ai/dist/index.mjs.map

AI SDK by Vercel - The AI Toolkit for TypeScript and JavaScript

{"version":3,"sources":["../src/index.ts","../src/generate-text/generate-text.ts","../src/error/index.ts","../src/error/invalid-argument-error.ts","../src/error/invalid-stream-part-error.ts","../src/error/invalid-tool-approval-error.ts","../src/error/invalid-tool-input-error.ts","../src/error/tool-call-not-found-for-approval-error.ts","../src/error/missing-tool-result-error.ts","../src/error/no-image-generated-error.ts","../src/error/no-object-generated-error.ts","../src/error/no-output-generated-error.ts","../src/error/no-speech-generated-error.ts","../src/error/no-transcript-generated-error.ts","../src/error/no-video-generated-error.ts","../src/error/no-such-tool-error.ts","../src/error/tool-call-repair-error.ts","../src/error/unsupported-model-version-error.ts","../src/error/ui-message-stream-error.ts","../src/prompt/invalid-data-content-error.ts","../src/prompt/invalid-message-role-error.ts","../src/prompt/message-conversion-error.ts","../src/util/retry-error.ts","../src/logger/log-warnings.ts","../src/model/resolve-model.ts","../src/util/log-v2-compatibility-warning.ts","../src/model/as-embedding-model-v3.ts","../src/model/as-image-model-v3.ts","../src/model/as-language-model-v3.ts","../src/model/as-speech-model-v3.ts","../src/model/as-transcription-model-v3.ts","../src/prompt/call-settings.ts","../src/prompt/convert-to-language-model-prompt.ts","../src/util/detect-media-type.ts","../src/util/download/download.ts","../src/version.ts","../src/util/download/download-function.ts","../src/prompt/data-content.ts","../src/prompt/split-data-url.ts","../src/util/as-array.ts","../src/prompt/create-tool-model-output.ts","../src/prompt/prepare-call-settings.ts","../src/prompt/prepare-tools-and-tool-choice.ts","../src/util/is-non-empty-object.ts","../src/prompt/standardize-prompt.ts","../src/prompt/message.ts","../src/types/provider-metadata.ts","../src/types/json-value.ts","../src/prompt/content-part.ts","../src/prompt/wrap-gateway-error.ts","../src/telemetry/assemble-operation-name.ts","../src/telemetry/get-base-telemetry-attributes.ts","../src/telemetry/get-tracer.ts","../src/telemetry/noop-tracer.ts","../src/telemetry/record-span.ts","../src/telemetry/select-telemetry-attributes.ts","../src/telemetry/stringify-for-telemetry.ts","../src/types/usage.ts","../src/util/merge-objects.ts","../src/util/retry-with-exponential-backoff.ts","../src/util/prepare-retries.ts","../src/generate-text/collect-tool-approvals.ts","../src/generate-text/execute-tool-call.ts","../src/util/now.ts","../src/generate-text/extract-reasoning-content.ts","../src/generate-text/extract-text-content.ts","../src/generate-text/generated-file.ts","../src/generate-text/is-approval-needed.ts","../src/generate-text/output.ts","../src/util/parse-partial-json.ts","../src/util/fix-json.ts","../src/generate-text/parse-tool-call.ts","../src/generate-text/step-result.ts","../src/generate-text/stop-condition.ts","../src/generate-text/to-response-messages.ts","../src/util/merge-abort-signals.ts","../src/generate-text/stream-text.ts","../src/util/prepare-headers.ts","../src/text-stream/create-text-stream-response.ts","../src/util/write-to-server-response.ts","../src/text-stream/pipe-text-stream-to-response.ts","../src/ui-message-stream/json-to-sse-transform-stream.ts","../src/ui-message-stream/ui-message-stream-headers.ts","../src/ui-message-stream/create-ui-message-stream-response.ts","../src/ui-message-stream/get-response-ui-message-id.ts","../src/ui/process-ui-message-stream.ts","../src/ui-message-stream/ui-message-chunks.ts","../src/ui/ui-messages.ts","../src/ui-message-stream/handle-ui-message-stream-finish.ts","../src/ui-message-stream/pipe-ui-message-stream-to-response.ts","../src/util/async-iterable-stream.ts","../src/util/consume-stream.ts","../src/util/create-resolvable-promise.ts","../src/util/create-stitchable-stream.ts","../src/generate-text/run-tools-transformation.ts","../src/agent/tool-loop-agent.ts","../src/ui-message-stream/create-ui-message-stream.ts","../src/ui-message-stream/read-ui-message-stream.ts","../src/ui/convert-to-model-messages.ts","../src/ui/validate-ui-messages.ts","../src/agent/create-agent-ui-stream.ts","../src/agent/create-agent-ui-stream-response.ts","../src/agent/pipe-agent-ui-stream-to-response.ts","../src/embed/embed.ts","../src/embed/embed-many.ts","../src/util/split-array.ts","../src/generate-image/generate-image.ts","../src/generate-image/index.ts","../src/generate-object/generate-object.ts","../src/generate-object/output-strategy.ts","../src/generate-object/parse-and-validate-object-result.ts","../src/generate-object/validate-object-generation-input.ts","../src/generate-object/stream-object.ts","../src/util/cosine-similarity.ts","../src/util/download/create-download.ts","../src/util/data-url.ts","../src/util/is-deep-equal-data.ts","../src/util/serial-job-executor.ts","../src/util/simulate-readable-stream.ts","../src/generate-speech/generate-speech.ts","../src/generate-speech/generated-audio-file.ts","../src/generate-text/prune-messages.ts","../src/generate-text/smooth-stream.ts","../src/generate-video/generate-video.ts","../src/middleware/default-embedding-settings-middleware.ts","../src/middleware/default-settings-middleware.ts","../src/middleware/extract-json-middleware.ts","../src/util/get-potential-start-index.ts","../src/middleware/extract-reasoning-middleware.ts","../src/middleware/simulate-streaming-middleware.ts","../src/middleware/add-tool-input-examples-middleware.ts","../src/middleware/wrap-language-model.ts","../src/middleware/wrap-embedding-model.ts","../src/middleware/wrap-image-model.ts","../src/model/as-provider-v3.ts","../src/middleware/wrap-provider.ts","../src/registry/custom-provider.ts","../src/registry/no-such-provider-error.ts","../src/registry/provider-registry.ts","../src/rerank/rerank.ts","../src/transcribe/transcribe.ts","../src/ui/call-completion-api.ts","../src/ui/process-text-stream.ts","../src/ui/chat.ts","../src/ui/convert-file-list-to-file-ui-parts.ts","../src/ui/default-chat-transport.ts","../src/ui/http-chat-transport.ts","../src/ui/direct-chat-transport.ts","../src/ui/last-assistant-message-is-complete-with-approval-responses.ts","../src/ui/last-assistant-message-is-complete-with-tool-calls.ts","../src/ui/transform-text-to-ui-message-stream.ts","../src/ui/text-stream-chat-transport.ts"],"sourcesContent":["// re-exports:\nexport { createGateway, gateway, type GatewayModelId } from '@ai-sdk/gateway';\nexport {\n  asSchema,\n  createIdGenerator,\n  dynamicTool,\n  generateId,\n  jsonSchema,\n  parseJsonEventStream,\n  tool,\n  zodSchema,\n  type FlexibleSchema,\n  type IdGenerator,\n  type InferSchema,\n  type InferToolInput,\n  type InferToolOutput,\n  type Schema,\n  type Tool,\n  type ToolApprovalRequest,\n  type ToolApprovalResponse,\n  type ToolCallOptions,\n  type ToolExecutionOptions,\n  type ToolExecuteFunction,\n} from '@ai-sdk/provider-utils';\n\n// directory exports\nexport * from './agent';\nexport * from './embed';\nexport * from './error';\nexport * from './generate-image';\nexport * from './generate-object';\nexport * from './generate-speech';\nexport * from './generate-text';\nexport * from './generate-video';\nexport * from './logger';\nexport * from './middleware';\nexport * from './prompt';\nexport * from './registry';\nexport * from './rerank';\nexport * from './text-stream';\nexport * from './transcribe';\nexport * from './types';\nexport * from './ui';\nexport * from './ui-message-stream';\nexport * from './util';\n\n// telemetry types:\nexport type { TelemetrySettings } from './telemetry/telemetry-settings';\n\n// import globals\nimport './global';\n","import {\n  LanguageModelV3,\n  LanguageModelV3Content,\n  LanguageModelV3ToolCall,\n  LanguageModelV3ToolChoice,\n} from '@ai-sdk/provider';\nimport {\n  createIdGenerator,\n  getErrorMessage,\n  IdGenerator,\n  ProviderOptions,\n  SystemModelMessage,\n  ToolApprovalResponse,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { Tracer } from '@opentelemetry/api';\nimport { NoOutputGeneratedError } from '../error';\nimport { logWarnings } from '../logger/log-warnings';\nimport { resolveLanguageModel } from '../model/resolve-model';\nimport { ModelMessage } from '../prompt';\nimport {\n  CallSettings,\n  getStepTimeoutMs,\n  getTotalTimeoutMs,\n  TimeoutConfiguration,\n} from '../prompt/call-settings';\nimport { convertToLanguageModelPrompt } from '../prompt/convert-to-language-model-prompt';\nimport { createToolModelOutput } from '../prompt/create-tool-model-output';\nimport { prepareCallSettings } from '../prompt/prepare-call-settings';\nimport { prepareToolsAndToolChoice } from '../prompt/prepare-tools-and-tool-choice';\nimport { Prompt } from '../prompt/prompt';\nimport { standardizePrompt } from '../prompt/standardize-prompt';\nimport { wrapGatewayError } from '../prompt/wrap-gateway-error';\nimport { ToolCallNotFoundForApprovalError } from '../error/tool-call-not-found-for-approval-error';\nimport { assembleOperationName } from '../telemetry/assemble-operation-name';\nimport { getBaseTelemetryAttributes } from '../telemetry/get-base-telemetry-attributes';\nimport { getTracer } from '../telemetry/get-tracer';\nimport { recordSpan } from '../telemetry/record-span';\nimport { selectTelemetryAttributes } from '../telemetry/select-telemetry-attributes';\nimport { stringifyForTelemetry } from '../telemetry/stringify-for-telemetry';\nimport { TelemetrySettings } from '../telemetry/telemetry-settings';\nimport {\n  LanguageModel,\n  LanguageModelRequestMetadata,\n  ToolChoice,\n} from '../types';\nimport {\n  addLanguageModelUsage,\n  asLanguageModelUsage,\n  LanguageModelUsage,\n} from '../types/usage';\nimport { asArray } from '../util/as-array';\nimport { DownloadFunction } from '../util/download/download-function';\nimport { mergeObjects } from '../util/merge-objects';\nimport { prepareRetries } from '../util/prepare-retries';\nimport { VERSION } from '../version';\nimport type {\n  OnFinishEvent,\n  OnStartEvent,\n  OnStepFinishEvent,\n  OnStepStartEvent,\n  OnToolCallFinishEvent,\n  OnToolCallStartEvent,\n} from './callback-events';\nimport { collectToolApprovals } from './collect-tool-approvals';\nimport { ContentPart } from './content-part';\nimport { executeToolCall } from './execute-tool-call';\nimport { extractReasoningContent } from './extract-reasoning-content';\nimport { extractTextContent } from './extract-text-content';\nimport { GenerateTextResult } from './generate-text-result';\nimport { DefaultGeneratedFile } from './generated-file';\nimport { isApprovalNeeded } from './is-approval-needed';\nimport { Output, text } from './output';\nimport { InferCompleteOutput } from './output-utils';\nimport { parseToolCall } from './parse-tool-call';\nimport { PrepareStepFunction } from './prepare-step';\nimport { ResponseMessage } from './response-message';\nimport { DefaultStepResult, StepResult } from './step-result';\nimport {\n  isStopConditionMet,\n  stepCountIs,\n  StopCondition,\n} from './stop-condition';\nimport { toResponseMessages } from './to-response-messages';\nimport { ToolApprovalRequestOutput } from './tool-approval-request-output';\nimport { TypedToolCall } from './tool-call';\nimport { ToolCallRepairFunction } from './tool-call-repair-function';\nimport { TypedToolError } from './tool-error';\nimport { ToolOutput } from './tool-output';\nimport { TypedToolResult } from './tool-result';\nimport { ToolSet } from './tool-set';\nimport { mergeAbortSignals } from '../util/merge-abort-signals';\n\nconst originalGenerateId = createIdGenerator({\n  prefix: 'aitxt',\n  size: 24,\n});\n\n/**\n * Include settings for generateText (requestBody and responseBody).\n */\ntype GenerateTextIncludeSettings = {\n  requestBody?: boolean;\n  responseBody?: boolean;\n};\n\n/**\n * Callback that is set using the `experimental_onStart` option.\n *\n * Called when the generateText operation begins, before any LLM calls.\n * Use this callback for logging, analytics, or initializing state at the\n * start of a generation.\n *\n * @param event - The event object containing generation configuration.\n */\nexport type GenerateTextOnStartCallback<\n  TOOLS extends ToolSet = ToolSet,\n  OUTPUT extends Output = Output,\n> = (\n  event: OnStartEvent<TOOLS, OUTPUT, GenerateTextIncludeSettings>,\n) => PromiseLike<void> | void;\n\n/**\n * Callback that is set using the `experimental_onStepStart` option.\n *\n * Called when a step (LLM call) begins, before the provider is called.\n * Each step represents a single LLM invocation. Multiple steps occur when\n * using tool calls (the model may be called multiple times in a loop).\n *\n * @param event - The event object containing step configuration.\n */\nexport type GenerateTextOnStepStartCallback<\n  TOOLS extends ToolSet = ToolSet,\n  OUTPUT extends Output = Output,\n> = (\n  event: OnStepStartEvent<TOOLS, OUTPUT, GenerateTextIncludeSettings>,\n) => PromiseLike<void> | void;\n\n/**\n * Callback that is set using the `experimental_onToolCallStart` option.\n *\n * Called when a tool execution begins, before the tool's `execute` function is invoked.\n * Use this for logging tool invocations, tracking tool usage, or pre-execution validation.\n *\n * @param event - The event object containing tool call information.\n */\nexport type GenerateTextOnToolCallStartCallback<\n  TOOLS extends ToolSet = ToolSet,\n> = (event: OnToolCallStartEvent<TOOLS>) => PromiseLike<void> | void;\n\n/**\n * Callback that is set using the `experimental_onToolCallFinish` option.\n *\n * Called when a tool execution completes, either successfully or with an error.\n * Use this for logging tool results, tracking execution time, or error handling.\n *\n * The event uses a discriminated union on the `success` field:\n * - When `success: true`: `output` contains the tool result, `error` is never present.\n * - When `success: false`: `error` contains the error, `output` is never present.\n *\n * @param event - The event object containing tool call result information.\n */\nexport type GenerateTextOnToolCallFinishCallback<\n  TOOLS extends ToolSet = ToolSet,\n> = (event: OnToolCallFinishEvent<TOOLS>) => PromiseLike<void> | void;\n\n/**\n * Callback that is set using the `onStepFinish` option.\n *\n * Called when a step (LLM call) completes. The event includes all step result\n * properties (text, tool calls, usage, etc.) along with additional metadata.\n *\n * @param stepResult - The result of the step.\n */\nexport type GenerateTextOnStepFinishCallback<TOOLS extends ToolSet> = (\n  event: OnStepFinishEvent<TOOLS>,\n) => Promise<void> | void;\n\n/**\n * Callback that is set using the `onFinish` option.\n *\n * Called when the entire generation completes (all steps finished).\n * The event includes the final step's result properties along with\n * aggregated data from all steps.\n *\n * @param event - The final result along with aggregated step data.\n */\nexport type GenerateTextOnFinishCallback<TOOLS extends ToolSet> = (\n  event: OnFinishEvent<TOOLS>,\n) => PromiseLike<void> | void;\n\n/**\n * Generate a text and call tools for a given prompt using a language model.\n *\n * This function does not stream the output. If you want to stream the output, use `streamText` instead.\n *\n * @param model - The language model to use.\n *\n * @param tools - Tools that are accessible to and can be called by the model. The model needs to support calling tools.\n * @param toolChoice - The tool choice strategy. Default: 'auto'.\n *\n * @param system - A system message that will be part of the prompt.\n * @param prompt - A simple text prompt. You can either use `prompt` or `messages` but not both.\n * @param messages - A list of messages. You can either use `prompt` or `messages` but not both.\n *\n * @param maxOutputTokens - Maximum number of tokens to generate.\n * @param temperature - Temperature setting.\n * The value is passed through to the provider. The range depends on the provider and model.\n * It is recommended to set either `temperature` or `topP`, but not both.\n * @param topP - Nucleus sampling.\n * The value is passed through to the provider. The range depends on the provider and model.\n * It is recommended to set either `temperature` or `topP`, but not both.\n * @param topK - Only sample from the top K options for each subsequent token.\n * Used to remove \"long tail\" low probability responses.\n * Recommended for advanced use cases only. You usually only need to use temperature.\n * @param presencePenalty - Presence penalty setting.\n * It affects the likelihood of the model to repeat information that is already in the prompt.\n * The value is passed through to the provider. The range depends on the provider and model.\n * @param frequencyPenalty - Frequency penalty setting.\n * It affects the likelihood of the model to repeatedly use the same words or phrases.\n * The value is passed through to the provider. The range depends on the provider and model.\n * @param stopSequences - Stop sequences.\n * If set, the model will stop generating text when one of the stop sequences is generated.\n * @param seed - The seed (integer) to use for random sampling.\n * If set and supported by the model, calls will generate deterministic results.\n *\n * @param maxRetries - Maximum number of retries. Set to 0 to disable retries. Default: 2.\n * @param abortSignal - An optional abort signal that can be used to cancel the call.\n * @param timeout - An optional timeout in milliseconds. The call will be aborted if it takes longer than the specified timeout.\n * @param headers - Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.\n *\n * @param experimental_context - User-defined context object that flows through the entire generation lifecycle.\n * @param experimental_onStart - Callback invoked when generation begins, before any LLM calls.\n * @param experimental_onStepStart - Callback invoked when each step begins, before the provider is called.\n * Receives step number, messages (in ModelMessage format), tools, and context.\n * @param experimental_onToolCallStart - Callback invoked before each tool execution begins.\n * Receives tool name, call ID, input, and context.\n * @param experimental_onToolCallFinish - Callback invoked after each tool execution completes.\n * Uses a discriminated union: check `success` to determine if `output` or `error` is present.\n * @param onStepFinish - Callback that is called when each step (LLM call) is finished, including intermediate steps.\n * @param onFinish - Callback that is called when all steps are finished and the response is complete.\n *\n * @returns\n * A result object that contains the generated text, the results of the tool calls, and additional information.\n */\nexport async function generateText<\n  TOOLS extends ToolSet,\n  OUTPUT extends Output = Output<string, string>,\n>({\n  model: modelArg,\n  tools,\n  toolChoice,\n  system,\n  prompt,\n  messages,\n  maxRetries: maxRetriesArg,\n  abortSignal,\n  timeout,\n  headers,\n  stopWhen = stepCountIs(1),\n  experimental_output,\n  output = experimental_output,\n  experimental_telemetry: telemetry,\n  providerOptions,\n  experimental_activeTools,\n  activeTools = experimental_activeTools,\n  experimental_prepareStep,\n  prepareStep = experimental_prepareStep,\n  experimental_repairToolCall: repairToolCall,\n  experimental_download: download,\n  experimental_context,\n  experimental_include: include,\n  _internal: { generateId = originalGenerateId } = {},\n  experimental_onStart: onStart,\n  experimental_onStepStart: onStepStart,\n  experimental_onToolCallStart: onToolCallStart,\n  experimental_onToolCallFinish: onToolCallFinish,\n  onStepFinish,\n  onFinish,\n  ...settings\n}: CallSettings &\n  Prompt & {\n    /**\n     * The language model to use.\n     */\n    model: LanguageModel;\n\n    /**\n     * The tools that the model can call. The model needs to support calling tools.\n     */\n    tools?: TOOLS;\n\n    /**\n     * The tool choice strategy. Default: 'auto'.\n     */\n    toolChoice?: ToolChoice<NoInfer<TOOLS>>;\n\n    /**\n     * Condition for stopping the generation when there are tool results in the last step.\n     * When the condition is an array, any of the conditions can be met to stop the generation.\n     *\n     * @default stepCountIs(1)\n     */\n    stopWhen?:\n      | StopCondition<NoInfer<TOOLS>>\n      | Array<StopCondition<NoInfer<TOOLS>>>;\n\n    /**\n     * Optional telemetry configuration (experimental).\n     */\n    experimental_telemetry?: TelemetrySettings;\n\n    /**\n     * Additional provider-specific options. They are passed through\n     * to the provider from the AI SDK and enable provider-specific\n     * functionality that can be fully encapsulated in the provider.\n     */\n    providerOptions?: ProviderOptions;\n\n    /**\n     * @deprecated Use `activeTools` instead.\n     */\n    experimental_activeTools?: Array<keyof NoInfer<TOOLS>>;\n\n    /**\n     * Limits the tools that are available for the model to call without\n     * changing the tool call and result types in the result.\n     */\n    activeTools?: Array<keyof NoInfer<TOOLS>>;\n\n    /**\n     * Optional specification for parsing structured outputs from the LLM response.\n     */\n    output?: OUTPUT;\n\n    /**\n     * Optional specification for parsing structured outputs from the LLM response.\n     *\n     * @deprecated Use `output` instead.\n     */\n    experimental_output?: OUTPUT;\n\n    /**\n     * Custom download function to use for URLs.\n     *\n     * By default, files are downloaded if the model does not support the URL for the given media type.\n     */\n    experimental_download?: DownloadFunction | undefined;\n\n    /**\n     * @deprecated Use `prepareStep` instead.\n     */\n    experimental_prepareStep?: PrepareStepFunction<NoInfer<TOOLS>>;\n\n    /**\n     * Optional function that you can use to provide different settings for a step.\n     */\n    prepareStep?: PrepareStepFunction<NoInfer<TOOLS>>;\n\n    /**\n     * A function that attempts to repair a tool call that failed to parse.\n     */\n    experimental_repairToolCall?: ToolCallRepairFunction<NoInfer<TOOLS>>;\n\n    /**\n     * Callback that is called when the generateText operation begins,\n     * before any LLM calls are made.\n     */\n    experimental_onStart?: GenerateTextOnStartCallback<NoInfer<TOOLS>, OUTPUT>;\n\n    /**\n     * Callback that is called when a step (LLM call) begins,\n     * before the provider is called.\n     */\n    experimental_onStepStart?: GenerateTextOnStepStartCallback<\n      NoInfer<TOOLS>,\n      OUTPUT\n    >;\n\n    /**\n     * Callback that is called right before a tool's execute function runs.\n     */\n    experimental_onToolCallStart?: GenerateTextOnToolCallStartCallback<\n      NoInfer<TOOLS>\n    >;\n\n    /**\n     * Callback that is called right after a tool's execute function completes (or errors).\n     */\n    experimental_onToolCallFinish?: GenerateTextOnToolCallFinishCallback<\n      NoInfer<TOOLS>\n    >;\n\n    /**\n     * Callback that is called when each step (LLM call) is finished, including intermediate steps.\n     */\n    onStepFinish?: GenerateTextOnStepFinishCallback<NoInfer<TOOLS>>;\n\n    /**\n     * Callback that is called when all steps are finished and the response is complete.\n     */\n    onFinish?: GenerateTextOnFinishCallback<NoInfer<TOOLS>>;\n\n    /**\n     * Context that is passed into tool execution.\n     *\n     * Experimental (can break in patch releases).\n     *\n     * @default undefined\n     */\n    experimental_context?: unknown;\n\n    /**\n     * Settings for controlling what data is included in step results.\n     * Disabling inclusion can help reduce memory usage when processing\n     * large payloads like images.\n     *\n     * By default, all data is included for backwards compatibility.\n     */\n    experimental_include?: {\n      /**\n       * Whether to retain the request body in step results.\n       * The request body can be large when sending images or files.\n       * @default true\n       */\n      requestBody?: boolean;\n\n      /**\n       * Whether to retain the response body in step results.\n       * @default true\n       */\n      responseBody?: boolean;\n    };\n\n    /**\n     * Internal. For test use only. May change without notice.\n     */\n    _internal?: {\n      generateId?: IdGenerator;\n    };\n  }): Promise<GenerateTextResult<TOOLS, OUTPUT>> {\n  const model = resolveLanguageModel(modelArg);\n  const stopConditions = asArray(stopWhen);\n\n  const totalTimeoutMs = getTotalTimeoutMs(timeout);\n  const stepTimeoutMs = getStepTimeoutMs(timeout);\n  const stepAbortController =\n    stepTimeoutMs != null ? new AbortController() : undefined;\n  const mergedAbortSignal = mergeAbortSignals(\n    abortSignal,\n    totalTimeoutMs != null ? AbortSignal.timeout(totalTimeoutMs) : undefined,\n    stepAbortController?.signal,\n  );\n\n  const { maxRetries, retry } = prepareRetries({\n    maxRetries: maxRetriesArg,\n    abortSignal: mergedAbortSignal,\n  });\n\n  const callSettings = prepareCallSettings(settings);\n\n  const headersWithUserAgent = withUserAgentSuffix(\n    headers ?? {},\n    `ai/${VERSION}`,\n  );\n\n  const baseTelemetryAttributes = getBaseTelemetryAttributes({\n    model,\n    telemetry,\n    headers: headersWithUserAgent,\n    settings: { ...callSettings, maxRetries },\n  });\n\n  const modelInfo = { provider: model.provider, modelId: model.modelId };\n\n  const initialPrompt = await standardizePrompt({\n    system,\n    prompt,\n    messages,\n  } as Prompt);\n\n  try {\n    await onStart?.({\n      model: modelInfo,\n      system,\n      prompt,\n      messages,\n      tools,\n      toolChoice,\n      activeTools,\n      maxOutputTokens: callSettings.maxOutputTokens,\n      temperature: callSettings.temperature,\n      topP: callSettings.topP,\n      topK: callSettings.topK,\n      presencePenalty: callSettings.presencePenalty,\n      frequencyPenalty: callSettings.frequencyPenalty,\n      stopSequences: callSettings.stopSequences,\n      seed: callSettings.seed,\n      maxRetries,\n      timeout,\n      headers,\n      providerOptions,\n      stopWhen,\n      output,\n      abortSignal,\n      include,\n      functionId: telemetry?.functionId,\n      metadata: telemetry?.metadata as Record<string, unknown> | undefined,\n      experimental_context,\n    });\n  } catch (_ignored) {\n    // Errors in callbacks should not break the generation flow.\n  }\n\n  const tracer = getTracer(telemetry);\n\n  try {\n    return await recordSpan({\n      name: 'ai.generateText',\n      attributes: selectTelemetryAttributes({\n        telemetry,\n        attributes: {\n          ...assembleOperationName({\n            operationId: 'ai.generateText',\n            telemetry,\n          }),\n          ...baseTelemetryAttributes,\n          // model:\n          'ai.model.provider': model.provider,\n          'ai.model.id': model.modelId,\n          // specific settings that only make sense on the outer level:\n          'ai.prompt': {\n            input: () => JSON.stringify({ system, prompt, messages }),\n          },\n        },\n      }),\n      tracer,\n      fn: async span => {\n        const initialMessages = initialPrompt.messages;\n        const responseMessages: Array<ResponseMessage> = [];\n\n        const { approvedToolApprovals, deniedToolApprovals } =\n          collectToolApprovals<TOOLS>({ messages: initialMessages });\n\n        const localApprovedToolApprovals = approvedToolApprovals.filter(\n          toolApproval => !toolApproval.toolCall.providerExecuted,\n        );\n\n        if (\n          deniedToolApprovals.length > 0 ||\n          localApprovedToolApprovals.length > 0\n        ) {\n          const toolOutputs = await executeTools({\n            toolCalls: localApprovedToolApprovals.map(\n              toolApproval => toolApproval.toolCall,\n            ),\n            tools: tools as TOOLS,\n            tracer,\n            telemetry,\n            messages: initialMessages,\n            abortSignal: mergedAbortSignal,\n            experimental_context,\n            stepNumber: 0,\n            model: modelInfo,\n            onToolCallStart: onToolCallStart,\n            onToolCallFinish: onToolCallFinish,\n          });\n\n          const toolContent: Array<any> = [];\n\n          // add regular tool results for approved tool calls:\n          for (const output of toolOutputs) {\n            const modelOutput = await createToolModelOutput({\n              toolCallId: output.toolCallId,\n              input: output.input,\n              tool: tools?.[output.toolName],\n              output:\n                output.type === 'tool-result' ? output.output : output.error,\n              errorMode: output.type === 'tool-error' ? 'json' : 'none',\n            });\n\n            toolContent.push({\n              type: 'tool-result' as const,\n              toolCallId: output.toolCallId,\n              toolName: output.toolName,\n              output: modelOutput,\n            });\n          }\n\n          // add execution denied tool results for all denied tool approvals:\n          for (const toolApproval of deniedToolApprovals) {\n            toolContent.push({\n              type: 'tool-result' as const,\n              toolCallId: toolApproval.toolCall.toolCallId,\n              toolName: toolApproval.toolCall.toolName,\n              output: {\n                type: 'execution-denied' as const,\n                reason: toolApproval.approvalResponse.reason,\n                // For provider-executed tools, include approvalId so provider can correlate\n                ...(toolApproval.toolCall.providerExecuted && {\n                  providerOptions: {\n                    openai: {\n                      approvalId: toolApproval.approvalResponse.approvalId,\n                    },\n                  },\n                }),\n              },\n            });\n          }\n\n          responseMessages.push({\n            role: 'tool',\n            content: toolContent,\n          });\n        }\n\n        // Forward provider-executed approval responses to the provider\n        const providerExecutedToolApprovals = [\n          ...approvedToolApprovals,\n          ...deniedToolApprovals,\n        ].filter(toolApproval => toolApproval.toolCall.providerExecuted);\n\n        if (providerExecutedToolApprovals.length > 0) {\n          responseMessages.push({\n            role: 'tool',\n            content: providerExecutedToolApprovals.map(\n              toolApproval =>\n                ({\n                  type: 'tool-approval-response',\n                  approvalId: toolApproval.approvalResponse.approvalId,\n                  approved: toolApproval.approvalResponse.approved,\n                  reason: toolApproval.approvalResponse.reason,\n                  providerExecuted: true,\n                }) satisfies ToolApprovalResponse,\n            ),\n          });\n        }\n\n        const callSettings = prepareCallSettings(settings);\n\n        let currentModelResponse: Awaited<\n          ReturnType<LanguageModelV3['doGenerate']>\n        > & { response: { id: string; timestamp: Date; modelId: string } };\n        let clientToolCalls: Array<TypedToolCall<TOOLS>> = [];\n        let clientToolOutputs: Array<ToolOutput<TOOLS>> = [];\n        const steps: GenerateTextResult<TOOLS, OUTPUT>['steps'] = [];\n\n        // Track provider-executed tool calls that support deferred results\n        // (e.g., code_execution in programmatic tool calling scenarios).\n        // These tools may not return their results in the same turn as their call.\n        const pendingDeferredToolCalls = new Map<\n          string,\n          { toolName: string }\n        >();\n\n        do {\n          // Set up step timeout if configured\n          const stepTimeoutId =\n            stepTimeoutMs != null\n              ? setTimeout(() => stepAbortController!.abort(), stepTimeoutMs)\n              : undefined;\n\n          try {\n            const stepInputMessages = [...initialMessages, ...responseMessages];\n\n            const prepareStepResult = await prepareStep?.({\n              model,\n              steps,\n              stepNumber: steps.length,\n              messages: stepInputMessages,\n              experimental_context,\n            });\n\n            const stepModel = resolveLanguageModel(\n              prepareStepResult?.model ?? model,\n            );\n            const stepModelInfo = {\n              provider: stepModel.provider,\n              modelId: stepModel.modelId,\n            };\n\n            const promptMessages = await convertToLanguageModelPrompt({\n              prompt: {\n                system: prepareStepResult?.system ?? initialPrompt.system,\n                messages: prepareStepResult?.messages ?? stepInputMessages,\n              },\n              supportedUrls: await stepModel.supportedUrls,\n              download,\n            });\n\n            experimental_context =\n              prepareStepResult?.experimental_context ?? experimental_context;\n\n            const stepActiveTools =\n              prepareStepResult?.activeTools ?? activeTools;\n\n            const { toolChoice: stepToolChoice, tools: stepTools } =\n              await prepareToolsAndToolChoice({\n                tools,\n                toolChoice: prepareStepResult?.toolChoice ?? toolChoice,\n                activeTools: stepActiveTools,\n              });\n\n            const stepMessages =\n              prepareStepResult?.messages ?? stepInputMessages;\n\n            const stepSystem =\n              prepareStepResult?.system ?? initialPrompt.system;\n\n            const stepProviderOptions = mergeObjects(\n              providerOptions,\n              prepareStepResult?.providerOptions,\n            );\n\n            try {\n              await onStepStart?.({\n                stepNumber: steps.length,\n                model: stepModelInfo,\n                system: stepSystem,\n                messages: stepMessages,\n                tools,\n                toolChoice: stepToolChoice,\n                activeTools: stepActiveTools,\n                steps: [...steps],\n                providerOptions: stepProviderOptions,\n                timeout,\n                headers,\n                stopWhen,\n                output,\n                abortSignal,\n                include,\n                functionId: telemetry?.functionId,\n                metadata: telemetry?.metadata as\n                  | Record<string, unknown>\n                  | undefined,\n                experimental_context,\n              });\n            } catch (_ignored) {\n              // Errors in callbacks should not break the generation flow.\n            }\n\n            currentModelResponse = await retry(() =>\n              recordSpan({\n                name: 'ai.generateText.doGenerate',\n                attributes: selectTelemetryAttributes({\n                  telemetry,\n                  attributes: {\n                    ...assembleOperationName({\n                      operationId: 'ai.generateText.doGenerate',\n                      telemetry,\n                    }),\n                    ...baseTelemetryAttributes,\n                    // model:\n                    'ai.model.provider': stepModel.provider,\n                    'ai.model.id': stepModel.modelId,\n                    // prompt:\n                    'ai.prompt.messages': {\n                      input: () => stringifyForTelemetry(promptMessages),\n                    },\n                    'ai.prompt.tools': {\n                      // convert the language model level tools:\n                      input: () => stepTools?.map(tool => JSON.stringify(tool)),\n                    },\n                    'ai.prompt.toolChoice': {\n                      input: () =>\n                        stepToolChoice != null\n                          ? JSON.stringify(stepToolChoice)\n                          : undefined,\n                    },\n\n                    // standardized gen-ai llm span attributes:\n                    'gen_ai.system': stepModel.provider,\n                    'gen_ai.request.model': stepModel.modelId,\n                    'gen_ai.request.frequency_penalty':\n                      settings.frequencyPenalty,\n                    'gen_ai.request.max_tokens': settings.maxOutputTokens,\n                    'gen_ai.request.presence_penalty': settings.presencePenalty,\n                    'gen_ai.request.stop_sequences': settings.stopSequences,\n                    'gen_ai.request.temperature':\n                      settings.temperature ?? undefined,\n                    'gen_ai.request.top_k': settings.topK,\n                    'gen_ai.request.top_p': settings.topP,\n                  },\n                }),\n                tracer,\n                fn: async span => {\n                  const result = await stepModel.doGenerate({\n                    ...callSettings,\n                    tools: stepTools,\n                    toolChoice: stepToolChoice,\n                    responseFormat: await output?.responseFormat,\n                    prompt: promptMessages,\n                    providerOptions: stepProviderOptions,\n                    abortSignal: mergedAbortSignal,\n                    headers: headersWithUserAgent,\n                  });\n\n                  // Fill in default values:\n                  const responseData = {\n                    id: result.response?.id ?? generateId(),\n                    timestamp: result.response?.timestamp ?? new Date(),\n                    modelId: result.response?.modelId ?? stepModel.modelId,\n                    headers: result.response?.headers,\n                    body: result.response?.body,\n                  };\n\n                  // Add response information to the span:\n                  span.setAttributes(\n                    await selectTelemetryAttributes({\n                      telemetry,\n                      attributes: {\n                        'ai.response.finishReason': result.finishReason.unified,\n                        'ai.response.text': {\n                          output: () => extractTextContent(result.content),\n                        },\n                        'ai.response.reasoning': {\n                          output: () => extractReasoningContent(result.content),\n                        },\n                        'ai.response.toolCalls': {\n                          output: () => {\n                            const toolCalls = asToolCalls(result.content);\n                            return toolCalls == null\n                              ? undefined\n                              : JSON.stringify(toolCalls);\n                          },\n                        },\n                        'ai.response.id': responseData.id,\n                        'ai.response.model': responseData.modelId,\n                        'ai.response.timestamp':\n                          responseData.timestamp.toISOString(),\n                        'ai.response.providerMetadata': JSON.stringify(\n                          result.providerMetadata,\n                        ),\n\n                        // TODO rename telemetry attributes to inputTokens and outputTokens\n                        'ai.usage.promptTokens': result.usage.inputTokens.total,\n                        'ai.usage.completionTokens':\n                          result.usage.outputTokens.total,\n\n                        // standardized gen-ai llm span attributes:\n                        'gen_ai.response.finish_reasons': [\n                          result.finishReason.unified,\n                        ],\n                        'gen_ai.response.id': responseData.id,\n                        'gen_ai.response.model': responseData.modelId,\n                        'gen_ai.usage.input_tokens':\n                          result.usage.inputTokens.total,\n                        'gen_ai.usage.output_tokens':\n                          result.usage.outputTokens.total,\n                      },\n                    }),\n                  );\n\n                  return { ...result, response: responseData };\n                },\n              }),\n            );\n\n            // parse tool calls:\n            const stepToolCalls: TypedToolCall<TOOLS>[] = await Promise.all(\n              currentModelResponse.content\n                .filter(\n                  (part): part is LanguageModelV3ToolCall =>\n                    part.type === 'tool-call',\n                )\n                .map(toolCall =>\n                  parseToolCall({\n                    toolCall,\n                    tools,\n                    repairToolCall,\n                    system,\n                    messages: stepInputMessages,\n                  }),\n                ),\n            );\n            const toolApprovalRequests: Record<\n              string,\n              ToolApprovalRequestOutput<TOOLS>\n            > = {};\n\n            // notify the tools that the tool calls are available:\n            for (const toolCall of stepToolCalls) {\n              if (toolCall.invalid) {\n                continue; // ignore invalid tool calls\n              }\n\n              const tool = tools?.[toolCall.toolName];\n\n              if (tool == null) {\n                // ignore tool calls for tools that are not available,\n                // e.g. provider-executed dynamic tools\n                continue;\n              }\n\n              if (tool?.onInputAvailable != null) {\n                await tool.onInputAvailable({\n                  input: toolCall.input,\n                  toolCallId: toolCall.toolCallId,\n                  messages: stepInputMessages,\n                  abortSignal: mergedAbortSignal,\n                  experimental_context,\n                });\n              }\n\n              if (\n                await isApprovalNeeded({\n                  tool,\n                  toolCall,\n                  messages: stepInputMessages,\n                  experimental_context,\n                })\n              ) {\n                toolApprovalRequests[toolCall.toolCallId] = {\n                  type: 'tool-approval-request',\n                  approvalId: generateId(),\n                  toolCall,\n                };\n              }\n            }\n\n            // insert error tool outputs for invalid tool calls:\n            // TODO AI SDK 6: invalid inputs should not require output parts\n            const invalidToolCalls = stepToolCalls.filter(\n              toolCall => toolCall.invalid && toolCall.dynamic,\n            );\n\n            clientToolOutputs = [];\n\n            for (const toolCall of invalidToolCalls) {\n              clientToolOutputs.push({\n                type: 'tool-error',\n                toolCallId: toolCall.toolCallId,\n                toolName: toolCall.toolName,\n                input: toolCall.input,\n                error: getErrorMessage(toolCall.error!),\n                dynamic: true,\n              });\n            }\n\n            // execute client tool calls:\n            clientToolCalls = stepToolCalls.filter(\n              toolCall => !toolCall.providerExecuted,\n            );\n\n            if (tools != null) {\n              clientToolOutputs.push(\n                ...(await executeTools({\n                  toolCalls: clientToolCalls.filter(\n                    toolCall =>\n                      !toolCall.invalid &&\n                      toolApprovalRequests[toolCall.toolCallId] == null,\n                  ),\n                  tools,\n                  tracer,\n                  telemetry,\n                  messages: stepInputMessages,\n                  abortSignal: mergedAbortSignal,\n                  experimental_context,\n                  stepNumber: steps.length,\n                  model: stepModelInfo,\n                  onToolCallStart: onToolCallStart,\n                  onToolCallFinish: onToolCallFinish,\n                })),\n              );\n            }\n\n            // Track provider-executed tool calls that support deferred results.\n            // In programmatic tool calling, a server tool (e.g., code_execution) may\n            // trigger a client tool, and the server tool's result is deferred until\n            // the client tool's result is sent back.\n            for (const toolCall of stepToolCalls) {\n              if (!toolCall.providerExecuted) continue;\n              const tool = tools?.[toolCall.toolName];\n              if (tool?.type === 'provider' && tool.supportsDeferredResults) {\n                // Check if this tool call already has a result in the current response\n                const hasResultInResponse = currentModelResponse.content.some(\n                  part =>\n                    part.type === 'tool-result' &&\n                    part.toolCallId === toolCall.toolCallId,\n                );\n                if (!hasResultInResponse) {\n                  pendingDeferredToolCalls.set(toolCall.toolCallId, {\n                    toolName: toolCall.toolName,\n                  });\n                }\n              }\n            }\n\n            // Mark deferred tool calls as resolved when we receive their results\n            for (const part of currentModelResponse.content) {\n              if (part.type === 'tool-result') {\n                pendingDeferredToolCalls.delete(part.toolCallId);\n              }\n            }\n\n            // content:\n            const stepContent = asContent({\n              content: currentModelResponse.content,\n              toolCalls: stepToolCalls,\n              toolOutputs: clientToolOutputs,\n              toolApprovalRequests: Object.values(toolApprovalRequests),\n              tools,\n            });\n\n            // append to messages for potential next step:\n            responseMessages.push(\n              ...(await toResponseMessages({\n                content: stepContent,\n                tools,\n              })),\n            );\n\n            // Add step information (after response messages are updated):\n            // Conditionally include request.body and response.body based on include settings.\n            // Large payloads (e.g., base64-encoded images) can cause memory issues.\n            const stepRequest: LanguageModelRequestMetadata =\n              (include?.requestBody ?? true)\n                ? (currentModelResponse.request ?? {})\n                : { ...currentModelResponse.request, body: undefined };\n\n            const stepResponse = {\n              ...currentModelResponse.response,\n              // deep clone msgs to avoid mutating past messages in multi-step:\n              messages: structuredClone(responseMessages),\n              // Conditionally include response body:\n              body:\n                (include?.responseBody ?? true)\n                  ? currentModelResponse.response?.body\n                  : undefined,\n            };\n\n            const stepNumber = steps.length;\n\n            const currentStepResult: StepResult<TOOLS> = new DefaultStepResult({\n              stepNumber,\n              model: stepModelInfo,\n              functionId: telemetry?.functionId,\n              metadata: telemetry?.metadata as\n                | Record<string, unknown>\n                | undefined,\n              experimental_context,\n              content: stepContent,\n              finishReason: currentModelResponse.finishReason.unified,\n              rawFinishReason: currentModelResponse.finishReason.raw,\n              usage: asLanguageModelUsage(currentModelResponse.usage),\n              warnings: currentModelResponse.warnings,\n              providerMetadata: currentModelResponse.providerMetadata,\n              request: stepRequest,\n              response: stepResponse,\n            });\n\n            logWarnings({\n              warnings: currentModelResponse.warnings ?? [],\n              provider: stepModelInfo.provider,\n              model: stepModelInfo.modelId,\n            });\n\n            steps.push(currentStepResult);\n            await onStepFinish?.(currentStepResult);\n          } finally {\n            if (stepTimeoutId != null) {\n              clearTimeout(stepTimeoutId);\n            }\n          }\n        } while (\n          // Continue if:\n          // 1. There are client tool calls that have all been executed, OR\n          // 2. There are pending deferred results from provider-executed tools\n          ((clientToolCalls.length > 0 &&\n            clientToolOutputs.length === clientToolCalls.length) ||\n            pendingDeferredToolCalls.size > 0) &&\n          // continue until a stop condition is met:\n          !(await isStopConditionMet({ stopConditions, steps }))\n        );\n\n        // Add response information to the span:\n        span.setAttributes(\n          await selectTelemetryAttributes({\n            telemetry,\n            attributes: {\n              'ai.response.finishReason':\n                currentModelResponse.finishReason.unified,\n              'ai.response.text': {\n                output: () => extractTextContent(currentModelResponse.content),\n              },\n              'ai.response.reasoning': {\n                output: () =>\n                  extractReasoningContent(currentModelResponse.content),\n              },\n              'ai.response.toolCalls': {\n                output: () => {\n                  const toolCalls = asToolCalls(currentModelResponse.content);\n                  return toolCalls == null\n                    ? undefined\n                    : JSON.stringify(toolCalls);\n                },\n              },\n              'ai.response.providerMetadata': JSON.s