@ai-sdk/openai/dist/internal/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-chat-language-model.ts","../../src/convert-to-openai-chat-messages.ts","../../src/get-response-metadata.ts","../../src/map-openai-finish-reason.ts","../../src/openai-chat-options.ts","../../src/openai-error.ts","../../src/openai-prepare-tools.ts","../../src/tool/file-search.ts","../../src/tool/web-search-preview.ts","../../src/openai-completion-language-model.ts","../../src/convert-to-openai-completion-prompt.ts","../../src/openai-completion-options.ts","../../src/openai-embedding-model.ts","../../src/openai-embedding-options.ts","../../src/openai-image-model.ts","../../src/openai-image-settings.ts","../../src/openai-transcription-model.ts","../../src/openai-transcription-options.ts","../../src/openai-speech-model.ts","../../src/responses/openai-responses-language-model.ts","../../src/responses/convert-to-openai-responses-messages.ts","../../src/responses/map-openai-responses-finish-reason.ts","../../src/responses/openai-responses-prepare-tools.ts"],"sourcesContent":["import {\n  InvalidResponseDataError,\n  LanguageModelV2,\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n  SharedV2ProviderMetadata,\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 { z } from 'zod/v4';\nimport { convertToOpenAIChatMessages } from './convert-to-openai-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  OpenAIChatModelId,\n  openaiProviderOptions,\n} from './openai-chat-options';\nimport {\n  openaiErrorDataSchema,\n  openaiFailedResponseHandler,\n} from './openai-error';\nimport { prepareTools } from './openai-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 LanguageModelV2 {\n  readonly specificationVersion = 'v2';\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  }: LanguageModelV2CallOptions) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    // Parse provider options\n    const openaiOptions =\n      (await parseProviderOptions({\n        provider: 'openai',\n        providerOptions,\n        schema: openaiProviderOptions,\n      })) ?? {};\n\n    const structuredOutputs = openaiOptions.structuredOutputs ?? true;\n\n    if (topK != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n      });\n    }\n\n    if (\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !structuredOutputs\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details:\n          'JSON response format schema is only supported with structuredOutputs',\n      });\n    }\n\n    const { messages, warnings: messageWarnings } = convertToOpenAIChatMessages(\n      {\n        prompt,\n        systemMessageMode: getSystemMessageMode(this.modelId),\n      },\n    );\n\n    warnings.push(...messageWarnings);\n\n    const strictJsonSchema = openaiOptions.strictJsonSchema ?? false;\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          ? structuredOutputs && responseFormat.schema != null\n            ? {\n                type: 'json_schema',\n                json_schema: {\n                  schema: responseFormat.schema,\n                  strict: strictJsonSchema,\n                  name: responseFormat.name ?? 'response',\n                  description: responseFormat.description,\n                },\n              }\n            : { type: 'json_object' }\n          : undefined,\n      stop: stopSequences,\n      seed,\n\n      // openai specific settings:\n      // TODO remove in next major version; 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\n      // messages:\n      messages,\n    };\n\n    if (isReasoningModel(this.modelId)) {\n      // remove unsupported settings for reasoning models\n      // see https://platform.openai.com/docs/guides/reasoning#limitations\n      if (baseArgs.temperature != null) {\n        baseArgs.temperature = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: '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-setting',\n          setting: 'topP',\n          details: 'topP is not supported for reasoning models',\n        });\n      }\n      if (baseArgs.frequency_penalty != null) {\n        baseArgs.frequency_penalty = undefined;\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: '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-setting',\n          setting: '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      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      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-setting',\n          setting: '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      !supportsFlexProcessing(this.modelId)\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details: 'flex processing is only available for o3 and o4-mini models',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    // Validate priority processing support\n    if (\n      openaiOptions.serviceTier === 'priority' &&\n      !supportsPriorityProcessing(this.modelId)\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'serviceTier',\n        details:\n          'priority processing is only available for supported models (GPT-4, o3, o4-mini) and requires Enterprise access',\n      });\n      baseArgs.service_tier = undefined;\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n      structuredOutputs,\n      strictJsonSchema,\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: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\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<LanguageModelV2Content> = [];\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,\n        title: annotation.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: SharedV2ProviderMetadata = { 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: mapOpenAIFinishReason(choice.finish_reason),\n      usage: {\n        inputTokens: response.usage?.prompt_tokens ?? undefined,\n        outputTokens: response.usage?.completion_tokens ?? undefined,\n        totalTokens: response.usage?.total_tokens ?? undefined,\n        reasoningTokens: completionTokenDetails?.reasoning_tokens ?? undefined,\n        cachedInputTokens: promptTokenDetails?.cached_tokens ?? undefined,\n      },\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: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\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: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    let isFirstChunk = true;\n    let isActiveText = false;\n\n    const providerMetadata: SharedV2ProviderMetadata = { openai: {} };\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof openaiChatChunkSchema>>,\n          LanguageModelV2StreamPart\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 = 'error';\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 = 'error';\n              controller.enqueue({ type: 'error', error: value.error });\n              return;\n            }\n\n            if (isFirstChunk) {\n              isFirstChunk = false;\n\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(value),\n              });\n            }\n\n            if (value.usage != null) {\n              usage.inputTokens = value.usage.prompt_tokens ?? undefined;\n              usage.outputTokens = value.usage.completion_tokens ?? undefined;\n              usage.totalTokens = value.usage.total_tokens ?? undefined;\n              usage.reasoningTokens =\n                value.usage.completion_tokens_details?.reasoning_tokens ??\n                undefined;\n              usage.cachedInputTokens =\n                value.usage.prompt_tokens_details?.cached_tokens ?? undefined;\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 = mapOpenAIFinishReason(choice.finish_reason);\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,\n                  title: annotation.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,\n              ...(providerMetadata != null ? { providerMetadata } : {}),\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nconst openaiTokenUsageSchema = 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// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst openaiChatResponseSchema = 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              start_index: z.number(),\n              end_index: z.number(),\n              url: z.string(),\n              title: z.string(),\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: openaiTokenUsageSchema,\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst openaiChatChunkSchema = 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                  start_index: z.number(),\n                  end_index: z.number(),\n                  url: z.string(),\n                  title: z.string(),\n                }),\n              )\n              .nullish(),\n          })\n          .nullish(),\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        index: z.number(),\n      }),\n    ),\n    usage: openaiTokenUsageSchema,\n  }),\n  openaiErrorDataSchema,\n]);\n\nfunction isReasoningModel(modelId: string) {\n  return modelId.startsWith('o');\n}\n\nfunction supportsFlexProcessing(modelId: string) {\n  return modelId.startsWith('o3') || modelId.startsWith('o4-mini');\n}\n\nfunction supportsPriorityProcessing(modelId: string) {\n  return (\n    modelId.startsWith('gpt-4') ||\n    modelId.startsWith('o3') ||\n    modelId.startsWith('o4-mini')\n  );\n}\n\nfunction getSystemMessageMode(modelId: string) {\n  if (!isReasoningModel(modelId)) {\n    return 'system';\n  }\n\n  return (\n    reasoningModels[modelId as keyof typeof reasoningModels]\n      ?.systemMessageMode ?? 'developer'\n  );\n}\n\nconst reasoningModels = {\n  'o1-mini': {\n    systemMessageMode: 'remove',\n  },\n  'o1-mini-2024-09-12': {\n    systemMessageMode: 'remove',\n  },\n  'o1-preview': {\n    systemMessageMode: 'remove',\n  },\n  'o1-preview-2024-09-12': {\n    systemMessageMode: 'remove',\n  },\n  o3: {\n    systemMessageMode: 'developer',\n  },\n  'o3-2025-04-16': {\n    systemMessageMode: 'developer',\n  },\n  'o3-mini': {\n    systemMessageMode: 'developer',\n  },\n  'o3-mini-2025-01-31': {\n    systemMessageMode: 'developer',\n  },\n  'o4-mini': {\n    systemMessageMode: 'developer',\n  },\n  'o4-mini-2025-04-16': {\n    systemMessageMode: 'developer',\n  },\n} as const;\n","import {\n  LanguageModelV2CallWarning,\n  LanguageModelV2Prompt,\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: LanguageModelV2Prompt;\n  systemMessageMode?: 'system' | 'developer' | 'remove';\n}): {\n  messages: OpenAIChatPrompt;\n  warnings: Array<LanguageModelV2CallWarning>;\n} {\n  const messages: OpenAIChatPrompt = [];\n  const warnings: Array<LanguageModelV2CallWarning> = [];\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 ${part.mediaType}`,\n                      });\n                    }\n                  }\n                } else if (part.mediaType === 'application/pdf') {\n                  if (part.data instanceof URL) {\n                    throw new UnsupportedFunctionalityError({\n                      functionality: 'PDF file parts with URLs',\n                    });\n                  }\n\n                  return {\n                    type: 'file',\n                    file: {\n                      filename: part.filename ?? `part-${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          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 '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 != null ? new Date(created * 1000) : undefined,\n  };\n}\n","import { LanguageModelV2FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV2FinishReason {\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 'unknown';\n  }\n}\n","import { z } from 'zod/v4';\n\n// https://platform.openai.com/docs/models\nexport type OpenAIChatModelId =\n  | 'o1'\n  | 'o1-2024-12-17'\n  | 'o1-mini'\n  | 'o1-mini-2024-09-12'\n  | 'o1-preview'\n  | 'o1-preview-2024-09-12'\n  | 'o3-mini'\n  | 'o3-mini-2025-01-31'\n  | 'o3'\n  | 'o3-2025-04-16'\n  | 'o4-mini'\n  | 'o4-mini-2025-04-16'\n  | 'gpt-4.1'\n  | 'gpt-4.1-2025-04-14'\n  | 'gpt-4.1-mini'\n  | 'gpt-4.1-mini-2025-04-14'\n  | 'gpt-4.1-nano'\n  | 'gpt-4.1-nano-2025-04-14'\n  | 'gpt-4o'\n  | 'gpt-4o-2024-05-13'\n  | 'gpt-4o-2024-08-06'\n  | 'gpt-4o-2024-11-20'\n  | 'gpt-4o-audio-preview'\n  | 'gpt-4o-audio-preview-2024-10-01'\n  | 'gpt-4o-audio-preview-2024-12-17'\n  | 'gpt-4o-search-preview'\n  | 'gpt-4o-search-preview-2025-03-11'\n  | 'gpt-4o-mini-search-preview'\n  | 'gpt-4o-mini-search-preview-2025-03-11'\n  | 'gpt-4o-mini'\n  | 'gpt-4o-mini-2024-07-18'\n  | 'gpt-4-turbo'\n  | 'gpt-4-turbo-2024-04-09'\n  | 'gpt-4-turbo-preview'\n  | 'gpt-4-0125-preview'\n  | 'gpt-4-1106-preview'\n  | 'gpt-4'\n  | 'gpt-4-0613'\n  | 'gpt-4.5-preview'\n  | 'gpt-4.5-preview-2025-02-27'\n  | 'gpt-3.5-turbo-0125'\n  | 'gpt-3.5-turbo'\n  | 'gpt-3.5-turbo-1106'\n  | 'chatgpt-4o-latest'\n  | (string & {});\n\nexport const openaiProviderOptions = z.object({\n  /**\n   * Modify the likelihood of specified tokens appearing in the completion.\n   *\n   * Accepts a JSON object that maps tokens (specified by their token ID in\n   * the GPT tokenizer) to an associated bias value from -100 to 100.\n   */\n  logitBias: z.record(z.coerce.number<string>(), z.number()).optional(),\n\n  /**\n   * Return the log probabilities of the tokens.\n   *\n   * Setting to true will return the log probabilities of the tokens that\n   * were generated.\n   *\n   * Setting to a number will return the log probabilities of the top n\n   * tokens that were generated.\n   */\n  logprobs: z.union([z.boolean(), z.number()]).optional(),\n\n  /**\n   * Whether to enable parallel function calling during tool use. Default to true.\n   */\n  parallelToolCalls: z.boolean().optional(),\n\n  /**\n   * A unique identifier representing your end-user, which can help OpenAI to\n   * monitor and detect abuse.\n   */\n  user: z.string().optional(),\n\n  /**\n   * Reasoning effort for reasoning models. Defaults to `medium`.\n   */\n  reasoningEffort: z.enum(['low', 'medium', 'high']).optional(),\n\n  /**\n   * Maximum number of completion tokens to generate. Useful for reasoning models.\n   */\n  maxCompletionTokens: z.number().optional(),\n\n  /**\n   * Whether to enable persistence in responses API.\n   */\n  store: z.boolean().optional(),\n\n  /**\n   * Metadata to associate with the request.\n   */\n  metadata: z.record(z.string().max(64), z.string().max(512)).optional(),\n\n  /**\n   * Parameters for prediction mode.\n   */\n  prediction: z.record(z.string(), z.any()).optional(),\n\n  /**\n   * Whether to use structured outputs.\n   *\n   * @default true\n   */\n  structuredOutputs: z.boolean().optional(),\n\n  /**\n   * Service tier for the request.\n   * - 'auto': Default service tier\n   * - 'flex': 50% cheaper processing at the cost of increased latency. Only available for o3 and o4-mini models.\n   * - 'priority': Higher-speed processing with predictably low latency at premium cost. Available for Enterprise customers.\n   *\n   * @default 'auto'\n   */\n  serviceTier: z.enum(['auto', 'flex', 'priority']).optional(),\n\n  /**\n   * Whether to use strict JSON schema validation.\n   *\n   * @default false\n   */\n  strictJsonSchema: z.boolean().optional(),\n});\n\nexport type OpenAIProviderOptions = z.infer<typeof openaiProviderOptions>;\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","import {\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { OpenAITools, OpenAIToolChoice } from './openai-types';\nimport { fileSearchArgsSchema } from './tool/file-search';\nimport { webSearchPreviewArgsSchema } from './tool/web-search-preview';\n\nexport function prepareTools({\n  tools,\n  toolChoice,\n  structuredOutputs,\n  strictJsonSchema,\n}: {\n  tools: LanguageModelV2CallOptions['tools'];\n  toolChoice?: LanguageModelV2CallOptions['toolChoice'];\n  structuredOutputs: boolean;\n  strictJsonSchema: boolean;\n}): {\n  tools?: OpenAITools;\n  toolChoice?: OpenAIToolChoice;\n  toolWarnings: Array<LanguageModelV2CallWarning>;\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  tools = tools?.length ? tools : undefined;\n\n  const toolWarnings: LanguageModelV2CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const openaiTools: OpenAITools = [];\n\n  for (const tool of tools) {\n    switch (tool.type) {\n      case 'function':\n        openaiTools.push({\n          type: 'function',\n          function: {\n            name: tool.name,\n            description: tool.description,\n            parameters: tool.inputSchema,\n            strict: structuredOutputs ? strictJsonSchema : undefined,\n          },\n        });\n        break;\n      case 'provider-defined':\n        switch (tool.id) {\n          case 'openai.file_search': {\n            const args = fileSearchArgsSchema.parse(tool.args);\n            openaiTools.push({\n              type: 'file_search',\n              vector_store_ids: args.vectorStoreIds,\n              max_num_results: args.maxNumResults,\n              ranking_options: args.ranking\n                ? { ranker: args.ranking.ranker }\n                : undefined,\n              filters: args.filters,\n            });\n            break;\n          }\n          case 'openai.web_search_preview': {\n            const args = webSearchPreviewArgsSchema.parse(tool.args);\n            openaiTools.push({\n              type: 'web_search_preview',\n              search_context_size: args.searchContextSize,\n              user_location: args.userLocation,\n            });\n            break;\n          }\n          default:\n            toolWarnings.push({ type: 'unsupported-tool', tool });\n            break;\n        }\n        break;\n      default:\n        toolWarnings.push({ type: 'unsupported-tool', tool });\n        break;\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: openaiTools, toolChoice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n    case 'required':\n      return { tools: openaiTools, toolChoice: type, toolWarnings };\n    case 'tool':\n      return {\n        tools: openaiTools,\n        toolChoice: {\n          type: 'function',\n          function: {\n            name: toolChoice.toolName,\n          },\n        },\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import { createProviderDefinedToolFactory } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// Filter schemas\nconst comparisonFilterSchema = z.object({\n  key: z.string(),\n  type: z.enum(['eq', 'ne', 'gt', 'gte', 'lt', 'lte']),\n  value: z.union([z.string(), z.number(), z.boolean()]),\n});\n\nconst compoundFilterSchema: z.ZodType<any> = z.object({\n  type: z.enum(['and', 'or']),\n  filters: z.array(\n    z.union([comparisonFilterSchema, z.lazy(() => compoundFilterSchema)]),\n  ),\n});\n\nconst filtersSchema = z.union([comparisonFilterSchema, compoundFilterSchema]);\n\n// Args validation schema\nexport const fileSearchArgsSchema = z.object({\n  /**\n   * List of vector store IDs to search through. If not provided, searches all available vector stores.\n   */\n  vectorStoreIds: z.array(z.string()).optional(),\n\n  /**\n   * Maximum number of search results to return. Defaults to 10.\n   */\n  maxNumResults: z.number().optional(),\n\n  /**\n   * Ranking options for the search.\n   */\n  ranking: z\n    .object({\n      ranker: z.enum(['auto', 'default-2024-08-21']).optional(),\n    })\n    .optional(),\n\n  /**\n   * A filter to apply based on file attributes.\n   */\n  filters: filtersSchema.optional(),\n});\n\nexport const fileSearch = createProviderDefinedToolFactory<\n  {\n    /**\n     * The search query to execute.\n     */\n    query: string;\n  },\n  {\n    /**\n     * List of vector store IDs to search through. If not provided, searches all available vector stores.\n     */\n    vectorStoreIds?: string[];\n\n    /**\n     * Maximum number of search results to return. Defaults to 10.\n     */\n    maxNumResults?: number;\n\n    /**\n     * Ranking options for the search.\n     */\n    ranking?: {\n      ranker?: 'auto' | 'default-2024-08-21';\n    };\n\n    /**\n     * A filter to apply based on file attributes.\n     */\n    filters?:\n      | {\n          key: string;\n          type: 'eq' | 'ne' | 'gt' | 'gte' | 'lt' | 'lte';\n          value: string | number | boolean;\n        }\n      | {\n          type: 'and' | 'or';\n          filters: any[];\n        };\n  }\n>({\n  id: 'openai.file_search',\n  name: 'file_search',\n  inputSchema: z.object({\n    query: z.string(),\n  }),\n});\n","import { createProviderDefinedToolFactory } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// Args validation schema\nexport const webSearchPreviewArgsSchema = z.object({\n  /**\n   * Search context size to use for the web search.\n   * - high: Most comprehensive context, highest cost, slower response\n   * - medium: Balanced context, cost, and latency (default)\n   * - low: Least context, lowest cost, fastest response\n   */\n  searchContextSize: z.enum(['low', 'medium', 'high']).optional(),\n\n  /**\n   * User location information to provide geographically relevant search results.\n   */\n  userLocation: z\n    .object({\n      /**\n       * Type of location (always 'approximate')\n       */\n      type: z.literal('approximate'),\n      /**\n       * Two-letter ISO country code (e.g., 'US', 'GB')\n       */\n      country: z.string().optional(),\n      /**\n       * City name (free text, e.g., 'Minneapolis')\n       */\n      city: z.string().optional(),\n      /**\n       * Region name (free text, e.g., 'Minnesota')\n       */\n      region: z.string().optional(),\n      /**\n       * IANA timezone (e.g., 'America/Chicago')\n       */\n      timezone: z.string().optional(),\n    })\n    .optional(),\n});\n\nexport const webSearchPreview = createProviderDefinedToolFactory<\n  {\n    // Web search doesn't take input parameters - it's controlled by the prompt\n  },\n  {\n    /**\n     * Search context size to use for the web search.\n     * - high: Most comprehensive context, highest cost, slower response\n     * - medium: Balanced context, cost, and latency (default)\n     * - low: Least context, lowest cost, fastest response\n     */\n    searchContextSize?: 'low' | 'medium' | 'high';\n\n    /**\n     * User location information to provide geographically relevant search results.\n     */\n    userLocation?: {\n      /**\n       * Type of location (always 'approximate')\n       */\n      type: 'approximate';\n      /**\n       * Two-letter ISO country code (e.g., 'US', 'GB')\n       */\n      country?: string;\n      /**\n       * City name (free text, e.g., 'Minneapolis')\n       */\n      city?: string;\n      /**\n       * Region name (free text, e.g., 'Minnesota')\n       */\n      region?: string;\n      /**\n       * IANA timezone (e.g., 'America/Chicago')\n       */\n      timezone?: string;\n    };\n  }\n>({\n  id: 'openai.web_search_preview',\n  name: 'web_search_preview',\n  inputSchema: z.object({}),\n});\n","import {\n  LanguageModelV2,\n  LanguageModelV2CallWarning,\n  LanguageModelV2FinishReason,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n  SharedV2ProviderMetadata,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { convertToOpenAICompletionPrompt } from './convert-to-openai-completion-prompt';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  OpenAICompletionModelId,\n  openaiCompletionProviderOptions,\n} from './openai-completion-options';\nimport {\n  openaiErrorDataSchema,\n  openaiFailedResponseHandler,\n} from './openai-error';\n\ntype OpenAICompletionConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { m