@ai-sdk/openai/dist/index.mjs.map

The **[OpenAI provider](https://ai-sdk.dev/providers/ai-sdk-providers/openai)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the OpenAI chat and completion APIs and embedding model support for the OpenAI embeddings API.

{"version":3,"sources":["../src/openai-provider.ts","../src/chat/openai-chat-language-model.ts","../src/openai-error.ts","../src/openai-language-model-capabilities.ts","../src/chat/convert-openai-chat-usage.ts","../src/chat/convert-to-openai-chat-messages.ts","../src/chat/get-response-metadata.ts","../src/chat/map-openai-finish-reason.ts","../src/chat/openai-chat-api.ts","../src/chat/openai-chat-options.ts","../src/chat/openai-chat-prepare-tools.ts","../src/completion/openai-completion-language-model.ts","../src/completion/convert-openai-completion-usage.ts","../src/completion/convert-to-openai-completion-prompt.ts","../src/completion/get-response-metadata.ts","../src/completion/map-openai-finish-reason.ts","../src/completion/openai-completion-api.ts","../src/completion/openai-completion-options.ts","../src/embedding/openai-embedding-model.ts","../src/embedding/openai-embedding-options.ts","../src/embedding/openai-embedding-api.ts","../src/image/openai-image-model.ts","../src/image/openai-image-api.ts","../src/image/openai-image-options.ts","../src/tool/apply-patch.ts","../src/tool/code-interpreter.ts","../src/tool/file-search.ts","../src/tool/image-generation.ts","../src/tool/local-shell.ts","../src/tool/shell.ts","../src/tool/web-search.ts","../src/tool/web-search-preview.ts","../src/tool/mcp.ts","../src/openai-tools.ts","../src/responses/openai-responses-language-model.ts","../src/responses/convert-openai-responses-usage.ts","../src/responses/convert-to-openai-responses-input.ts","../src/responses/map-openai-responses-finish-reason.ts","../src/responses/openai-responses-api.ts","../src/responses/openai-responses-options.ts","../src/responses/openai-responses-prepare-tools.ts","../src/speech/openai-speech-model.ts","../src/speech/openai-speech-options.ts","../src/transcription/openai-transcription-model.ts","../src/transcription/openai-transcription-api.ts","../src/transcription/openai-transcription-options.ts","../src/version.ts"],"sourcesContent":["import {\n  EmbeddingModelV3,\n  ImageModelV3,\n  LanguageModelV3,\n  ProviderV3,\n  SpeechModelV3,\n  TranscriptionModelV3,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  loadApiKey,\n  loadOptionalSetting,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { OpenAIChatLanguageModel } from './chat/openai-chat-language-model';\nimport { OpenAIChatModelId } from './chat/openai-chat-options';\nimport { OpenAICompletionLanguageModel } from './completion/openai-completion-language-model';\nimport { OpenAICompletionModelId } from './completion/openai-completion-options';\nimport { OpenAIEmbeddingModel } from './embedding/openai-embedding-model';\nimport { OpenAIEmbeddingModelId } from './embedding/openai-embedding-options';\nimport { OpenAIImageModel } from './image/openai-image-model';\nimport { OpenAIImageModelId } from './image/openai-image-options';\nimport { openaiTools } from './openai-tools';\nimport { OpenAIResponsesLanguageModel } from './responses/openai-responses-language-model';\nimport { OpenAIResponsesModelId } from './responses/openai-responses-options';\nimport { OpenAISpeechModel } from './speech/openai-speech-model';\nimport { OpenAISpeechModelId } from './speech/openai-speech-options';\nimport { OpenAITranscriptionModel } from './transcription/openai-transcription-model';\nimport { OpenAITranscriptionModelId } from './transcription/openai-transcription-options';\nimport { VERSION } from './version';\n\nexport interface OpenAIProvider extends ProviderV3 {\n  (modelId: OpenAIResponsesModelId): LanguageModelV3;\n\n  /**\nCreates an OpenAI model for text generation.\n   */\n  languageModel(modelId: OpenAIResponsesModelId): LanguageModelV3;\n\n  /**\nCreates an OpenAI chat model for text generation.\n   */\n  chat(modelId: OpenAIChatModelId): LanguageModelV3;\n\n  /**\nCreates an OpenAI responses API model for text generation.\n   */\n  responses(modelId: OpenAIResponsesModelId): LanguageModelV3;\n\n  /**\nCreates an OpenAI completion model for text generation.\n   */\n  completion(modelId: OpenAICompletionModelId): LanguageModelV3;\n\n  /**\nCreates a model for text embeddings.\n   */\n  embedding(modelId: OpenAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\nCreates a model for text embeddings.\n   */\n  embeddingModel(modelId: OpenAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\n   * @deprecated Use `embedding` instead.\n   */\n  textEmbedding(modelId: OpenAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(modelId: OpenAIEmbeddingModelId): EmbeddingModelV3;\n\n  /**\nCreates a model for image generation.\n   */\n  image(modelId: OpenAIImageModelId): ImageModelV3;\n\n  /**\nCreates a model for image generation.\n   */\n  imageModel(modelId: OpenAIImageModelId): ImageModelV3;\n\n  /**\nCreates a model for transcription.\n   */\n  transcription(modelId: OpenAITranscriptionModelId): TranscriptionModelV3;\n\n  /**\nCreates a model for speech generation.\n   */\n  speech(modelId: OpenAISpeechModelId): SpeechModelV3;\n\n  /**\nOpenAI-specific tools.\n   */\n  tools: typeof openaiTools;\n}\n\nexport interface OpenAIProviderSettings {\n  /**\nBase URL for the OpenAI API calls.\n     */\n  baseURL?: string;\n\n  /**\nAPI key for authenticating requests.\n     */\n  apiKey?: string;\n\n  /**\nOpenAI Organization.\n     */\n  organization?: string;\n\n  /**\nOpenAI project.\n     */\n  project?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: Record<string, string>;\n\n  /**\nProvider name. Overrides the `openai` default name for 3rd party providers.\n   */\n  name?: string;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n}\n\n/**\nCreate an OpenAI provider instance.\n */\nexport function createOpenAI(\n  options: OpenAIProviderSettings = {},\n): OpenAIProvider {\n  const baseURL =\n    withoutTrailingSlash(\n      loadOptionalSetting({\n        settingValue: options.baseURL,\n        environmentVariableName: 'OPENAI_BASE_URL',\n      }),\n    ) ?? 'https://api.openai.com/v1';\n\n  const providerName = options.name ?? 'openai';\n\n  const getHeaders = () =>\n    withUserAgentSuffix(\n      {\n        Authorization: `Bearer ${loadApiKey({\n          apiKey: options.apiKey,\n          environmentVariableName: 'OPENAI_API_KEY',\n          description: 'OpenAI',\n        })}`,\n        'OpenAI-Organization': options.organization,\n        'OpenAI-Project': options.project,\n        ...options.headers,\n      },\n      `ai-sdk/openai/${VERSION}`,\n    );\n\n  const createChatModel = (modelId: OpenAIChatModelId) =>\n    new OpenAIChatLanguageModel(modelId, {\n      provider: `${providerName}.chat`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createCompletionModel = (modelId: OpenAICompletionModelId) =>\n    new OpenAICompletionLanguageModel(modelId, {\n      provider: `${providerName}.completion`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createEmbeddingModel = (modelId: OpenAIEmbeddingModelId) =>\n    new OpenAIEmbeddingModel(modelId, {\n      provider: `${providerName}.embedding`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createImageModel = (modelId: OpenAIImageModelId) =>\n    new OpenAIImageModel(modelId, {\n      provider: `${providerName}.image`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createTranscriptionModel = (modelId: OpenAITranscriptionModelId) =>\n    new OpenAITranscriptionModel(modelId, {\n      provider: `${providerName}.transcription`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createSpeechModel = (modelId: OpenAISpeechModelId) =>\n    new OpenAISpeechModel(modelId, {\n      provider: `${providerName}.speech`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createLanguageModel = (modelId: OpenAIResponsesModelId) => {\n    if (new.target) {\n      throw new Error(\n        'The OpenAI model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createResponsesModel(modelId);\n  };\n\n  const createResponsesModel = (modelId: OpenAIResponsesModelId) => {\n    return new OpenAIResponsesLanguageModel(modelId, {\n      provider: `${providerName}.responses`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      fileIdPrefixes: ['file-'],\n    });\n  };\n\n  const provider = function (modelId: OpenAIResponsesModelId) {\n    return createLanguageModel(modelId);\n  };\n\n  provider.specificationVersion = 'v3' as const;\n  provider.languageModel = createLanguageModel;\n  provider.chat = createChatModel;\n  provider.completion = createCompletionModel;\n  provider.responses = createResponsesModel;\n  provider.embedding = createEmbeddingModel;\n  provider.embeddingModel = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n\n  provider.transcription = createTranscriptionModel;\n  provider.transcriptionModel = createTranscriptionModel;\n\n  provider.speech = createSpeechModel;\n  provider.speechModel = createSpeechModel;\n\n  provider.tools = openaiTools;\n\n  return provider as OpenAIProvider;\n}\n\n/**\nDefault OpenAI provider instance.\n */\nexport const openai = createOpenAI();\n","import {\n  InvalidResponseDataError,\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  isParsableJson,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { openaiFailedResponseHandler } from '../openai-error';\nimport { getOpenAILanguageModelCapabilities } from '../openai-language-model-capabilities';\nimport {\n  OpenAIChatUsage,\n  convertOpenAIChatUsage,\n} from './convert-openai-chat-usage';\nimport { convertToOpenAIChatMessages } from './convert-to-openai-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  OpenAIChatChunk,\n  openaiChatChunkSchema,\n  openaiChatResponseSchema,\n} from './openai-chat-api';\nimport {\n  OpenAIChatModelId,\n  openaiChatLanguageModelOptions,\n} from './openai-chat-options';\nimport { prepareChatTools } from './openai-chat-prepare-tools';\n\ntype OpenAIChatConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class OpenAIChatLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\n\n  readonly modelId: OpenAIChatModelId;\n\n  readonly supportedUrls = {\n    'image/*': [/^https?:\\/\\/.*$/],\n  };\n\n  private readonly config: OpenAIChatConfig;\n\n  constructor(modelId: OpenAIChatModelId, config: OpenAIChatConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    tools,\n    toolChoice,\n    providerOptions,\n  }: LanguageModelV3CallOptions) {\n    const warnings: SharedV3Warning[] = [];\n\n    // Parse provider options\n    const openaiOptions =\n      (await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiChatLanguageModelOptions,\n      })) ?? {};\n\n    const modelCapabilities = getOpenAILanguageModelCapabilities(this.modelId);\n    const isReasoningModel =\n      openaiOptions.forceReasoning ?? modelCapabilities.isReasoningModel;\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported', feature: 'topK' });\n    }\n\n    const { messages, warnings: messageWarnings } = convertToOpenAIChatMessages(\n      {\n        prompt,\n        systemMessageMode:\n          openaiOptions.systemMessageMode ??\n          (isReasoningModel\n            ? 'developer'\n            : modelCapabilities.systemMessageMode),\n      },\n    );\n\n    warnings.push(...messageWarnings);\n\n    const strictJsonSchema = openaiOptions.strictJsonSchema ?? true;\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      logit_bias: openaiOptions.logitBias,\n      logprobs:\n        openaiOptions.logprobs === true ||\n        typeof openaiOptions.logprobs === 'number'\n          ? true\n          : undefined,\n      top_logprobs:\n        typeof openaiOptions.logprobs === 'number'\n          ? openaiOptions.logprobs\n          : typeof openaiOptions.logprobs === 'boolean'\n            ? openaiOptions.logprobs\n              ? 0\n              : undefined\n            : undefined,\n      user: openaiOptions.user,\n      parallel_tool_calls: openaiOptions.parallelToolCalls,\n\n      // standardized settings:\n      max_tokens: maxOutputTokens,\n      temperature,\n      top_p: topP,\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      response_format:\n        responseFormat?.type === 'json'\n          ? responseFormat.schema != null\n            ? {\n                type: 'json_schema',\n                json_schema: {\n                  schema: responseFormat.schema,\n                  strict: strictJsonSchema,\n                  name: responseFormat.name ?? 'response',\n                  description: responseFormat.description,\n                },\n              }\n            : { type: 'json_object' }\n          : undefined,\n      stop: stopSequences,\n      seed,\n      verbosity: openaiOptions.textVerbosity,\n\n      // openai specific settings:\n      // TODO AI SDK 6: remove, we auto-map maxOutputTokens now\n      max_completion_tokens: openaiOptions.maxCompletionTokens,\n      store: openaiOptions.store,\n      metadata: openaiOptions.metadata,\n      prediction: openaiOptions.prediction,\n      reasoning_effort: openaiOptions.reasoningEffort,\n      service_tier: openaiOptions.serviceTier,\n      prompt_cache_key: openaiOptions.promptCacheKey,\n      prompt_cache_retention: openaiOptions.promptCacheRetention,\n      safety_identifier: openaiOptions.safetyIdentifier,\n\n      // messages:\n      messages,\n    };\n\n    // remove unsupported settings for reasoning models\n    // see https://platform.openai.com/docs/guides/reasoning#limitations\n    if (isReasoningModel) {\n      // when reasoning effort is none, gpt-5.1 models allow temperature, topP, logprobs\n      //  https://platform.openai.com/docs/guides/latest-model#gpt-5-1-parameter-compatibility\n      if (\n        openaiOptions.reasoningEffort !== 'none' ||\n        !modelCapabilities.supportsNonReasoningParameters\n      ) {\n        if (baseArgs.temperature != null) {\n          baseArgs.temperature = undefined;\n          warnings.push({\n            type: 'unsupported',\n            feature: 'temperature',\n            details: 'temperature is not supported for reasoning models',\n          });\n        }\n        if (baseArgs.top_p != null) {\n          baseArgs.top_p = undefined;\n          warnings.push({\n            type: 'unsupported',\n            feature: 'topP',\n            details: 'topP is not supported for reasoning models',\n          });\n        }\n        if (baseArgs.logprobs != null) {\n          baseArgs.logprobs = undefined;\n          warnings.push({\n            type: 'other',\n            message: 'logprobs is not supported for reasoning models',\n          });\n        }\n      }\n\n      if (baseArgs.frequency_penalty != null) {\n        baseArgs.frequency_penalty = undefined;\n        warnings.push({\n          type: 'unsupported',\n          feature: 'frequencyPenalty',\n          details: 'frequencyPenalty is not supported for reasoning models',\n        });\n      }\n      if (baseArgs.presence_penalty != null) {\n        baseArgs.presence_penalty = undefined;\n        warnings.push({\n          type: 'unsupported',\n          feature: 'presencePenalty',\n          details: 'presencePenalty is not supported for reasoning models',\n        });\n      }\n      if (baseArgs.logit_bias != null) {\n        baseArgs.logit_bias = undefined;\n        warnings.push({\n          type: 'other',\n          message: 'logitBias is not supported for reasoning models',\n        });\n      }\n\n      if (baseArgs.top_logprobs != null) {\n        baseArgs.top_logprobs = undefined;\n        warnings.push({\n          type: 'other',\n          message: 'topLogprobs is not supported for reasoning models',\n        });\n      }\n\n      // reasoning models use max_completion_tokens instead of max_tokens:\n      if (baseArgs.max_tokens != null) {\n        if (baseArgs.max_completion_tokens == null) {\n          baseArgs.max_completion_tokens = baseArgs.max_tokens;\n        }\n        baseArgs.max_tokens = undefined;\n      }\n    } else if (\n      this.modelId.startsWith('gpt-4o-search-preview') ||\n      this.modelId.startsWith('gpt-4o-mini-search-preview')\n    ) {\n      if (baseArgs.temperature != null) {\n        baseArgs.temperature = undefined;\n        warnings.push({\n          type: 'unsupported',\n          feature: 'temperature',\n          details:\n            'temperature is not supported for the search preview models and has been removed.',\n        });\n      }\n    }\n\n    // Validate flex processing support\n    if (\n      openaiOptions.serviceTier === 'flex' &&\n      !modelCapabilities.supportsFlexProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'serviceTier',\n        details:\n          'flex processing is only available for o3, o4-mini, and gpt-5 models',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    // Validate priority processing support\n    if (\n      openaiOptions.serviceTier === 'priority' &&\n      !modelCapabilities.supportsPriorityProcessing\n    ) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'serviceTier',\n        details:\n          'priority processing is only available for supported models (gpt-4, gpt-5, gpt-5-mini, o3, o4-mini) and requires Enterprise access. gpt-5-nano is not supported',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = prepareChatTools({\n      tools,\n      toolChoice,\n    });\n\n    return {\n      args: {\n        ...baseArgs,\n        tools: openaiTools,\n        tool_choice: openaiToolChoice,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args: body, warnings } = await this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: this.config.url({\n        path: '/chat/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // text content:\n    const text = choice.message.content;\n    if (text != null && text.length > 0) {\n      content.push({ type: 'text', text });\n    }\n\n    // tool calls:\n    for (const toolCall of choice.message.tool_calls ?? []) {\n      content.push({\n        type: 'tool-call' as const,\n        toolCallId: toolCall.id ?? generateId(),\n        toolName: toolCall.function.name,\n        input: toolCall.function.arguments!,\n      });\n    }\n\n    // annotations/citations:\n    for (const annotation of choice.message.annotations ?? []) {\n      content.push({\n        type: 'source',\n        sourceType: 'url',\n        id: generateId(),\n        url: annotation.url_citation.url,\n        title: annotation.url_citation.title,\n      });\n    }\n\n    // provider metadata:\n    const completionTokenDetails = response.usage?.completion_tokens_details;\n    const promptTokenDetails = response.usage?.prompt_tokens_details;\n    const providerMetadata: SharedV3ProviderMetadata = { openai: {} };\n    if (completionTokenDetails?.accepted_prediction_tokens != null) {\n      providerMetadata.openai.acceptedPredictionTokens =\n        completionTokenDetails?.accepted_prediction_tokens;\n    }\n    if (completionTokenDetails?.rejected_prediction_tokens != null) {\n      providerMetadata.openai.rejectedPredictionTokens =\n        completionTokenDetails?.rejected_prediction_tokens;\n    }\n    if (choice.logprobs?.content != null) {\n      providerMetadata.openai.logprobs = choice.logprobs.content;\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapOpenAIFinishReason(choice.finish_reason),\n        raw: choice.finish_reason ?? undefined,\n      },\n      usage: convertOpenAIChatUsage(response.usage),\n      request: { body },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n      providerMetadata,\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const body = {\n      ...args,\n      stream: true,\n      stream_options: {\n        include_usage: true,\n      },\n    };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/chat/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiChatChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const toolCalls: Array<{\n      id: string;\n      type: 'function';\n      function: {\n        name: string;\n        arguments: string;\n      };\n      hasFinished: boolean;\n    }> = [];\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage: OpenAIChatUsage | undefined = undefined;\n    let metadataExtracted = false;\n    let isActiveText = false;\n\n    const providerMetadata: SharedV3ProviderMetadata = { openai: {} };\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<OpenAIChatChunk>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            // handle error chunks:\n            if ('error' in value) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({ type: 'error', error: value.error });\n              return;\n            }\n\n            // extract and emit response metadata once. Usually it comes in the first chunk.\n            // Azure may prepend a chunk with a `\"prompt_filter_results\"` key which does not contain other metadata,\n            // https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/content-filter-annotations?tabs=powershell\n            if (!metadataExtracted) {\n              const metadata = getResponseMetadata(value);\n              if (Object.values(metadata).some(Boolean)) {\n                metadataExtracted = true;\n                controller.enqueue({\n                  type: 'response-metadata',\n                  ...getResponseMetadata(value),\n                });\n              }\n            }\n\n            if (value.usage != null) {\n              usage = value.usage;\n\n              if (\n                value.usage.completion_tokens_details\n                  ?.accepted_prediction_tokens != null\n              ) {\n                providerMetadata.openai.acceptedPredictionTokens =\n                  value.usage.completion_tokens_details?.accepted_prediction_tokens;\n              }\n              if (\n                value.usage.completion_tokens_details\n                  ?.rejected_prediction_tokens != null\n              ) {\n                providerMetadata.openai.rejectedPredictionTokens =\n                  value.usage.completion_tokens_details?.rejected_prediction_tokens;\n              }\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = {\n                unified: mapOpenAIFinishReason(choice.finish_reason),\n                raw: choice.finish_reason,\n              };\n            }\n\n            if (choice?.logprobs?.content != null) {\n              providerMetadata.openai.logprobs = choice.logprobs.content;\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            if (delta.content != null) {\n              if (!isActiveText) {\n                controller.enqueue({ type: 'text-start', id: '0' });\n                isActiveText = true;\n              }\n\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: delta.content,\n              });\n            }\n\n            if (delta.tool_calls != null) {\n              for (const toolCallDelta of delta.tool_calls) {\n                const index = toolCallDelta.index;\n\n                // Tool call start. OpenAI returns all information except the arguments in the first chunk.\n                if (toolCalls[index] == null) {\n                  if (toolCallDelta.type !== 'function') {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function' type.`,\n                    });\n                  }\n\n                  if (toolCallDelta.id == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'id' to be a string.`,\n                    });\n                  }\n\n                  if (toolCallDelta.function?.name == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function.name' to be a string.`,\n                    });\n                  }\n\n                  controller.enqueue({\n                    type: 'tool-input-start',\n                    id: toolCallDelta.id,\n                    toolName: toolCallDelta.function.name,\n                  });\n\n                  toolCalls[index] = {\n                    id: toolCallDelta.id,\n                    type: 'function',\n                    function: {\n                      name: toolCallDelta.function.name,\n                      arguments: toolCallDelta.function.arguments ?? '',\n                    },\n                    hasFinished: false,\n                  };\n\n                  const toolCall = toolCalls[index];\n\n                  if (\n                    toolCall.function?.name != null &&\n                    toolCall.function?.arguments != null\n                  ) {\n                    // send delta if the argument text has already started:\n                    if (toolCall.function.arguments.length > 0) {\n                      controller.enqueue({\n                        type: 'tool-input-delta',\n                        id: toolCall.id,\n                        delta: toolCall.function.arguments,\n                      });\n                    }\n\n                    // check if tool call is complete\n                    // (some providers send the full tool call in one chunk):\n                    if (isParsableJson(toolCall.function.arguments)) {\n                      controller.enqueue({\n                        type: 'tool-input-end',\n                        id: toolCall.id,\n                      });\n\n                      controller.enqueue({\n                        type: 'tool-call',\n                        toolCallId: toolCall.id ?? generateId(),\n                        toolName: toolCall.function.name,\n                        input: toolCall.function.arguments,\n                      });\n                      toolCall.hasFinished = true;\n                    }\n                  }\n\n                  continue;\n                }\n\n                // existing tool call, merge if not finished\n                const toolCall = toolCalls[index];\n\n                if (toolCall.hasFinished) {\n                  continue;\n                }\n\n                if (toolCallDelta.function?.arguments != null) {\n                  toolCall.function!.arguments +=\n                    toolCallDelta.function?.arguments ?? '';\n                }\n\n                // send delta\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCall.id,\n                  delta: toolCallDelta.function.arguments ?? '',\n                });\n\n                // check if tool call is complete\n                if (\n                  toolCall.function?.name != null &&\n                  toolCall.function?.arguments != null &&\n                  isParsableJson(toolCall.function.arguments)\n                ) {\n                  controller.enqueue({\n                    type: 'tool-input-end',\n                    id: toolCall.id,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId: toolCall.id ?? generateId(),\n                    toolName: toolCall.function.name,\n                    input: toolCall.function.arguments,\n                  });\n                  toolCall.hasFinished = true;\n                }\n              }\n            }\n\n            // annotations/citations:\n            if (delta.annotations != null) {\n              for (const annotation of delta.annotations) {\n                controller.enqueue({\n                  type: 'source',\n                  sourceType: 'url',\n                  id: generateId(),\n                  url: annotation.url_citation.url,\n                  title: annotation.url_citation.title,\n                });\n              }\n            }\n          },\n\n          flush(controller) {\n            if (isActiveText) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertOpenAIChatUsage(usage),\n              ...(providerMetadata != null ? { providerMetadata } : {}),\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n","import { z } from 'zod/v4';\nimport { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\n\nexport const openaiErrorDataSchema = z.object({\n  error: z.object({\n    message: z.string(),\n\n    // The additional information below is handled loosely to support\n    // OpenAI-compatible providers that have slightly different error\n    // responses:\n    type: z.string().nullish(),\n    param: z.any().nullish(),\n    code: z.union([z.string(), z.number()]).nullish(),\n  }),\n});\n\nexport type OpenAIErrorData = z.infer<typeof openaiErrorDataSchema>;\n\nexport const openaiFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: openaiErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","export type OpenAILanguageModelCapabilities = {\n  isReasoningModel: boolean;\n  systemMessageMode: 'remove' | 'system' | 'developer';\n  supportsFlexProcessing: boolean;\n  supportsPriorityProcessing: boolean;\n\n  /**\n   * Allow temperature, topP, logProbs when reasoningEffort is none.\n   */\n  supportsNonReasoningParameters: boolean;\n};\n\nexport function getOpenAILanguageModelCapabilities(\n  modelId: string,\n): OpenAILanguageModelCapabilities {\n  const supportsFlexProcessing =\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini') ||\n    (modelId.startsWith('gpt-5') && !modelId.startsWith('gpt-5-chat'));\n\n  const supportsPriorityProcessing =\n    modelId.startsWith('gpt-4') ||\n    modelId.startsWith('gpt-5-mini') ||\n    (modelId.startsWith('gpt-5') &&\n      !modelId.startsWith('gpt-5-nano') &&\n      !modelId.startsWith('gpt-5-chat')) ||\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini');\n\n  // Use allowlist approach: only known reasoning models should use 'developer' role\n  // This prevents issues with fine-tuned models, third-party models, and custom models\n  const isReasoningModel =\n    modelId.startsWith('o1') ||\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini') ||\n    modelId.startsWith('codex-mini') ||\n    modelId.startsWith('computer-use-preview') ||\n    (modelId.startsWith('gpt-5') && !modelId.startsWith('gpt-5-chat'));\n\n  // https://platform.openai.com/docs/guides/latest-model#gpt-5-1-parameter-compatibility\n  // GPT-5.1 and GPT-5.2 support temperature, topP, logProbs when reasoningEffort is none\n  const supportsNonReasoningParameters =\n    modelId.startsWith('gpt-5.1') || modelId.startsWith('gpt-5.2');\n\n  const systemMessageMode = isReasoningModel ? 'developer' : 'system';\n\n  return {\n    supportsFlexProcessing,\n    supportsPriorityProcessing,\n    isReasoningModel,\n    systemMessageMode,\n    supportsNonReasoningParameters,\n  };\n}\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport type OpenAIChatUsage = {\n  prompt_tokens?: number | null;\n  completion_tokens?: number | null;\n  total_tokens?: number | null;\n  prompt_tokens_details?: {\n    cached_tokens?: number | null;\n  } | null;\n  completion_tokens_details?: {\n    reasoning_tokens?: number | null;\n    accepted_prediction_tokens?: number | null;\n    rejected_prediction_tokens?: number | null;\n  } | null;\n};\n\nexport function convertOpenAIChatUsage(\n  usage: OpenAIChatUsage | undefined | null,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.prompt_tokens ?? 0;\n  const completionTokens = usage.completion_tokens ?? 0;\n  const cachedTokens = usage.prompt_tokens_details?.cached_tokens ?? 0;\n  const reasoningTokens =\n    usage.completion_tokens_details?.reasoning_tokens ?? 0;\n\n  return {\n    inputTokens: {\n      total: promptTokens,\n      noCache: promptTokens - cachedTokens,\n      cacheRead: cachedTokens,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: completionTokens,\n      text: completionTokens - reasoningTokens,\n      reasoning: reasoningTokens,\n    },\n    raw: usage,\n  };\n}\n","import {\n  SharedV3Warning,\n  LanguageModelV3Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { OpenAIChatPrompt } from './openai-chat-prompt';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\n\nexport function convertToOpenAIChatMessages({\n  prompt,\n  systemMessageMode = 'system',\n}: {\n  prompt: LanguageModelV3Prompt;\n  systemMessageMode?: 'system' | 'developer' | 'remove';\n}): {\n  messages: OpenAIChatPrompt;\n  warnings: Array<SharedV3Warning>;\n} {\n  const messages: OpenAIChatPrompt = [];\n  const warnings: Array<SharedV3Warning> = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        switch (systemMessageMode) {\n          case 'system': {\n            messages.push({ role: 'system', content });\n            break;\n          }\n          case 'developer': {\n            messages.push({ role: 'developer', content });\n            break;\n          }\n          case 'remove': {\n            warnings.push({\n              type: 'other',\n              message: 'system messages are removed for this model',\n            });\n            break;\n          }\n          default: {\n            const _exhaustiveCheck: never = systemMessageMode;\n            throw new Error(\n              `Unsupported system message mode: ${_exhaustiveCheck}`,\n            );\n          }\n        }\n        break;\n      }\n\n      case 'user': {\n        if (content.length === 1 && content[0].type === 'text') {\n          messages.push({ role: 'user', content: content[0].text });\n          break;\n        }\n\n        messages.push({\n          role: 'user',\n          content: content.map((part, index) => {\n            switch (part.type) {\n              case 'text': {\n                return { type: 'text', text: part.text };\n              }\n              case 'file': {\n                if (part.mediaType.startsWith('image/')) {\n                  const mediaType =\n                    part.mediaType === 'image/*'\n                      ? 'image/jpeg'\n                      : part.mediaType;\n\n                  return {\n                    type: 'image_url',\n                    image_url: {\n                      url:\n                        part.data instanceof URL\n                          ? part.data.toString()\n                          : `data:${mediaType};base64,${convertToBase64(part.data)}`,\n\n                      // OpenAI specific extension: image detail\n                      detail: part.providerOptions?.openai?.imageDetail,\n                    },\n                  };\n                } else if (part.mediaType.startsWith('audio/')) {\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality: 'audio file parts with URLs',\n                    });\n                  }\n\n                  switch (part.mediaType) {\n                    case 'audio/wav': {\n                      return {\n                        type: 'input_audio',\n                        input_audio: {\n                          data: convertToBase64(part.data),\n                          format: 'wav',\n                        },\n                      };\n                    }\n                    case 'audio/mp3':\n                    case 'audio/mpeg': {\n                      return {\n                        type: 'input_audio',\n                        input_audio: {\n                          data: convertToBase64(part.data),\n                          format: 'mp3',\n                        },\n                      };\n                    }\n\n                    default: {\n                      throw new UnsupportedFunctionalityError({\n                        functionality: `audio content parts with media type ${part.mediaType}`,\n                      });\n                    }\n                  }\n                } else if (part.mediaType === 'application/pdf') {\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality: 'PDF file parts with URLs',\n                    });\n                  }\n\n                  return {\n                    type: 'file',\n                    file:\n                      typeof part.data === 'string' &&\n                      part.data.startsWith('file-')\n                        ? { file_id: part.data }\n                        : {\n                            filename: part.filename ?? `part-${index}.pdf`,\n                            file_data: `data:application/pdf;base64,${convertToBase64(part.data)}`,\n                          },\n                  };\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: `file part media type ${part.mediaType}`,\n                  });\n                }\n              }\n            }\n          }),\n        });\n\n        break;\n      }\n\n      case 'assistant': {\n        let text = '';\n        const toolCalls: Array<{\n          id: string;\n          type: 'function';\n          function: { name: string; arguments: string };\n        }> = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              text += part.text;\n              break;\n            }\n            case 'tool-call': {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: 'function',\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.input),\n                },\n              });\n              break;\n            }\n          }\n        }\n\n        messages.push({\n          role: 'assistant',\n          content: text,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        });\n\n        break;\n      }\n\n      case 'tool': {\n        for (const toolResponse of content) {\n          if (toolResponse.type === 'tool-approval-response') {\n            continue;\n          }\n          const output = toolResponse.output;\n\n          let contentValue: string;\n          switch (output.type) {\n            case 'text':\n            case 'error-text':\n              contentValue = output.value;\n              break;\n            case 'execution-denied':\n              contentValue = output.reason ?? 'Tool execution denied.';\n              break;\n            case 'content':\n            case 'json':\n            case 'error-json':\n              contentValue = JSON.stringify(output.value);\n              break;\n          }\n\n          messages.push({\n            role: 'tool',\n            tool_call_id: toolResponse.toolCallId,\n            content: contentValue,\n          });\n        }\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return { messages, warnings };\n}\n","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | null;\n  model?: string | undefined | null;\n}) {\n  return {\n    id: id ?? undefined,\n    modelId: model ?? undefined,\n    timestamp: created ? new Date(created * 1000) : undefined,\n  };\n}\n","import { LanguageModelV3FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV3FinishReason['unified'] {\n  switch (finishReason) {\n    case 'stop':\n      return 'stop';\n    case 'length':\n      return 'length';\n    case 'content_filter':\n      return 'content-filter';\n    case 'function_call':\n    case 'tool_calls':\n      return 'tool-calls';\n    default:\n      return 'other';\n  }\n}\n","import { JSONSchema7 } from '@ai-sdk/provider';\nimport { InferSchema, lazySchema, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { openaiErrorDataSchema } from '../openai-error';\n\nexport interface OpenAIChatFunctionTool {\n  type: 'function';\n  function: {\n    name: string;\n    description: string | undefined;\n    parameters: JSONSchema7;\n    strict?: boolean;\n  };\n}\n\nexport type OpenAIChatToolChoice =\n  | 'auto'\n  | 'none'\n  | 'required'\n  | { type: 'function'; function: { name: string } };\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiChatResponseSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      id: z.string().nullish(),\n      created: z.number().nullish(),\n      model: z.string().nullish(),\n      choices: z.array(\n        z.object({\n          message: z.object({\n            role: z.literal('assistant').nullish(),\n            content: z.string().nullish(),\n            tool_calls: z\n              .array(\n                z.object({\n                  id: z.string().nullish(),\n                  type: z.literal('function'),\n                  function: z.object({\n                    name: z.string(),\n                    arguments: z.string(),\n                  }),\n                }),\n              )\n              .nullish(),\n            annotations: z\n              .array(\n                z.object({\n                  type: z.literal('url_citation'),\n                  url_citation: z.object({\n                    start_index: z.number(),\n                    end_index: z.number(),\n                    url: z.string(),\n                    title: z.string(),\n                  }),\n                }),\n              )\n              .nullish(),\n          }),\n          index: z.number(),\n          logprobs: z\n            .object({\n              content: z\n                .array(\n                  z.object({\n                    token: z.string(),\n                    logprob: z.number(),\n                    top_logprobs: z.array(\n                      z.object({\n                        token: z.string(),\n                        logprob: z.number(),\n                      }),\n                    ),\n                  }),\n                )\n                .nullish(),\n            })\n            .nullish(),\n          finish_reason: z.string().nullish(),\n        }),\n      ),\n      usage: z\n        .object({\n          prompt_tokens: z.number().nullish(),\n          completion_tokens: z.number().nullish(),\n          total_tokens: z.number().nullish(),\n          prompt_tokens_details: z\n            .object({\n              cached_tokens: z.number().nullish(),\n            })\n            .nullish(),\n          completion_tokens_details: z\n            .object({\n              reasoning_tokens: z.number().nullish(),\n              accepted_prediction_tokens: z.number().nullish(),\n              rejected_prediction_tokens: z.number().nullish(),\n            })\n            .nullish(),\n        })\n        .nullish(),\n    }),\n  ),\n);\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const openaiChatChunkSchema = lazySchema(() =>\n  zodSchema(\n    z.union([\n      z.object({\n        id: z.string().nullish(),\n        created: z.number().nullish(),\n        model: z.string().nullish(),\n        choices: z.array(\n          z.object({\n            delta: z\n              .object({\n                role: z.enum(['assistant']).nullish(),\n                content: z.string().nullish(),\n                tool_calls: z\n                  .array(\n                    z.object({\n                      index: z.number(),\n                      id: z.string().nullish(),\n                      type: z.literal('function').nullish(),\n                      function: z.object({\n                        name: z.string().nullish(),\n                        arguments: z.string().nullish(),\n                      }),\n                    }),\n                  )\n                  .nullish(),\n                annotations: z\n                  .array(\n                    z.object({\n                      type: z.literal('url_citation'),\n                      url_citation: z.object({\n                        start_index: z.number(),\n                        end_index: z.number(),\n                        url: z.string(),\n                        title: z.string(),\n                      }),\n                    }),\n                  )\n                  .nullish(),\n              })\n              .nullish(),\n            logprobs: z\n              .object({\n                content: z\n                  .array(\n