@friendliai/ai-provider/dist/index.mjs.map

Learn how to use the FriendliAI provider for the Vercel AI SDK.

{"version":3,"sources":["../src/friendli-provider.ts","../src/friendli-settings.ts","../src/friendli-chat-language-model.ts","../src/friendli-error.ts","../src/friendli-prepare-tools.ts"],"sourcesContent":["import { LanguageModelV1, NoSuchModelError } from '@ai-sdk/provider'\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils'\nimport { OpenAICompatibleCompletionLanguageModel } from '@ai-sdk/openai-compatible'\n\nimport {\n  FriendliAILanguageModelId,\n  FriendliAIBetaChatModelId,\n  FriendliAIChatSettings,\n  FriendliAICompletionSettings,\n  FriendliAIServerlessModelIds,\n  FriendliAIServerlessModelId,\n} from './friendli-settings'\nimport { FriendliAIChatLanguageModel } from './friendli-chat-language-model'\nimport { friendliaiErrorStructure } from './friendli-error'\n\nexport interface FriendliAIProviderSettings {\n  /**\n   * FriendliAI API key. (FRIENDLI__TOKEN)\n   */\n  apiKey?: string\n  /**\n   * Base URL for the API calls.\n   */\n  baseURL?: string\n  /**\n   * Custom headers to include in the requests.\n   */\n  headers?: Record<string, string>\n  /**\n   * FriendliAI Team ID.\n   */\n  teamId?: string\n  /**\n   * Custom fetch implementation. You can use it as a middleware to intercept requests,\n   * or to provide a custom fetch implementation for e.g. testing.\n   */\n  fetch?: FetchFunction\n}\n\nexport interface FriendliAIProvider {\n  /**\n   * Creates a model for text generation.\n   */\n  (\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  /**\n   * A model that has not yet been officially released\n   */\n  beta(\n    modelId: FriendliAIBetaChatModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  /**\n   * Creates a chat model for text generation.\n   */\n  chat(\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  chatModel(\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  /**\n   * Creates a completion model for text generation.\n   */\n  completion(\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  completionModel(\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  /**\n   * Creates a text embedding model for text generation.\n   */\n  embedding(\n    modelId: string & {},\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n  textEmbeddingModel(\n    modelId: string & {},\n    settings?: FriendliAIChatSettings,\n  ): LanguageModelV1\n}\n\n/**\nCreate an FriendliAI provider instance.\n */\nexport function createFriendli(\n  options: FriendliAIProviderSettings = {},\n): FriendliAIProvider {\n  const getHeaders = () => ({\n    Authorization: `Bearer ${loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: 'FRIENDLI_TOKEN',\n      description: 'FRIENDLI_TOKEN',\n    })}`,\n    'X-Friendli-Team': options.teamId,\n    ...options.headers,\n  })\n\n  const baseURLAutoSelect = (\n    modelId: string,\n    endpoint: 'auto' | 'dedicated' | 'serverless' | 'beta',\n    baseURL: string | undefined,\n    tools?: Array<unknown>,\n  ): {\n    baseURL: string\n    type: 'dedicated' | 'serverless' | 'tools' | 'custom' | 'beta'\n  } => {\n    // Ignore options if baseURL is specified\n    const customBaseURL = withoutTrailingSlash(baseURL)\n    if (typeof customBaseURL === 'string') {\n      return { baseURL: customBaseURL, type: 'custom' }\n    }\n\n    const FriendliBaseURL = {\n      beta: 'https://api.friendli.ai/serverless/beta',\n      serverless: 'https://api.friendli.ai/serverless/v1',\n      tools: 'https://api.friendli.ai/serverless/tools/v1',\n      dedicated: 'https://api.friendli.ai/dedicated/v1',\n    }\n\n    if (endpoint === 'beta') {\n      return {\n        baseURL: FriendliBaseURL.beta,\n        type: 'beta',\n      }\n    }\n\n    if (\n      // If the endpoint setting is serverless or auto and the model is floating on serverless,\n      endpoint === 'serverless' ||\n      (endpoint === 'auto' &&\n        Object.values(FriendliAIServerlessModelIds).includes(\n          modelId as FriendliAIServerlessModelId,\n        ))\n    ) {\n      if (tools && tools.length > 0) {\n        return {\n          baseURL: FriendliBaseURL.tools,\n          type: 'tools',\n        }\n      }\n\n      return {\n        baseURL: FriendliBaseURL.serverless,\n        type: 'serverless',\n      }\n    } else {\n      return {\n        baseURL: FriendliBaseURL.dedicated,\n        type: 'dedicated',\n      }\n    }\n  }\n\n  const createChatModel = (\n    modelId: FriendliAILanguageModelId,\n    settings: FriendliAIChatSettings = {},\n  ) => {\n    const { baseURL, type } = baseURLAutoSelect(\n      modelId,\n      settings.endpoint || 'auto',\n      options.baseURL,\n      settings.tools,\n    )\n\n    return new FriendliAIChatLanguageModel(modelId, settings, {\n      provider: `friendliai.${type}.chat`,\n      url: ({ path }: { path: string }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      defaultObjectGenerationMode: 'json',\n    })\n  }\n\n  const createCompletionModel = (\n    modelId: FriendliAILanguageModelId,\n    settings: FriendliAICompletionSettings = {},\n  ) => {\n    const { baseURL, type } = baseURLAutoSelect(\n      modelId,\n      settings.endpoint || 'auto',\n      options.baseURL,\n    )\n\n    return new OpenAICompatibleCompletionLanguageModel(modelId, settings, {\n      provider: `friendliai.${type}.completion`,\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      errorStructure: friendliaiErrorStructure,\n    })\n  }\n\n  const createBetaModel = (\n    modelId: FriendliAIBetaChatModelId,\n    settings: FriendliAIChatSettings = {},\n  ) => {\n    const { baseURL, type } = baseURLAutoSelect(\n      modelId,\n      'beta',\n      options.baseURL,\n    )\n\n    return new FriendliAIChatLanguageModel(modelId, settings, {\n      provider: `friendliai.${type}.chat`,\n      url: ({ path }: { path: string }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      defaultObjectGenerationMode: 'json',\n    })\n  }\n\n  const createTextEmbeddingModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' })\n  }\n\n  const provider = function (\n    modelId: FriendliAILanguageModelId,\n    settings?: FriendliAIChatSettings,\n  ) {\n    return createChatModel(modelId, settings)\n  }\n\n  provider.beta = createBetaModel\n\n  provider.chat = createChatModel\n  provider.chatModel = createChatModel\n\n  provider.completion = createCompletionModel\n  provider.completionModel = createCompletionModel\n\n  provider.embedding = createTextEmbeddingModel\n  provider.textEmbeddingModel = createTextEmbeddingModel\n\n  return provider as FriendliAIProvider\n}\n\nexport const friendli = createFriendli({})\n","import {\n  OpenAICompatibleChatSettings,\n  OpenAICompatibleCompletionSettings,\n} from '@ai-sdk/openai-compatible'\n\n// https://friendli.ai/products/serverless-endpoints\n// Below is just a subset of the available models.\nexport const FriendliAIServerlessModelIds = [\n  'meta-llama-3.1-8b-instruct',\n  'meta-llama-3.1-70b-instruct',\n  'meta-llama-3.3-70b-instruct',\n  'deepseek-r1',\n] as const\n\nexport type FriendliAIServerlessModelId =\n  (typeof FriendliAIServerlessModelIds)[number]\n\nexport type FriendliAILanguageModelId =\n  | FriendliAIServerlessModelId\n  | (string & {})\n\nexport type FriendliAIBetaChatModelId =\n  | 'llama-3.2-11b-vision-instruct'\n  | (string & {})\n\ninterface FriendliAISharedSettings {\n  /**\n   * Sets the endpoint to which the request will be sent.\n   * auto: automatically selected based on model_id\n   * dedicated: Fixed to \"/dedicated/v1\"\n   * serverless: automatically selected as one of \"/serverless/beta\", \"/serverless/v1\", or \"/serverless/tools/v1\"\n   * Ignored if baseURL is specified.\n   */\n  endpoint?: 'auto' | 'dedicated' | 'serverless'\n}\n\nexport interface FriendliAIChatSettings\n  extends FriendliAISharedSettings,\n    OpenAICompatibleChatSettings {\n  /**\n   * BETA FEATURE: Include the model's training loss in the response.\n   */\n  tools?: Array<{\n    type:\n      | 'web:url'\n      | 'web:search'\n      | 'math:calendar'\n      | 'math:statistics'\n      | 'math:calculator'\n      | 'code:python-interpreter'\n  }>\n\n  /**\n   * Whether to enable parallel function calling during tool use. Default to true.\n   */\n  parallelToolCalls?: boolean\n\n  /**\n   * BETA FEATURE: You can write a regular expression to force output that satisfies that regular expression.\n   */\n  regex?: RegExp\n}\n\nexport interface FriendliAICompletionSettings\n  extends FriendliAISharedSettings,\n    OpenAICompatibleCompletionSettings {}\n","import {\n  APICallError,\n  InvalidResponseDataError,\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1ProviderMetadata,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider'\nimport {\n  ParseResult,\n  ResponseHandler,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonErrorResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  isParsableJson,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils'\nimport {\n  convertToOpenAICompatibleChatMessages,\n  getResponseMetadata,\n  mapOpenAICompatibleFinishReason,\n  OpenAICompatibleChatConfig,\n} from '@ai-sdk/openai-compatible/internal'\n\nimport { z } from 'zod'\n\nimport {\n  FriendliAILanguageModelId,\n  FriendliAIChatSettings,\n} from './friendli-settings'\nimport {\n  friendliaiErrorSchema,\n  friendliaiErrorStructure,\n  friendliaiFailedResponseHandler,\n} from './friendli-error'\nimport { prepareTools } from './friendli-prepare-tools'\n\nexport class FriendliAIChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1'\n\n  readonly supportsStructuredOutputs: boolean\n\n  readonly modelId: FriendliAILanguageModelId\n  readonly settings: FriendliAIChatSettings\n\n  private readonly config: OpenAICompatibleChatConfig\n  private readonly failedResponseHandler: ResponseHandler<APICallError>\n\n  constructor(\n    modelId: FriendliAILanguageModelId,\n    settings: FriendliAIChatSettings,\n    config: OpenAICompatibleChatConfig,\n  ) {\n    this.modelId = modelId\n    this.settings = settings\n    this.config = config\n\n    this.failedResponseHandler = createJsonErrorResponseHandler(\n      friendliaiErrorStructure,\n    )\n\n    this.supportsStructuredOutputs = config.supportsStructuredOutputs ?? true\n  }\n\n  get defaultObjectGenerationMode(): 'json' | 'tool' {\n    return this.config.defaultObjectGenerationMode ?? 'json'\n  }\n\n  get provider(): string {\n    return this.config.provider\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  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type\n\n    const warnings: LanguageModelV1CallWarning[] = []\n\n    if (\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !this.supportsStructuredOutputs\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 baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\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      top_k: topK,\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      response_format:\n        responseFormat?.type === 'json'\n          ? this.supportsStructuredOutputs === true &&\n            responseFormat.schema != null\n            ? {\n                type: 'json_schema',\n                json_schema: {\n                  schema: responseFormat.schema,\n                  description: responseFormat.description,\n                },\n              }\n            : { type: 'json_object' }\n          : undefined,\n\n      stop: stopSequences,\n      seed,\n\n      // messages:\n      messages: convertToOpenAICompatibleChatMessages(prompt),\n    }\n\n    if (this.settings.regex != null && type !== 'regular') {\n      throw new UnsupportedFunctionalityError({\n        functionality:\n          'egular expression is only supported with regular mode (generateText, streamText)',\n      })\n    }\n\n    switch (type) {\n      case 'regular': {\n        if (this.settings.regex != null) {\n          if (this.settings.tools != null || mode.tools != null) {\n            throw new UnsupportedFunctionalityError({\n              functionality:\n                'Regular expression and tools cannot be used together. Use either regular expression or tools.',\n            })\n          }\n\n          return {\n            args: {\n              ...baseArgs,\n              response_format: {\n                type: 'regex',\n                schema: this.settings.regex.source,\n              },\n            },\n            warnings,\n          }\n        }\n\n        const { tools, tool_choice, toolWarnings } = prepareTools({\n          mode,\n          tools: this.settings.tools,\n        })\n\n        return {\n          args: { ...baseArgs, tools, tool_choice },\n          warnings: [...warnings, ...toolWarnings],\n        }\n      }\n\n      case 'object-json': {\n        return {\n          args: {\n            ...baseArgs,\n            response_format:\n              this.supportsStructuredOutputs === true && mode.schema != null\n                ? {\n                    type: 'json_schema',\n                    json_schema: {\n                      schema: mode.schema,\n                      description: mode.description,\n                    },\n                  }\n                : { type: 'json_object' },\n          },\n          warnings,\n        }\n      }\n\n      case 'object-tool': {\n        return {\n          args: {\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                },\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 body = JSON.stringify({ ...args, stream: false })\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: false,\n      },\n      failedResponseHandler: this.failedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        friendliAIChatResponseSchema,\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    return {\n      text: choice.message.content ?? undefined,\n      toolCalls: choice.message.tool_calls?.map((toolCall) => ({\n        toolCallType: 'function',\n        toolCallId: toolCall.id ?? generateId(),\n        toolName: toolCall.function.name,\n        args:\n          typeof toolCall.function.arguments! === 'string'\n            ? toolCall.function.arguments!\n            : JSON.stringify(toolCall.function.arguments),\n      })),\n      finishReason: mapOpenAICompatibleFinishReason(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      request: { body },\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 body = JSON.stringify({ ...args, stream: true })\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        stream_options: { include_usage: true },\n      },\n      failedResponseHandler: friendliaiFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        friendliaiChatChunkSchema,\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 isFirstChunk = true\n\n    let providerMetadata: LanguageModelV1ProviderMetadata | undefined\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof friendliaiChatChunkSchema>>,\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            // hosted tool execution case\n            if ('status' in value) {\n              switch (value.status) {\n                case 'STARTED':\n                  break\n\n                case 'UPDATING':\n                  break\n\n                case 'ENDED':\n                  break\n\n                case 'ERRORED':\n                  finishReason = 'error'\n                  break\n\n                default:\n                  finishReason = 'error'\n                  controller.enqueue({\n                    type: 'error',\n                    error: new Error(\n                      `Unsupported tool call status: ${value.status}`,\n                    ),\n                  })\n              }\n              return\n            }\n\n            // handle error chunks:\n            if ('message' in value) {\n              console.error('Error chunk:', value)\n              finishReason = 'error'\n              controller.enqueue({ type: 'error', error: value.message })\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 = mapOpenAICompatibleFinishReason(\n                choice.finish_reason,\n              )\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            if (delta.tool_calls != null) {\n              for (const toolCallDelta of delta.tool_calls) {\n                const index = toolCallDelta.index\n\n                // Tool call start. FriendliAI 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              usage: {\n                promptTokens: usage.promptTokens ?? NaN,\n                completionTokens: usage.completionTokens ?? 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