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

<!-- header start --> <p align="center"> <img src="https://huggingface.co/datasets/FriendliAI/documentation-images/resolve/main/model-card-assets/friendliai.png" width="100%" alt="FriendliAI Logo"> </p> <!-- header end -->

{"version":3,"sources":["../src/friendli-provider.ts","../src/friendli-chat-language-model.ts","../src/friendli-error.ts","../src/friendli-prepare-tools.ts","../src/friendli-settings.ts","../src/friendli-tools.ts","../src/get-available-models.ts"],"sourcesContent":["import { OpenAICompatibleCompletionLanguageModel } from '@ai-sdk/openai-compatible';\nimport {\n  EmbeddingModelV3,\n  ImageModelV3,\n  LanguageModelV3,\n  NoSuchModelError,\n  ProviderV3,\n  SpeechModelV3,\n  TranscriptionModelV3,\n} from '@ai-sdk/provider';\nimport { FetchFunction, loadApiKey, withoutTrailingSlash } from '@ai-sdk/provider-utils';\n\nimport { FriendliAIChatLanguageModel } from './friendli-chat-language-model';\nimport { friendliaiErrorStructure } from './friendli-error';\nimport {\n  FriendliAILanguageModelId,\n  FriendliAIServerlessModelId,\n  FriendliAIServerlessModelIds,\n} from './friendli-settings';\nimport { friendliTools } from './friendli-tools';\nimport type { FriendliAvailableModelsResponse } from './get-available-models';\nimport { getAvailableModelsImpl } from './get-available-models';\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 | 'auto' | 'dedicated' | 'serverless' | 'serverless-tools';\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   * Whether to include usage information in the response.\n   */\n  includeUsage?: boolean;\n}\n\nexport interface FriendliAIProvider extends ProviderV3 {\n  /**\n   * Creates a model for text generation.\n   */\n  (modelId: FriendliAILanguageModelId): LanguageModelV3;\n  /**\n   * Creates a chat model for text generation.\n   */\n  languageModel(modelId: FriendliAILanguageModelId): LanguageModelV3;\n  /**\n   * Creates a chat model for text generation.\n   */\n  chat(modelId: FriendliAILanguageModelId): LanguageModelV3;\n  /**\n   * Creates a completion model for text generation.\n   */\n  completion(modelId: FriendliAILanguageModelId): LanguageModelV3;\n  /**\n   * Creates an embedding model for text generation.\n   * TODO: Implement for Dedicated users\n   */\n  embedding(modelId: string & {}): LanguageModelV3;\n  embeddingModel(modelId: string & {}): LanguageModelV3;\n  /**\n   * Returns the available models and their metadata.\n   */\n  getAvailableModels(options?: { graphqlURL?: string }): Promise<FriendliAvailableModelsResponse>;\n  embedding(modelId: string & {}): EmbeddingModelV3;\n  embeddingModel(modelId: string & {}): EmbeddingModelV3;\n  /**\n   * Creates a model for image generation.\n   * TODO: Implement for Dedicated users\n   */\n  imageModel(modelId: string & {}): ImageModelV3;\n\n  /**\n   * Creates a model for transcription.\n   * TODO: Implement for Dedicated users\n   */\n  transcription(modelId: string & {}): TranscriptionModelV3;\n\n  /**\n   * Creates a model for speech generation.\n   * TODO: Implement for Dedicated users\n   */\n  speech(modelId: string & {}): SpeechModelV3;\n\n  /**\n   * Friendli-specific tools.\n   */\n  tools: typeof friendliTools;\n}\n\n/**\nCreate an FriendliAI provider instance.\n */\nexport function createFriendli(options: FriendliAIProviderSettings = {}): 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    baseURL: string | 'dedicated' | 'serverless' | 'serverless-tools' | undefined\n  ): {\n    baseURL: string;\n    type: 'dedicated' | 'serverless' | 'serverless-tools' | 'custom';\n  } => {\n    const FriendliBaseURL = {\n      serverless: 'https://api.friendli.ai/serverless/v1',\n      serverless_tools: 'https://api.friendli.ai/serverless/tools/v1',\n      dedicated: 'https://api.friendli.ai/dedicated/v1',\n    };\n\n    // Ignore options if baseURL is specified\n    const customBaseURL = withoutTrailingSlash(baseURL);\n    if (\n      typeof customBaseURL === 'string' &&\n      customBaseURL !== 'dedicated' &&\n      customBaseURL !== 'serverless' &&\n      customBaseURL !== 'serverless-tools'\n    ) {\n      return { baseURL: customBaseURL, type: 'custom' };\n    }\n\n    switch (baseURL) {\n      case 'dedicated':\n        return {\n          baseURL: FriendliBaseURL.dedicated,\n          type: 'dedicated',\n        };\n      case 'serverless':\n        return {\n          baseURL: FriendliBaseURL.serverless,\n          type: 'serverless',\n        };\n      case 'serverless-tools':\n        return {\n          baseURL: FriendliBaseURL.serverless_tools,\n          type: 'serverless-tools',\n        };\n      default:\n        if (FriendliAIServerlessModelIds.includes(modelId as FriendliAIServerlessModelId)) {\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\n  const createLanguageModel = (modelId: FriendliAILanguageModelId) => {\n    const { baseURL, type } = baseURLAutoSelect(modelId, options.baseURL);\n\n    return new FriendliAIChatLanguageModel(modelId, {\n      provider: `friendliai.${type}.chat`,\n      url: ({ path }: { path: string }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      includeUsage: options.includeUsage,\n    });\n  };\n\n  const createCompletionModel = (modelId: FriendliAILanguageModelId) => {\n    const { baseURL, type } = baseURLAutoSelect(modelId, options.baseURL);\n\n    return new OpenAICompatibleCompletionLanguageModel(modelId, {\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 createTextEmbeddingModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n  };\n  const createImageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n  const createTranscriptionModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'languageModel' });\n  };\n  const createSpeechModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'languageModel' });\n  };\n\n  const provider = (modelId: FriendliAILanguageModelId) => createLanguageModel(modelId);\n\n  provider.languageModel = createLanguageModel;\n  provider.chat = createLanguageModel;\n  provider.completion = createCompletionModel;\n\n  // TODO: Implement for Dedicated users\n  provider.embedding = createTextEmbeddingModel;\n  provider.embeddingModel = createTextEmbeddingModel;\n  (provider as unknown as FriendliAIProvider).getAvailableModels = async (opts?: {\n    graphqlURL?: string;\n  }) => {\n    const defaultURL = 'https://api-internal.friendli.ai/api/graphql';\n    const graphqlURL = opts?.graphqlURL ?? defaultURL;\n    const apiKey = options.apiKey;\n    const teamId = options.teamId;\n    const headers = options.headers;\n    return getAvailableModelsImpl({ apiKey, teamId, headers, graphqlURL });\n  };\n  provider.imageModel = createImageModel;\n  provider.transcription = createTranscriptionModel;\n  provider.speech = createSpeechModel;\n\n  provider.tools = friendliTools;\n\n  // 'getAvailableModels' is declared here.\n  return provider as unknown as FriendliAIProvider;\n}\n\n/**\n * Default FriendliAI provider instance.\n */\nexport const friendli = createFriendli();\n","import { MetadataExtractor, ProviderErrorStructure } from '@ai-sdk/openai-compatible';\nimport {\n  convertOpenAICompatibleChatUsage,\n  convertToOpenAICompatibleChatMessages,\n  getResponseMetadata,\n  mapOpenAICompatibleFinishReason,\n} from '@ai-sdk/openai-compatible/internal';\nimport {\n  APICallError,\n  InvalidResponseDataError,\n  LanguageModelV3,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3Prompt,\n  LanguageModelV3StreamPart,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  FetchFunction,\n  generateId,\n  isParsableJson,\n  ParseResult,\n  parseProviderOptions,\n  postJsonToApi,\n  ResponseHandler,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nimport {\n  friendliaiErrorSchema,\n  friendliaiErrorStructure,\n  friendliaiFailedResponseHandler,\n  tryWrapFriendliJsonEnvelopeError,\n} from './friendli-error';\nimport { prepareTools } from './friendli-prepare-tools';\nimport { FriendliAILanguageModelId } from './friendli-settings';\n\nexport type OpenAICompatibleChatConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n  includeUsage?: boolean;\n  errorStructure?: ProviderErrorStructure<z.infer<typeof friendliaiErrorSchema>>;\n  metadataExtractor?: MetadataExtractor;\n\n  /**\n   * Whether the model supports structured outputs.\n   */\n  supportsStructuredOutputs?: boolean;\n\n  /**\n   * The supported URLs for the model.\n   */\n  supportedUrls?: () => LanguageModelV3['supportedUrls'];\n};\n\ntype HostedToolExecutionChunk = {\n  name: string;\n  status: 'ENDED' | 'STARTED' | 'ERRORED' | 'UPDATING';\n  message: null;\n  parameters: Array<{ name: string; value: string }>;\n  result: string | null;\n  error: { type: 'INVALID_PARAMETER' | 'UNKNOWN'; msg: string } | null;\n  timestamp: number;\n  usage: null;\n  tool_call_id: string | null;\n};\n\ntype OpenAIChatUsage = {\n  prompt_tokens?: number | null;\n  completion_tokens?: number | null;\n  total_tokens?: number | null;\n  prompt_tokens_details?: { cached_tokens?: number | null } | null;\n  completion_tokens_details?: {\n    reasoning_tokens?: number | null;\n    accepted_prediction_tokens?: number | null;\n    rejected_prediction_tokens?: number | null;\n  } | null;\n};\n\ntype OpenAIChatToolCallDelta = {\n  index: number;\n  id?: string | null;\n  type?: 'function' | null;\n  function?: { name?: string | null; arguments?: string | null } | null;\n};\n\ntype OpenAIChatDelta = {\n  role?: 'assistant' | null;\n  content?: string | null;\n  reasoning_content?: string | null;\n  tool_calls?: OpenAIChatToolCallDelta[] | null;\n};\n\ntype OpenAIChatChoice = {\n  delta?: OpenAIChatDelta | null;\n  finish_reason?: string | null;\n};\n\ntype OpenAIChatChunk = {\n  id?: string | null;\n  created?: number | null;\n  model?: string | null;\n  choices: OpenAIChatChoice[];\n  usage?: OpenAIChatUsage | null;\n};\n\nfunction isRecord(value: unknown): value is Record<string, unknown> {\n  return typeof value === 'object' && value != null;\n}\n\nfunction isHostedToolExecutionChunk(value: unknown): value is HostedToolExecutionChunk {\n  if (!isRecord(value)) return false;\n  return (\n    typeof value.status === 'string' &&\n    typeof value.name === 'string' &&\n    Array.isArray(value.parameters)\n  );\n}\n\nfunction getChunkErrorMessage(value: unknown): string | undefined {\n  if (!isRecord(value)) return undefined;\n\n  if (typeof value.message === 'string') {\n    return value.message;\n  }\n\n  const nestedError = value.error;\n  if (isRecord(nestedError) && typeof nestedError.message === 'string') {\n    return nestedError.message;\n  }\n\n  return undefined;\n}\n\nfunction isOpenAIChatChunk(value: unknown): value is OpenAIChatChunk {\n  if (!isRecord(value)) return false;\n  return Array.isArray(value.choices);\n}\n\n/**\n * Adds reasoning_content field to assistant messages for interleaved thinking support.\n * This enables models like MiniMax-M2.5, GLM-5.1 to maintain reasoning context across conversation turns.\n *\n * Note: We use `reasoning_content` which is consistent with FriendliAI's response format.\n */\nfunction addReasoningToMessages<T extends Array<{ role: string; reasoning_content?: string }>>(\n  prompt: LanguageModelV3Prompt,\n  messages: T\n): T {\n  // Track assistant message indices in both arrays\n  let promptAssistantIndex = 0;\n\n  for (const promptMessage of prompt) {\n    if (promptMessage.role === 'assistant') {\n      // Extract reasoning from the original prompt\n      const reasoningText = promptMessage.content\n        .filter((part): part is { type: 'reasoning'; text: string } => part.type === 'reasoning')\n        .map((part) => part.text)\n        .join('\\n');\n\n      if (reasoningText) {\n        // Find the corresponding assistant message in converted messages\n        let messagesAssistantIndex = 0;\n        for (let i = 0; i < messages.length; i++) {\n          if (messages[i].role === 'assistant') {\n            if (messagesAssistantIndex === promptAssistantIndex) {\n              messages[i].reasoning_content = reasoningText;\n              break;\n            }\n            messagesAssistantIndex++;\n          }\n        }\n      }\n      promptAssistantIndex++;\n    }\n  }\n\n  return messages;\n}\n\nexport class FriendliAIChatLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\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  private readonly chunkSchema; // type inferred via constructor\n\n  constructor(modelId: FriendliAILanguageModelId, config: OpenAICompatibleChatConfig) {\n    this.modelId = modelId;\n    // this.settings = settings\n    this.config = config;\n\n    const errorStructure = friendliaiErrorStructure;\n    this.chunkSchema = createOpenAICompatibleChatChunkSchema(errorStructure.errorSchema);\n\n    this.failedResponseHandler = friendliaiFailedResponseHandler;\n\n    this.supportsStructuredOutputs = config.supportsStructuredOutputs ?? true;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get supportedUrls() {\n    return this.config.supportedUrls?.() ?? {};\n  }\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    providerOptions,\n    stopSequences,\n    responseFormat,\n    seed,\n    toolChoice,\n    tools,\n    stream,\n  }: Parameters<LanguageModelV3['doGenerate']>[0] & {\n    stream: boolean;\n  }) {\n    const warnings: SharedV3Warning[] = [];\n\n    const friendliOptions = await parseProviderOptions({\n      provider: 'friendliai',\n      providerOptions,\n      schema: friendliProviderOptionsSchema,\n    });\n\n    const legacyFriendliOptions = await parseProviderOptions({\n      provider: 'friendli',\n      providerOptions,\n      schema: friendliProviderOptionsSchema,\n    });\n\n    const options = {\n      ...legacyFriendliOptions,\n      ...friendliOptions,\n    };\n\n    if (\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !this.supportsStructuredOutputs\n    ) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'responseFormat',\n        details: 'JSON response format schema is only supported with structuredOutputs',\n      });\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n    });\n\n    const isToolsPresent = openaiTools != null && openaiTools.length > 0;\n\n    if (isToolsPresent && (responseFormat != null || options?.regex != null)) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'responseFormat',\n        details: 'response_format is not supported when tools are present.',\n      });\n    }\n\n    return {\n      args: {\n        // >>> hard-coded default options >>>\n        parse_reasoning: true,\n        // <<< hard-coded default options <<<\n\n        model: this.modelId,\n\n        // standardized settings:\n        stream: stream,\n        max_tokens: maxOutputTokens,\n        temperature,\n        top_p: topP,\n        top_k: topK,\n        frequency_penalty: frequencyPenalty,\n        presence_penalty: presencePenalty,\n        response_format:\n          isToolsPresent === false\n            ? responseFormat?.type === 'json'\n              ? this.supportsStructuredOutputs === true && responseFormat.schema != null\n                ? {\n                    type: 'json_schema',\n                    json_schema: {\n                      schema: responseFormat.schema,\n                      name: responseFormat.name ?? 'response',\n                      description: responseFormat.description,\n                    },\n                  }\n                : { type: 'json_object' }\n              : options?.regex != null\n                ? {\n                    type: 'regex',\n                    schema: options.regex,\n                  }\n                : undefined\n            : undefined,\n\n        stop: stopSequences,\n        seed,\n\n        min_p: options?.minP,\n        repetition_penalty: options?.repetitionPenalty,\n        xtc_threshold: options?.xtcThreshold,\n        xtc_probability: options?.xtcProbability,\n\n        ...(options?.chat_template_kwargs\n          ? { chat_template_kwargs: options.chat_template_kwargs }\n          : {}),\n\n        // messages:\n        // Use addReasoningToMessages to include reasoning_content in assistant messages\n        // for interleaved thinking support\n        messages: addReasoningToMessages(prompt, convertToOpenAICompatibleChatMessages(prompt)),\n\n        // tools:\n        tools: openaiTools,\n        tool_choice: openaiToolChoice,\n        parallel_tool_calls: options?.parallelToolCalls,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV3['doGenerate']>[0]\n  ): Promise<Awaited<ReturnType<LanguageModelV3['doGenerate']>>> {\n    const { args, warnings } = await this.getArgs({ ...options, stream: false });\n\n    const body = JSON.stringify(args);\n\n    const response = await (async () => {\n      try {\n        return 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: this.failedResponseHandler,\n          successfulResponseHandler: createJsonResponseHandler(OpenAICompatibleChatResponseSchema),\n          abortSignal: options.abortSignal,\n          fetch: this.config.fetch,\n        });\n      } catch (error) {\n        const wrappedError = await tryWrapFriendliJsonEnvelopeError(error);\n\n        if (wrappedError != null) {\n          throw wrappedError;\n        }\n\n        throw error;\n      }\n    })();\n\n    const { responseHeaders, value: responseBody, rawValue: rawResponse } = response;\n\n    const choice = responseBody.choices[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // text content:\n    const text = choice.message.content;\n    if (text != null && text.length > 0) {\n      content.push({ type: 'text', text });\n    }\n\n    // reasoning content:\n    const reasoning = choice.message.reasoning_content;\n    if (reasoning != null && reasoning.length > 0) {\n      content.push({\n        type: 'reasoning',\n        text: reasoning,\n      });\n    }\n\n    // tool calls:\n    if (choice.message.tool_calls != null) {\n      for (const toolCall of choice.message.tool_calls) {\n        content.push({\n          type: 'tool-call',\n          toolCallId: toolCall.id ?? generateId(),\n          toolName: toolCall.function.name,\n          input: toolCall.function.arguments!,\n        });\n      }\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n        raw: choice.finish_reason ?? undefined,\n      },\n      usage: convertOpenAICompatibleChatUsage(responseBody.usage),\n      // providerMetadata,\n      request: { body },\n      response: {\n        ...getResponseMetadata(responseBody),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV3['doStream']>[0]\n  ): Promise<Awaited<ReturnType<LanguageModelV3['doStream']>>> {\n    const { args, warnings } = await this.getArgs({ ...options, stream: true });\n\n    const body = {\n      ...args,\n      stream: true,\n\n      // only include stream_options when in strict compatibility mode:\n      stream_options: this.config.includeUsage ? { include_usage: true } : undefined,\n    };\n\n    const metadataExtractor = this.config.metadataExtractor?.createStreamExtractor();\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: this.failedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(this.chunkSchema),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const toolCalls: Array<{\n      id: string;\n      type: 'function';\n      function: {\n        name: string;\n        arguments: string;\n      };\n      hasFinished: boolean;\n    }> = [];\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage: z.infer<typeof openaiCompatibleTokenUsageSchema> | undefined = undefined;\n    let isFirstChunk = true;\n    const providerOptionsName = 'friendliai';\n\n    // Track IDs for text and reasoning events\n    let currentTextId: string | null = null;\n    let currentReasoningId: string | null = null;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof this.chunkSchema>>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          // NOTE: Chunk values can contain OpenAI-compatible deltas, hosted tool events, and error events.\n          // We narrow with type guards for safe handling.\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n            const value: unknown = chunk.value;\n\n            metadataExtractor?.processChunk(chunk.rawValue);\n\n            // hosted tool execution case\n            if (isHostedToolExecutionChunk(value)) {\n              const toolCallId = value.tool_call_id ?? generateId();\n              switch (value.status) {\n                case 'STARTED':\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId,\n                    toolName: value.name,\n                    input: JSON.stringify(\n                      Object.fromEntries(value.parameters.map((p) => [p.name, p.value]))\n                    ),\n                    providerExecuted: true,\n                  });\n                  break;\n\n                case 'UPDATING':\n                  // Optionally handle progress if needed, but LanguageModelV3StreamPart doesn't have a direct \"progress\" type for tools\n                  break;\n\n                case 'ENDED':\n                  controller.enqueue({\n                    type: 'tool-result',\n                    toolCallId,\n                    toolName: value.name,\n                    result: value.result ?? '',\n                  });\n                  break;\n\n                case 'ERRORED':\n                  finishReason = { unified: 'error', raw: undefined };\n                  controller.enqueue({\n                    type: 'tool-result',\n                    toolCallId,\n                    toolName: value.name,\n                    result: value.error?.msg ?? 'Unknown error',\n                    isError: true,\n                  });\n                  break;\n\n                default:\n                  finishReason = { unified: 'error', raw: undefined };\n                  controller.enqueue({\n                    type: 'error',\n                    error: new Error(`Unsupported tool call status: ${value.status}`),\n                  });\n              }\n              return;\n            }\n\n            const chunkErrorMessage = getChunkErrorMessage(value);\n            if (chunkErrorMessage != null) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({ type: 'error', error: chunkErrorMessage });\n              return;\n            }\n\n            if (!isOpenAIChatChunk(value)) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({\n                type: 'error',\n                error: new Error('Unsupported chunk shape'),\n              });\n              return;\n            }\n\n            const chunkValue = value;\n\n            if (isFirstChunk) {\n              isFirstChunk = false;\n\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(chunkValue),\n              });\n            }\n\n            if (chunkValue.usage != null) {\n              usage = chunkValue.usage;\n            }\n\n            const choice = chunkValue.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = {\n                unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n                raw: choice.finish_reason,\n              };\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            // enqueue reasoning before text deltas:\n            if (delta.reasoning_content != null) {\n              if (currentReasoningId == null) {\n                currentReasoningId = generateId();\n                // Enqueue reasoning-start event for the first reasoning delta\n                controller.enqueue({\n                  type: 'reasoning-start',\n                  id: currentReasoningId,\n                });\n              }\n              controller.enqueue({\n                type: 'reasoning-delta',\n                id: currentReasoningId,\n                delta: delta.reasoning_content,\n              });\n            }\n\n            if (delta.content != null) {\n              if (currentTextId == null) {\n                currentTextId = generateId();\n                // Enqueue text-start event for the first text delta\n                controller.enqueue({\n                  type: 'text-start',\n                  id: currentTextId,\n                });\n              }\n              controller.enqueue({\n                type: 'text-delta',\n                id: currentTextId,\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                // 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                    hasFinished: false,\n                  };\n\n                  controller.enqueue({\n                    type: 'tool-input-start',\n                    id: toolCallDelta.id,\n                    toolName: toolCallDelta.function.name,\n                  });\n\n                  const toolCall = toolCalls[index];\n\n                  if (toolCall.function?.name != null && toolCall.function?.arguments != null) {\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 += 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\n          flush(controller) {\n            // Emit end events before finish\n            if (currentReasoningId != null) {\n              controller.enqueue({\n                type: 'reasoning-end',\n                id: currentReasoningId,\n              });\n            }\n\n            if (currentTextId != null) {\n              controller.enqueue({\n                type: 'text-end',\n                id: currentTextId,\n              });\n            }\n\n            for (const toolCall of toolCalls.filter(\n              (pendingToolCall) => !pendingToolCall.hasFinished\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            }\n\n            const providerMetadata: SharedV3ProviderMetadata = {\n              [providerOptionsName]: {},\n              ...metadataExtractor?.buildMetadata(),\n            };\n            if (usage?.completion_tokens_details?.accepted_prediction_tokens != null) {\n              providerMetadata[providerOptionsName].acceptedPredictionTokens =\n                usage.completion_tokens_details.accepted_prediction_tokens;\n            }\n            if (usage?.completion_tokens_details?.rejected_prediction_tokens != null) {\n              providerMetadata[providerOptionsName].rejectedPredictionTokens =\n                usage.completion_tokens_details.rejected_prediction_tokens;\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertOpenAICompatibleChatUsage(usage),\n              providerMetadata,\n            });\n          },\n        })\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nconst openaiCompatibleTokenUsageSchema = 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 OpenAICompatibleChatResponseSchema = 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        reasoning_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      }),\n      finish_reason: z.string().nullish(),\n    })\n  ),\n  usage: openaiCompatibleTokenUsageSchema,\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 createOpenAICompatibleChatChunkSchema = <ERROR_SCHEMA extends z.ZodType>(\n  errorSchema: ERROR_SCHEMA\n) =>\n  z.union([\n    z.object({\n      id: z.string().nullish(),\n      created: z.number().nullish(),\n      model: z.string().nullish(),\n      choices: z.array(\n        z.object({\n          delta: z\n            .object({\n              role: z.enum(['assistant']).nullish(),\n              content: z.string().nullish(),\n              reasoning_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            })\n            .nullish(),\n          finish_reason: z.string().nullish(),\n        })\n      ),\n      usage: openaiCompatibleTokenUsageSchema,\n    }),\n    z.object({\n      name: z.string(),\n      status: z.enum(['ENDED', 'STARTED', 'ERRORED', 'UPDATING']),\n      message: z.null(),\n      parameters: z.array(\n        z.object({\n          name: z.string(),\n          value: z.string(),\n        })\n      ),\n      result: z.string().nullable(),\n      error: z\n        .object({\n          type: z.enum(['INVALID_PARAMETER', 'UNKNOWN']),\n          msg: z.string(),\n        })\n        .nullable(),\n      timestamp: z.number(),\n      usage: z.null(),\n      tool_call_id: z.string().nullable(),\n    }),\n    errorSchema,\n  ]);\n\nconst friendliProviderOptionsSchema = z.object({\n  /**\n   * Whether to enable parallel function calling during tool use. Default to true.\n   */\n  parallelToolCalls: z.boolean().nullish(),\n\n  /**\n   * BETA FEATURE: You can write a regular expression to force output that satisfies that regular expression.\n   */\n  // regex: z.instanceof(RegExp).nullish(),\n  regex: z.string().nullish(),\n\n  chat_template_kwargs: z.record(z.string(), z.any()).nullish(),\n\n  /**\n   * A scaling factor used to determine the minimum token probability threshold.\n   */\n  minP: z.number().nullish(),\n\n  /**\n   * Penalizes tokens that have already appeared in the generated result.\n   */\n  repetitionPenalty: z.number().nullish(),\n\n  /**\n   * A probability threshold used to identify “top choice” tokens for exclusion in XTC sampling.\n   */\n  xtcThreshold: z.number().nullish(),\n\n  /**\n   * The probability that XTC (Exclude Top Choices) filtering will be applied for each sampling decision.\n   */\n  xtcProbability: z.number().nullish(),\n});\n\nexport type FriendliProviderOptions = z.infer<typeof friendliProviderOptionsSchema>;\n","import { ProviderErrorStructure } from '@ai-sdk/openai-compatible';\nimport { APICallError } from '@ai-sdk/provider';\nimport { type ResponseHandler, safeParseJSON } from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\n\nconst friendliErrorResponseSchema = z.object({\n  message: z.string(),\n  error: z.record(z.string(), z.any()).optional(),\n});\n\nconst openAIStyleErrorResponseSchema = z\n  .object({\n    error: z\n      .object({\n        message: z.string(),\n      })\n      .loose(),\n  })\n  .loose();\n\nexport const friendliaiErrorSchema = z.union([\n  // OpenAI/OpenRouter style error: { \"error\": { \"message\": \"...\" } }\n  openAIStyleErrorResponseSchema,\n  // Friendli style error: { \"message\": \"...\", \"error\": { ... } }\n  friendliErrorResponseSchema,\n]);\n\nexport type FriendliAIErrorData = z.infer<typeof friendliaiErrorSchema>;\n\nexport const friendliaiErrorStructure: ProviderErrorStructure<FriendliAIErrorData> = {\n  errorSchema: friendliaiErrorSchema,\n  errorToMessage: (data) => {\n    if (\n      typeof data === 'object' &&\n      data != null &&\n      'error' in data &&\n      typeof data.error === 'object' &&\n      data.error != null &&\n      'message' in data.error &&\n      typeof data.error.message === 'string'\n    ) {\n      return data.error.message;\n    }\n\n    if (\n      typeof data === 'object' &&\n      data != null &&\n      'message' in data &&\n      typeof data.message === 'string'\n    ) {\n      return data.message;\n    }\n\n    return 'Unknown error';\n  },\n};\n\nexport const friendliaiFailedResponseHandler: ResponseHandler<APICallError> = async ({\n  response,\n  url,\n  requestBodyValues,\n}) => {\n  const responseBody = await response.text();\n  const responseHeaders: Record<string, string> = {};\n  response.headers.forEach((value, key) => {\n    responseHeaders[key] = value;\n  });\n\n  const baseErrorOptions = {\n    url,\n    requestBodyValues,\n    statusCode: response.status,\n    responseHeaders,\n    responseBody,\n  } as const;\n\n  const trimmedBody = responseBody.trim();\n\n  if (trimmedBody === '') {\n    const fallback = response.statusText || `Request failed with status ${response.status}`;\n    return {\n      responseHeaders,\n      value: new APICallError({\n        message: fallback,\n        ...baseErrorOptions,\n      }),\n    };\n  }\n\n  const parsedError = await safeParseJSON({\n    text: responseBody,\n    schema: friendliaiErrorSchema,\n  });\n\n  if (parsedError.success) {\n    return {\n      responseHeaders,\n      value: new APICallError({\n        message: friendliaiErrorStructure.errorToMessage(parsedError.value),\n        data: parsedError.value,\n        ...baseErrorOptions,\n      }),\n    };\n  }\n\n  const fallback =\n    trimmedBody || response.statusText || `Request failed with status ${response.status}`;\n\n  return {\n    responseHeaders,\n    value: new APICallError({\n      message: fallback,\n      cause: parsedError.error,\n      ...baseErrorOptions,\n    }),\n  };\n};\n\nexport const tryWrapFriendliJsonEnvelopeError = async (\n  error: unknown\n): Promise<APICallError | undefined> => {\n  if (!APICallError.isInstance(error)) {\n    return undefined;\n  }\n\n  const responseBody = error.responseBody;\n\n  if (typeof responseBody !== 'string' || responseBody.trim() === '') {\n    return undefined;\n  }\n\n  const parsedError = await safeParseJSON({\n    text: responseBody,\n    schema: friendliaiErrorSchema,\n  });\n\n  if (!parsedError.success) {\n    return undefined;\n  }\n\n  return new APICallError({\n    message: friendliaiErrorStructure.errorToMessage(parsedError.value),\n    url: error.url,\n    requestBodyValues: error.requestBodyValues,\n    statusCode: error.statusCode,\n    responseHeaders: error.responseHeaders,\n    responseBody: error.responseBody,\n    cause: error,\n    isRetryable: error.isRetryable,\n    data: parsedError.value,\n  });\n};\n","import {\n  LanguageModelV3CallOptions,\n  SharedV3Warning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\n\nexport function prepareTools({\n  tools,\n  toolChoice,\n}: {\n  tools: LanguageModelV3CallOptions['tools'];\n  toolChoice?: LanguageModelV3CallOptions['toolChoice'];\n}): {\n  tools:\n    | undefined\n    | Array<{\n        type: string;\n        files?: string[];\n      }>\n    | Array<{\n        type: 'function';\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }>;\n  toolChoice:\n    | { type: 'function'; function: { name: string } }\n    | 'auto'\n    | 'none'\n    | 'required'\n    | undefined;\n  toolWarnings: SharedV3Warning[];\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: SharedV3Warning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const openaiCompatTools: Array<\n    | {\n        type: 'function';\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }\n    | {\n        type: string;\n      }\n  > = [];\n\n  for (const tool of tools) {\n    if (tool.type === 'provider') {\n      openaiCompatTools.push({\n        // NOTE: Friendli tool-assisted API expects provider tool types like \"web:search\".\n        // We derive it from the provider tool id (e.g. \"friendli.web:search\" -> \"web:search\")\n        // instead of tool.name (often \"web_search\").\n        type: tool.id.split('.')[1] ?? 'unknown',\n      });\n    } else {\n      openaiCompatTools.push({\n        type: 'function',\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.inputSchema,\n        },\n      });\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: openaiCompatTools, 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: openaiCompatTools, toolChoice: type, toolWarnings };\n    case 'tool':\n      return {\n        tools: openaiCompatTools,\n        toolChoice: {\n          type: 'function',\n          function: { name: toolChoice.toolName },\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","// https://friendli.ai/product/model-apis\n// Below is just a subset of the available models.\nexport const FriendliAIServerlessModelIds = [\n  'google/gemma-4-31B-it',\n  'zai-org/GLM-5.1',\n  'zai-org/GLM-5',\n  'meta-llama/Llama-3.3-70B-Instruct',\n  'meta-llama-3.3-70b-instruct',\n  'meta-llama/Llama-3.1-8B-Instruct',\n  'meta-llama-3.1-8b-instruct',\n  'Qwen/Qwen3-235B-A22B-Instruct-2507',\n  'deepseek-ai/DeepSeek-V3.2',\n  'openai/whisper-large-v3',\n  'MiniMaxAI/MiniMax-M2.5',\n  'LGAI-EXAONE/K-EXAONE-236B-A23B',\n] as const;\n\nexport type FriendliAIServerlessModelId = (typeof FriendliAIServerlessModelIds)[number];\n\nexport type FriendliAILanguageModelId = FriendliAIServerlessModelId | (string & {});\n","import { createProviderToolFactoryWithOutputSchema, type Tool } from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\n\n/**\n * Friendli built-in tools for serverless tool-assisted API.\n *\n * @remarks\n * These tools are currently in **Beta**. While we strive to provide a stable\n * and reliable experience, this feature is still under active development.\n *\n * @see https://friendli.ai/docs/guides/model-apis/tool-assisted-api\n */\n\nconst inputSchema = z.object({}).loose();\nconst outputSchema = z.unknown();\n\nexport const webSearchTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.web:search',\n  inputSchema,\n  outputSchema,\n});\n\nexport const webUrlTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.web:url',\n  inputSchema,\n  outputSchema,\n});\n\nexport const mathCalendarTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.math:calendar',\n  inputSchema,\n  outputSchema,\n});\n\nexport const mathStatisticsTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.math:statistics',\n  inputSchema,\n  outputSchema,\n});\n\nexport const mathCalculatorTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.math:calculator',\n  inputSchema,\n  outputSchema,\n});\n\nexport const codePythonInterpreterTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.code:python-interpreter',\n  inputSchema,\n  outputSchema,\n});\n\nexport const linkupSearchTool = createProviderToolFactoryWithOutputSchema({\n  id: 'friendli.linkup:search',\n  inputSchema,\n  outputSchema,\n});\n\n/**\n * Web search tool - searches the web for information.\n * @beta\n */\nfunction webSearch(): Tool {\n  return webSearchTool({});\n}\n\n/**\n * Web URL tool - fetches content from a specific URL.\n * @beta\n */\nfunction webUrl(): Tool {\n  return webUrlTool({});\n}\n\n/**\n * Math calendar tool - performs calendar-related calculations.\n * @beta\n */\nfunction mathCalendar(): Tool {\n  return mathCalendarTool({});\n}\n\n/**\n * Math statistics tool - performs statistical calculations.\n * @beta\n */\nfunction mathStatistics(): Tool {\n  return mathStatisticsTool({});\n}\n\n/**\n * Math calculator tool - performs arithmetic calculations.\n * @beta\n */\nfunction mathCalculator(): Tool {\n  return mathCalculatorTool({});\n}\n\n/**\n * Python interpreter tool - executes Python code.\n * @beta\n */\nfunction codePythonInterpreter(): Tool {\n  return codePythonInterpreterTool({});\n}\n\n/**\n * Linkup search tool - searches the web for real-time information with citations.\n * @see https://www.linkup.so\n */\nfunction linkupSearch(): Tool {\n  return linkupSearchTool({});\n}\n\nexport const friendliTools = {\n  webSearch,\n  webUrl,\n  mathCalendar,\n  mathStatistics,\n  mathCalculator,\n  codePythonInterpreter,\n  linkupSearch,\n};\n","import { loadApiKey } from '@ai-sdk/provider-utils';\n\ntype Pricing = {\n  inputToken?: number;\n  cachedInputToken?: number;\n  outputToken?: number;\n  responseTime?: number;\n  audioMinute?: number;\n  unitType?: 'TOKEN' | 'SECOND' | 'AUDIO_MINUTE';\n  currency?: string;\n  unit?: string;\n};\n\nexport type FriendliAvailableModel = {\n  id: string;\n  name?: string | null;\n  description?: string | null;\n  pricing?: Pricing;\n  warm?: boolean;\n  col