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@ai-sdk/groq
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@ai-sdk/groq - npm Package Compare versions
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| # @ai-sdk/groq | ||
| ## 4.0.10 | ||
| ### Patch Changes | ||
| - Updated dependencies [4be62c1] | ||
| - Updated dependencies [7805e4a] | ||
| - Updated dependencies [cd12954] | ||
| - @ai-sdk/provider-utils@5.0.9 | ||
| ## 4.0.9 | ||
| ### Patch Changes | ||
| - Updated dependencies [e193290] | ||
| - @ai-sdk/provider-utils@5.0.8 | ||
| ## 4.0.8 | ||
@@ -4,0 +20,0 @@ |
+1
-1
@@ -963,3 +963,3 @@ // src/groq-provider.ts | ||
| // src/version.ts | ||
| var VERSION = true ? "4.0.8" : "0.0.0-test"; | ||
| var VERSION = true ? "4.0.10" : "0.0.0-test"; | ||
@@ -966,0 +966,0 @@ // src/groq-provider.ts |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"sources":["../src/groq-provider.ts","../src/groq-chat-language-model.ts","../src/convert-groq-usage.ts","../src/convert-to-groq-chat-messages.ts","../src/get-response-metadata.ts","../src/groq-chat-language-model-options.ts","../src/groq-error.ts","../src/groq-prepare-tools.ts","../src/groq-browser-search-models.ts","../src/map-groq-finish-reason.ts","../src/groq-transcription-model.ts","../src/groq-transcription-model-options.ts","../src/tool/browser-search.ts","../src/groq-tools.ts","../src/version.ts"],"sourcesContent":["import {\n NoSuchModelError,\n type LanguageModelV4,\n type ProviderV4,\n type TranscriptionModelV4,\n} from '@ai-sdk/provider';\nimport {\n loadApiKey,\n withoutTrailingSlash,\n withUserAgentSuffix,\n type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport { GroqChatLanguageModel } from './groq-chat-language-model';\nimport type { GroqChatModelId } from './groq-chat-language-model-options';\nimport type { GroqTranscriptionModelId } from './groq-transcription-model-options';\nimport { GroqTranscriptionModel } from './groq-transcription-model';\n\nimport { groqTools } from './groq-tools';\nimport { VERSION } from './version';\nexport interface GroqProvider extends ProviderV4 {\n /**\n * Creates a model for text generation.\n */\n (modelId: GroqChatModelId): LanguageModelV4;\n\n /**\n * Creates an Groq chat model for text generation.\n */\n languageModel(modelId: GroqChatModelId): LanguageModelV4;\n\n /**\n * Creates a model for transcription.\n */\n transcription(modelId: GroqTranscriptionModelId): TranscriptionModelV4;\n\n /**\n * Tools provided by Groq.\n */\n tools: typeof groqTools;\n\n /**\n * @deprecated Use `embeddingModel` instead.\n */\n textEmbeddingModel(modelId: string): never;\n}\n\nexport interface GroqProviderSettings {\n /**\n * Base URL for the Groq API calls.\n */\n baseURL?: string;\n\n /**\n * API key for authenticating requests.\n */\n apiKey?: string;\n\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record<string, string>;\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/**\n * Create an Groq provider instance.\n */\nexport function createGroq(options: GroqProviderSettings = {}): GroqProvider {\n const baseURL =\n withoutTrailingSlash(options.baseURL) ?? 'https://api.groq.com/openai/v1';\n\n const getHeaders = () =>\n withUserAgentSuffix(\n {\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'GROQ_API_KEY',\n description: 'Groq',\n })}`,\n ...options.headers,\n },\n `ai-sdk/groq/${VERSION}`,\n );\n\n const createChatModel = (modelId: GroqChatModelId) =>\n new GroqChatLanguageModel(modelId, {\n provider: 'groq.chat',\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createLanguageModel = (modelId: GroqChatModelId) => {\n if (new.target) {\n throw new Error(\n 'The Groq model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n const createTranscriptionModel = (modelId: GroqTranscriptionModelId) => {\n return new GroqTranscriptionModel(modelId, {\n provider: 'groq.transcription',\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n };\n\n const provider = function (modelId: GroqChatModelId) {\n return createLanguageModel(modelId);\n };\n\n provider.specificationVersion = 'v4' as const;\n provider.languageModel = createLanguageModel;\n provider.chat = createChatModel;\n\n provider.embeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n };\n provider.textEmbeddingModel = provider.embeddingModel;\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n provider.transcription = createTranscriptionModel;\n provider.transcriptionModel = createTranscriptionModel;\n\n provider.tools = groqTools;\n\n return provider;\n}\n\n/**\n * Default Groq provider instance.\n */\nexport const groq = createGroq();\n","import type {\n LanguageModelV4,\n LanguageModelV4CallOptions,\n LanguageModelV4Content,\n LanguageModelV4FinishReason,\n LanguageModelV4GenerateResult,\n LanguageModelV4StreamPart,\n LanguageModelV4StreamResult,\n SharedV4ProviderMetadata,\n SharedV4Warning,\n} from '@ai-sdk/provider';\nimport {\n StreamingToolCallTracker,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n generateId,\n isCustomReasoning,\n mapReasoningToProviderEffort,\n parseProviderOptions,\n postJsonToApi,\n serializeModelOptions,\n WORKFLOW_SERIALIZE,\n WORKFLOW_DESERIALIZE,\n type FetchFunction,\n type ParseResult,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { convertGroqUsage } from './convert-groq-usage';\nimport { convertToGroqChatMessages } from './convert-to-groq-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport {\n groqLanguageModelChatOptions,\n type GroqChatModelId,\n} from './groq-chat-language-model-options';\nimport { groqErrorDataSchema, groqFailedResponseHandler } from './groq-error';\nimport { prepareTools } from './groq-prepare-tools';\nimport { mapGroqFinishReason } from './map-groq-finish-reason';\n\ntype GroqChatConfig = {\n provider: string;\n headers?: () => Record<string, string | undefined>;\n url: (options: { modelId: string; path: string }) => string;\n fetch?: FetchFunction;\n};\n\nexport class GroqChatLanguageModel implements LanguageModelV4 {\n readonly specificationVersion = 'v4';\n\n readonly modelId: GroqChatModelId;\n\n readonly supportedUrls = {\n 'image/*': [/^https?:\\/\\/.*$/],\n };\n\n private readonly config: GroqChatConfig;\n\n static [WORKFLOW_SERIALIZE](model: GroqChatLanguageModel) {\n return serializeModelOptions({\n modelId: model.modelId,\n config: model.config,\n });\n }\n\n static [WORKFLOW_DESERIALIZE](options: {\n modelId: GroqChatModelId;\n config: GroqChatConfig;\n }) {\n return new GroqChatLanguageModel(options.modelId, options.config);\n }\n\n constructor(modelId: GroqChatModelId, config: GroqChatConfig) {\n this.modelId = modelId;\n this.config = config;\n }\n\n get provider(): string {\n return this.config.provider;\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 reasoning,\n tools,\n toolChoice,\n providerOptions,\n }: LanguageModelV4CallOptions) {\n const warnings: SharedV4Warning[] = [];\n\n const groqOptions = await parseProviderOptions({\n provider: 'groq',\n providerOptions,\n schema: groqLanguageModelChatOptions,\n });\n\n const structuredOutputs = groqOptions?.structuredOutputs ?? true;\n const strictJsonSchema = groqOptions?.strictJsonSchema ?? true;\n\n if (topK != null) {\n warnings.push({ type: 'unsupported', feature: 'topK' });\n }\n\n if (\n responseFormat?.type === 'json' &&\n responseFormat.schema != null &&\n !structuredOutputs\n ) {\n warnings.push({\n type: 'unsupported',\n feature: 'responseFormat',\n details:\n 'JSON response format schema is only supported with structuredOutputs',\n });\n }\n\n const {\n tools: groqTools,\n toolChoice: groqToolChoice,\n toolWarnings,\n } = prepareTools({ tools, toolChoice, modelId: this.modelId });\n\n return {\n args: {\n // model id:\n model: this.modelId,\n\n // model specific settings:\n user: groqOptions?.user,\n parallel_tool_calls: groqOptions?.parallelToolCalls,\n\n // standardized settings:\n max_tokens: maxOutputTokens,\n temperature,\n top_p: topP,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n stop: stopSequences,\n seed,\n\n // response format:\n response_format:\n responseFormat?.type === 'json'\n ? structuredOutputs && responseFormat.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: responseFormat.schema,\n strict: strictJsonSchema,\n name: responseFormat.name ?? 'response',\n description: responseFormat.description,\n },\n }\n : { type: 'json_object' }\n : undefined,\n\n // provider options:\n reasoning_format: groqOptions?.reasoningFormat,\n reasoning_effort:\n groqOptions?.reasoningEffort ??\n (isCustomReasoning(reasoning) && reasoning !== 'none'\n ? mapReasoningToProviderEffort({\n reasoning,\n effortMap: {\n minimal: 'low',\n low: 'low',\n medium: 'medium',\n high: 'high',\n xhigh: 'high',\n },\n warnings,\n })\n : undefined),\n service_tier: groqOptions?.serviceTier,\n\n // messages:\n messages: convertToGroqChatMessages(prompt),\n\n // tools:\n tools: groqTools,\n tool_choice: groqToolChoice,\n },\n warnings: [...warnings, ...toolWarnings],\n };\n }\n\n async doGenerate(\n options: LanguageModelV4CallOptions,\n ): Promise<LanguageModelV4GenerateResult> {\n const { args, warnings } = await this.getArgs(options);\n\n const body = JSON.stringify(args);\n\n const {\n responseHeaders,\n value: response,\n rawValue: rawResponse,\n } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n body: args,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n groqChatResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const choice = response.choices[0];\n const content: Array<LanguageModelV4Content> = [];\n\n // text content:\n const text = choice.message.content;\n if (text != null && text.length > 0) {\n content.push({ type: 'text', text: text });\n }\n\n // reasoning:\n const reasoning = choice.message.reasoning;\n if (reasoning != null && reasoning.length > 0) {\n content.push({\n type: 'reasoning',\n text: reasoning,\n });\n }\n\n // tool calls:\n if (choice.message.tool_calls != null) {\n for (const toolCall of choice.message.tool_calls) {\n content.push({\n type: 'tool-call',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments!,\n });\n }\n }\n\n return {\n content,\n finishReason: {\n unified: mapGroqFinishReason(choice.finish_reason),\n raw: choice.finish_reason ?? undefined,\n },\n usage: convertGroqUsage(response.usage),\n response: {\n ...getResponseMetadata(response),\n headers: responseHeaders,\n body: rawResponse,\n },\n warnings,\n request: { body },\n };\n }\n\n async doStream(\n options: LanguageModelV4CallOptions,\n ): Promise<LanguageModelV4StreamResult> {\n const { args, warnings } = await this.getArgs(options);\n\n const body = { ...args, stream: true };\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n body,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler:\n createEventSourceResponseHandler(groqChatChunkSchema),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n let toolCallTracker: StreamingToolCallTracker;\n\n let finishReason: LanguageModelV4FinishReason = {\n unified: 'other',\n raw: undefined,\n };\n let usage:\n | {\n prompt_tokens?: number | null | undefined;\n completion_tokens?: number | null | undefined;\n prompt_tokens_details?:\n | {\n cached_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n completion_tokens_details?:\n | {\n reasoning_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n }\n | undefined = undefined;\n let isFirstChunk = true;\n let isActiveText = false;\n let isActiveReasoning = false;\n\n let providerMetadata: SharedV4ProviderMetadata | undefined;\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult<z.infer<typeof groqChatChunkSchema>>,\n LanguageModelV4StreamPart\n >({\n start(controller) {\n toolCallTracker = new StreamingToolCallTracker(controller, {\n generateId,\n typeValidation: 'required',\n });\n controller.enqueue({ type: 'stream-start', warnings });\n },\n\n transform(chunk, controller) {\n // Emit raw chunk if requested (before anything else)\n if (options.includeRawChunks) {\n controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n }\n\n // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = {\n unified: 'error',\n raw: undefined,\n };\n controller.enqueue({ type: 'error', error: chunk.error });\n return;\n }\n\n const value = chunk.value;\n\n // handle error chunks:\n if ('error' in value) {\n finishReason = {\n unified: 'error',\n raw: undefined,\n };\n controller.enqueue({ type: 'error', error: value.error });\n return;\n }\n\n if (isFirstChunk) {\n isFirstChunk = false;\n\n controller.enqueue({\n type: 'response-metadata',\n ...getResponseMetadata(value),\n });\n }\n\n if (value.x_groq?.usage != null) {\n usage = value.x_groq.usage;\n }\n\n const choice = value.choices[0];\n\n if (choice?.finish_reason != null) {\n finishReason = {\n unified: mapGroqFinishReason(choice.finish_reason),\n raw: choice.finish_reason,\n };\n }\n\n if (choice?.delta == null) {\n return;\n }\n\n const delta = choice.delta;\n\n if (delta.reasoning != null && delta.reasoning.length > 0) {\n if (!isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-start',\n id: 'reasoning-0',\n });\n isActiveReasoning = true;\n }\n\n controller.enqueue({\n type: 'reasoning-delta',\n id: 'reasoning-0',\n delta: delta.reasoning,\n });\n }\n\n if (delta.content != null && delta.content.length > 0) {\n // end active reasoning block before text starts\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n if (!isActiveText) {\n controller.enqueue({ type: 'text-start', id: 'txt-0' });\n isActiveText = true;\n }\n\n controller.enqueue({\n type: 'text-delta',\n id: 'txt-0',\n delta: delta.content,\n });\n }\n\n if (delta.tool_calls != null) {\n // end active reasoning block before tool calls start\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n for (const toolCallDelta of delta.tool_calls) {\n toolCallTracker.processDelta(toolCallDelta);\n }\n }\n },\n\n flush(controller) {\n if (isActiveReasoning) {\n controller.enqueue({ type: 'reasoning-end', id: 'reasoning-0' });\n }\n\n if (isActiveText) {\n controller.enqueue({ type: 'text-end', id: 'txt-0' });\n }\n\n toolCallTracker.flush();\n\n controller.enqueue({\n type: 'finish',\n finishReason,\n usage: convertGroqUsage(usage),\n ...(providerMetadata != null ? { providerMetadata } : {}),\n });\n },\n }),\n ),\n request: { body: JSON.stringify(body) },\n response: { headers: responseHeaders },\n };\n }\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 groqChatResponseSchema = z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n message: z.object({\n content: z.string().nullish(),\n reasoning: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n id: z.string().nullish(),\n type: z.literal('function'),\n function: z.object({\n name: z.string(),\n arguments: z.string(),\n }),\n }),\n )\n .nullish(),\n }),\n index: z.number(),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n total_tokens: z.number().nullish(),\n prompt_tokens_details: z\n .object({\n cached_tokens: z.number().nullish(),\n })\n .nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish(),\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst groqChatChunkSchema = z.union([\n z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n delta: z\n .object({\n content: z.string().nullish(),\n reasoning: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n index: z.number(),\n id: z.string().nullish(),\n type: z.literal('function').optional(),\n function: z.object({\n name: z.string().nullish(),\n arguments: z.string().nullish(),\n }),\n }),\n )\n .nullish(),\n })\n .nullish(),\n finish_reason: z.string().nullable().optional(),\n index: z.number(),\n }),\n ),\n x_groq: z\n .object({\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n total_tokens: z.number().nullish(),\n prompt_tokens_details: z\n .object({\n cached_tokens: z.number().nullish(),\n })\n .nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish(),\n })\n .nullish(),\n }),\n groqErrorDataSchema,\n]);\n","import type { LanguageModelV4Usage } from '@ai-sdk/provider';\n\nexport function convertGroqUsage(\n usage:\n | {\n prompt_tokens?: number | null | undefined;\n completion_tokens?: number | null | undefined;\n prompt_tokens_details?:\n | {\n cached_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n completion_tokens_details?:\n | {\n reasoning_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n }\n | undefined\n | null,\n): LanguageModelV4Usage {\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.prompt_tokens ?? 0;\n const cacheReadTokens =\n usage.prompt_tokens_details?.cached_tokens ?? undefined;\n const completionTokens = usage.completion_tokens ?? 0;\n const reasoningTokens =\n usage.completion_tokens_details?.reasoning_tokens ?? undefined;\n const textTokens =\n reasoningTokens != null\n ? completionTokens - reasoningTokens\n : completionTokens;\n\n return {\n inputTokens: {\n total: promptTokens,\n noCache:\n cacheReadTokens != null ? promptTokens - cacheReadTokens : promptTokens,\n cacheRead: cacheReadTokens,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: completionTokens,\n text: textTokens,\n reasoning: reasoningTokens,\n },\n raw: usage,\n };\n}\n","import {\n UnsupportedFunctionalityError,\n type LanguageModelV4Prompt,\n} from '@ai-sdk/provider';\nimport type { GroqChatPrompt } from './groq-api-types';\nimport {\n convertToBase64,\n getTopLevelMediaType,\n resolveFullMediaType,\n} from '@ai-sdk/provider-utils';\n\nexport function convertToGroqChatMessages(\n prompt: LanguageModelV4Prompt,\n): GroqChatPrompt {\n const messages: GroqChatPrompt = [];\n\n for (const { role, content } of prompt) {\n switch (role) {\n case 'system': {\n messages.push({ role: 'system', content });\n break;\n }\n\n case 'user': {\n if (content.length === 1 && content[0].type === 'text') {\n messages.push({ role: 'user', content: content[0].text });\n break;\n }\n\n messages.push({\n role: 'user',\n content: content.map(part => {\n switch (part.type) {\n case 'text': {\n return { type: 'text', text: part.text };\n }\n case 'file': {\n switch (part.data.type) {\n case 'reference': {\n throw new UnsupportedFunctionalityError({\n functionality: 'file parts with provider references',\n });\n }\n case 'text': {\n throw new UnsupportedFunctionalityError({\n functionality: 'text file parts',\n });\n }\n case 'url':\n case 'data': {\n if (getTopLevelMediaType(part.mediaType) !== 'image') {\n throw new UnsupportedFunctionalityError({\n functionality: 'Non-image file content parts',\n });\n }\n\n return {\n type: 'image_url',\n image_url: {\n url:\n part.data.type === 'url'\n ? part.data.url.toString()\n : `data:${resolveFullMediaType({ part })};base64,${convertToBase64(part.data.data)}`,\n },\n };\n }\n }\n }\n }\n }),\n });\n\n break;\n }\n\n case 'assistant': {\n let text = '';\n let reasoning = '';\n const toolCalls: Array<{\n id: string;\n type: 'function';\n function: { name: string; arguments: string };\n }> = [];\n\n for (const part of content) {\n switch (part.type) {\n // groq supports reasoning for tool-calls in multi-turn conversations\n // https://github.com/vercel/ai/issues/7860\n case 'reasoning': {\n reasoning += part.text;\n break;\n }\n\n case 'text': {\n text += part.text;\n break;\n }\n\n case 'tool-call': {\n toolCalls.push({\n id: part.toolCallId,\n type: 'function',\n function: {\n name: part.toolName,\n arguments: JSON.stringify(part.input),\n },\n });\n break;\n }\n }\n }\n\n messages.push({\n role: 'assistant',\n content: text,\n ...(reasoning.length > 0 ? { reasoning } : null),\n ...(toolCalls.length > 0 ? { tool_calls: toolCalls } : null),\n });\n\n break;\n }\n\n case 'tool': {\n for (const toolResponse of content) {\n if (toolResponse.type === 'tool-approval-response') {\n continue;\n }\n const output = toolResponse.output;\n\n let contentValue: string;\n switch (output.type) {\n case 'text':\n case 'error-text':\n contentValue = output.value;\n break;\n case 'execution-denied':\n contentValue = output.reason ?? 'Tool call execution denied.';\n break;\n case 'content':\n case 'json':\n case 'error-json':\n contentValue = JSON.stringify(output.value);\n break;\n }\n\n messages.push({\n role: 'tool',\n tool_call_id: toolResponse.toolCallId,\n content: contentValue,\n });\n }\n break;\n }\n\n default: {\n const _exhaustiveCheck: never = role;\n throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n }\n }\n }\n\n return messages;\n}\n","export function getResponseMetadata({\n id,\n model,\n created,\n}: {\n id?: string | undefined | null;\n created?: number | undefined | null;\n model?: string | undefined | null;\n}) {\n return {\n id: id ?? undefined,\n modelId: model ?? undefined,\n timestamp: created != null ? new Date(created * 1000) : undefined,\n };\n}\n","import { z } from 'zod/v4';\n\n// https://console.groq.com/docs/models\nexport type GroqChatModelId =\n // production models\n | 'gemma2-9b-it'\n | 'llama-3.1-8b-instant'\n | 'llama-3.3-70b-versatile'\n | 'meta-llama/llama-guard-4-12b'\n | 'openai/gpt-oss-120b'\n | 'openai/gpt-oss-20b'\n // preview models (selection)\n | 'deepseek-r1-distill-llama-70b'\n | 'meta-llama/llama-4-maverick-17b-128e-instruct'\n | 'meta-llama/llama-4-scout-17b-16e-instruct'\n | 'meta-llama/llama-prompt-guard-2-22m'\n | 'meta-llama/llama-prompt-guard-2-86m'\n | 'moonshotai/kimi-k2-instruct-0905'\n | 'qwen/qwen3-32b'\n | 'llama-guard-3-8b'\n | 'llama3-70b-8192'\n | 'llama3-8b-8192'\n | 'mixtral-8x7b-32768'\n | 'qwen-qwq-32b'\n | 'qwen-2.5-32b'\n | 'deepseek-r1-distill-qwen-32b'\n | (string & {});\n\nexport const groqLanguageModelChatOptions = z.object({\n reasoningFormat: z.enum(['parsed', 'raw', 'hidden']).optional(),\n\n /**\n * Specifies the reasoning effort level for model inference.\n * @see https://console.groq.com/docs/reasoning#reasoning-effort\n */\n reasoningEffort: z\n .enum(['none', 'default', 'low', 'medium', 'high'])\n .optional(),\n\n /**\n * Whether to enable parallel function calling during tool use. Default to true.\n */\n parallelToolCalls: z.boolean().optional(),\n\n /**\n * A unique identifier representing your end-user, which can help OpenAI to\n * monitor and detect abuse. Learn more.\n */\n user: z.string().optional(),\n\n /**\n * Whether to use structured outputs.\n *\n * @default true\n */\n structuredOutputs: z.boolean().optional(),\n\n /**\n * Whether to use strict JSON schema validation.\n * When true, the model uses constrained decoding to guarantee schema compliance.\n * Only used when structured outputs are enabled and a schema is provided.\n *\n * @default true\n */\n strictJsonSchema: z.boolean().optional(),\n\n /**\n * Service tier for the request.\n * - 'on_demand': Default tier with consistent performance and fairness\n * - 'performance': Prioritized tier for latency-sensitive workloads\n * - 'flex': Higher throughput tier optimized for workloads that can handle occasional request failures\n * - 'auto': Uses on_demand rate limits, then falls back to flex tier if exceeded\n *\n * @default 'on_demand'\n */\n serviceTier: z.enum(['on_demand', 'performance', 'flex', 'auto']).optional(),\n});\n\nexport type GroqLanguageModelChatOptions = z.infer<\n typeof groqLanguageModelChatOptions\n>;\n","import { z } from 'zod/v4';\nimport { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\n\nexport const groqErrorDataSchema = z.object({\n error: z.object({\n message: z.string(),\n type: z.string(),\n }),\n});\n\nexport type GroqErrorData = z.infer<typeof groqErrorDataSchema>;\n\nexport const groqFailedResponseHandler = createJsonErrorResponseHandler({\n errorSchema: groqErrorDataSchema,\n errorToMessage: data => data.error.message,\n});\n","import {\n UnsupportedFunctionalityError,\n type LanguageModelV4CallOptions,\n type SharedV4Warning,\n} from '@ai-sdk/provider';\nimport {\n getSupportedModelsString,\n isBrowserSearchSupportedModel,\n} from './groq-browser-search-models';\nimport type { GroqChatModelId } from './groq-chat-language-model-options';\n\nexport function prepareTools({\n tools,\n toolChoice,\n modelId,\n}: {\n tools: LanguageModelV4CallOptions['tools'];\n toolChoice?: LanguageModelV4CallOptions['toolChoice'];\n modelId: GroqChatModelId;\n}): {\n tools:\n | undefined\n | Array<\n | {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: unknown;\n strict?: boolean;\n };\n }\n | {\n type: 'browser_search';\n }\n >;\n toolChoice:\n | { type: 'function'; function: { name: string } }\n | 'auto'\n | 'none'\n | 'required'\n | undefined;\n toolWarnings: SharedV4Warning[];\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: SharedV4Warning[] = [];\n\n if (tools == null) {\n return { tools: undefined, toolChoice: undefined, toolWarnings };\n }\n\n const groqTools: Array<\n | {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: unknown;\n strict?: boolean;\n };\n }\n | {\n type: 'browser_search';\n }\n > = [];\n\n for (const tool of tools) {\n if (tool.type === 'provider') {\n if (tool.id === 'groq.browser_search') {\n if (!isBrowserSearchSupportedModel(modelId)) {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details: `Browser search is only supported on the following models: ${getSupportedModelsString()}. Current model: ${modelId}`,\n });\n } else {\n groqTools.push({\n type: 'browser_search',\n });\n }\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n });\n }\n } else {\n groqTools.push({\n type: 'function',\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.inputSchema,\n ...(tool.strict != null ? { strict: tool.strict } : {}),\n },\n });\n }\n }\n\n if (toolChoice == null) {\n return { tools: groqTools, toolChoice: undefined, toolWarnings };\n }\n\n const type = toolChoice.type;\n\n switch (type) {\n case 'auto':\n case 'none':\n case 'required':\n return { tools: groqTools, toolChoice: type, toolWarnings };\n case 'tool':\n return {\n tools: groqTools,\n toolChoice: {\n type: 'function',\n function: {\n name: 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 type { GroqChatModelId } from './groq-chat-language-model-options';\n\n/**\n * Models that support browser search functionality.\n * Based on: https://console.groq.com/docs/browser-search\n */\nexport const BROWSER_SEARCH_SUPPORTED_MODELS: readonly GroqChatModelId[] = [\n 'openai/gpt-oss-20b',\n 'openai/gpt-oss-120b',\n] as const;\n\n/**\n * Check if a model supports browser search functionality.\n */\nexport function isBrowserSearchSupportedModel(\n modelId: GroqChatModelId,\n): boolean {\n return BROWSER_SEARCH_SUPPORTED_MODELS.includes(modelId);\n}\n\n/**\n * Get a formatted list of supported models for error messages.\n */\nexport function getSupportedModelsString(): string {\n return BROWSER_SEARCH_SUPPORTED_MODELS.join(', ');\n}\n","import type { LanguageModelV4FinishReason } from '@ai-sdk/provider';\n\nexport function mapGroqFinishReason(\n finishReason: string | null | undefined,\n): LanguageModelV4FinishReason['unified'] {\n switch (finishReason) {\n case 'stop':\n return 'stop';\n case 'length':\n return 'length';\n case 'content_filter':\n return 'content-filter';\n case 'function_call':\n case 'tool_calls':\n return 'tool-calls';\n default:\n return 'other';\n }\n}\n","import type { TranscriptionModelV4, SharedV4Warning } from '@ai-sdk/provider';\nimport {\n combineHeaders,\n convertBase64ToUint8Array,\n createJsonResponseHandler,\n mediaTypeToExtension,\n parseProviderOptions,\n postFormDataToApi,\n serializeModelOptions,\n WORKFLOW_SERIALIZE,\n WORKFLOW_DESERIALIZE,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport type { GroqConfig } from './groq-config';\nimport { groqFailedResponseHandler } from './groq-error';\nimport {\n groqTranscriptionModelOptions,\n type GroqTranscriptionModelId,\n} from './groq-transcription-model-options';\nimport type { GroqTranscriptionAPITypes } from './groq-api-types';\n\ninterface GroqTranscriptionModelConfig extends GroqConfig {\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class GroqTranscriptionModel implements TranscriptionModelV4 {\n readonly specificationVersion = 'v4';\n\n get provider(): string {\n return this.config.provider;\n }\n\n static [WORKFLOW_SERIALIZE](model: GroqTranscriptionModel) {\n return serializeModelOptions({\n modelId: model.modelId,\n config: model.config,\n });\n }\n\n static [WORKFLOW_DESERIALIZE](options: {\n modelId: GroqTranscriptionModelId;\n config: GroqTranscriptionModelConfig;\n }) {\n return new GroqTranscriptionModel(options.modelId, options.config);\n }\n\n constructor(\n readonly modelId: GroqTranscriptionModelId,\n private readonly config: GroqTranscriptionModelConfig,\n ) {}\n\n private async getArgs({\n audio,\n mediaType,\n providerOptions,\n }: Parameters<TranscriptionModelV4['doGenerate']>[0]) {\n const warnings: SharedV4Warning[] = [];\n\n // Parse provider options\n const groqOptions = await parseProviderOptions({\n provider: 'groq',\n providerOptions,\n schema: groqTranscriptionModelOptions,\n });\n\n // Create form data with base fields\n const formData = new FormData();\n const blob =\n audio instanceof Uint8Array\n ? new Blob([audio])\n : new Blob([convertBase64ToUint8Array(audio)]);\n\n formData.append('model', this.modelId);\n const fileExtension = mediaTypeToExtension(mediaType);\n formData.append(\n 'file',\n new File([blob], 'audio', { type: mediaType }),\n `audio.${fileExtension}`,\n );\n\n // Add provider-specific options\n if (groqOptions) {\n const transcriptionModelOptions: Omit<\n GroqTranscriptionAPITypes,\n 'model'\n > = {\n language: groqOptions.language ?? undefined,\n prompt: groqOptions.prompt ?? undefined,\n response_format: groqOptions.responseFormat ?? undefined,\n temperature: groqOptions.temperature ?? undefined,\n timestamp_granularities:\n groqOptions.timestampGranularities ?? undefined,\n };\n\n for (const key in transcriptionModelOptions) {\n const value =\n transcriptionModelOptions[\n key as keyof Omit<GroqTranscriptionAPITypes, 'model'>\n ];\n if (value !== undefined) {\n if (Array.isArray(value)) {\n for (const item of value) {\n formData.append(`${key}[]`, String(item));\n }\n } else {\n formData.append(key, String(value));\n }\n }\n }\n }\n\n return {\n formData,\n warnings,\n };\n }\n\n async doGenerate(\n options: Parameters<TranscriptionModelV4['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<TranscriptionModelV4['doGenerate']>>> {\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { formData, warnings } = await this.getArgs(options);\n\n const {\n value: response,\n responseHeaders,\n rawValue: rawResponse,\n } = await postFormDataToApi({\n url: this.config.url({\n path: '/audio/transcriptions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n formData,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n groqTranscriptionResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n text: response.text,\n segments:\n response.segments?.map(segment => ({\n text: segment.text,\n startSecond: segment.start,\n endSecond: segment.end,\n })) ?? [],\n language: response.language ?? undefined,\n durationInSeconds: response.duration ?? undefined,\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n body: rawResponse,\n },\n };\n }\n}\n\nconst groqTranscriptionResponseSchema = z.object({\n text: z.string(),\n x_groq: z.object({\n id: z.string(),\n }),\n // additional properties are returned when `response_format: 'verbose_json'` is\n task: z.string().nullish(),\n language: z.string().nullish(),\n duration: z.number().nullish(),\n segments: z\n .array(\n z.object({\n id: z.number(),\n seek: z.number(),\n start: z.number(),\n end: z.number(),\n text: z.string(),\n tokens: z.array(z.number()),\n temperature: z.number(),\n avg_logprob: z.number(),\n compression_ratio: z.number(),\n no_speech_prob: z.number(),\n }),\n )\n .nullish(),\n});\n","import {\n lazySchema,\n zodSchema,\n type InferSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type GroqTranscriptionModelId =\n | 'whisper-large-v3-turbo'\n | 'whisper-large-v3'\n | (string & {});\n\n// https://console.groq.com/docs/speech-to-text\nexport const groqTranscriptionModelOptions = lazySchema(() =>\n zodSchema(\n z.object({\n language: z.string().nullish(),\n prompt: z.string().nullish(),\n responseFormat: z.string().nullish(),\n temperature: z.number().min(0).max(1).nullish(),\n timestampGranularities: z.array(z.string()).nullish(),\n }),\n ),\n);\n\nexport type GroqTranscriptionModelOptions = InferSchema<\n typeof groqTranscriptionModelOptions\n>;\n","import {\n createProviderExecutedToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n/**\n * Browser search tool for Groq models.\n *\n * Provides interactive browser search capabilities that go beyond traditional web search\n * by navigating websites interactively and providing more detailed results.\n *\n * Currently supported on:\n * - openai/gpt-oss-20b\n * - openai/gpt-oss-120b\n *\n * @see https://console.groq.com/docs/browser-search\n */\nexport const browserSearch = createProviderExecutedToolFactory<\n {\n // Browser search doesn't take input parameters - it's controlled by the prompt\n // The tool is activated automatically when included in the tools array\n },\n {\n // Browser search doesn't have any output parameters\n },\n {\n // No configuration options needed - the tool works automatically\n // when included in the tools array for supported models\n }\n>({\n id: 'groq.browser_search',\n inputSchema: lazySchema(() => zodSchema(z.object({}))),\n outputSchema: lazySchema(() => zodSchema(z.object({}))),\n});\n","import { browserSearch } from './tool/browser-search';\n\nexport const groqTools = {\n browserSearch,\n};\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : 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| {"version":3,"sources":["../src/groq-provider.ts","../src/groq-chat-language-model.ts","../src/convert-groq-usage.ts","../src/convert-to-groq-chat-messages.ts","../src/get-response-metadata.ts","../src/groq-chat-language-model-options.ts","../src/groq-error.ts","../src/groq-prepare-tools.ts","../src/groq-browser-search-models.ts","../src/map-groq-finish-reason.ts","../src/groq-transcription-model.ts","../src/groq-transcription-model-options.ts","../src/tool/browser-search.ts","../src/groq-tools.ts","../src/version.ts"],"sourcesContent":["import {\n NoSuchModelError,\n type LanguageModelV4,\n type ProviderV4,\n type TranscriptionModelV4,\n} from '@ai-sdk/provider';\nimport {\n loadApiKey,\n withoutTrailingSlash,\n withUserAgentSuffix,\n type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport { GroqChatLanguageModel } from './groq-chat-language-model';\nimport type { GroqChatModelId } from './groq-chat-language-model-options';\nimport type { GroqTranscriptionModelId } from './groq-transcription-model-options';\nimport { GroqTranscriptionModel } from './groq-transcription-model';\n\nimport { groqTools } from './groq-tools';\nimport { VERSION } from './version';\nexport interface GroqProvider extends ProviderV4 {\n /**\n * Creates a model for text generation.\n */\n (modelId: GroqChatModelId): LanguageModelV4;\n\n /**\n * Creates an Groq chat model for text generation.\n */\n languageModel(modelId: GroqChatModelId): LanguageModelV4;\n\n /**\n * Creates a model for transcription.\n */\n transcription(modelId: GroqTranscriptionModelId): TranscriptionModelV4;\n\n /**\n * Tools provided by Groq.\n */\n tools: typeof groqTools;\n\n /**\n * @deprecated Use `embeddingModel` instead.\n */\n textEmbeddingModel(modelId: string): never;\n}\n\nexport interface GroqProviderSettings {\n /**\n * Base URL for the Groq API calls.\n */\n baseURL?: string;\n\n /**\n * API key for authenticating requests.\n */\n apiKey?: string;\n\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record<string, string>;\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/**\n * Create an Groq provider instance.\n */\nexport function createGroq(options: GroqProviderSettings = {}): GroqProvider {\n const baseURL =\n withoutTrailingSlash(options.baseURL) ?? 'https://api.groq.com/openai/v1';\n\n const getHeaders = () =>\n withUserAgentSuffix(\n {\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'GROQ_API_KEY',\n description: 'Groq',\n })}`,\n ...options.headers,\n },\n `ai-sdk/groq/${VERSION}`,\n );\n\n const createChatModel = (modelId: GroqChatModelId) =>\n new GroqChatLanguageModel(modelId, {\n provider: 'groq.chat',\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createLanguageModel = (modelId: GroqChatModelId) => {\n if (new.target) {\n throw new Error(\n 'The Groq model function cannot be called with the new keyword.',\n );\n }\n\n return createChatModel(modelId);\n };\n\n const createTranscriptionModel = (modelId: GroqTranscriptionModelId) => {\n return new GroqTranscriptionModel(modelId, {\n provider: 'groq.transcription',\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n };\n\n const provider = function (modelId: GroqChatModelId) {\n return createLanguageModel(modelId);\n };\n\n provider.specificationVersion = 'v4' as const;\n provider.languageModel = createLanguageModel;\n provider.chat = createChatModel;\n\n provider.embeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n };\n provider.textEmbeddingModel = provider.embeddingModel;\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n provider.transcription = createTranscriptionModel;\n provider.transcriptionModel = createTranscriptionModel;\n\n provider.tools = groqTools;\n\n return provider;\n}\n\n/**\n * Default Groq provider instance.\n */\nexport const groq = createGroq();\n","import type {\n LanguageModelV4,\n LanguageModelV4CallOptions,\n LanguageModelV4Content,\n LanguageModelV4FinishReason,\n LanguageModelV4GenerateResult,\n LanguageModelV4StreamPart,\n LanguageModelV4StreamResult,\n SharedV4ProviderMetadata,\n SharedV4Warning,\n} from '@ai-sdk/provider';\nimport {\n StreamingToolCallTracker,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n generateId,\n isCustomReasoning,\n mapReasoningToProviderEffort,\n parseProviderOptions,\n postJsonToApi,\n serializeModelOptions,\n WORKFLOW_SERIALIZE,\n WORKFLOW_DESERIALIZE,\n type FetchFunction,\n type ParseResult,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { convertGroqUsage } from './convert-groq-usage';\nimport { convertToGroqChatMessages } from './convert-to-groq-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport {\n groqLanguageModelChatOptions,\n type GroqChatModelId,\n} from './groq-chat-language-model-options';\nimport { groqErrorDataSchema, groqFailedResponseHandler } from './groq-error';\nimport { prepareTools } from './groq-prepare-tools';\nimport { mapGroqFinishReason } from './map-groq-finish-reason';\n\ntype GroqChatConfig = {\n provider: string;\n headers?: () => Record<string, string | undefined>;\n url: (options: { modelId: string; path: string }) => string;\n fetch?: FetchFunction;\n};\n\nexport class GroqChatLanguageModel implements LanguageModelV4 {\n readonly specificationVersion = 'v4';\n\n readonly modelId: GroqChatModelId;\n\n readonly supportedUrls = {\n 'image/*': [/^https?:\\/\\/.*$/],\n };\n\n private readonly config: GroqChatConfig;\n\n static [WORKFLOW_SERIALIZE](model: GroqChatLanguageModel) {\n return serializeModelOptions({\n modelId: model.modelId,\n config: model.config,\n });\n }\n\n static [WORKFLOW_DESERIALIZE](options: {\n modelId: GroqChatModelId;\n config: GroqChatConfig;\n }) {\n return new GroqChatLanguageModel(options.modelId, options.config);\n }\n\n constructor(modelId: GroqChatModelId, config: GroqChatConfig) {\n this.modelId = modelId;\n this.config = config;\n }\n\n get provider(): string {\n return this.config.provider;\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 reasoning,\n tools,\n toolChoice,\n providerOptions,\n }: LanguageModelV4CallOptions) {\n const warnings: SharedV4Warning[] = [];\n\n const groqOptions = await parseProviderOptions({\n provider: 'groq',\n providerOptions,\n schema: groqLanguageModelChatOptions,\n });\n\n const structuredOutputs = groqOptions?.structuredOutputs ?? true;\n const strictJsonSchema = groqOptions?.strictJsonSchema ?? true;\n\n if (topK != null) {\n warnings.push({ type: 'unsupported', feature: 'topK' });\n }\n\n if (\n responseFormat?.type === 'json' &&\n responseFormat.schema != null &&\n !structuredOutputs\n ) {\n warnings.push({\n type: 'unsupported',\n feature: 'responseFormat',\n details:\n 'JSON response format schema is only supported with structuredOutputs',\n });\n }\n\n const {\n tools: groqTools,\n toolChoice: groqToolChoice,\n toolWarnings,\n } = prepareTools({ tools, toolChoice, modelId: this.modelId });\n\n return {\n args: {\n // model id:\n model: this.modelId,\n\n // model specific settings:\n user: groqOptions?.user,\n parallel_tool_calls: groqOptions?.parallelToolCalls,\n\n // standardized settings:\n max_tokens: maxOutputTokens,\n temperature,\n top_p: topP,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n stop: stopSequences,\n seed,\n\n // response format:\n response_format:\n responseFormat?.type === 'json'\n ? structuredOutputs && responseFormat.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: responseFormat.schema,\n strict: strictJsonSchema,\n name: responseFormat.name ?? 'response',\n description: responseFormat.description,\n },\n }\n : { type: 'json_object' }\n : undefined,\n\n // provider options:\n reasoning_format: groqOptions?.reasoningFormat,\n reasoning_effort:\n groqOptions?.reasoningEffort ??\n (isCustomReasoning(reasoning) && reasoning !== 'none'\n ? mapReasoningToProviderEffort({\n reasoning,\n effortMap: {\n minimal: 'low',\n low: 'low',\n medium: 'medium',\n high: 'high',\n xhigh: 'high',\n },\n warnings,\n })\n : undefined),\n service_tier: groqOptions?.serviceTier,\n\n // messages:\n messages: convertToGroqChatMessages(prompt),\n\n // tools:\n tools: groqTools,\n tool_choice: groqToolChoice,\n },\n warnings: [...warnings, ...toolWarnings],\n };\n }\n\n async doGenerate(\n options: LanguageModelV4CallOptions,\n ): Promise<LanguageModelV4GenerateResult> {\n const { args, warnings } = await this.getArgs(options);\n\n const body = JSON.stringify(args);\n\n const {\n responseHeaders,\n value: response,\n rawValue: rawResponse,\n } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n body: args,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n groqChatResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const choice = response.choices[0];\n const content: Array<LanguageModelV4Content> = [];\n\n // text content:\n const text = choice.message.content;\n if (text != null && text.length > 0) {\n content.push({ type: 'text', text: text });\n }\n\n // reasoning:\n const reasoning = choice.message.reasoning;\n if (reasoning != null && reasoning.length > 0) {\n content.push({\n type: 'reasoning',\n text: reasoning,\n });\n }\n\n // tool calls:\n if (choice.message.tool_calls != null) {\n for (const toolCall of choice.message.tool_calls) {\n content.push({\n type: 'tool-call',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments!,\n });\n }\n }\n\n return {\n content,\n finishReason: {\n unified: mapGroqFinishReason(choice.finish_reason),\n raw: choice.finish_reason ?? undefined,\n },\n usage: convertGroqUsage(response.usage),\n response: {\n ...getResponseMetadata(response),\n headers: responseHeaders,\n body: rawResponse,\n },\n warnings,\n request: { body },\n };\n }\n\n async doStream(\n options: LanguageModelV4CallOptions,\n ): Promise<LanguageModelV4StreamResult> {\n const { args, warnings } = await this.getArgs(options);\n\n const body = { ...args, stream: true };\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n body,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler:\n createEventSourceResponseHandler(groqChatChunkSchema),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n let toolCallTracker: StreamingToolCallTracker;\n\n let finishReason: LanguageModelV4FinishReason = {\n unified: 'other',\n raw: undefined,\n };\n let usage:\n | {\n prompt_tokens?: number | null | undefined;\n completion_tokens?: number | null | undefined;\n prompt_tokens_details?:\n | {\n cached_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n completion_tokens_details?:\n | {\n reasoning_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n }\n | undefined = undefined;\n let isFirstChunk = true;\n let isActiveText = false;\n let isActiveReasoning = false;\n\n let providerMetadata: SharedV4ProviderMetadata | undefined;\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult<z.infer<typeof groqChatChunkSchema>>,\n LanguageModelV4StreamPart\n >({\n start(controller) {\n toolCallTracker = new StreamingToolCallTracker(controller, {\n generateId,\n typeValidation: 'required',\n });\n controller.enqueue({ type: 'stream-start', warnings });\n },\n\n transform(chunk, controller) {\n // Emit raw chunk if requested (before anything else)\n if (options.includeRawChunks) {\n controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n }\n\n // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = {\n unified: 'error',\n raw: undefined,\n };\n controller.enqueue({ type: 'error', error: chunk.error });\n return;\n }\n\n const value = chunk.value;\n\n // handle error chunks:\n if ('error' in value) {\n finishReason = {\n unified: 'error',\n raw: undefined,\n };\n controller.enqueue({ type: 'error', error: value.error });\n return;\n }\n\n if (isFirstChunk) {\n isFirstChunk = false;\n\n controller.enqueue({\n type: 'response-metadata',\n ...getResponseMetadata(value),\n });\n }\n\n if (value.x_groq?.usage != null) {\n usage = value.x_groq.usage;\n }\n\n const choice = value.choices[0];\n\n if (choice?.finish_reason != null) {\n finishReason = {\n unified: mapGroqFinishReason(choice.finish_reason),\n raw: choice.finish_reason,\n };\n }\n\n if (choice?.delta == null) {\n return;\n }\n\n const delta = choice.delta;\n\n if (delta.reasoning != null && delta.reasoning.length > 0) {\n if (!isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-start',\n id: 'reasoning-0',\n });\n isActiveReasoning = true;\n }\n\n controller.enqueue({\n type: 'reasoning-delta',\n id: 'reasoning-0',\n delta: delta.reasoning,\n });\n }\n\n if (delta.content != null && delta.content.length > 0) {\n // end active reasoning block before text starts\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n if (!isActiveText) {\n controller.enqueue({ type: 'text-start', id: 'txt-0' });\n isActiveText = true;\n }\n\n controller.enqueue({\n type: 'text-delta',\n id: 'txt-0',\n delta: delta.content,\n });\n }\n\n if (delta.tool_calls != null) {\n // end active reasoning block before tool calls start\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n for (const toolCallDelta of delta.tool_calls) {\n toolCallTracker.processDelta(toolCallDelta);\n }\n }\n },\n\n flush(controller) {\n if (isActiveReasoning) {\n controller.enqueue({ type: 'reasoning-end', id: 'reasoning-0' });\n }\n\n if (isActiveText) {\n controller.enqueue({ type: 'text-end', id: 'txt-0' });\n }\n\n toolCallTracker.flush();\n\n controller.enqueue({\n type: 'finish',\n finishReason,\n usage: convertGroqUsage(usage),\n ...(providerMetadata != null ? { providerMetadata } : {}),\n });\n },\n }),\n ),\n request: { body: JSON.stringify(body) },\n response: { headers: responseHeaders },\n };\n }\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 groqChatResponseSchema = z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n message: z.object({\n content: z.string().nullish(),\n reasoning: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n id: z.string().nullish(),\n type: z.literal('function'),\n function: z.object({\n name: z.string(),\n arguments: z.string(),\n }),\n }),\n )\n .nullish(),\n }),\n index: z.number(),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n total_tokens: z.number().nullish(),\n prompt_tokens_details: z\n .object({\n cached_tokens: z.number().nullish(),\n })\n .nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish(),\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst groqChatChunkSchema = z.union([\n z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n delta: z\n .object({\n content: z.string().nullish(),\n reasoning: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n index: z.number(),\n id: z.string().nullish(),\n type: z.literal('function').optional(),\n function: z.object({\n name: z.string().nullish(),\n arguments: z.string().nullish(),\n }),\n }),\n )\n .nullish(),\n })\n .nullish(),\n finish_reason: z.string().nullable().optional(),\n index: z.number(),\n }),\n ),\n x_groq: z\n .object({\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n total_tokens: z.number().nullish(),\n prompt_tokens_details: z\n .object({\n cached_tokens: z.number().nullish(),\n })\n .nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish(),\n })\n .nullish(),\n }),\n groqErrorDataSchema,\n]);\n","import type { LanguageModelV4Usage } from '@ai-sdk/provider';\n\nexport function convertGroqUsage(\n usage:\n | {\n prompt_tokens?: number | null | undefined;\n completion_tokens?: number | null | undefined;\n prompt_tokens_details?:\n | {\n cached_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n completion_tokens_details?:\n | {\n reasoning_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n }\n | undefined\n | null,\n): LanguageModelV4Usage {\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.prompt_tokens ?? 0;\n const cacheReadTokens =\n usage.prompt_tokens_details?.cached_tokens ?? undefined;\n const completionTokens = usage.completion_tokens ?? 0;\n const reasoningTokens =\n usage.completion_tokens_details?.reasoning_tokens ?? undefined;\n const textTokens =\n reasoningTokens != null\n ? completionTokens - reasoningTokens\n : completionTokens;\n\n return {\n inputTokens: {\n total: promptTokens,\n noCache:\n cacheReadTokens != null ? promptTokens - cacheReadTokens : promptTokens,\n cacheRead: cacheReadTokens,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: completionTokens,\n text: textTokens,\n reasoning: reasoningTokens,\n },\n raw: usage,\n };\n}\n","import {\n UnsupportedFunctionalityError,\n type LanguageModelV4Prompt,\n} from '@ai-sdk/provider';\nimport type { GroqChatPrompt } from './groq-api-types';\nimport {\n convertToBase64,\n getTopLevelMediaType,\n resolveFullMediaType,\n} from '@ai-sdk/provider-utils';\n\nexport function convertToGroqChatMessages(\n prompt: LanguageModelV4Prompt,\n): GroqChatPrompt {\n const messages: GroqChatPrompt = [];\n\n for (const { role, content } of prompt) {\n switch (role) {\n case 'system': {\n messages.push({ role: 'system', content });\n break;\n }\n\n case 'user': {\n if (content.length === 1 && content[0].type === 'text') {\n messages.push({ role: 'user', content: content[0].text });\n break;\n }\n\n messages.push({\n role: 'user',\n content: content.map(part => {\n switch (part.type) {\n case 'text': {\n return { type: 'text', text: part.text };\n }\n case 'file': {\n switch (part.data.type) {\n case 'reference': {\n throw new UnsupportedFunctionalityError({\n functionality: 'file parts with provider references',\n });\n }\n case 'text': {\n throw new UnsupportedFunctionalityError({\n functionality: 'text file parts',\n });\n }\n case 'url':\n case 'data': {\n if (getTopLevelMediaType(part.mediaType) !== 'image') {\n throw new UnsupportedFunctionalityError({\n functionality: 'Non-image file content parts',\n });\n }\n\n return {\n type: 'image_url',\n image_url: {\n url:\n part.data.type === 'url'\n ? part.data.url.toString()\n : `data:${resolveFullMediaType({ part })};base64,${convertToBase64(part.data.data)}`,\n },\n };\n }\n }\n }\n }\n }),\n });\n\n break;\n }\n\n case 'assistant': {\n let text = '';\n let reasoning = '';\n const toolCalls: Array<{\n id: string;\n type: 'function';\n function: { name: string; arguments: string };\n }> = [];\n\n for (const part of content) {\n switch (part.type) {\n // groq supports reasoning for tool-calls in multi-turn conversations\n // https://github.com/vercel/ai/issues/7860\n case 'reasoning': {\n reasoning += part.text;\n break;\n }\n\n case 'text': {\n text += part.text;\n break;\n }\n\n case 'tool-call': {\n toolCalls.push({\n id: part.toolCallId,\n type: 'function',\n function: {\n name: part.toolName,\n arguments: JSON.stringify(part.input),\n },\n });\n break;\n }\n }\n }\n\n messages.push({\n role: 'assistant',\n content: text,\n ...(reasoning.length > 0 ? { reasoning } : null),\n ...(toolCalls.length > 0 ? { tool_calls: toolCalls } : null),\n });\n\n break;\n }\n\n case 'tool': {\n for (const toolResponse of content) {\n if (toolResponse.type === 'tool-approval-response') {\n continue;\n }\n const output = toolResponse.output;\n\n let contentValue: string;\n switch (output.type) {\n case 'text':\n case 'error-text':\n contentValue = output.value;\n break;\n case 'execution-denied':\n contentValue = output.reason ?? 'Tool call execution denied.';\n break;\n case 'content':\n case 'json':\n case 'error-json':\n contentValue = JSON.stringify(output.value);\n break;\n }\n\n messages.push({\n role: 'tool',\n tool_call_id: toolResponse.toolCallId,\n content: contentValue,\n });\n }\n break;\n }\n\n default: {\n const _exhaustiveCheck: never = role;\n throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n }\n }\n }\n\n return messages;\n}\n","export function getResponseMetadata({\n id,\n model,\n created,\n}: {\n id?: string | undefined | null;\n created?: number | undefined | null;\n model?: string | undefined | null;\n}) {\n return {\n id: id ?? undefined,\n modelId: model ?? undefined,\n timestamp: created != null ? new Date(created * 1000) : undefined,\n };\n}\n","import { z } from 'zod/v4';\n\n// https://console.groq.com/docs/models\nexport type GroqChatModelId =\n // production models\n | 'gemma2-9b-it'\n | 'llama-3.1-8b-instant'\n | 'llama-3.3-70b-versatile'\n | 'meta-llama/llama-guard-4-12b'\n | 'openai/gpt-oss-120b'\n | 'openai/gpt-oss-20b'\n // preview models (selection)\n | 'deepseek-r1-distill-llama-70b'\n | 'meta-llama/llama-4-maverick-17b-128e-instruct'\n | 'meta-llama/llama-4-scout-17b-16e-instruct'\n | 'meta-llama/llama-prompt-guard-2-22m'\n | 'meta-llama/llama-prompt-guard-2-86m'\n | 'moonshotai/kimi-k2-instruct-0905'\n | 'qwen/qwen3-32b'\n | 'llama-guard-3-8b'\n | 'llama3-70b-8192'\n | 'llama3-8b-8192'\n | 'mixtral-8x7b-32768'\n | 'qwen-qwq-32b'\n | 'qwen-2.5-32b'\n | 'deepseek-r1-distill-qwen-32b'\n | (string & {});\n\nexport const groqLanguageModelChatOptions = z.object({\n reasoningFormat: z.enum(['parsed', 'raw', 'hidden']).optional(),\n\n /**\n * Specifies the reasoning effort level for model inference.\n * @see https://console.groq.com/docs/reasoning#reasoning-effort\n */\n reasoningEffort: z\n .enum(['none', 'default', 'low', 'medium', 'high'])\n .optional(),\n\n /**\n * Whether to enable parallel function calling during tool use. Default to true.\n */\n parallelToolCalls: z.boolean().optional(),\n\n /**\n * A unique identifier representing your end-user, which can help OpenAI to\n * monitor and detect abuse. Learn more.\n */\n user: z.string().optional(),\n\n /**\n * Whether to use structured outputs.\n *\n * @default true\n */\n structuredOutputs: z.boolean().optional(),\n\n /**\n * Whether to use strict JSON schema validation.\n * When true, the model uses constrained decoding to guarantee schema compliance.\n * Only used when structured outputs are enabled and a schema is provided.\n *\n * @default true\n */\n strictJsonSchema: z.boolean().optional(),\n\n /**\n * Service tier for the request.\n * - 'on_demand': Default tier with consistent performance and fairness\n * - 'performance': Prioritized tier for latency-sensitive workloads\n * - 'flex': Higher throughput tier optimized for workloads that can handle occasional request failures\n * - 'auto': Uses on_demand rate limits, then falls back to flex tier if exceeded\n *\n * @default 'on_demand'\n */\n serviceTier: z.enum(['on_demand', 'performance', 'flex', 'auto']).optional(),\n});\n\nexport type GroqLanguageModelChatOptions = z.infer<\n typeof groqLanguageModelChatOptions\n>;\n","import { z } from 'zod/v4';\nimport { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\n\nexport const groqErrorDataSchema = z.object({\n error: z.object({\n message: z.string(),\n type: z.string(),\n }),\n});\n\nexport type GroqErrorData = z.infer<typeof groqErrorDataSchema>;\n\nexport const groqFailedResponseHandler = createJsonErrorResponseHandler({\n errorSchema: groqErrorDataSchema,\n errorToMessage: data => data.error.message,\n});\n","import {\n UnsupportedFunctionalityError,\n type LanguageModelV4CallOptions,\n type SharedV4Warning,\n} from '@ai-sdk/provider';\nimport {\n getSupportedModelsString,\n isBrowserSearchSupportedModel,\n} from './groq-browser-search-models';\nimport type { GroqChatModelId } from './groq-chat-language-model-options';\n\nexport function prepareTools({\n tools,\n toolChoice,\n modelId,\n}: {\n tools: LanguageModelV4CallOptions['tools'];\n toolChoice?: LanguageModelV4CallOptions['toolChoice'];\n modelId: GroqChatModelId;\n}): {\n tools:\n | undefined\n | Array<\n | {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: unknown;\n strict?: boolean;\n };\n }\n | {\n type: 'browser_search';\n }\n >;\n toolChoice:\n | { type: 'function'; function: { name: string } }\n | 'auto'\n | 'none'\n | 'required'\n | undefined;\n toolWarnings: SharedV4Warning[];\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: SharedV4Warning[] = [];\n\n if (tools == null) {\n return { tools: undefined, toolChoice: undefined, toolWarnings };\n }\n\n const groqTools: Array<\n | {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: unknown;\n strict?: boolean;\n };\n }\n | {\n type: 'browser_search';\n }\n > = [];\n\n for (const tool of tools) {\n if (tool.type === 'provider') {\n if (tool.id === 'groq.browser_search') {\n if (!isBrowserSearchSupportedModel(modelId)) {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n details: `Browser search is only supported on the following models: ${getSupportedModelsString()}. Current model: ${modelId}`,\n });\n } else {\n groqTools.push({\n type: 'browser_search',\n });\n }\n } else {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n });\n }\n } else {\n groqTools.push({\n type: 'function',\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.inputSchema,\n ...(tool.strict != null ? { strict: tool.strict } : {}),\n },\n });\n }\n }\n\n if (toolChoice == null) {\n return { tools: groqTools, toolChoice: undefined, toolWarnings };\n }\n\n const type = toolChoice.type;\n\n switch (type) {\n case 'auto':\n case 'none':\n case 'required':\n return { tools: groqTools, toolChoice: type, toolWarnings };\n case 'tool':\n return {\n tools: groqTools,\n toolChoice: {\n type: 'function',\n function: {\n name: 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 type { GroqChatModelId } from './groq-chat-language-model-options';\n\n/**\n * Models that support browser search functionality.\n * Based on: https://console.groq.com/docs/browser-search\n */\nexport const BROWSER_SEARCH_SUPPORTED_MODELS: readonly GroqChatModelId[] = [\n 'openai/gpt-oss-20b',\n 'openai/gpt-oss-120b',\n] as const;\n\n/**\n * Check if a model supports browser search functionality.\n */\nexport function isBrowserSearchSupportedModel(\n modelId: GroqChatModelId,\n): boolean {\n return BROWSER_SEARCH_SUPPORTED_MODELS.includes(modelId);\n}\n\n/**\n * Get a formatted list of supported models for error messages.\n */\nexport function getSupportedModelsString(): string {\n return BROWSER_SEARCH_SUPPORTED_MODELS.join(', ');\n}\n","import type { LanguageModelV4FinishReason } from '@ai-sdk/provider';\n\nexport function mapGroqFinishReason(\n finishReason: string | null | undefined,\n): LanguageModelV4FinishReason['unified'] {\n switch (finishReason) {\n case 'stop':\n return 'stop';\n case 'length':\n return 'length';\n case 'content_filter':\n return 'content-filter';\n case 'function_call':\n case 'tool_calls':\n return 'tool-calls';\n default:\n return 'other';\n }\n}\n","import type { TranscriptionModelV4, SharedV4Warning } from '@ai-sdk/provider';\nimport {\n combineHeaders,\n convertBase64ToUint8Array,\n createJsonResponseHandler,\n mediaTypeToExtension,\n parseProviderOptions,\n postFormDataToApi,\n serializeModelOptions,\n WORKFLOW_SERIALIZE,\n WORKFLOW_DESERIALIZE,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport type { GroqConfig } from './groq-config';\nimport { groqFailedResponseHandler } from './groq-error';\nimport {\n groqTranscriptionModelOptions,\n type GroqTranscriptionModelId,\n} from './groq-transcription-model-options';\nimport type { GroqTranscriptionAPITypes } from './groq-api-types';\n\ninterface GroqTranscriptionModelConfig extends GroqConfig {\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class GroqTranscriptionModel implements TranscriptionModelV4 {\n readonly specificationVersion = 'v4';\n\n get provider(): string {\n return this.config.provider;\n }\n\n static [WORKFLOW_SERIALIZE](model: GroqTranscriptionModel) {\n return serializeModelOptions({\n modelId: model.modelId,\n config: model.config,\n });\n }\n\n static [WORKFLOW_DESERIALIZE](options: {\n modelId: GroqTranscriptionModelId;\n config: GroqTranscriptionModelConfig;\n }) {\n return new GroqTranscriptionModel(options.modelId, options.config);\n }\n\n constructor(\n readonly modelId: GroqTranscriptionModelId,\n private readonly config: GroqTranscriptionModelConfig,\n ) {}\n\n private async getArgs({\n audio,\n mediaType,\n providerOptions,\n }: Parameters<TranscriptionModelV4['doGenerate']>[0]) {\n const warnings: SharedV4Warning[] = [];\n\n // Parse provider options\n const groqOptions = await parseProviderOptions({\n provider: 'groq',\n providerOptions,\n schema: groqTranscriptionModelOptions,\n });\n\n // Create form data with base fields\n const formData = new FormData();\n const blob =\n audio instanceof Uint8Array\n ? new Blob([audio])\n : new Blob([convertBase64ToUint8Array(audio)]);\n\n formData.append('model', this.modelId);\n const fileExtension = mediaTypeToExtension(mediaType);\n formData.append(\n 'file',\n new File([blob], 'audio', { type: mediaType }),\n `audio.${fileExtension}`,\n );\n\n // Add provider-specific options\n if (groqOptions) {\n const transcriptionModelOptions: Omit<\n GroqTranscriptionAPITypes,\n 'model'\n > = {\n language: groqOptions.language ?? undefined,\n prompt: groqOptions.prompt ?? undefined,\n response_format: groqOptions.responseFormat ?? undefined,\n temperature: groqOptions.temperature ?? undefined,\n timestamp_granularities:\n groqOptions.timestampGranularities ?? undefined,\n };\n\n for (const key in transcriptionModelOptions) {\n const value =\n transcriptionModelOptions[\n key as keyof Omit<GroqTranscriptionAPITypes, 'model'>\n ];\n if (value !== undefined) {\n if (Array.isArray(value)) {\n for (const item of value) {\n formData.append(`${key}[]`, String(item));\n }\n } else {\n formData.append(key, String(value));\n }\n }\n }\n }\n\n return {\n formData,\n warnings,\n };\n }\n\n async doGenerate(\n options: Parameters<TranscriptionModelV4['doGenerate']>[0],\n ): Promise<Awaited<ReturnType<TranscriptionModelV4['doGenerate']>>> {\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { formData, warnings } = await this.getArgs(options);\n\n const {\n value: response,\n responseHeaders,\n rawValue: rawResponse,\n } = await postFormDataToApi({\n url: this.config.url({\n path: '/audio/transcriptions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers?.(), options.headers),\n formData,\n failedResponseHandler: groqFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n groqTranscriptionResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n text: response.text,\n segments:\n response.segments?.map(segment => ({\n text: segment.text,\n startSecond: segment.start,\n endSecond: segment.end,\n })) ?? [],\n language: response.language ?? undefined,\n durationInSeconds: response.duration ?? undefined,\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n body: rawResponse,\n },\n };\n }\n}\n\nconst groqTranscriptionResponseSchema = z.object({\n text: z.string(),\n x_groq: z.object({\n id: z.string(),\n }),\n // additional properties are returned when `response_format: 'verbose_json'` is\n task: z.string().nullish(),\n language: z.string().nullish(),\n duration: z.number().nullish(),\n segments: z\n .array(\n z.object({\n id: z.number(),\n seek: z.number(),\n start: z.number(),\n end: z.number(),\n text: z.string(),\n tokens: z.array(z.number()),\n temperature: z.number(),\n avg_logprob: z.number(),\n compression_ratio: z.number(),\n no_speech_prob: z.number(),\n }),\n )\n .nullish(),\n});\n","import {\n lazySchema,\n zodSchema,\n type InferSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type GroqTranscriptionModelId =\n | 'whisper-large-v3-turbo'\n | 'whisper-large-v3'\n | (string & {});\n\n// https://console.groq.com/docs/speech-to-text\nexport const groqTranscriptionModelOptions = lazySchema(() =>\n zodSchema(\n z.object({\n language: z.string().nullish(),\n prompt: z.string().nullish(),\n responseFormat: z.string().nullish(),\n temperature: z.number().min(0).max(1).nullish(),\n timestampGranularities: z.array(z.string()).nullish(),\n }),\n ),\n);\n\nexport type GroqTranscriptionModelOptions = InferSchema<\n typeof groqTranscriptionModelOptions\n>;\n","import {\n createProviderExecutedToolFactory,\n lazySchema,\n zodSchema,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n/**\n * Browser search tool for Groq models.\n *\n * Provides interactive browser search capabilities that go beyond traditional web search\n * by navigating websites interactively and providing more detailed results.\n *\n * Currently supported on:\n * - openai/gpt-oss-20b\n * - openai/gpt-oss-120b\n *\n * @see https://console.groq.com/docs/browser-search\n */\nexport const browserSearch = createProviderExecutedToolFactory<\n {\n // Browser search doesn't take input parameters - it's controlled by the prompt\n // The tool is activated automatically when included in the tools array\n },\n {\n // Browser search doesn't have any output parameters\n },\n {\n // No configuration options needed - the tool works automatically\n // when included in the tools array for supported models\n }\n>({\n id: 'groq.browser_search',\n inputSchema: lazySchema(() => zodSchema(z.object({}))),\n outputSchema: lazySchema(() => zodSchema(z.object({}))),\n});\n","import { browserSearch } from './tool/browser-search';\n\nexport const groqTools = {\n browserSearch,\n};\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : 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|
+4
-4
| { | ||
| "name": "@ai-sdk/groq", | ||
| "version": "4.0.8", | ||
| "version": "4.0.10", | ||
| "type": "module", | ||
@@ -33,3 +33,3 @@ "license": "Apache-2.0", | ||
| "@ai-sdk/provider": "4.0.3", | ||
| "@ai-sdk/provider-utils": "5.0.7" | ||
| "@ai-sdk/provider-utils": "5.0.9" | ||
| }, | ||
@@ -41,4 +41,4 @@ "devDependencies": { | ||
| "zod": "3.25.76", | ||
| "@vercel/ai-tsconfig": "0.0.0", | ||
| "@ai-sdk/test-server": "2.0.0" | ||
| "@ai-sdk/test-server": "2.0.0", | ||
| "@vercel/ai-tsconfig": "0.0.0" | ||
| }, | ||
@@ -45,0 +45,0 @@ "peerDependencies": { |
No alert changes
Improved metrics
- Total package byte prevSize
252196
0.1%Dependency changes
+ Added
@ai-sdk/provider-utils@5.0.9(transitive)- Removed
@ai-sdk/provider-utils@5.0.7(transitive)Updated
@ai-sdk/provider-utils@5.0.9