ai/dist/index.mjs.map

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

{"version":3,"sources":["../core/index.ts","../core/data-stream/create-data-stream.ts","../core/util/prepare-response-headers.ts","../core/data-stream/create-data-stream-response.ts","../core/util/prepare-outgoing-http-headers.ts","../core/util/write-to-server-response.ts","../core/data-stream/pipe-data-stream-to-response.ts","../errors/invalid-argument-error.ts","../util/retry-with-exponential-backoff.ts","../util/retry-error.ts","../core/prompt/prepare-retries.ts","../core/telemetry/assemble-operation-name.ts","../core/telemetry/get-base-telemetry-attributes.ts","../core/telemetry/get-tracer.ts","../core/telemetry/noop-tracer.ts","../core/telemetry/record-span.ts","../core/telemetry/select-telemetry-attributes.ts","../core/embed/embed.ts","../core/util/split-array.ts","../core/embed/embed-many.ts","../errors/no-image-generated-error.ts","../core/generate-text/generated-file.ts","../core/util/detect-mimetype.ts","../core/generate-image/generate-image.ts","../core/generate-object/generate-object.ts","../errors/no-object-generated-error.ts","../util/download-error.ts","../util/download.ts","../core/prompt/data-content.ts","../core/prompt/invalid-data-content-error.ts","../core/prompt/invalid-message-role-error.ts","../core/prompt/split-data-url.ts","../core/prompt/convert-to-language-model-prompt.ts","../core/prompt/prepare-call-settings.ts","../core/prompt/standardize-prompt.ts","../core/prompt/attachments-to-parts.ts","../core/prompt/message-conversion-error.ts","../core/prompt/convert-to-core-messages.ts","../core/prompt/message.ts","../core/types/provider-metadata.ts","../core/types/json-value.ts","../core/prompt/content-part.ts","../core/prompt/tool-result-content.ts","../core/types/usage.ts","../core/generate-object/inject-json-instruction.ts","../core/generate-object/output-strategy.ts","../core/util/async-iterable-stream.ts","../core/generate-object/validate-object-generation-input.ts","../core/prompt/stringify-for-telemetry.ts","../core/generate-object/stream-object.ts","../util/delayed-promise.ts","../util/create-resolvable-promise.ts","../core/util/create-stitchable-stream.ts","../core/util/now.ts","../core/generate-text/generate-text.ts","../errors/no-output-specified-error.ts","../errors/tool-execution-error.ts","../core/prompt/prepare-tools-and-tool-choice.ts","../core/util/is-non-empty-object.ts","../core/util/split-on-last-whitespace.ts","../core/util/remove-text-after-last-whitespace.ts","../core/generate-text/parse-tool-call.ts","../errors/invalid-tool-arguments-error.ts","../errors/no-such-tool-error.ts","../errors/tool-call-repair-error.ts","../core/generate-text/reasoning-detail.ts","../core/generate-text/to-response-messages.ts","../core/generate-text/output.ts","../errors/index.ts","../errors/invalid-stream-part-error.ts","../errors/mcp-client-error.ts","../core/generate-text/smooth-stream.ts","../core/generate-text/stream-text.ts","../util/as-array.ts","../util/consume-stream.ts","../core/util/merge-streams.ts","../core/generate-text/run-tools-transformation.ts","../errors/no-speech-generated-error.ts","../core/generate-speech/generated-audio-file.ts","../core/generate-speech/generate-speech.ts","../errors/no-transcript-generated-error.ts","../core/transcribe/transcribe.ts","../core/util/merge-objects.ts","../core/middleware/default-settings-middleware.ts","../core/util/get-potential-start-index.ts","../core/middleware/extract-reasoning-middleware.ts","../core/middleware/simulate-streaming-middleware.ts","../core/middleware/wrap-language-model.ts","../core/prompt/append-client-message.ts","../core/prompt/append-response-messages.ts","../core/registry/custom-provider.ts","../core/registry/no-such-provider-error.ts","../core/registry/provider-registry.ts","../core/tool/mcp/mcp-client.ts","../core/tool/tool.ts","../core/tool/mcp/mcp-sse-transport.ts","../core/tool/mcp/json-rpc-message.ts","../core/tool/mcp/types.ts","../core/tool/mcp/mcp-transport.ts","../core/util/cosine-similarity.ts","../core/util/simulate-readable-stream.ts","../streams/assistant-response.ts","../streams/langchain-adapter.ts","../streams/stream-callbacks.ts","../streams/llamaindex-adapter.ts","../streams/stream-data.ts","../util/constants.ts"],"sourcesContent":["// re-exports:\nexport { createIdGenerator, generateId } from '@ai-sdk/provider-utils';\nexport type { IDGenerator } from '@ai-sdk/provider-utils';\nexport {\n  formatAssistantStreamPart,\n  formatDataStreamPart,\n  jsonSchema,\n  parseAssistantStreamPart,\n  parseDataStreamPart,\n  processDataStream,\n  processTextStream,\n  zodSchema,\n} from '@ai-sdk/ui-utils';\nexport type {\n  AssistantMessage,\n  AssistantStatus,\n  Attachment,\n  ChatRequest,\n  ChatRequestOptions,\n  CreateMessage,\n  DataMessage,\n  DataStreamPart,\n  DeepPartial,\n  IdGenerator,\n  JSONValue,\n  Message,\n  UIMessage,\n  RequestOptions,\n  Schema,\n  ToolInvocation,\n  UseAssistantOptions,\n} from '@ai-sdk/ui-utils';\n\n// directory exports:\nexport * from './data-stream';\nexport * from './embed';\nexport * from './generate-image';\nexport * from './generate-object';\nexport * from './generate-text';\nexport * from './generate-speech';\nexport * from './transcribe';\nexport * from './middleware';\nexport * from './prompt';\nexport * from './registry';\nexport * from './tool';\nexport * from './types';\n\n// telemetry types:\nexport type { TelemetrySettings } from './telemetry/telemetry-settings';\n\n// util exports:\nexport { cosineSimilarity } from './util/cosine-similarity';\nexport { simulateReadableStream } from './util/simulate-readable-stream';\n","import { DataStreamString, formatDataStreamPart } from '@ai-sdk/ui-utils';\nimport { DataStreamWriter } from './data-stream-writer';\n\nexport function createDataStream({\n  execute,\n  onError = () => 'An error occurred.', // mask error messages for safety by default\n}: {\n  execute: (dataStream: DataStreamWriter) => Promise<void> | void;\n  onError?: (error: unknown) => string;\n}): ReadableStream<DataStreamString> {\n  let controller!: ReadableStreamDefaultController<string>;\n\n  const ongoingStreamPromises: Promise<void>[] = [];\n\n  const stream = new ReadableStream({\n    start(controllerArg) {\n      controller = controllerArg;\n    },\n  });\n\n  function safeEnqueue(data: DataStreamString) {\n    try {\n      controller.enqueue(data);\n    } catch (error) {\n      // suppress errors when the stream has been closed\n    }\n  }\n\n  try {\n    const result = execute({\n      write(data: DataStreamString) {\n        safeEnqueue(data);\n      },\n      writeData(data) {\n        safeEnqueue(formatDataStreamPart('data', [data]));\n      },\n      writeMessageAnnotation(annotation) {\n        safeEnqueue(formatDataStreamPart('message_annotations', [annotation]));\n      },\n      writeSource(source) {\n        safeEnqueue(formatDataStreamPart('source', source));\n      },\n      merge(streamArg) {\n        ongoingStreamPromises.push(\n          (async () => {\n            const reader = streamArg.getReader();\n            while (true) {\n              const { done, value } = await reader.read();\n              if (done) break;\n              safeEnqueue(value);\n            }\n          })().catch(error => {\n            safeEnqueue(formatDataStreamPart('error', onError(error)));\n          }),\n        );\n      },\n      onError,\n    });\n\n    if (result) {\n      ongoingStreamPromises.push(\n        result.catch(error => {\n          safeEnqueue(formatDataStreamPart('error', onError(error)));\n        }),\n      );\n    }\n  } catch (error) {\n    safeEnqueue(formatDataStreamPart('error', onError(error)));\n  }\n\n  // Wait until all ongoing streams are done. This approach enables merging\n  // streams even after execute has returned, as long as there is still an\n  // open merged stream. This is important to e.g. forward new streams and\n  // from callbacks.\n  const waitForStreams: Promise<void> = new Promise(async resolve => {\n    while (ongoingStreamPromises.length > 0) {\n      await ongoingStreamPromises.shift();\n    }\n    resolve();\n  });\n\n  waitForStreams.finally(() => {\n    try {\n      controller.close();\n    } catch (error) {\n      // suppress errors when the stream has been closed\n    }\n  });\n\n  return stream;\n}\n","export function prepareResponseHeaders(\n  headers: HeadersInit | undefined,\n  {\n    contentType,\n    dataStreamVersion,\n  }: { contentType: string; dataStreamVersion?: 'v1' | undefined },\n) {\n  const responseHeaders = new Headers(headers ?? {});\n\n  if (!responseHeaders.has('Content-Type')) {\n    responseHeaders.set('Content-Type', contentType);\n  }\n\n  if (dataStreamVersion !== undefined) {\n    responseHeaders.set('X-Vercel-AI-Data-Stream', dataStreamVersion);\n  }\n\n  return responseHeaders;\n}\n","import { prepareResponseHeaders } from '../util/prepare-response-headers';\nimport { createDataStream } from './create-data-stream';\nimport { DataStreamWriter } from './data-stream-writer';\n\nexport function createDataStreamResponse({\n  status,\n  statusText,\n  headers,\n  execute,\n  onError,\n}: ResponseInit & {\n  execute: (dataStream: DataStreamWriter) => Promise<void> | void;\n  onError?: (error: unknown) => string;\n}): Response {\n  return new Response(\n    createDataStream({ execute, onError }).pipeThrough(new TextEncoderStream()),\n    {\n      status,\n      statusText,\n      headers: prepareResponseHeaders(headers, {\n        contentType: 'text/plain; charset=utf-8',\n        dataStreamVersion: 'v1',\n      }),\n    },\n  );\n}\n","export function prepareOutgoingHttpHeaders(\n  headers: HeadersInit | undefined,\n  {\n    contentType,\n    dataStreamVersion,\n  }: { contentType: string; dataStreamVersion?: 'v1' | undefined },\n) {\n  const outgoingHeaders: Record<string, string | number | string[]> = {};\n\n  if (headers != null) {\n    for (const [key, value] of Object.entries(headers)) {\n      outgoingHeaders[key] = value;\n    }\n  }\n\n  if (outgoingHeaders['Content-Type'] == null) {\n    outgoingHeaders['Content-Type'] = contentType;\n  }\n\n  if (dataStreamVersion !== undefined) {\n    outgoingHeaders['X-Vercel-AI-Data-Stream'] = dataStreamVersion;\n  }\n\n  return outgoingHeaders;\n}\n","import { ServerResponse } from 'node:http';\n\n/**\n * Writes the content of a stream to a server response.\n */\nexport function writeToServerResponse({\n  response,\n  status,\n  statusText,\n  headers,\n  stream,\n}: {\n  response: ServerResponse;\n  status?: number;\n  statusText?: string;\n  headers?: Record<string, string | number | string[]>;\n  stream: ReadableStream<Uint8Array>;\n}): void {\n  response.writeHead(status ?? 200, statusText, headers);\n\n  const reader = stream.getReader();\n  const read = async () => {\n    try {\n      while (true) {\n        const { done, value } = await reader.read();\n        if (done) break;\n        response.write(value);\n      }\n    } catch (error) {\n      throw error;\n    } finally {\n      response.end();\n    }\n  };\n\n  read();\n}\n","import { ServerResponse } from 'node:http';\nimport { prepareOutgoingHttpHeaders } from '../util/prepare-outgoing-http-headers';\nimport { writeToServerResponse } from '../util/write-to-server-response';\nimport { createDataStream } from './create-data-stream';\nimport { DataStreamWriter } from './data-stream-writer';\n\nexport function pipeDataStreamToResponse(\n  response: ServerResponse,\n  {\n    status,\n    statusText,\n    headers,\n    execute,\n    onError,\n  }: ResponseInit & {\n    execute: (writer: DataStreamWriter) => Promise<void> | void;\n    onError?: (error: unknown) => string;\n  },\n): void {\n  writeToServerResponse({\n    response,\n    status,\n    statusText,\n    headers: prepareOutgoingHttpHeaders(headers, {\n      contentType: 'text/plain; charset=utf-8',\n      dataStreamVersion: 'v1',\n    }),\n    stream: createDataStream({ execute, onError }).pipeThrough(\n      new TextEncoderStream(),\n    ),\n  });\n}\n","import { AISDKError } from '@ai-sdk/provider';\n\nconst name = 'AI_InvalidArgumentError';\nconst marker = `vercel.ai.error.${name}`;\nconst symbol = Symbol.for(marker);\n\nexport class InvalidArgumentError extends AISDKError {\n  private readonly [symbol] = true; // used in isInstance\n\n  readonly parameter: string;\n  readonly value: unknown;\n\n  constructor({\n    parameter,\n    value,\n    message,\n  }: {\n    parameter: string;\n    value: unknown;\n    message: string;\n  }) {\n    super({\n      name,\n      message: `Invalid argument for parameter ${parameter}: ${message}`,\n    });\n\n    this.parameter = parameter;\n    this.value = value;\n  }\n\n  static isInstance(error: unknown): error is InvalidArgumentError {\n    return AISDKError.hasMarker(error, marker);\n  }\n}\n","import { APICallError } from '@ai-sdk/provider';\nimport { delay, getErrorMessage, isAbortError } from '@ai-sdk/provider-utils';\nimport { RetryError } from './retry-error';\n\nexport type RetryFunction = <OUTPUT>(\n  fn: () => PromiseLike<OUTPUT>,\n) => PromiseLike<OUTPUT>;\n\n/**\nThe `retryWithExponentialBackoff` strategy retries a failed API call with an exponential backoff.\nYou can configure the maximum number of retries, the initial delay, and the backoff factor.\n */\nexport const retryWithExponentialBackoff =\n  ({\n    maxRetries = 2,\n    initialDelayInMs = 2000,\n    backoffFactor = 2,\n  } = {}): RetryFunction =>\n  async <OUTPUT>(f: () => PromiseLike<OUTPUT>) =>\n    _retryWithExponentialBackoff(f, {\n      maxRetries,\n      delayInMs: initialDelayInMs,\n      backoffFactor,\n    });\n\nasync function _retryWithExponentialBackoff<OUTPUT>(\n  f: () => PromiseLike<OUTPUT>,\n  {\n    maxRetries,\n    delayInMs,\n    backoffFactor,\n  }: { maxRetries: number; delayInMs: number; backoffFactor: number },\n  errors: unknown[] = [],\n): Promise<OUTPUT> {\n  try {\n    return await f();\n  } catch (error) {\n    if (isAbortError(error)) {\n      throw error; // don't retry when the request was aborted\n    }\n\n    if (maxRetries === 0) {\n      throw error; // don't wrap the error when retries are disabled\n    }\n\n    const errorMessage = getErrorMessage(error);\n    const newErrors = [...errors, error];\n    const tryNumber = newErrors.length;\n\n    if (tryNumber > maxRetries) {\n      throw new RetryError({\n        message: `Failed after ${tryNumber} attempts. Last error: ${errorMessage}`,\n        reason: 'maxRetriesExceeded',\n        errors: newErrors,\n      });\n    }\n\n    if (\n      error instanceof Error &&\n      APICallError.isInstance(error) &&\n      error.isRetryable === true &&\n      tryNumber <= maxRetries\n    ) {\n      await delay(delayInMs);\n      return _retryWithExponentialBackoff(\n        f,\n        { maxRetries, delayInMs: backoffFactor * delayInMs, backoffFactor },\n        newErrors,\n      );\n    }\n\n    if (tryNumber === 1) {\n      throw error; // don't wrap the error when a non-retryable error occurs on the first try\n    }\n\n    throw new RetryError({\n      message: `Failed after ${tryNumber} attempts with non-retryable error: '${errorMessage}'`,\n      reason: 'errorNotRetryable',\n      errors: newErrors,\n    });\n  }\n}\n","import { AISDKError } from '@ai-sdk/provider';\n\nconst name = 'AI_RetryError';\nconst marker = `vercel.ai.error.${name}`;\nconst symbol = Symbol.for(marker);\n\nexport type RetryErrorReason =\n  | 'maxRetriesExceeded'\n  | 'errorNotRetryable'\n  | 'abort';\n\nexport class RetryError extends AISDKError {\n  private readonly [symbol] = true; // used in isInstance\n\n  // note: property order determines debugging output\n  readonly reason: RetryErrorReason;\n  readonly lastError: unknown;\n  readonly errors: Array<unknown>;\n\n  constructor({\n    message,\n    reason,\n    errors,\n  }: {\n    message: string;\n    reason: RetryErrorReason;\n    errors: Array<unknown>;\n  }) {\n    super({ name, message });\n\n    this.reason = reason;\n    this.errors = errors;\n\n    // separate our last error to make debugging via log easier:\n    this.lastError = errors[errors.length - 1];\n  }\n\n  static isInstance(error: unknown): error is RetryError {\n    return AISDKError.hasMarker(error, marker);\n  }\n}\n","import { InvalidArgumentError } from '../../errors/invalid-argument-error';\nimport {\n  RetryFunction,\n  retryWithExponentialBackoff,\n} from '../../util/retry-with-exponential-backoff';\n\n/**\n * Validate and prepare retries.\n */\nexport function prepareRetries({\n  maxRetries,\n}: {\n  maxRetries: number | undefined;\n}): {\n  maxRetries: number;\n  retry: RetryFunction;\n} {\n  if (maxRetries != null) {\n    if (!Number.isInteger(maxRetries)) {\n      throw new InvalidArgumentError({\n        parameter: 'maxRetries',\n        value: maxRetries,\n        message: 'maxRetries must be an integer',\n      });\n    }\n\n    if (maxRetries < 0) {\n      throw new InvalidArgumentError({\n        parameter: 'maxRetries',\n        value: maxRetries,\n        message: 'maxRetries must be >= 0',\n      });\n    }\n  }\n\n  const maxRetriesResult = maxRetries ?? 2;\n\n  return {\n    maxRetries: maxRetriesResult,\n    retry: retryWithExponentialBackoff({ maxRetries: maxRetriesResult }),\n  };\n}\n","import { TelemetrySettings } from './telemetry-settings';\n\nexport function assembleOperationName({\n  operationId,\n  telemetry,\n}: {\n  operationId: string;\n  telemetry?: TelemetrySettings;\n}) {\n  return {\n    // standardized operation and resource name:\n    'operation.name': `${operationId}${\n      telemetry?.functionId != null ? ` ${telemetry.functionId}` : ''\n    }`,\n    'resource.name': telemetry?.functionId,\n\n    // detailed, AI SDK specific data:\n    'ai.operationId': operationId,\n    'ai.telemetry.functionId': telemetry?.functionId,\n  };\n}\n","import { Attributes } from '@opentelemetry/api';\nimport { CallSettings } from '../prompt/call-settings';\nimport { TelemetrySettings } from './telemetry-settings';\n\nexport function getBaseTelemetryAttributes({\n  model,\n  settings,\n  telemetry,\n  headers,\n}: {\n  model: { modelId: string; provider: string };\n  settings: Omit<CallSettings, 'abortSignal' | 'headers'>;\n  telemetry: TelemetrySettings | undefined;\n  headers: Record<string, string | undefined> | undefined;\n}): Attributes {\n  return {\n    'ai.model.provider': model.provider,\n    'ai.model.id': model.modelId,\n\n    // settings:\n    ...Object.entries(settings).reduce((attributes, [key, value]) => {\n      attributes[`ai.settings.${key}`] = value;\n      return attributes;\n    }, {} as Attributes),\n\n    // add metadata as attributes:\n    ...Object.entries(telemetry?.metadata ?? {}).reduce(\n      (attributes, [key, value]) => {\n        attributes[`ai.telemetry.metadata.${key}`] = value;\n        return attributes;\n      },\n      {} as Attributes,\n    ),\n\n    // request headers\n    ...Object.entries(headers ?? {}).reduce((attributes, [key, value]) => {\n      if (value !== undefined) {\n        attributes[`ai.request.headers.${key}`] = value;\n      }\n      return attributes;\n    }, {} as Attributes),\n  };\n}\n","import { Tracer, trace } from '@opentelemetry/api';\nimport { noopTracer } from './noop-tracer';\n\nexport function getTracer({\n  isEnabled = false,\n  tracer,\n}: {\n  isEnabled?: boolean;\n  tracer?: Tracer;\n} = {}): Tracer {\n  if (!isEnabled) {\n    return noopTracer;\n  }\n\n  if (tracer) {\n    return tracer;\n  }\n\n  return trace.getTracer('ai');\n}\n","import { Span, SpanContext, Tracer } from '@opentelemetry/api';\n\n/**\n * Tracer implementation that does nothing (null object).\n */\nexport const noopTracer: Tracer = {\n  startSpan(): Span {\n    return noopSpan;\n  },\n\n  startActiveSpan<F extends (span: Span) => unknown>(\n    name: unknown,\n    arg1: unknown,\n    arg2?: unknown,\n    arg3?: F,\n  ): ReturnType<any> {\n    if (typeof arg1 === 'function') {\n      return arg1(noopSpan);\n    }\n    if (typeof arg2 === 'function') {\n      return arg2(noopSpan);\n    }\n    if (typeof arg3 === 'function') {\n      return arg3(noopSpan);\n    }\n  },\n};\n\nconst noopSpan: Span = {\n  spanContext() {\n    return noopSpanContext;\n  },\n  setAttribute() {\n    return this;\n  },\n  setAttributes() {\n    return this;\n  },\n  addEvent() {\n    return this;\n  },\n  addLink() {\n    return this;\n  },\n  addLinks() {\n    return this;\n  },\n  setStatus() {\n    return this;\n  },\n  updateName() {\n    return this;\n  },\n  end() {\n    return this;\n  },\n  isRecording() {\n    return false;\n  },\n  recordException() {\n    return this;\n  },\n};\n\nconst noopSpanContext: SpanContext = {\n  traceId: '',\n  spanId: '',\n  traceFlags: 0,\n};\n","import { Attributes, Span, Tracer, SpanStatusCode } from '@opentelemetry/api';\n\nexport function recordSpan<T>({\n  name,\n  tracer,\n  attributes,\n  fn,\n  endWhenDone = true,\n}: {\n  name: string;\n  tracer: Tracer;\n  attributes: Attributes;\n  fn: (span: Span) => Promise<T>;\n  endWhenDone?: boolean;\n}) {\n  return tracer.startActiveSpan(name, { attributes }, async span => {\n    try {\n      const result = await fn(span);\n\n      if (endWhenDone) {\n        span.end();\n      }\n\n      return result;\n    } catch (error) {\n      try {\n        if (error instanceof Error) {\n          span.recordException({\n            name: error.name,\n            message: error.message,\n            stack: error.stack,\n          });\n          span.setStatus({\n            code: SpanStatusCode.ERROR,\n            message: error.message,\n          });\n        } else {\n          span.setStatus({ code: SpanStatusCode.ERROR });\n        }\n      } finally {\n        // always stop the span when there is an error:\n        span.end();\n      }\n\n      throw error;\n    }\n  });\n}\n","import type { Attributes, AttributeValue } from '@opentelemetry/api';\nimport type { TelemetrySettings } from './telemetry-settings';\n\nexport function selectTelemetryAttributes({\n  telemetry,\n  attributes,\n}: {\n  telemetry?: TelemetrySettings;\n  attributes: {\n    [attributeKey: string]:\n      | AttributeValue\n      | { input: () => AttributeValue | undefined }\n      | { output: () => AttributeValue | undefined }\n      | undefined;\n  };\n}): Attributes {\n  // when telemetry is disabled, return an empty object to avoid serialization overhead:\n  if (telemetry?.isEnabled !== true) {\n    return {};\n  }\n\n  return Object.entries(attributes).reduce((attributes, [key, value]) => {\n    if (value === undefined) {\n      return attributes;\n    }\n\n    // input value, check if it should be recorded:\n    if (\n      typeof value === 'object' &&\n      'input' in value &&\n      typeof value.input === 'function'\n    ) {\n      // default to true:\n      if (telemetry?.recordInputs === false) {\n        return attributes;\n      }\n\n      const result = value.input();\n\n      return result === undefined\n        ? attributes\n        : { ...attributes, [key]: result };\n    }\n\n    // output value, check if it should be recorded:\n    if (\n      typeof value === 'object' &&\n      'output' in value &&\n      typeof value.output === 'function'\n    ) {\n      // default to true:\n      if (telemetry?.recordOutputs === false) {\n        return attributes;\n      }\n\n      const result = value.output();\n\n      return result === undefined\n        ? attributes\n        : { ...attributes, [key]: result };\n    }\n\n    // value is an attribute value already:\n    return { ...attributes, [key]: value };\n  }, {});\n}\n","import { prepareRetries } from '../prompt/prepare-retries';\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 { TelemetrySettings } from '../telemetry/telemetry-settings';\nimport { EmbeddingModel } from '../types';\nimport { EmbedResult } from './embed-result';\n\n/**\nEmbed a value using an embedding model. The type of the value is defined by the embedding model.\n\n@param model - The embedding model to use.\n@param value - The value that should be embedded.\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 headers - Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.\n\n@returns A result object that contains the embedding, the value, and additional information.\n */\nexport async function embed<VALUE>({\n  model,\n  value,\n  maxRetries: maxRetriesArg,\n  abortSignal,\n  headers,\n  experimental_telemetry: telemetry,\n}: {\n  /**\nThe embedding model to use.\n     */\n  model: EmbeddingModel<VALUE>;\n\n  /**\nThe value that should be embedded.\n   */\n  value: VALUE;\n\n  /**\nMaximum number of retries per embedding model call. Set to 0 to disable retries.\n\n@default 2\n   */\n  maxRetries?: number;\n\n  /**\nAbort signal.\n */\n  abortSignal?: AbortSignal;\n\n  /**\nAdditional headers to include in the request.\nOnly applicable for HTTP-based providers.\n */\n  headers?: Record<string, string>;\n\n  /**\n   * Optional telemetry configuration (experimental).\n   */\n  experimental_telemetry?: TelemetrySettings;\n}): Promise<EmbedResult<VALUE>> {\n  const { maxRetries, retry } = prepareRetries({ maxRetries: maxRetriesArg });\n\n  const baseTelemetryAttributes = getBaseTelemetryAttributes({\n    model,\n    telemetry,\n    headers,\n    settings: { maxRetries },\n  });\n\n  const tracer = getTracer(telemetry);\n\n  return recordSpan({\n    name: 'ai.embed',\n    attributes: selectTelemetryAttributes({\n      telemetry,\n      attributes: {\n        ...assembleOperationName({ operationId: 'ai.embed', telemetry }),\n        ...baseTelemetryAttributes,\n        'ai.value': { input: () => JSON.stringify(value) },\n      },\n    }),\n    tracer,\n    fn: async span => {\n      const { embedding, usage, rawResponse } = await retry(() =>\n        // nested spans to align with the embedMany telemetry data:\n        recordSpan({\n          name: 'ai.embed.doEmbed',\n          attributes: selectTelemetryAttributes({\n            telemetry,\n            attributes: {\n              ...assembleOperationName({\n                operationId: 'ai.embed.doEmbed',\n                telemetry,\n              }),\n              ...baseTelemetryAttributes,\n              // specific settings that only make sense on the outer level:\n              'ai.values': { input: () => [JSON.stringify(value)] },\n            },\n          }),\n          tracer,\n          fn: async doEmbedSpan => {\n            const modelResponse = await model.doEmbed({\n              values: [value],\n              abortSignal,\n              headers,\n            });\n\n            const embedding = modelResponse.embeddings[0];\n            const usage = modelResponse.usage ?? { tokens: NaN };\n\n            doEmbedSpan.setAttributes(\n              selectTelemetryAttributes({\n                telemetry,\n                attributes: {\n                  'ai.embeddings': {\n                    output: () =>\n                      modelResponse.embeddings.map(embedding =>\n                        JSON.stringify(embedding),\n                      ),\n                  },\n                  'ai.usage.tokens': usage.tokens,\n                },\n              }),\n            );\n\n            return {\n              embedding,\n              usage,\n              rawResponse: modelResponse.rawResponse,\n            };\n          },\n        }),\n      );\n\n      span.setAttributes(\n        selectTelemetryAttributes({\n          telemetry,\n          attributes: {\n            'ai.embedding': { output: () => JSON.stringify(embedding) },\n            'ai.usage.tokens': usage.tokens,\n          },\n        }),\n      );\n\n      return new DefaultEmbedResult({ value, embedding, usage, rawResponse });\n    },\n  });\n}\n\nclass DefaultEmbedResult<VALUE> implements EmbedResult<VALUE> {\n  readonly value: EmbedResult<VALUE>['value'];\n  readonly embedding: EmbedResult<VALUE>['embedding'];\n  readonly usage: EmbedResult<VALUE>['usage'];\n  readonly rawResponse: EmbedResult<VALUE>['rawResponse'];\n\n  constructor(options: {\n    value: EmbedResult<VALUE>['value'];\n    embedding: EmbedResult<VALUE>['embedding'];\n    usage: EmbedResult<VALUE>['usage'];\n    rawResponse?: EmbedResult<VALUE>['rawResponse'];\n  }) {\n    this.value = options.value;\n    this.embedding = options.embedding;\n    this.usage = options.usage;\n    this.rawResponse = options.rawResponse;\n  }\n}\n","/**\n * Splits an array into chunks of a specified size.\n *\n * @template T - The type of elements in the array.\n * @param {T[]} array - The array to split.\n * @param {number} chunkSize - The size of each chunk.\n * @returns {T[][]} - A new array containing the chunks.\n */\nexport function splitArray<T>(array: T[], chunkSize: number): T[][] {\n  if (chunkSize <= 0) {\n    throw new Error('chunkSize must be greater than 0');\n  }\n\n  const result = [];\n  for (let i = 0; i < array.length; i += chunkSize) {\n    result.push(array.slice(i, i + chunkSize));\n  }\n\n  return result;\n}\n","import { prepareRetries } from '../prompt/prepare-retries';\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 { TelemetrySettings } from '../telemetry/telemetry-settings';\nimport { Embedding, EmbeddingModel } from '../types';\nimport { splitArray } from '../util/split-array';\nimport { EmbedManyResult } from './embed-many-result';\n\n/**\nEmbed several values using an embedding model. The type of the value is defined\nby the embedding model.\n\n`embedMany` automatically splits large requests into smaller chunks if the model\nhas a limit on how many embeddings can be generated in a single call.\n\n@param model - The embedding model to use.\n@param values - The values that should be embedded.\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 headers - Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.\n\n@returns A result object that contains the embeddings, the value, and additional information.\n */\nexport async function embedMany<VALUE>({\n  model,\n  values,\n  maxRetries: maxRetriesArg,\n  abortSignal,\n  headers,\n  experimental_telemetry: telemetry,\n}: {\n  /**\nThe embedding model to use.\n     */\n  model: EmbeddingModel<VALUE>;\n\n  /**\nThe values that should be embedded.\n   */\n  values: Array<VALUE>;\n\n  /**\nMaximum number of retries per embedding model call. Set to 0 to disable retries.\n\n@default 2\n   */\n  maxRetries?: number;\n\n  /**\nAbort signal.\n */\n  abortSignal?: AbortSignal;\n\n  /**\nAdditional headers to include in the request.\nOnly applicable for HTTP-based providers.\n */\n  headers?: Record<string, string>;\n\n  /**\n   * Optional telemetry configuration (experimental).\n   */\n  experimental_telemetry?: TelemetrySettings;\n}): Promise<EmbedManyResult<VALUE>> {\n  const { maxRetries, retry } = prepareRetries({ maxRetries: maxRetriesArg });\n\n  const baseTelemetryAttributes = getBaseTelemetryAttributes({\n    model,\n    telemetry,\n    headers,\n    settings: { maxRetries },\n  });\n\n  const tracer = getTracer(telemetry);\n\n  return recordSpan({\n    name: 'ai.embedMany',\n    attributes: selectTelemetryAttributes({\n      telemetry,\n      attributes: {\n        ...assembleOperationName({ operationId: 'ai.embedMany', telemetry }),\n        ...baseTelemetryAttributes,\n        // specific settings that only make sense on the outer level:\n        'ai.values': {\n          input: () => values.map(value => JSON.stringify(value)),\n        },\n      },\n    }),\n    tracer,\n    fn: async span => {\n      const maxEmbeddingsPerCall = model.maxEmbeddingsPerCall;\n\n      // the model has not specified limits on\n      // how many embeddings can be generated in a single call\n      if (maxEmbeddingsPerCall == null) {\n        const { embeddings, usage } = await retry(() => {\n          // nested spans to align with the embedMany telemetry data:\n          return recordSpan({\n            name: 'ai.embedMany.doEmbed',\n            attributes: selectTelemetryAttributes({\n              telemetry,\n              attributes: {\n                ...assembleOperationName({\n                  operationId: 'ai.embedMany.doEmbed',\n                  telemetry,\n                }),\n                ...baseTelemetryAttributes,\n                // specific settings that only make sense on the outer level:\n                'ai.values': {\n                  input: () => values.map(value => JSON.stringify(value)),\n                },\n              },\n            }),\n            tracer,\n            fn: async doEmbedSpan => {\n              const modelResponse = await model.doEmbed({\n                values,\n                abortSignal,\n                headers,\n              });\n\n              const embeddings = modelResponse.embeddings;\n              const usage = modelResponse.usage ?? { tokens: NaN };\n\n              doEmbedSpan.setAttributes(\n                selectTelemetryAttributes({\n                  telemetry,\n                  attributes: {\n                    'ai.embeddings': {\n                      output: () =>\n                        embeddings.map(embedding => JSON.stringify(embedding)),\n                    },\n                    'ai.usage.tokens': usage.tokens,\n                  },\n                }),\n              );\n\n              return { embeddings, usage };\n            },\n          });\n        });\n\n        span.setAttributes(\n          selectTelemetryAttributes({\n            telemetry,\n            attributes: {\n              'ai.embeddings': {\n                output: () =>\n                  embeddings.map(embedding => JSON.stringify(embedding)),\n              },\n              'ai.usage.tokens': usage.tokens,\n            },\n          }),\n        );\n\n        return new DefaultEmbedManyResult({ values, embeddings, usage });\n      }\n\n      // split the values into chunks that are small enough for the model:\n      const valueChunks = splitArray(values, maxEmbeddingsPerCall);\n\n      // serially embed the chunks:\n      const embeddings: Array<Embedding> = [];\n      let tokens = 0;\n\n      for (const chunk of valueChunks) {\n        const { embeddings: responseEmbeddings, usage } = await retry(() => {\n          // nested spans to align with the embedMany telemetry data:\n          return recordSpan({\n            name: 'ai.embedMany.doEmbed',\n            attributes: selectTelemetryAttributes({\n              telemetry,\n              attributes: {\n                ...assembleOperationName({\n                  operationId: 'ai.embedMany.doEmbed',\n                  telemetry,\n                }),\n                ...baseTelemetryAttributes,\n                // specific settings that only make sense on the outer level:\n                'ai.values': {\n                  input: () => chunk.map(value => JSON.stringify(value)),\n                },\n              },\n            }),\n            tracer,\n            fn: async doEmbedSpan => {\n              const modelResponse = await model.doEmbed({\n                values: chunk,\n                abortSignal,\n                headers,\n              });\n\n              const embeddings = modelResponse.embeddings;\n              const usage = modelResponse.usage ?? { tokens: NaN };\n\n              doEmbedSpan.setAttributes(\n                selectTelemetryAttributes({\n                  telemetry,\n                  attributes: {\n                    'ai.embeddings': {\n                      output: () =>\n                        embeddings.map(embedding => JSON.stringify(embedding)),\n                    },\n                    'ai.usage.tokens': usage.tokens,\n                  },\n                }),\n              );\n\n              return { embeddings, usage };\n            },\n          });\n        });\n\n        embeddings.push(...responseEmbeddings);\n        tokens += usage.tokens;\n      }\n\n      span.setAttributes(\n        selectTelemetryAttributes({\n          telemetry,\n          attributes: {\n            'ai.embeddings': {\n              output: () =>\n                embeddings.map(embedding => JSON.stringify(embedding)),\n            },\n            'ai.usage.tokens': tokens,\n          },\n        }),\n      );\n\n      return new DefaultEmbedManyResult({\n        values,\n        embeddings,\n        usage: { tokens },\n      });\n    },\n  });\n}\n\nclass DefaultEmbedManyResult<VALUE> implements EmbedManyResult<VALUE> {\n  readonly values: EmbedManyResult<VALUE>['values'];\n  readonly embeddings: EmbedManyResult<VALUE>['embeddings'];\n  readonly usage: EmbedManyResult<VALUE>['usage'];\n\n  constructor(options: {\n    values: EmbedManyResult<VALUE>['values'];\n    embeddings: EmbedManyResult<VALUE>['embeddings'];\n    usage: EmbedManyResult<VALUE>['usage'];\n  }) {\n    this.values = options.values;\n    this.embeddings = options.embeddings;\n    this.usage = options.usage;\n  }\n}\n","import { AISDKError } from '@ai-sdk/provider';\nimport { ImageModelResponseMetadata } from '../core/types/image-model-response-metadata';\n\nconst name = 'AI_NoImageGeneratedError';\nconst marker = `vercel.ai.error.${name}`;\nconst symbol = Symbol.for(marker);\n\n/**\nThrown when no image could be generated. This can have multiple causes:\n\n- The model failed to generate a response.\n- The model generated a response that could not be parsed.\n */\nexport class NoImageGeneratedError extends AISDKError {\n  private readonly [symbol] = true; // used in isInstance\n\n  /**\nThe response metadata for each call.\n   */\n  readonly responses: Array<ImageModelResponseMetadata> | undefined;\n\n  constructor({\n    message = 'No image generated.',\n    cause,\n    responses,\n  }: {\n    message?: string;\n    cause?: Error;\n    responses?: Array<ImageModelResponseMetadata>;\n  }) {\n    super({ name, message, cause });\n\n    this.responses = responses;\n  }\n\n  static isInstance(error: unknown): error is NoImageGeneratedError {\n    return AISDKError.hasMarker(error, marker);\n  }\n}\n","import {\n  convertBase64ToUint8Array,\n  convertUint8ArrayToBase64,\n} from '@ai-sdk/provider-utils';\n\n/**\n * A generated file.\n */\nexport interface GeneratedFile {\n  /**\nFile as a base64 encoded string.\n     */\n  readonly base64: string;\n\n  /**\nFile as a Uint8Array.\n     */\n  readonly uint8Array: Uint8Array;\n\n  /**\nMIME type of the file\n   */\n  readonly mimeType: string;\n}\n\nexport class DefaultGeneratedFile implements GeneratedFile {\n  private base64Data: string | undefined;\n  private uint8ArrayData: Uint8Array | undefined;\n\n  readonly mimeType: string;\n\n  constructor({\n    data,\n    mimeType,\n  }: {\n    data: string | Uint8Array;\n    mimeType: string;\n  }) {\n    const isUint8Array = data instanceof Uint8Array;\n    this.base64Data = isUint8Array ? undefined : data;\n    this.uint8ArrayData = isUint8Array ? data : undefined;\n    this.mimeType = mimeType;\n  }\n\n  // lazy conversion with caching to avoid unnecessary conversion overhead:\n  get base64() {\n    if (this.base64Data == null) {\n      this.base64Data = convertUint8ArrayToBase64(this.uint8ArrayData!);\n    }\n    return this.base64Data;\n  }\n\n  // lazy conversion with caching to avoid unnecessary conversion overhead:\n  get uint8Array() {\n    if (this.uint8ArrayData == null) {\n      this.uint8ArrayData = convertBase64ToUint8Array(this.base64Data!);\n    }\n    return this.uint8ArrayData;\n  }\n}\n\nexport class DefaultGeneratedFileWithType extends DefaultGeneratedFile {\n  readonly type = 'file';\n\n  constructor(options: { data: string | Uint8Array; mimeType: string }) {\n    super(options);\n  }\n}\n","import { convertBase64ToUint8Array } from '@ai-sdk/provider-utils';\n\nexport const imageMimeTypeSignatures = [\n  {\n    mimeType: 'image/gif' as const,\n    bytesPrefix: [0x47, 0x49, 0x46],\n    base64Prefix: 'R0lG',\n  },\n  {\n    mimeType: 'image/png' as const,\n    bytesPrefix: [0x89, 0x50, 0x4e, 0x47],\n    base64Prefix: 'iVBORw',\n  },\n  {\n    mimeType: 'image/jpeg' as const,\n    bytesPrefix: [0xff, 0xd8],\n    base64Prefix: '/9j/',\n  },\n  {\n    mimeType: 'image/webp' as const,\n    bytesPrefix: [0x52, 0x49, 0x46, 0x46],\n    base64Prefix: 'UklGRg',\n  },\n  {\n    mimeType: 'image/bmp' as const,\n    bytesPrefix: [0x42, 0x4d],\n    base64Prefix: 'Qk',\n  },\n  {\n    mimeType: 'image/tiff' as const,\n    bytesPrefix: [0x49, 0x49, 0x2a, 0x00],\n    base64Prefix: 'SUkqAA',\n  },\n  {\n    mimeType: 'image/tiff' as const,\n    bytesPrefix: [0x4d, 0x4d, 0x00, 0x2a],\n    base64Prefix: 'TU0AKg',\n  },\n  {\n    mimeType: 'image/avif' as const,\n    bytesPrefix: [\n      0x00, 0x00, 0x00, 0x20, 0x66, 0x74, 0x79, 0x70, 0x61, 0x76, 0x69, 0x66,\n    ],\n    base64Prefix: 'AAAAIGZ0eXBhdmlm',\n  },\n  {\n    mimeType: 'image/heic' as const,\n    bytesPrefix: [\n      0x00, 0x00, 0x00, 0x20, 0x66, 0x74, 0x79, 0x70, 0x68, 0x65, 0x69, 0x63,\n    ],\n    base64Prefix: 'AAAAIGZ0eXBoZWlj',\n  },\n] as const;\n\nexport const audioMimeTypeSignatures = [\n  {\n    mimeType: 'audio/mpeg' as const,\n    bytesPrefix: [0xff, 0xfb],\n    base64Prefix: '//s=',\n  },\n  {\n    mimeType: 'audio/wav' as const,\n    bytesPrefix: [0x52, 0x49, 0x46, 0x46],\n    base64Prefix: 'UklGR',\n  },\n  {\n    mimeType: 'audio/ogg' as const,\n    bytesPrefix: [0x4f, 0x67, 0x67, 0x53],\n    base64Prefix: 'T2dnUw',\n  },\n  {\n    mimeType: 'audio/flac' as const,\n    bytesPrefix: [0x66, 0x4c, 0x61, 0x43],\n    base64Prefix: 'ZkxhQw',\n  },\n  {\n    mimeType: 'audio/aac' as const,\n    bytesPrefix: [0x40, 0x15, 0x00, 0x00],\n    base64Prefix: 'QBUA',\n  },\n  {\n    mimeType: 'audio/mp4' as const,\n    bytesPrefix: [0x66, 0x74, 0x79, 0x70],\n    base64Prefix: 'ZnR5cA',\n  },\n] as const;\n\nconst stripID3 = (data: Uint8Array | string) => {\n  const bytes =\n    typeof data === 'string' ? convertBase64ToUint8Array(data) : data;\n  const id3Size =\n    ((bytes[6] & 0x7f) << 21) |\n    ((bytes[7] & 0x7f) << 14) |\n    ((bytes[8] & 0x7f) << 7) |\n    (bytes[9] & 0x7f);\n\n  // The raw MP3 starts here\n  return bytes.slice(id3Size + 10);\n};\n\nfunction stripID3TagsIfPresent(data: Uint8Array | string): Uint8Array | string {\n  const hasId3 =\n    (typeof data === 'string' && data.startsWith('SUQz')) ||\n    (typeof data !== 'string' &&\n      data.length > 10 &&\n      data[0] === 0x49 && // 'I'\n      data[1] === 0x44 && // 'D'\n      data[2] === 0x33); // '3'\n\n  return hasId3 ? stripID3(data) : data;\n}\n\nexport function detectMimeType({\n  data,\n  signatures,\n}: {\n  data: Uint8Array | string;\n  signatures: typeof audioMimeTypeSignatures | typeof imageMimeTypeSignatures;\n}): (typeof signatures)[number]['mimeType'] | undefined {\n  const processedData = stripID3TagsIfPresent(data);\n\n  for (const signature of signatures) {\n    if (\n      typeof processedData === 'string'\n        ? processedData.startsWith(signature.base64Prefix)\n        : processedData.length >= signature.bytesPrefix.length &&\n          signature.bytesPrefix.every(\n            (byte, index) => processedData[index] === byte,\n          )\n    ) {\n      return signature.mimeType;\n    }\n  }\n\n  return undefined;\n}\n","import { AISDKError, ImageModelV1, JSONValue } from '@ai-sdk/provider';\nimport { NoImageGeneratedError } from '../../errors/no-image-generated-error';\nimport {\n  DefaultGeneratedFile,\n  GeneratedFile,\n} from '../generate-text/generated-file';\nimport { prepareRetries } from '../prompt/prepare-retries';\nimport { ImageGenerationWarning } from '../types/image-model';\nimport { ImageModelResponseMetadata } from '../types/image-model-response-metadata';\nimport { GenerateImageResult } from './generate-image-result';\nimport {\n  detectMimeType,\n  imageMimeTypeSignatures,\n} from '../util/detect-mimetype';\n\n/**\nGenerates images using an image model.\n\n@param model - The image model to use.\n@param prompt - The prompt that should be used to generate the image.\n@param n - Number of images to generate. Default: 1.\n@param size - Size of the images to generate. Must have the format `{width}x{height}`.\n@param aspectRatio - Aspect ratio of the images to generate. Must have the format `{width}:{height}`.\n@param seed - Seed for the image generation.\n@param providerOptions - Additional provider-specific options that are passed through to the provider\nas body parameters.\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 headers - Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.\n\n@returns A result object that contains the generated images.\n */\nexport async function generateImage({\n  model,\n  prompt,\n  n = 1,\n  size,\n  aspectRatio,\n  seed,\n  providerOptions,\n  maxRetries: maxRetriesArg,\n  abortSignal,\n  headers,\n}: {\n  /**\nThe image model to use.\n     */\n  model: ImageModelV1;\n\n  /**\nThe prompt that should be used to generate the image.\n   */\n  prompt: string;\n\n  /**\nNumber of images to generate.\n   */\n  n?: number;\n\n  /**\nSize of the images to generate. Must have the format `{width}x{height}`. If not provided, the default size will be used.\n   */\n  size?: `${number}x${number}`;\n\n  /**\nAspect ratio of the images to generate. Must have the format `{width}:{height}`. If not provided, the default aspect ratio will be used.\n   */\n  aspectRatio?: `${number}:${number}`;\n\n  /**\nSeed for the image generation. If not provided, the default seed will be used.\n   */\n  seed?: number;\n\n  /**\nAdditional provider-specific options that are passed through to the provider\nas body parameters.\n\nThe outer record is keyed by the provider name, and the inner\nrecord is keyed by the provider-specific metadata key.\n```ts\n{\n  \"openai\": {\n    \"style\": \"vivid\"\n  }\n}\n```\n     */\n  providerOptions?: Record<string, Record<string, JSONValue>>;\n\n  /**\nMaximum number of retries per embedding model call. Set to 0 to disable retries.\n\n@default 2\n   */\n  maxRetries?: number;\n\n  /**\nAbort signal.\n */\n  abortSignal?: AbortSignal;\n\n  /**\nAdditional headers to include in the request.\nOnly applicable for HTTP-based providers.\n */\n  headers?: Record<string, string>;\n}): Promise<GenerateImageResult> {\n  const { retry } = prepareRetries({ maxRetries: maxRetriesArg });\n\n  // default to 1 if the model has not specified limits on\n  // how many images can be generated in a single call\n  const maxImagesPerCall = model.maxImagesPerCall ?? 1;\n\n  // parallelize calls to the model:\n  const callCount = Math.ceil(n / maxImagesPerCall);\n  const callImageCounts = Array.from({ length: callCount }, (_, i) => {\n    if (i < callCount - 1) {\n      return maxImagesPerCall;\n    }\n\n    const remainder = n % maxImagesPerCall;\n    return remainder === 0 ? maxImagesPerCall : remainder;\n  });\n  const results = await Promise.all(\n    callImageCounts.map(async callImageCount =>\n      retry(() =>\n        model.doGenerate({\n          prompt,\n          n: callImageCount,\n          abortSignal,\n          headers,\n          size,\n          aspectRatio,\n          seed,\n          providerOptions: providerOptions ?? {},\n        }),\n      ),\n    ),\n  );\n\n  // collect result images, warnings, and response metadata\n  const images: Array<DefaultGeneratedFile> = [];\n  const warnings: Array<ImageGenerationWarning> = [];\n  const responses: Array<ImageModelResponseMetadata> = [];\n  for (const result of results) {\n    images.push(\n      ...result.images.map(\n        image =>\n          new DefaultGeneratedFile({\n            data: image,\n            mimeType:\n              detectMimeType({\n                data: image,\n                signatures: imageMimeTypeSignatures,\n              }) ?? 'image/png',\n          }),\n      ),\n    );\n    warnings.push(...result.warnings);\n    responses.push(result.response);\n  }\n\n  if (!images.length) {\n    throw new NoImageGeneratedError({ responses });\n  }\n\n  return new DefaultGenerateImageResult({ images, warnings, responses });\n}\n\nclass DefaultGenerateImageResult implements GenerateImageResult {\n  readonly images: Array<GeneratedFile>;\n  readonly warnings: Array<ImageGenerationWarning>;\n  readonly responses: Array<ImageModelResponseMetadata>;\n\n  constructor(options: {\n    images: Array<GeneratedFile>;\n    warnings: Array<ImageGenerationWarning>;\n    responses: Array<ImageModelResponseMetadata>;\n  }) {\n    this.images = options.images;\n    this.warnings = options.warnings;\n    this.responses = options.responses;\n  }\n\n  get image() {\n    return this.images[0];\n  }\n}\n","import {\n  JSONParseError,\n  JSONValue,\n  TypeValidationError,\n} from '@ai-sdk/provider';\nimport { createIdGenerator, safeParseJSON } from '@ai-sdk/provider-utils';\nimport { Schema } from '@ai-sdk/ui-utils';\nimport { z } from 'zod';\nimport { NoObjectGeneratedError } from '../../errors/no-object