ai/dist/index.mjs.map

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

{"version":3,"sources":["../streams/index.ts","../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/delay.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","../core/generate-image/generate-image.ts","../core/generate-object/generate-object.ts","../util/download-error.ts","../util/download.ts","../core/util/detect-image-mimetype.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/message.ts","../core/types/provider-metadata.ts","../core/types/json-value.ts","../core/prompt/content-part.ts","../core/prompt/tool-result-content.ts","../core/prompt/detect-prompt-type.ts","../core/prompt/attachments-to-parts.ts","../core/prompt/message-conversion-error.ts","../core/prompt/convert-to-core-messages.ts","../core/types/usage.ts","../core/generate-object/inject-json-instruction.ts","../core/generate-object/no-object-generated-error.ts","../core/generate-object/output-strategy.ts","../core/util/async-iterable-stream.ts","../core/generate-object/validate-object-generation-input.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/index.ts","../errors/invalid-tool-arguments-error.ts","../errors/no-such-tool-error.ts","../errors/tool-call-repair-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","../core/generate-text/to-response-messages.ts","../core/generate-text/output.ts","../core/generate-text/stream-text.ts","../core/util/merge-streams.ts","../core/generate-text/run-tools-transformation.ts","../core/middleware/wrap-language-model.ts","../core/registry/custom-provider.ts","../core/registry/no-such-provider-error.ts","../core/registry/provider-registry.ts","../core/tool/tool.ts","../core/util/cosine-similarity.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":["// forwarding exports from ui-utils:\nexport {\n  formatAssistantStreamPart,\n  formatDataStreamPart,\n  parseAssistantStreamPart,\n  parseDataStreamPart,\n  processDataStream,\n  processTextStream,\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  IdGenerator,\n  JSONValue,\n  Message,\n  RequestOptions,\n  ToolInvocation,\n  UseAssistantOptions,\n} from '@ai-sdk/ui-utils';\n\nexport { generateId } from '@ai-sdk/provider-utils';\n\nexport * from '../core/index';\nexport * from '../errors/index';\n\nexport * from './assistant-response';\nexport * as LangChainAdapter from './langchain-adapter';\nexport * as LlamaIndexAdapter from './llamaindex-adapter';\nexport * from './stream-data';\n","export { jsonSchema } from '@ai-sdk/ui-utils';\nexport type { DeepPartial, Schema } from '@ai-sdk/ui-utils';\nexport * from './data-stream';\nexport * from './embed';\nexport * from './generate-image';\nexport * from './generate-object';\nexport * from './generate-text';\nexport * from './middleware';\nexport * from './prompt';\nexport * from './registry';\nexport * from './tool';\nexport * from './types';\nexport { cosineSimilarity } from './util/cosine-similarity';\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  try {\n    const result = execute({\n      writeData(data) {\n        controller.enqueue(formatDataStreamPart('data', [data]));\n      },\n      writeMessageAnnotation(annotation) {\n        controller.enqueue(\n          formatDataStreamPart('message_annotations', [annotation]),\n        );\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              controller.enqueue(value);\n            }\n          })().catch(error => {\n            controller.enqueue(formatDataStreamPart('error', onError(error)));\n          }),\n        );\n      },\n      onError,\n    });\n\n    if (result) {\n      ongoingStreamPromises.push(\n        result.catch(error => {\n          controller.enqueue(formatDataStreamPart('error', onError(error)));\n        }),\n      );\n    }\n  } catch (error) {\n    controller!.enqueue(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    controller!.close();\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 { getErrorMessage, isAbortError } from '@ai-sdk/provider-utils';\nimport { delay } from './delay';\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","export async function delay(delayInMs?: number): Promise<void> {\n  return delayInMs === undefined\n    ? Promise.resolve()\n    : new Promise(resolve => setTimeout(resolve, delayInMs));\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 { ImageModelV1, JSONValue } from '@ai-sdk/provider';\nimport { convertBase64ToUint8Array } from '@ai-sdk/provider-utils';\nimport { prepareRetries } from '../prompt/prepare-retries';\nimport { GeneratedImage, GenerateImageResult } from './generate-image-result';\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 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,\n  size,\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}`.\n   */\n  size?: `${number}x${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  const { images } = await retry(() =>\n    model.doGenerate({\n      prompt,\n      n: n ?? 1,\n      abortSignal,\n      headers,\n      size,\n      providerOptions: providerOptions ?? {},\n    }),\n  );\n\n  return new DefaultGenerateImageResult({ base64Images: images });\n}\n\nclass DefaultGenerateImageResult implements GenerateImageResult {\n  readonly images: Array<GeneratedImage>;\n\n  constructor(options: { base64Images: Array<string> }) {\n    this.images = options.base64Images.map(base64 => ({\n      base64,\n      get uint8Array() {\n        return convertBase64ToUint8Array(this.base64);\n      },\n    }));\n  }\n\n  get image() {\n    return this.images[0];\n  }\n}\n","import { JSONValue } from '@ai-sdk/provider';\nimport { createIdGenerator, safeParseJSON } from '@ai-sdk/provider-utils';\nimport { Schema } from '@ai-sdk/ui-utils';\nimport { z } from 'zod';\nimport { CallSettings } from '../prompt/call-settings';\nimport { convertToLanguageModelPrompt } from '../prompt/convert-to-language-model-prompt';\nimport { prepareCallSettings } from '../prompt/prepare-call-settings';\nimport { prepareRetries } from '../prompt/prepare-retries';\nimport { Prompt } from '../prompt/prompt';\nimport { standardizePrompt } from '../prompt/standardize-prompt';\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 {\n  CallWarning,\n  FinishReason,\n  LanguageModel,\n  LogProbs,\n  ProviderMetadata,\n} from '../types';\nimport { LanguageModelRequestMetadata } from '../types/language-model-request-metadata';\nimport { LanguageModelResponseMetadata } from '../types/language-model-response-metadata';\nimport { calculateLanguageModelUsage } from '../types/usage';\nimport { prepareResponseHeaders } from '../util/prepare-response-headers';\nimport { GenerateObjectResult } from './generate-object-result';\nimport { injectJsonInstruction } from './inject-json-instruction';\nimport { NoObjectGeneratedError } from './no-object-generated-error';\nimport { getOutputStrategy } from './output-strategy';\nimport { validateObjectGenerationInput } from './validate-object-generation-input';\n\nconst originalGenerateId = createIdGenerator({ prefix: 'aiobj', size: 24 });\n\n/**\nGenerate a structured, typed object for a given prompt and schema using a language model.\n\nThis function does not stream the output. If you want to stream the output, use `streamObject` instead.\n\n@returns\nA result object that contains the generated object, the finish reason, the token usage, and additional information.\n */\nexport async function generateObject<OBJECT>(\n  options: Omit<CallSettings, 'stopSequences'> &\n    Prompt & {\n      output?: 'object' | undefined;\n\n      /**\nThe language model to use.\n     */\n      model: LanguageModel;\n\n      /**\nThe schema of the object that the model should generate.\n     */\n      schema: z.Schema<OBJECT, z.ZodTypeDef, any> | Schema<OBJECT>;\n\n      /**\nOptional name of the output that should be generated.\nUsed by some providers for additional LLM guidance, e.g.\nvia tool or schema name.\n     */\n      schemaName?: string;\n\n      /**\nOptional description of the output that should be generated.\nUsed by some providers for additional LLM guidance, e.g.\nvia tool or schema description.\n     */\n      schemaDescription?: string;\n\n      /**\nThe mode to use for object generation.\n\nThe schema is converted into a JSON schema and used in one of the following ways\n\n- 'auto': The provider will choose the best mode for the model.\n- 'tool': A tool with the JSON schema as parameters is provided and the provider is instructed to use it.\n- 'json': The JSON schema and an instruction are injected into the prompt. If the provider supports JSON mode, it is enabled. If the provider supports JSON grammars, the grammar is used.\n\nPlease note that most providers do not support all modes.\n\nDefault and recommended: 'auto' (best mode for the model).\n     */\n      mode?: 'auto' | 'json' | 'tool';\n\n      /**\nOptional telemetry configuration (experimental).\n       */\n\n      experimental_telemetry?: TelemetrySettings;\n\n      /**\nAdditional provider-specific metadata. They are passed through\nto the provider from the AI SDK and enable provider-specific\nfunctionality that can be fully encapsulated in the provider.\n */\n      experimental_providerMetadata?: ProviderMetadata;\n\n      /**\n       * Internal. For test use only. May change without notice.\n       */\n      _internal?: {\n        generateId?: () => string;\n        currentDate?: () => Date;\n      };\n    },\n): Promise<GenerateObjectResult<OBJECT>>;\n/**\nGenerate an array with structured, typed elements for a given prompt and element schema using a language model.\n\nThis function does not stream the output. If you want to stream the output, use `streamObject` instead.\n\n@return\nA result object that contains the generated object, the finish reason, the token usage, and additional information.\n */\nexport async function generateObject<ELEMENT>(\n  options: Omit<CallSettings, 'stopSequences'> &\n    Prompt & {\n      output: 'array';\n\n      /**\nThe language model to use.\n     */\n      model: LanguageModel;\n\n      /**\nThe element schema of the array that the model should generate.\n */\n      schema: z.Schema<ELEMENT, z.ZodTypeDef, any> | Schema<ELEMENT>;\n\n      /**\nOptional name of the array that should be generated.\nUsed by some providers for additional LLM guidance, e.g.\nvia tool or schema name.\n     */\n      schemaName?: string;\n\n      /**\nOptional description of the array that should be generated.\nUsed by some providers for additional LLM guidance, e.g.\nvia tool or schema description.\n */\n      schemaDescription?: string;\n\n      /**\nThe mode to use for object generation.\n\nThe schema is converted into a JSON schema and used in one of the following ways\n\n- 'auto': The provider will choose the best mode for the model.\n- 'tool': A tool with the JSON schema as parameters is provided and the provider is instructed to use it.\n- 'json': The JSON schema and an instruction are injected into the prompt. If the provider supports JSON mode, it is enabled. If the provider supports JSON grammars, the grammar is used.\n\nPlease note that most providers do not support all modes.\n\nDefault and recommended: 'auto' (best mode for the model).\n     */\n      mode?: 'auto' | 'json' | 'tool';\n\n      /**\nOptional telemetry configuration (experimental).\n     */\n      experimental_telemetry?: TelemetrySettings;\n\n      /**\nAdditional provider-specific metadata. They are passed through\nto the provider from the AI SDK and enable provider-specific\nfunctionality that can be fully encapsulated in the provider.\n */\n      experimental_providerMetadata?: ProviderMetadata;\n\n      /**\n       * Internal. For test use only. May change without notice.\n       */\n      _internal?: {\n        generateId?: () => string;\n        currentDate?: () => Date;\n      };\n    },\n): Promise<GenerateObjectResult<Array<ELEMENT>>>;\n/**\nGenerate a value from an enum (limited list of string values) using a language model.\n\nThis function does not stream the output.\n\n@return\nA result object that contains the generated value, the finish reason, the token usage, and additional information.\n */\nexport async function generateObject<ENUM extends string>(\n  options: Omit<CallSettings, 'stopSequences'> &\n    Prompt & {\n      output: 'enum';\n\n      /**\nThe language model to use.\n     */\n      model: LanguageModel;\n\n      /**\nThe enum values that the model should use.\n     */\n      enum: Array<ENUM>;\n\n      /**\nThe mode to use for object generation.\n\nThe schema is converted into a JSON schema and used in one of the following ways\n\n- 'auto': The provider will choose the best mode for the model.\n- 'tool': A tool with the JSON schema as parameters is provided and the provider is instructed to use it.\n- 'json': The JSON schema and an instruction are injected into the prompt. If the provider supports JSON mode, it is enabled. If the provider supports JSON grammars, the grammar is used.\n\nPlease note that most providers do not support all modes.\n\nDefault and recommended: 'auto' (best mode for the model).\n     */\n      mode?: 'auto' | 'json' | 'tool';\n\n      /**\nOptional telemetry configuration (experimental).\n     */\n      experimental_telemetry?: TelemetrySettings;\n\n      /**\nAdditional provider-specific metadata. They are passed through\nto the provider from the AI SDK and enable provider-specific\nfunctionality that can be fully encapsulated in the provider.\n */\n      experimental_providerMetadata?: ProviderMetadata;\n\n      /**\n       * Internal. For test use only. May change without notice.\n       */\n      _internal?: {\n        generateId?: () => string;\n        currentDate?: () => Date;\n      };\n    },\n): Promise<GenerateObjectResult<ENUM>>;\n/**\nGenerate JSON with any schema for a given prompt using a language model.\n\nThis function does not stream the output. If you want to stream the output, use `streamObject` instead.\n\n@returns\nA result object that contains the generated object, the finish reason, the token usage, and additional information.\n */\nexport async function generateObject(\n  options: Omit<CallSettings, 'stopSequences'> &\n    Prompt & {\n      output: 'no-schema';\n\n      /**\nThe language model to use.\n     */\n      model: LanguageModel;\n\n      /**\nThe mode to use for object generation. Must be \"json\" for no-schema output.\n     */\n      mode?: 'json';\n\n      /**\nOptional telemetry configuration (experimental).\n       */\n      experimental_telemetry?: TelemetrySettings;\n\n      /**\nAdditional provider-specific metadata. They are passed through\nto the provider from the AI SDK and enable provider-specific\nfunctionality that can be fully encapsulated in the provider.\n */\n      experimental_providerMetadata?: ProviderMetadata;\n\n      /**\n       * Internal. For test use only. May change without notice.\n       */\n      _internal?: {\n        generateId?: () => string;\n        currentDate?: () => Date;\n      };\n    },\n): Promise<GenerateObjectResult<JSONValue>>;\nexport async function generateObject<SCHEMA, RESULT>({\n  model,\n  enum: enumValues, // rename bc enum is reserved by typescript\n  schema: inputSchema,\n  schemaName,\n  schemaDescription,\n  mode,\n  output = 'object',\n  system,\n  prompt,\n  messages,\n  maxRetries: maxRetriesArg,\n  abortSignal,\n  headers,\n  experimental_telemetry: telemetry,\n  experimental_providerMetadata: providerMetadata,\