptions,\n 'responseModalities' | 'imageConfig'\n >) ?? {}),\n } satisfies GoogleLanguageModelOptions,\n },\n headers,\n abortSignal,\n });\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n\n // Extract images from language model response\n const images: string[] = [];\n for (const part of result.content) {\n if (part.type === 'file' && part.mediaType.startsWith('image/')) {\n images.push(convertToBase64(part.data));\n }\n }\n\n return {\n images,\n warnings,\n providerMetadata: {\n google: {\n images: images.map(() => ({})),\n },\n },\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: result.response?.headers,\n },\n usage: result.usage\n ? {\n inputTokens: result.usage.inputTokens.total,\n outputTokens: result.usage.outputTokens.total,\n totalTokens:\n (result.usage.inputTokens.total ?? 0) +\n (result.usage.outputTokens.total ?? 0),\n }\n : undefined,\n };\n }\n}\n\nfunction isGeminiModel(modelId: string): boolean {\n return modelId.startsWith('gemini-');\n}\n\n// minimal version of the schema\nconst googleImageResponseSchema = lazySchema(() =>\n zodSchema(\n z.object({\n predictions: z\n .array(z.object({ bytesBase64Encoded: z.string() }))\n .default([]),\n }),\n ),\n);\n\n// Note: For the initial GA launch of Imagen 3, safety filters are not configurable.\n// https://ai.google.dev/gemini-api/docs/imagen#imagen-model\nconst googleImageModelOptionsSchema = lazySchema(() =>\n zodSchema(\n z.object({\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .nullish(),\n aspectRatio: z.enum(['1:1', '3:4', '4:3', '9:16', '16:9']).nullish(),\n }),\n ),\n);\n\nexport type GoogleImageModelOptions = InferSchema<\n typeof googleImageModelOptionsSchema\n>;\n","import {\n AISDKError,\n type Experimental_VideoModelV3,\n type SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n convertUint8ArrayToBase64,\n createJsonResponseHandler,\n delay,\n type FetchFunction,\n getFromApi,\n lazySchema,\n parseProviderOptions,\n postJsonToApi,\n type Resolvable,\n resolve,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { googleFailedResponseHandler } from './google-error';\nimport type { GoogleGenerativeAIVideoModelId } from './google-generative-ai-video-settings';\n\nexport type GoogleVideoModelOptions = {\n // Polling configuration\n pollIntervalMs?: number | null;\n pollTimeoutMs?: number | null;\n\n // Video generation options\n personGeneration?: 'dont_allow' | 'allow_adult' | 'allow_all' | null;\n negativePrompt?: string | null;\n\n // Reference images (for style/asset reference)\n referenceImages?: Array<{\n bytesBase64Encoded?: string;\n gcsUri?: string;\n }> | null;\n\n [key: string]: unknown; // For passthrough\n};\n\ninterface GoogleGenerativeAIVideoModelConfig {\n provider: string;\n baseURL: string;\n headers?: Resolvable<Record<string, string | undefined>>;\n fetch?: FetchFunction;\n generateId?: () => string;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class GoogleGenerativeAIVideoModel implements Experimental_VideoModelV3 {\n readonly specificationVersion = 'v3';\n\n get provider(): string {\n return this.config.provider;\n }\n\n get maxVideosPerCall(): number {\n // Google supports multiple videos via sampleCount\n return 4;\n }\n\n constructor(\n readonly modelId: GoogleGenerativeAIVideoModelId,\n private readonly config: GoogleGenerativeAIVideoModelConfig,\n ) {}\n\n async doGenerate(\n options: Parameters<Experimental_VideoModelV3['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<Experimental_VideoModelV3['doGenerate']>>> {\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const warnings: SharedV3Warning[] = [];\n\n const googleOptions = (await parseProviderOptions({\n provider: 'google',\n providerOptions: options.providerOptions,\n schema: googleVideoModelOptionsSchema,\n })) as GoogleVideoModelOptions | undefined;\n\n const instances: Array<Record<string, unknown>> = [{}];\n const instance = instances[0];\n\n if (options.prompt != null) {\n instance.prompt = options.prompt;\n }\n\n // Handle image-to-video: convert image to base64\n if (options.image != null) {\n if (options.image.type === 'url') {\n warnings.push({\n type: 'unsupported',\n feature: 'URL-based image input',\n details:\n 'Google Generative AI video models require base64-encoded images. URL will be ignored.',\n });\n } else {\n const base64Data =\n typeof options.image.data === 'string'\n ? options.image.data\n : convertUint8ArrayToBase64(options.image.data);\n\n instance.image = {\n inlineData: {\n mimeType: options.image.mediaType || 'image/png',\n data: base64Data,\n },\n };\n }\n }\n\n if (googleOptions?.referenceImages != null) {\n instance.referenceImages = googleOptions.referenceImages.map(refImg => {\n if (refImg.bytesBase64Encoded) {\n return {\n inlineData: {\n mimeType: 'image/png',\n data: refImg.bytesBase64Encoded,\n },\n };\n } else if (refImg.gcsUri) {\n return {\n gcsUri: refImg.gcsUri,\n };\n }\n return refImg;\n });\n }\n\n const parameters: Record<string, unknown> = {\n sampleCount: options.n,\n };\n\n if (options.aspectRatio) {\n parameters.aspectRatio = options.aspectRatio;\n }\n\n if (options.resolution) {\n const resolutionMap: Record<string, string> = {\n '1280x720': '720p',\n '1920x1080': '1080p',\n '3840x2160': '4k',\n };\n parameters.resolution =\n resolutionMap[options.resolution] || options.resolution;\n }\n\n if (options.duration) {\n parameters.durationSeconds = options.duration;\n }\n\n if (options.seed) {\n parameters.seed = options.seed;\n }\n\n if (googleOptions != null) {\n const opts = googleOptions as GoogleVideoModelOptions;\n\n if (\n opts.personGeneration !== undefined &&\n opts.personGeneration !== null\n ) {\n parameters.personGeneration = opts.personGeneration;\n }\n if (opts.negativePrompt !== undefined && opts.negativePrompt !== null) {\n parameters.negativePrompt = opts.negativePrompt;\n }\n\n for (const [key, value] of Object.entries(opts)) {\n if (\n ![\n 'pollIntervalMs',\n 'pollTimeoutMs',\n 'personGeneration',\n 'negativePrompt',\n 'referenceImages',\n ].includes(key)\n ) {\n parameters[key] = value;\n }\n }\n }\n\n const { value: operation } = await postJsonToApi({\n url: `${this.config.baseURL}/models/${this.modelId}:predictLongRunning`,\n headers: combineHeaders(\n await resolve(this.config.headers),\n options.headers,\n ),\n body: {\n instances,\n parameters,\n },\n successfulResponseHandler: createJsonResponseHandler(\n googleOperationSchema,\n ),\n failedResponseHandler: googleFailedResponseHandler,\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const operationName = operation.name;\n if (!operationName) {\n throw new AISDKError({\n name: 'GOOGLE_VIDEO_GENERATION_ERROR',\n message: 'No operation name returned from API',\n });\n }\n\n const pollIntervalMs = googleOptions?.pollIntervalMs ?? 10000; // 10 seconds (per Google docs)\n const pollTimeoutMs = googleOptions?.pollTimeoutMs ?? 600000; // 10 minutes\n\n const startTime = Date.now();\n let finalOperation = operation;\n let responseHeaders: Record<string, string> | undefined;\n\n while (!finalOperation.done) {\n if (Date.now() - startTime > pollTimeoutMs) {\n throw new AISDKError({\n name: 'GOOGLE_VIDEO_GENERATION_TIMEOUT',\n message: `Video generation timed out after ${pollTimeoutMs}ms`,\n });\n }\n\n await delay(pollIntervalMs);\n\n if (options.abortSignal?.aborted) {\n throw new AISDKError({\n name: 'GOOGLE_VIDEO_GENERATION_ABORTED',\n message: 'Video generation request was aborted',\n });\n }\n\n const { value: statusOperation, responseHeaders: pollHeaders } =\n await getFromApi({\n url: `${this.config.baseURL}/${operationName}`,\n headers: combineHeaders(\n await resolve(this.config.headers),\n options.headers,\n ),\n successfulResponseHandler: createJsonResponseHandler(\n googleOperationSchema,\n ),\n failedResponseHandler: googleFailedResponseHandler,\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n finalOperation = statusOperation;\n responseHeaders = pollHeaders;\n }\n\n if (finalOperation.error) {\n throw new AISDKError({\n name: 'GOOGLE_VIDEO_GENERATION_FAILED',\n message: `Video generation failed: ${finalOperation.error.message}`,\n });\n }\n\n const response = finalOperation.response;\n if (\n !response?.generateVideoResponse?.generatedSamples ||\n response.generateVideoResponse.generatedSamples.length === 0\n ) {\n throw new AISDKError({\n name: 'GOOGLE_VIDEO_GENERATION_ERROR',\n message: `No videos in response. Response: ${JSON.stringify(finalOperation)}`,\n });\n }\n\n const videos: Array<{ type: 'url'; url: string; mediaType: string }> = [];\n const videoMetadata: Array<{ uri: string }> = [];\n\n // Get API key from headers to append to download URLs\n const resolvedHeaders = await resolve(this.config.headers);\n const apiKey = resolvedHeaders?.['x-goog-api-key'];\n\n for (const generatedSample of response.generateVideoResponse\n .generatedSamples) {\n if (generatedSample.video?.uri) {\n // Append API key to URL for authentication during download\n const urlWithAuth = apiKey\n ? `${generatedSample.video.uri}${generatedSample.video.uri.includes('?') ? 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