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

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

{"version":3,"sources":["../src/openai-facade.ts","../src/openai-chat-language-model.ts","../src/convert-to-openai-chat-messages.ts","../src/map-openai-chat-logprobs.ts","../src/map-openai-finish-reason.ts","../src/openai-error.ts","../src/get-response-metadata.ts","../src/openai-completion-language-model.ts","../src/convert-to-openai-completion-prompt.ts","../src/map-openai-completion-logprobs.ts","../src/openai-provider.ts","../src/openai-embedding-model.ts"],"sourcesContent":["import { loadApiKey, withoutTrailingSlash } from '@ai-sdk/provider-utils';\nimport { OpenAIChatLanguageModel } from './openai-chat-language-model';\nimport { OpenAIChatModelId, OpenAIChatSettings } from './openai-chat-settings';\nimport { OpenAICompletionLanguageModel } from './openai-completion-language-model';\nimport {\n  OpenAICompletionModelId,\n  OpenAICompletionSettings,\n} from './openai-completion-settings';\nimport { OpenAIProviderSettings } from './openai-provider';\n\n/**\n@deprecated Use `createOpenAI` instead.\n */\nexport class OpenAI {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.openai.com/v1`.\n   */\n  readonly baseURL: string;\n\n  /**\nAPI key that is being send using the `Authorization` header.\nIt defaults to the `OPENAI_API_KEY` environment variable.\n */\n  readonly apiKey?: string;\n\n  /**\nOpenAI Organization.\n   */\n  readonly organization?: string;\n\n  /**\nOpenAI project.\n   */\n  readonly project?: string;\n\n  /**\nCustom headers to include in the requests.\n   */\n  readonly headers?: Record<string, string>;\n\n  /**\n   * Creates a new OpenAI provider instance.\n   */\n  constructor(options: OpenAIProviderSettings = {}) {\n    this.baseURL =\n      withoutTrailingSlash(options.baseURL ?? options.baseUrl) ??\n      'https://api.openai.com/v1';\n    this.apiKey = options.apiKey;\n    this.organization = options.organization;\n    this.project = options.project;\n    this.headers = options.headers;\n  }\n\n  private get baseConfig() {\n    return {\n      organization: this.organization,\n      baseURL: this.baseURL,\n      headers: () => ({\n        Authorization: `Bearer ${loadApiKey({\n          apiKey: this.apiKey,\n          environmentVariableName: 'OPENAI_API_KEY',\n          description: 'OpenAI',\n        })}`,\n        'OpenAI-Organization': this.organization,\n        'OpenAI-Project': this.project,\n        ...this.headers,\n      }),\n    };\n  }\n\n  chat(modelId: OpenAIChatModelId, settings: OpenAIChatSettings = {}) {\n    return new OpenAIChatLanguageModel(modelId, settings, {\n      provider: 'openai.chat',\n      ...this.baseConfig,\n      compatibility: 'strict',\n      url: ({ path }) => `${this.baseURL}${path}`,\n    });\n  }\n\n  completion(\n    modelId: OpenAICompletionModelId,\n    settings: OpenAICompletionSettings = {},\n  ) {\n    return new OpenAICompletionLanguageModel(modelId, settings, {\n      provider: 'openai.completion',\n      ...this.baseConfig,\n      compatibility: 'strict',\n      url: ({ path }) => `${this.baseURL}${path}`,\n    });\n  }\n}\n","import {\n  InvalidResponseDataError,\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1LogProbs,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  isParsableJson,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\nimport { convertToOpenAIChatMessages } from './convert-to-openai-chat-messages';\nimport { mapOpenAIChatLogProbsOutput } from './map-openai-chat-logprobs';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport { OpenAIChatModelId, OpenAIChatSettings } from './openai-chat-settings';\nimport {\n  openAIErrorDataSchema,\n  openaiFailedResponseHandler,\n} from './openai-error';\nimport { getResponseMetadata } from './get-response-metadata';\n\ntype OpenAIChatConfig = {\n  provider: string;\n  compatibility: 'strict' | 'compatible';\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class OpenAIChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n\n  readonly modelId: OpenAIChatModelId;\n  readonly settings: OpenAIChatSettings;\n\n  private readonly config: OpenAIChatConfig;\n\n  constructor(\n    modelId: OpenAIChatModelId,\n    settings: OpenAIChatSettings,\n    config: OpenAIChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get supportsStructuredOutputs(): boolean {\n    return this.settings.structuredOutputs === true;\n  }\n\n  get defaultObjectGenerationMode() {\n    return this.supportsStructuredOutputs ? 'json' : 'tool';\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get supportsImageUrls(): boolean {\n    // image urls can be sent if downloadImages is disabled (default):\n    return !this.settings.downloadImages;\n  }\n\n  private getArgs({\n    mode,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    providerMetadata,\n  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\n\n    if (topK != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n      });\n    }\n\n    if (\n      responseFormat != null &&\n      responseFormat.type === 'json' &&\n      responseFormat.schema != null\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'JSON response format schema is not supported',\n      });\n    }\n\n    const useLegacyFunctionCalling = this.settings.useLegacyFunctionCalling;\n\n    if (useLegacyFunctionCalling && this.settings.parallelToolCalls === true) {\n      throw new UnsupportedFunctionalityError({\n        functionality: 'useLegacyFunctionCalling with parallelToolCalls',\n      });\n    }\n\n    if (useLegacyFunctionCalling && this.settings.structuredOutputs === true) {\n      throw new UnsupportedFunctionalityError({\n        functionality: 'structuredOutputs with useLegacyFunctionCalling',\n      });\n    }\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      logit_bias: this.settings.logitBias,\n      logprobs:\n        this.settings.logprobs === true ||\n        typeof this.settings.logprobs === 'number'\n          ? true\n          : undefined,\n      top_logprobs:\n        typeof this.settings.logprobs === 'number'\n          ? this.settings.logprobs\n          : typeof this.settings.logprobs === 'boolean'\n          ? this.settings.logprobs\n            ? 0\n            : undefined\n          : undefined,\n      user: this.settings.user,\n      parallel_tool_calls: this.settings.parallelToolCalls,\n\n      // standardized settings:\n      max_tokens: maxTokens,\n      temperature,\n      top_p: topP,\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      stop: stopSequences,\n      seed,\n\n      // openai specific settings:\n      max_completion_tokens:\n        providerMetadata?.openai?.maxCompletionTokens ?? undefined,\n\n      // response format:\n      response_format:\n        responseFormat?.type === 'json' ? { type: 'json_object' } : undefined,\n\n      // messages:\n      messages: convertToOpenAIChatMessages({\n        prompt,\n        useLegacyFunctionCalling,\n      }),\n    };\n\n    // reasoning models have fixed params, remove them if they are set:\n    if (isReasoningModel(this.modelId)) {\n      baseArgs.temperature = undefined;\n      baseArgs.top_p = undefined;\n      baseArgs.frequency_penalty = undefined;\n      baseArgs.presence_penalty = undefined;\n    }\n\n    switch (type) {\n      case 'regular': {\n        return {\n          args: {\n            ...baseArgs,\n            ...prepareToolsAndToolChoice({\n              mode,\n              useLegacyFunctionCalling,\n              structuredOutputs: this.settings.structuredOutputs,\n            }),\n          },\n          warnings,\n        };\n      }\n\n      case 'object-json': {\n        return {\n          args: {\n            ...baseArgs,\n            response_format:\n              this.settings.structuredOutputs === true\n                ? {\n                    type: 'json_schema',\n                    json_schema: {\n                      schema: mode.schema,\n                      strict: true,\n                      name: mode.name ?? 'response',\n                      description: mode.description,\n                    },\n                  }\n                : { type: 'json_object' },\n          },\n          warnings,\n        };\n      }\n\n      case 'object-tool': {\n        return {\n          args: useLegacyFunctionCalling\n            ? {\n                ...baseArgs,\n                function_call: {\n                  name: mode.tool.name,\n                },\n                functions: [\n                  {\n                    name: mode.tool.name,\n                    description: mode.tool.description,\n                    parameters: mode.tool.parameters,\n                  },\n                ],\n              }\n            : {\n                ...baseArgs,\n                tool_choice: {\n                  type: 'function',\n                  function: { name: mode.tool.name },\n                },\n                tools: [\n                  {\n                    type: 'function',\n                    function: {\n                      name: mode.tool.name,\n                      description: mode.tool.description,\n                      parameters: mode.tool.parameters,\n                      strict:\n                        this.settings.structuredOutputs === true\n                          ? true\n                          : undefined,\n                    },\n                  },\n                ],\n              },\n          warnings,\n        };\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { args, warnings } = this.getArgs(options);\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: args,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openAIChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n    const choice = response.choices[0];\n\n    const providerMetadata =\n      response.usage?.completion_tokens_details?.reasoning_tokens != null\n        ? {\n            openai: {\n              reasoningTokens:\n                response.usage?.completion_tokens_details?.reasoning_tokens,\n            },\n          }\n        : undefined;\n\n    return {\n      text: choice.message.content ?? undefined,\n      toolCalls:\n        this.settings.useLegacyFunctionCalling && choice.message.function_call\n          ? [\n              {\n                toolCallType: 'function',\n                toolCallId: generateId(),\n                toolName: choice.message.function_call.name,\n                args: choice.message.function_call.arguments,\n              },\n            ]\n          : choice.message.tool_calls?.map(toolCall => ({\n              toolCallType: 'function',\n              toolCallId: toolCall.id ?? generateId(),\n              toolName: toolCall.function.name,\n              args: toolCall.function.arguments!,\n            })),\n      finishReason: mapOpenAIFinishReason(choice.finish_reason),\n      usage: {\n        promptTokens: response.usage?.prompt_tokens ?? NaN,\n        completionTokens: response.usage?.completion_tokens ?? NaN,\n      },\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      response: getResponseMetadata(response),\n      warnings,\n      logprobs: mapOpenAIChatLogProbsOutput(choice.logprobs),\n      providerMetadata,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doStream']>>> {\n    // reasoning models don't support streaming, we simulate it:\n    if (isReasoningModel(this.modelId)) {\n      const result = await this.doGenerate(options);\n\n      const simulatedStream = new ReadableStream<LanguageModelV1StreamPart>({\n        start(controller) {\n          controller.enqueue({ type: 'response-metadata', ...result.response });\n\n          if (result.text) {\n            controller.enqueue({\n              type: 'text-delta',\n              textDelta: result.text,\n            });\n          }\n\n          if (result.toolCalls) {\n            for (const toolCall of result.toolCalls) {\n              controller.enqueue({\n                type: 'tool-call',\n                ...toolCall,\n              });\n            }\n          }\n\n          controller.enqueue({\n            type: 'finish',\n            finishReason: result.finishReason,\n            usage: result.usage,\n            logprobs: result.logprobs,\n            providerMetadata: result.providerMetadata,\n          });\n\n          controller.close();\n        },\n      });\n\n      return {\n        stream: simulatedStream,\n        rawCall: result.rawCall,\n        rawResponse: result.rawResponse,\n        warnings: result.warnings,\n      };\n    }\n\n    const { args, warnings } = this.getArgs(options);\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        ...args,\n        stream: true,\n\n        // only include stream_options when in strict compatibility mode:\n        stream_options:\n          this.config.compatibility === 'strict'\n            ? { include_usage: true }\n            : undefined,\n      },\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiChatChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n\n    const toolCalls: Array<{\n      id: string;\n      type: 'function';\n      function: {\n        name: string;\n        arguments: string;\n      };\n    }> = [];\n\n    let finishReason: LanguageModelV1FinishReason = 'unknown';\n    let usage: {\n      promptTokens: number | undefined;\n      completionTokens: number | undefined;\n    } = {\n      promptTokens: undefined,\n      completionTokens: undefined,\n    };\n    let logprobs: LanguageModelV1LogProbs;\n    let isFirstChunk = true;\n\n    const { useLegacyFunctionCalling } = this.settings;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof openaiChatChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\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 = {\n                promptTokens: value.usage.prompt_tokens ?? undefined,\n                completionTokens: value.usage.completion_tokens ?? undefined,\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?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            if (delta.content != null) {\n              controller.enqueue({\n                type: 'text-delta',\n                textDelta: delta.content,\n              });\n            }\n\n            const mappedLogprobs = mapOpenAIChatLogProbsOutput(\n              choice?.logprobs,\n            );\n            if (mappedLogprobs?.length) {\n              if (logprobs === undefined) logprobs = [];\n              logprobs.push(...mappedLogprobs);\n            }\n\n            const mappedToolCalls: typeof delta.tool_calls =\n              useLegacyFunctionCalling && delta.function_call != null\n                ? [\n                    {\n                      type: 'function',\n                      id: generateId(),\n                      function: delta.function_call,\n                      index: 0,\n                    },\n                  ]\n                : delta.tool_calls;\n\n            if (mappedToolCalls != null) {\n              for (const toolCallDelta of mappedToolCalls) {\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                  toolCalls[index] = {\n                    id: toolCallDelta.id,\n                    type: 'function',\n                    function: {\n                      name: toolCallDelta.function.name,\n                      arguments: toolCallDelta.function.arguments ?? '',\n                    },\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-call-delta',\n                        toolCallType: 'function',\n                        toolCallId: toolCall.id,\n                        toolName: toolCall.function.name,\n                        argsTextDelta: 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-call',\n                        toolCallType: 'function',\n                        toolCallId: toolCall.id ?? generateId(),\n                        toolName: toolCall.function.name,\n                        args: toolCall.function.arguments,\n                      });\n                    }\n                  }\n\n                  continue;\n                }\n\n                // existing tool call, merge\n                const toolCall = toolCalls[index];\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-call-delta',\n                  toolCallType: 'function',\n                  toolCallId: toolCall.id,\n                  toolName: toolCall.function.name,\n                  argsTextDelta: 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-call',\n                    toolCallType: 'function',\n                    toolCallId: toolCall.id ?? generateId(),\n                    toolName: toolCall.function.name,\n                    args: toolCall.function.arguments,\n                  });\n                }\n              }\n            }\n          },\n\n          flush(controller) {\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              logprobs,\n              usage: {\n                promptTokens: usage.promptTokens ?? NaN,\n                completionTokens: usage.completionTokens ?? NaN,\n              },\n            });\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      warnings,\n    };\n  }\n}\n\nconst openAITokenUsageSchema = z\n  .object({\n    prompt_tokens: z.number().nullish(),\n    completion_tokens: z.number().nullish(),\n    completion_tokens_details: z\n      .object({\n        reasoning_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        function_call: z\n          .object({\n            arguments: z.string(),\n            name: z.string(),\n          })\n          .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      }),\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            .nullable(),\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            function_call: z\n              .object({\n                name: z.string().optional(),\n                arguments: z.string().optional(),\n              })\n              .nullish(),\n            tool_calls: z\n              .array(\n                z.object({\n                  index: z.number(),\n                  id: z.string().nullish(),\n                  type: z.literal('function').optional(),\n                  function: z.object({\n                    name: z.string().nullish(),\n                    arguments: z.string().nullish(),\n                  }),\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              .nullable(),\n          })\n          .nullish(),\n        finish_reason: z.string().nullable().optional(),\n        index: z.number(),\n      }),\n    ),\n    usage: openAITokenUsageSchema,\n  }),\n  openAIErrorDataSchema,\n]);\n\nfunction prepareToolsAndToolChoice({\n  mode,\n  useLegacyFunctionCalling = false,\n  structuredOutputs = false,\n}: {\n  mode: Parameters<LanguageModelV1['doGenerate']>[0]['mode'] & {\n    type: 'regular';\n  };\n  useLegacyFunctionCalling?: boolean;\n  structuredOutputs?: boolean;\n}) {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  const tools = mode.tools?.length ? mode.tools : undefined;\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: undefined };\n  }\n\n  const toolChoice = mode.toolChoice;\n\n  if (useLegacyFunctionCalling) {\n    const mappedFunctions = tools.map(tool => ({\n      name: tool.name,\n      description: tool.description,\n      parameters: tool.parameters,\n    }));\n\n    if (toolChoice == null) {\n      return { functions: mappedFunctions, function_call: undefined };\n    }\n\n    const type = toolChoice.type;\n\n    switch (type) {\n      case 'auto':\n      case 'none':\n      case undefined:\n        return {\n          functions: mappedFunctions,\n          function_call: undefined,\n        };\n      case 'required':\n        throw new UnsupportedFunctionalityError({\n          functionality: 'useLegacyFunctionCalling and toolChoice: required',\n        });\n      default:\n        return {\n          functions: mappedFunctions,\n          function_call: { name: toolChoice.toolName },\n        };\n    }\n  }\n\n  const mappedTools = tools.map(tool => ({\n    type: 'function',\n    function: {\n      name: tool.name,\n      description: tool.description,\n      parameters: tool.parameters,\n      strict: structuredOutputs === true ? true : undefined,\n    },\n  }));\n\n  if (toolChoice == null) {\n    return { tools: mappedTools, tool_choice: undefined };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n    case 'required':\n      return { tools: mappedTools, tool_choice: type };\n    case 'tool':\n      return {\n        tools: mappedTools,\n        tool_choice: {\n          type: 'function',\n          function: {\n            name: toolChoice.toolName,\n          },\n        },\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new Error(`Unsupported tool choice type: ${_exhaustiveCheck}`);\n    }\n  }\n}\n\nfunction isReasoningModel(modelId: string) {\n  return modelId.startsWith('o1-');\n}\n","import {\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/provider-utils';\nimport { OpenAIChatPrompt } from './openai-chat-prompt';\n\nexport function convertToOpenAIChatMessages({\n  prompt,\n  useLegacyFunctionCalling = false,\n}: {\n  prompt: LanguageModelV1Prompt;\n  useLegacyFunctionCalling?: boolean;\n}): OpenAIChatPrompt {\n  const messages: OpenAIChatPrompt = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        messages.push({ role: 'system', content });\n        break;\n      }\n\n      case 'user': {\n        if (content.length === 1 && content[0].type === 'text') {\n          messages.push({ role: 'user', content: content[0].text });\n          break;\n        }\n\n        messages.push({\n          role: 'user',\n          content: content.map(part => {\n            switch (part.type) {\n              case 'text': {\n                return { type: 'text', text: part.text };\n              }\n              case 'image': {\n                return {\n                  type: 'image_url',\n                  image_url: {\n                    url:\n                      part.image instanceof URL\n                        ? part.image.toString()\n                        : `data:${\n                            part.mimeType ?? 'image/jpeg'\n                          };base64,${convertUint8ArrayToBase64(part.image)}`,\n\n                    // OpenAI specific extension: image detail\n                    detail: part.providerMetadata?.openai?.imageDetail,\n                  },\n                };\n              }\n              case 'file': {\n                throw new UnsupportedFunctionalityError({\n                  functionality: 'File content parts in user messages',\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.args),\n                },\n              });\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = part;\n              throw new Error(`Unsupported part: ${_exhaustiveCheck}`);\n            }\n          }\n        }\n\n        if (useLegacyFunctionCalling) {\n          if (toolCalls.length > 1) {\n            throw new UnsupportedFunctionalityError({\n              functionality:\n                'useLegacyFunctionCalling with multiple tool calls in one message',\n            });\n          }\n\n          messages.push({\n            role: 'assistant',\n            content: text,\n            function_call:\n              toolCalls.length > 0 ? toolCalls[0].function : undefined,\n          });\n        } else {\n          messages.push({\n            role: 'assistant',\n            content: text,\n            tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n          });\n        }\n\n        break;\n      }\n\n      case 'tool': {\n        for (const toolResponse of content) {\n          if (useLegacyFunctionCalling) {\n            messages.push({\n              role: 'function',\n              name: toolResponse.toolName,\n              content: JSON.stringify(toolResponse.result),\n            });\n          } else {\n            messages.push({\n              role: 'tool',\n              tool_call_id: toolResponse.toolCallId,\n              content: JSON.stringify(toolResponse.result),\n            });\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;\n}\n","import { LanguageModelV1LogProbs } from '@ai-sdk/provider';\n\ntype OpenAIChatLogProbs = {\n  content:\n    | {\n        token: string;\n        logprob: number;\n        top_logprobs:\n          | {\n              token: string;\n              logprob: number;\n            }[]\n          | null;\n      }[]\n    | null;\n};\n\nexport function mapOpenAIChatLogProbsOutput(\n  logprobs: OpenAIChatLogProbs | null | undefined,\n): LanguageModelV1LogProbs | undefined {\n  return (\n    logprobs?.content?.map(({ token, logprob, top_logprobs }) => ({\n      token,\n      logprob,\n      topLogprobs: top_logprobs\n        ? top_logprobs.map(({ token, logprob }) => ({\n            token,\n            logprob,\n          }))\n        : [],\n    })) ?? undefined\n  );\n}\n","import { LanguageModelV1FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAIFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\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';\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 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 {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1LogProbs,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\nimport { convertToOpenAICompletionPrompt } from './convert-to-openai-completion-prompt';\nimport { mapOpenAICompletionLogProbs } from './map-openai-completion-logprobs';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n  OpenAICompletionModelId,\n  OpenAICompletionSettings,\n} from './openai-completion-settings';\nimport {\n  openAIErrorDataSchema,\n  openaiFailedResponseHandler,\n} from './openai-error';\nimport { getResponseMetadata } from './get-response-metadata';\n\ntype OpenAICompletionConfig = {\n  provider: string;\n  compatibility: 'strict' | 'compatible';\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class OpenAICompletionLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n  readonly defaultObjectGenerationMode = undefined;\n\n  readonly modelId: OpenAICompletionModelId;\n  readonly settings: OpenAICompletionSettings;\n\n  private readonly config: OpenAICompletionConfig;\n\n  constructor(\n    modelId: OpenAICompletionModelId,\n    settings: OpenAICompletionSettings,\n    config: OpenAICompletionConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private getArgs({\n    mode,\n    inputFormat,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences: userStopSequences,\n    responseFormat,\n    seed,\n  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\n\n    if (topK != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n      });\n    }\n\n    if (responseFormat != null && responseFormat.type !== 'text') {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'JSON response format is not supported.',\n      });\n    }\n\n    const { prompt: completionPrompt, stopSequences } =\n      convertToOpenAICompletionPrompt({ prompt, inputFormat });\n\n    const stop = [...(stopSequences ?? []), ...(userStopSequences ?? [])];\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      echo: this.settings.echo,\n      logit_bias: this.settings.logitBias,\n      logprobs:\n        typeof this.settings.logprobs === 'number'\n          ? this.settings.logprobs\n          : typeof this.settings.logprobs === 'boolean'\n          ? this.settings.logprobs\n            ? 0\n            : undefined\n          : undefined,\n      suffix: this.settings.suffix,\n      user: this.settings.user,\n\n      // standardized settings:\n      max_tokens: maxTokens,\n      temperature,\n      top_p: topP,\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      seed,\n\n      // prompt:\n      prompt: completionPrompt,\n\n      // stop sequences:\n      stop: stop.length > 0 ? stop : undefined,\n    };\n\n    switch (type) {\n      case 'regular': {\n        if (mode.tools?.length) {\n          throw new UnsupportedFunctionalityError({\n            functionality: 'tools',\n          });\n        }\n\n        if (mode.toolChoice) {\n          throw new UnsupportedFunctionalityError({\n            functionality: 'toolChoice',\n          });\n        }\n\n        return { args: baseArgs, warnings };\n      }\n\n      case 'object-json': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'object-json mode',\n        });\n      }\n\n      case 'object-tool': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'object-tool mode',\n        });\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { args, warnings } = this.getArgs(options);\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openAICompletionResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { prompt: rawPrompt, ...rawSettings } = args;\n    const choice = response.choices[0];\n\n    return {\n      text: choice.text,\n      usage: {\n        promptTokens: response.usage.prompt_tokens,\n        completionTokens: response.usage.completion_tokens,\n      },\n      finishReason: mapOpenAIFinishReason(choice.finish_reason),\n      logprobs: mapOpenAICompletionLogProbs(choice.logprobs),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      response: getResponseMetadata(response),\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doStream']>>> {\n    const { args, warnings } = this.getArgs(options);\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: this.config.url({\n        path: '/completions',\n        modelId: this.modelId,\n      }),\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: {\n        ...args,\n        stream: true,\n\n        // only include stream_options when in strict compatibility mode:\n        stream_options:\n          this.config.compatibility === 'strict'\n            ? { include_usage: true }\n            : undefined,\n      },\n      failedResponseHandler: openaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        openaiCompletionChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { prompt: rawPrompt, ...rawSettings } = args;\n\n    let finishReason: LanguageModelV1FinishReason = 'unknown';\n    let usage: { promptTokens: number; completionTokens: number } = {\n      promptTokens: Number.NaN,\n      completionTokens: Number.NaN,\n    };\n    let logprobs: LanguageModelV1LogProbs;\n    let isFirstChunk = true;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof openaiCompletionChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\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 = {\n                promptTokens: value.usage.prompt_tokens,\n                completionTokens: value.usage.completion_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?.text != null) {\n              controller.enqueue({\n                type: 'text-delta',\n                textDelta: choice.text,\n              });\n            }\n\n            const mappedLogprobs = mapOpenAICompletionLogProbs(\n              choice?.logprobs,\n            );\n            if (mappedLogprobs?.length) {\n              if (logprobs === undefined) logprobs = [];\n              logprobs.push(...mappedLogprobs);\n            }\n          },\n\n          flush(controller) {\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              logprobs,\n              usage,\n            });\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      warnings,\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\nconst openAICompletionResponseSchema = 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      text: z.string(),\n      finish_reason: z.string(),\n      logprobs: z\n        .object({\n          tokens: z.array(z.string()),\n          token_logprobs: z.array(z.number()),\n          top_logprobs: z.array(z.record(z.string(), z.number())).nullable(),\n        })\n        .nullish(),\n    }),\n  ),\n  usage: z.object({\n    prompt_tokens: z.number(),\n    completion_tokens: z.number(),\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\nconst openaiCompletionChunkSchema = 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        text: z.string(),\n        finish_reason: z.string().nullish(),\n        index: z.number(),\n        logprobs: z\n          .object({\n            tokens: z.array(z.string()),\n            token_logprobs: z.array(z.number()),\n            top_logprobs: z.array(z.record(z.string(), z.number())).nullable(),\n          })\n          .nullish(),\n      }),\n    ),\n    usage: z\n      .object({\n        prompt_tokens: z.number(),\n        completion_tokens: z.number(),\n      })\n      .nullish(),\n  }),\n  openAIErrorDataSchema,\n]);\n","import {\n  InvalidPromptError,\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\n\nexport function convertToOpenAICompletionPrompt({\n  prompt,\n  inputFormat,\n  user = 'user',\n  assistant = 'assistant',\n}: {\n  prompt: LanguageModelV1Prompt;\n  inputFormat: 'prompt' | 'messages';\n  user?: string;\n  assistant?: string;\n}): {\n  prompt: string;\n  stopSequences?: string[];\n} {\n  // When the user supplied a prompt input, we don't transform it:\n  if (\n    inputFormat === 'prompt' &&\n    prompt.length === 1 &&\n    prompt[0].role === 'user' &&\n    prompt[0].content.length === 1 &&\n    prompt[0].content[0].type === 'text'\n  ) {\n    return { prompt: prompt[0].content[0].text };\n  }\n\n  // otherwise transform to a chat message format:\n  let text = '';\n\n  // if first message is a system message, add it to the text:\n  if (prompt[0].role === 'system') {\n    text += `${prompt[0].content}\\n\\n`;\n    prompt = prompt.slice(1);\n  }\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        throw new InvalidPromptError({\n          message: 'Unexpected system message in prompt: ${content}',\n          prompt,\n        });\n      }\n\n      case 'user': {\n        const userMessage = content\n          .map(part => {\n            switch (part.type) {\n              case 'text': {\n                return part.text;\n              }\n              case 'image': {\n                throw new UnsupportedFunctionalityError({\n                  functionality: 'images',\n                });\n              }\n            }\n          })\n          .join('');\n\n        text += `${user}:\\n${userMessage}\\n\\n`;\n        break;\n      }\n\n      case 'assistant': {\n        const assistantMessage = content\n          .map(part => {\n            switch (part.type) {\n              case 'text': {\n                return part.text;\n              }\n              case 'tool-call': {\n                throw new UnsupportedFunctionalityError({\n                  functionality: 'tool-call messages',\n                });\n              }\n            }\n          })\n          .join('');\n\n        text += `${assistant}:\\n${assistantMessage}\\n\\n`;\n        break;\n      }\n\n      case 'tool': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'tool messages',\n        });\n      }\n\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  // Assistant message prefix:\n  text += `${assistant}:\\n`;\n\n  return {\n    prompt: text,\n    stopSequences: [`\\n${user}:`],\n  };\n}\n","import { LanguageModelV1LogProbs } from '@ai-sdk/provider';\n\ntype OpenAICompletionLogProps = {\n  tokens: string[];\n  token_logprobs: number[];\n  top_logprobs: Record<string, number>[] | null;\n};\n\nexport function mapOpenAICompletionLogProbs(\n  logprobs: OpenAICompletionLogProps | null | undefined,\n): LanguageModelV1LogProbs | undefined {\n  return logprobs?.tokens.map((token, index) => ({\n    token,\n    logprob: logprobs.token_logprobs[index],\n    topLogprobs: logprobs.top_logprobs\n      ? Object.entries(logprob