@langchain/community/dist/chat_models/alibaba_tongyi.cjs.map

Third-party integrations for LangChain.js

{"version":3,"file":"alibaba_tongyi.cjs","names":["AIMessage","convertLangChainToolCallToOpenAI","ChatMessage","BaseChatModel","RunnableLambda","RunnablePassthrough","RunnableSequence","ChatGenerationChunk","AIMessageChunk","IterableReadableStream"],"sources":["../../src/chat_models/alibaba_tongyi.ts"],"sourcesContent":["import { type CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n  BaseChatModel,\n  BaseChatModelCallOptions,\n  BindToolsInput,\n  type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport {\n  AIMessage,\n  type BaseMessage,\n  ChatMessage,\n  AIMessageChunk,\n  type OpenAIToolCall,\n  type UsageMetadata,\n  type ToolMessage,\n} from \"@langchain/core/messages\";\nimport { type ChatResult } from \"@langchain/core/outputs\";\nimport { ChatGenerationChunk } from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport { IterableReadableStream } from \"@langchain/core/utils/stream\";\nimport {\n  convertLangChainToolCallToOpenAI,\n  makeInvalidToolCall,\n  parseToolCall,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport {\n  BaseLanguageModelInput,\n  StructuredOutputMethodOptions,\n  ToolDefinition,\n} from \"@langchain/core/language_models/base\";\nimport { type ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport {\n  Runnable,\n  RunnableLambda,\n  RunnablePassthrough,\n  RunnableSequence,\n} from \"@langchain/core/runnables\";\nimport {\n  getSchemaDescription,\n  InteropZodType,\n  isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport { toJsonSchema } from \"@langchain/core/utils/json_schema\";\n\n/**\n * Type representing the role of a message in the Tongyi chat model.\n */\nexport type TongyiMessageRole = \"system\" | \"assistant\" | \"user\" | \"tool\";\n\n/**\n * Interface representing a message in the Tongyi chat model.\n */\ninterface TongyiToolCall {\n  id?: string;\n  type?: string;\n  index?: number;\n  function?: {\n    name?: string;\n    arguments?: string | Record<string, unknown>;\n  };\n}\n\ninterface TongyiMessage {\n  role: TongyiMessageRole;\n  content: string;\n  tool_call_id?: string;\n  tool_calls?: TongyiToolCall[];\n}\n\ntype TongyiFinishReason = \"stop\" | \"tool_calls\" | \"length\" | \"null\" | null;\ntype TongyiToolChoice =\n  | \"auto\"\n  | \"none\"\n  | {\n      type: \"function\";\n      function: {\n        name: string;\n      };\n    };\n\ninterface TongyiChoiceMessage {\n  role?: string;\n  content?: string | null;\n  tool_calls?: TongyiToolCall[] | null;\n}\n\ninterface TongyiResponseChoice {\n  index?: number;\n  finish_reason?: TongyiFinishReason;\n  message?: TongyiChoiceMessage;\n  delta?: TongyiChoiceMessage;\n  tool_calls?: TongyiToolCall[];\n}\n\n/**\n * Interface representing a request for a chat completion.\n *\n * See https://help.aliyun.com/zh/dashscope/developer-reference/model-square/\n */\ninterface ChatCompletionRequest {\n  model:\n    | (string & NonNullable<unknown>)\n    | \"qwen-turbo\"\n    | \"qwen-plus\"\n    | \"qwen-max\"\n    | \"qwen-max-1201\"\n    | \"qwen-max-longcontext\"\n    // 通义千问开源系列\n    | \"qwen-7b-chat\"\n    | \"qwen-14b-chat\"\n    | \"qwen-72b-chat\"\n    // LLAMA2\n    | \"llama2-7b-chat-v2\"\n    | \"llama2-13b-chat-v2\"\n    // 百川\n    | \"baichuan-7b-v1\"\n    | \"baichuan2-13b-chat-v1\"\n    | \"baichuan2-7b-chat-v1\"\n    // ChatGLM\n    | \"chatglm3-6b\"\n    | \"chatglm-6b-v2\";\n  input: {\n    messages: TongyiMessage[];\n  };\n  parameters: {\n    stream?: boolean;\n    result_format?: \"text\" | \"message\";\n    seed?: number | null;\n    max_tokens?: number | null;\n    top_p?: number | null;\n    top_k?: number | null;\n    repetition_penalty?: number | null;\n    temperature?: number | null;\n    enable_search?: boolean | null;\n    incremental_output?: boolean | null;\n    parallel_tool_calls?: boolean | null;\n    tools?: ToolDefinition[];\n    tool_choice?: TongyiToolChoice;\n  };\n}\n\n/**\n * Interface representing a response from a chat completion.\n */\ninterface ChatCompletionResponse {\n  code?: string;\n  message?: string;\n  request_id?: string;\n  requestId?: string;\n  usage?: {\n    output_tokens: number;\n    input_tokens: number;\n    total_tokens: number;\n  };\n  output?: {\n    text?: string;\n    finish_reason?: TongyiFinishReason;\n    choices?: TongyiResponseChoice[];\n  };\n}\n\nexport interface ChatAlibabaTongyiCallOptions extends BaseChatModelCallOptions {\n  tools?: BindToolsInput[];\n  parallel_tool_calls?: boolean;\n  parallelToolCalls?: boolean;\n}\n\n/**\n * Interface defining the input to the ChatAlibabaTongyi class.\n */\ninterface AlibabaTongyiChatInput {\n  /**\n   * Model name to use. Available options are: qwen-turbo, qwen-plus, qwen-max, or Other compatible models.\n   * Alias for `model`\n   * @default \"qwen-turbo\"\n   */\n  modelName: string;\n\n  /** Model name to use. Available options are: qwen-turbo, qwen-plus, qwen-max, or Other compatible models.\n   * @default \"qwen-turbo\"\n   */\n  model: string;\n\n  /** Whether to stream the results or not. Defaults to false. */\n  streaming?: boolean;\n\n  /** Messages to pass as a prefix to the prompt */\n  prefixMessages?: TongyiMessage[];\n\n  /**\n   * API key to use when making requests. Defaults to the value of\n   * `ALIBABA_API_KEY` environment variable.\n   */\n  alibabaApiKey?: string;\n\n  /**\n   * Region for the Alibaba Tongyi API endpoint.\n   *\n   * Available base URLs (used with `/api/v1/services/aigc/text-generation/generation`):\n   * - 'china' (default): https://dashscope.aliyuncs.com/\n   * - 'singapore': https://dashscope-intl.aliyuncs.com/\n   * - 'us': https://dashscope-us.aliyuncs.com/\n   *\n   * @default \"china\"\n   */\n  region?: \"china\" | \"singapore\" | \"us\";\n\n  /** Amount of randomness injected into the response. Ranges\n   * from 0 to 1 (0 is not included). Use temp closer to 0 for analytical /\n   * multiple choice, and temp closer to 1 for creative\n   * and generative tasks. Defaults to 0.95.\n   */\n  temperature?: number;\n\n  /** Total probability mass of tokens to consider at each step. Range\n   * from 0 to 1.0. Defaults to 0.8.\n   */\n  topP?: number;\n\n  topK?: number;\n\n  enableSearch?: boolean;\n\n  maxTokens?: number;\n\n  seed?: number;\n\n  /** Penalizes repeated tokens according to frequency. Range\n   * from 1.0 to 2.0. Defaults to 1.0.\n   */\n  repetitionPenalty?: number;\n\n  /** Experimental passthrough to allow parallel tool calls. */\n  parallelToolCalls?: boolean;\n\n  /** Custom API URL. Overrides auto-detected URL based on region and model type.\n   * If not provided, text models use the text-generation endpoint and\n   * VL (vision-language) models use the multimodal-generation endpoint.\n   */\n  apiUrl?: string;\n}\n\n/**\n * Function that extracts the custom role of a generic chat message.\n * @param message Chat message from which to extract the custom role.\n * @returns The custom role of the chat message.\n */\nfunction extractGenericMessageCustomRole(message: ChatMessage) {\n  if (![\"system\", \"assistant\", \"user\", \"tool\"].includes(message.role)) {\n    console.warn(`Unknown message role: ${message.role}`);\n  }\n\n  return message.role as TongyiMessageRole;\n}\n\nfunction normalizeToolCall(rawToolCall: TongyiToolCall): TongyiToolCall {\n  const rawArguments = rawToolCall.function?.arguments;\n  const normalizedArguments =\n    typeof rawArguments === \"string\"\n      ? rawArguments\n      : JSON.stringify(rawArguments ?? {});\n  return {\n    ...rawToolCall,\n    function: rawToolCall.function\n      ? {\n          ...rawToolCall.function,\n          arguments: normalizedArguments,\n        }\n      : undefined,\n  };\n}\n\nfunction normalizeToolChoice(\n  toolChoice: BaseChatModelCallOptions[\"tool_choice\"] | undefined\n): TongyiToolChoice | undefined {\n  if (toolChoice === undefined) {\n    return undefined;\n  }\n  if (toolChoice === \"auto\" || toolChoice === \"none\") {\n    return toolChoice;\n  }\n  if (toolChoice === \"any\" || toolChoice === \"required\") {\n    console.warn(\n      `ChatAlibabaTongyi received tool_choice=\"${toolChoice}\", which DashScope does not support directly. Falling back to \"auto\" (forced tool use is not guaranteed).`\n    );\n    return \"auto\";\n  }\n  if (typeof toolChoice === \"string\") {\n    return {\n      type: \"function\",\n      function: { name: toolChoice },\n    };\n  }\n  if (typeof toolChoice === \"object\" && toolChoice !== null) {\n    if (\n      \"type\" in toolChoice &&\n      toolChoice.type === \"function\" &&\n      \"function\" in toolChoice &&\n      typeof toolChoice.function === \"object\" &&\n      toolChoice.function !== null &&\n      \"name\" in toolChoice.function &&\n      typeof toolChoice.function.name === \"string\"\n    ) {\n      return {\n        type: \"function\",\n        function: {\n          name: toolChoice.function.name,\n        },\n      };\n    }\n    if (\n      \"type\" in toolChoice &&\n      toolChoice.type === \"tool\" &&\n      \"name\" in toolChoice &&\n      typeof toolChoice.name === \"string\"\n    ) {\n      return {\n        type: \"function\",\n        function: { name: toolChoice.name },\n      };\n    }\n    if (\"type\" in toolChoice && toolChoice.type === \"auto\") {\n      return \"auto\";\n    }\n    if (\"type\" in toolChoice && toolChoice.type === \"none\") {\n      return \"none\";\n    }\n  }\n  throw new Error(\n    `Unsupported tool_choice value for ChatAlibabaTongyi: ${JSON.stringify(\n      toolChoice\n    )}`\n  );\n}\n\nfunction convertRawToolCallsToToolCallChunks(\n  rawToolCalls: TongyiToolCall[]\n): ToolCallChunk[] {\n  return rawToolCalls.map((rawToolCall) => {\n    const normalizedToolCall = normalizeToolCall(rawToolCall);\n    return {\n      type: \"tool_call_chunk\",\n      id: normalizedToolCall.id,\n      name: normalizedToolCall.function?.name,\n      args: normalizedToolCall.function?.arguments as string | undefined,\n      index: normalizedToolCall.index,\n    };\n  });\n}\n\nfunction convertRawToolCallsToOpenAIToolCalls(\n  rawToolCalls: TongyiToolCall[]\n): OpenAIToolCall[] {\n  return rawToolCalls.map((rawToolCall, index) => {\n    const normalizedToolCall = normalizeToolCall(rawToolCall);\n    return {\n      id: normalizedToolCall.id ?? `tool_call_${index}`,\n      type: \"function\",\n      function: {\n        name: normalizedToolCall.function?.name ?? \"\",\n        arguments: (normalizedToolCall.function?.arguments ?? \"{}\") as string,\n      },\n      index: normalizedToolCall.index,\n    };\n  });\n}\n\nfunction mergeToolCallStringValue(\n  previousValue: string | undefined,\n  deltaValue: string | undefined\n): string | undefined {\n  if (deltaValue === undefined) {\n    return previousValue;\n  }\n  if (previousValue === undefined) {\n    return deltaValue;\n  }\n  if (deltaValue.startsWith(previousValue)) {\n    return deltaValue;\n  }\n  if (previousValue.endsWith(deltaValue)) {\n    return previousValue;\n  }\n  return `${previousValue}${deltaValue}`;\n}\n\nfunction mergeToolCallDelta(\n  existingToolCall: TongyiToolCall | undefined,\n  deltaToolCall: TongyiToolCall\n): TongyiToolCall {\n  const existingArgs = normalizeToolCall(existingToolCall ?? {}).function\n    ?.arguments as string | undefined;\n  const deltaArgs = normalizeToolCall(deltaToolCall).function?.arguments as\n    | string\n    | undefined;\n  const existingName = existingToolCall?.function?.name;\n  const deltaName = deltaToolCall.function?.name;\n\n  return {\n    id: mergeToolCallStringValue(existingToolCall?.id, deltaToolCall.id),\n    index: deltaToolCall.index ?? existingToolCall?.index,\n    type: deltaToolCall.type ?? existingToolCall?.type,\n    function: {\n      name: mergeToolCallStringValue(existingName, deltaName),\n      arguments: mergeToolCallStringValue(existingArgs, deltaArgs),\n    },\n  };\n}\n\nfunction getToolCallDeltaKey(toolCall: TongyiToolCall, fallbackIndex: number) {\n  if (toolCall.index !== undefined) {\n    return `index:${toolCall.index}`;\n  }\n  if (toolCall.id) {\n    return `id:${toolCall.id}`;\n  }\n  return `fallback:${fallbackIndex}`;\n}\n\nfunction applyToolCallDeltas(\n  toolCallState: Map<string, TongyiToolCall>,\n  deltaToolCalls: TongyiToolCall[]\n): TongyiToolCall[] {\n  deltaToolCalls.forEach((deltaToolCall, index) => {\n    const key = getToolCallDeltaKey(deltaToolCall, index);\n    const mergedToolCall = mergeToolCallDelta(\n      toolCallState.get(key),\n      deltaToolCall\n    );\n    toolCallState.set(key, mergedToolCall);\n  });\n  return [...toolCallState.values()];\n}\n\nfunction parseRawToolCalls(rawToolCalls: TongyiToolCall[], partial = false) {\n  const toolCalls: Array<{\n    name: string;\n    args: Record<string, unknown>;\n    type: \"tool_call\";\n    id?: string;\n  }> = [];\n  const invalidToolCalls: ReturnType<typeof makeInvalidToolCall>[] = [];\n  for (const rawToolCall of rawToolCalls) {\n    const normalizedToolCall = normalizeToolCall(rawToolCall);\n    try {\n      const parsedToolCall = parseToolCall(normalizedToolCall, {\n        returnId: true,\n        partial,\n      });\n      if (parsedToolCall) {\n        toolCalls.push(\n          parsedToolCall as {\n            name: string;\n            args: Record<string, unknown>;\n            type: \"tool_call\";\n            id?: string;\n          }\n        );\n      }\n    } catch (error) {\n      const errorMessage =\n        typeof error === \"object\" &&\n        error !== null &&\n        \"message\" in error &&\n        typeof error.message === \"string\"\n          ? error.message\n          : \"Failed to parse tool call\";\n      invalidToolCalls.push(\n        makeInvalidToolCall(normalizedToolCall, errorMessage)\n      );\n    }\n  }\n  return { toolCalls, invalidToolCalls };\n}\n\nfunction convertMessagesToTongyiParams(\n  messages: BaseMessage[]\n): TongyiMessage[] {\n  return messages.map((message): TongyiMessage => {\n    if (typeof message.content !== \"string\") {\n      throw new Error(\n        \"ChatAlibabaTongyi only supports string message content. Complex content types (arrays, objects) are not supported.\"\n      );\n    }\n    const completionParam: TongyiMessage = {\n      role: messageToTongyiRole(message),\n      content: message.content,\n    };\n    if (AIMessage.isInstance(message) && !!message.tool_calls?.length) {\n      completionParam.tool_calls = message.tool_calls.map(\n        convertLangChainToolCallToOpenAI\n      );\n      return completionParam;\n    }\n    if (message.additional_kwargs.tool_calls != null) {\n      completionParam.tool_calls = message.additional_kwargs\n        .tool_calls as TongyiToolCall[];\n    }\n    if ((message as ToolMessage).tool_call_id != null) {\n      completionParam.tool_call_id = (message as ToolMessage).tool_call_id;\n    }\n    return completionParam;\n  });\n}\n\nfunction extractOutputMessage(output?: ChatCompletionResponse[\"output\"]): {\n  text: string;\n  finishReason?: TongyiFinishReason;\n  rawToolCalls: TongyiToolCall[];\n} {\n  if (!output) {\n    return { text: \"\", finishReason: undefined, rawToolCalls: [] };\n  }\n  if (output.choices?.length) {\n    // Keep first-choice semantics in non-stream responses for compatibility.\n    const firstChoice = output.choices[0];\n    const choiceMessage = firstChoice.message ?? firstChoice.delta;\n    return {\n      text: choiceMessage?.content ?? \"\",\n      finishReason: firstChoice.finish_reason ?? output.finish_reason,\n      rawToolCalls: choiceMessage?.tool_calls ?? firstChoice.tool_calls ?? [],\n    };\n  }\n  return {\n    text: output.text ?? \"\",\n    finishReason: output.finish_reason,\n    rawToolCalls: [],\n  };\n}\n\nfunction extractOutputFromStreamChunk(\n  output?: ChatCompletionResponse[\"output\"]\n): {\n  text: string;\n  finishReason?: TongyiFinishReason;\n  rawToolCalls: TongyiToolCall[];\n} {\n  if (!output) {\n    return { text: \"\", finishReason: undefined, rawToolCalls: [] };\n  }\n  if (output.choices?.length) {\n    let text = \"\";\n    let finishReason = output.finish_reason;\n    const rawToolCalls: TongyiToolCall[] = [];\n    for (const choice of output.choices) {\n      const choiceMessage = choice.delta ?? choice.message;\n      text += choiceMessage?.content ?? \"\";\n      if (choice.finish_reason) {\n        finishReason = choice.finish_reason;\n      }\n      if (choiceMessage?.tool_calls?.length) {\n        rawToolCalls.push(...choiceMessage.tool_calls);\n      } else if (choice.tool_calls?.length) {\n        rawToolCalls.push(...choice.tool_calls);\n      }\n    }\n    return { text, finishReason, rawToolCalls };\n  }\n  return {\n    text: output.text ?? \"\",\n    finishReason: output.finish_reason,\n    rawToolCalls: [],\n  };\n}\n\n/**\n * Function that converts a base message to a Tongyi message role.\n * @param message Base message to convert.\n * @returns The Tongyi message role.\n */\nfunction messageToTongyiRole(message: BaseMessage): TongyiMessageRole {\n  const type = message._getType();\n  switch (type) {\n    case \"ai\":\n      return \"assistant\";\n    case \"human\":\n      return \"user\";\n    case \"system\":\n      return \"system\";\n    case \"tool\":\n      return \"tool\";\n    case \"function\":\n      throw new Error(\"Function messages not supported\");\n    case \"generic\": {\n      if (!ChatMessage.isInstance(message))\n        throw new Error(\"Invalid generic chat message\");\n      return extractGenericMessageCustomRole(message);\n    }\n    default:\n      throw new Error(`Unknown message type: ${type}`);\n  }\n}\n\n/**\n * Wrapper around Ali Tongyi large language models that use the Chat endpoint.\n *\n * To use you should have the `ALIBABA_API_KEY`\n * environment variable set.\n *\n * @augments BaseLLM\n * @augments AlibabaTongyiChatInput\n * @example\n * ```typescript\n * // Default - uses China region\n * const qwen = new ChatAlibabaTongyi({\n *   alibabaApiKey: \"YOUR-API-KEY\",\n * });\n *\n * // Specify region explicitly\n * const qwen = new ChatAlibabaTongyi({\n *   model: \"qwen-turbo\",\n *   temperature: 1,\n *   region: \"singapore\", // or \"us\" or \"china\"\n *   alibabaApiKey: \"YOUR-API-KEY\",\n * });\n *\n * const messages = [new HumanMessage(\"Hello\")];\n *\n * await qwen.call(messages);\n * ```\n */\nexport class ChatAlibabaTongyi\n  extends BaseChatModel<ChatAlibabaTongyiCallOptions>\n  implements AlibabaTongyiChatInput\n{\n  static lc_name() {\n    return \"ChatAlibabaTongyi\";\n  }\n\n  get callKeys() {\n    return [\n      \"stop\",\n      \"signal\",\n      \"options\",\n      \"tools\",\n      \"tool_choice\",\n      \"parallel_tool_calls\",\n      \"parallelToolCalls\",\n    ];\n  }\n\n  get lc_secrets() {\n    return {\n      alibabaApiKey: \"ALIBABA_API_KEY\",\n    };\n  }\n\n  get lc_aliases() {\n    return undefined;\n  }\n\n  lc_serializable: boolean;\n\n  alibabaApiKey?: string;\n\n  streaming: boolean;\n\n  prefixMessages?: TongyiMessage[];\n\n  modelName: ChatCompletionRequest[\"model\"];\n\n  model: ChatCompletionRequest[\"model\"];\n\n  apiUrl: string;\n\n  maxTokens?: number | undefined;\n\n  temperature?: number | undefined;\n\n  topP?: number | undefined;\n\n  topK?: number | undefined;\n\n  repetitionPenalty?: number | undefined;\n\n  seed?: number | undefined;\n\n  enableSearch?: boolean | undefined;\n\n  parallelToolCalls?: boolean | undefined;\n\n  region: \"china\" | \"singapore\" | \"us\";\n\n  /**\n   * Get the API URL based on the specified region.\n   *\n   * @param region - The region to get the URL for ('china', 'singapore', or 'us')\n   * @returns The base URL for the specified region\n   */\n  private getRegionBaseUrl(region: \"china\" | \"singapore\" | \"us\"): string {\n    const regionUrls = {\n      china: \"https://dashscope.aliyuncs.com/\",\n      singapore: \"https://dashscope-intl.aliyuncs.com/\",\n      us: \"https://dashscope-us.aliyuncs.com/\",\n    };\n    return regionUrls[region];\n  }\n\n  private isMultimodalModel(modelName: string): boolean {\n    return /[-_]vl([-_.]|$)/i.test(modelName);\n  }\n\n  constructor(\n    fields: Partial<AlibabaTongyiChatInput> & BaseChatModelParams = {}\n  ) {\n    super(fields);\n\n    this.alibabaApiKey =\n      fields?.alibabaApiKey ?? getEnvironmentVariable(\"ALIBABA_API_KEY\");\n    if (!this.alibabaApiKey) {\n      throw new Error(\"Ali API key not found\");\n    }\n\n    // Set region (default to china)\n    this.region =\n      fields.region ??\n      (getEnvironmentVariable(\"ALIBABA_REGION\") as\n        | \"china\"\n        | \"singapore\"\n        | \"us\"\n        | undefined) ??\n      \"china\";\n\n    // Model must be known before computing apiUrl\n    this.modelName = fields?.model ?? fields.modelName ?? \"qwen-turbo\";\n    this.model = this.modelName;\n\n    // Set API URL: explicit override > auto-detect from model name\n    if (fields.apiUrl) {\n      this.apiUrl = fields.apiUrl;\n    } else {\n      const servicePath = this.isMultimodalModel(this.model)\n        ? \"api/v1/services/aigc/multimodal-generation/generation\"\n        : \"api/v1/services/aigc/text-generation/generation\";\n      this.apiUrl = `${this.getRegionBaseUrl(this.region)}${servicePath}`;\n    }\n\n    this.lc_serializable = true;\n    this.streaming = fields.streaming ?? false;\n    this.prefixMessages = fields.prefixMessages ?? [];\n    this.temperature = fields.temperature;\n    this.topP = fields.topP;\n    this.topK = fields.topK;\n    this.seed = fields.seed;\n    this.maxTokens = fields.maxTokens;\n    this.repetitionPenalty = fields.repetitionPenalty;\n    this.enableSearch = fields.enableSearch;\n    this.parallelToolCalls = fields.parallelToolCalls;\n  }\n\n  /**\n   * Get the parameters used to invoke the model\n   */\n  invocationParams(\n    options?: this[\"ParsedCallOptions\"]\n  ): ChatCompletionRequest[\"parameters\"] {\n    const tools =\n      options?.tools?.map((tool) => convertToOpenAITool(tool)) ?? [];\n    const parallelToolCalls =\n      options?.parallel_tool_calls ??\n      options?.parallelToolCalls ??\n      this.parallelToolCalls;\n    const hasTools = tools.length > 0;\n    const parameters: ChatCompletionRequest[\"parameters\"] = {\n      stream: this.streaming,\n      temperature: this.temperature,\n      top_p: this.topP,\n      top_k: this.topK,\n      seed: this.seed,\n      max_tokens: this.maxTokens,\n      result_format: hasTools ? \"message\" : \"text\",\n      enable_search: this.enableSearch,\n    };\n    if (hasTools) {\n      parameters.tools = tools;\n    }\n    if (parallelToolCalls !== undefined) {\n      parameters.parallel_tool_calls = parallelToolCalls;\n    }\n    const toolChoice = normalizeToolChoice(options?.tool_choice);\n    if (toolChoice !== undefined) {\n      parameters.tool_choice = toolChoice;\n    }\n\n    if (this.streaming) {\n      parameters.incremental_output = true;\n    } else {\n      // DashScope generation examples include repetition_penalty in non-stream mode.\n      parameters.repetition_penalty = this.repetitionPenalty;\n    }\n\n    return parameters;\n  }\n\n  /**\n   * Get the identifying parameters for the model\n   */\n  identifyingParams(): ChatCompletionRequest[\"parameters\"] &\n    Pick<ChatCompletionRequest, \"model\"> {\n    return {\n      model: this.model,\n      ...this.invocationParams(),\n    };\n  }\n\n  override bindTools(\n    tools: BindToolsInput[],\n    kwargs?: Partial<ChatAlibabaTongyiCallOptions>\n  ): Runnable<\n    BaseLanguageModelInput,\n    AIMessageChunk,\n    ChatAlibabaTongyiCallOptions\n  > {\n    return this.withConfig({\n      tools,\n      ...kwargs,\n    });\n  }\n\n  withStructuredOutput<\n    // oxlint-disable-next-line typescript/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>,\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // oxlint-disable-next-line typescript/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<false>\n  ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n  withStructuredOutput<\n    // oxlint-disable-next-line typescript/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>,\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // oxlint-disable-next-line typescript/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<true>\n  ): Runnable<\n    BaseLanguageModelInput,\n    { raw: AIMessage | AIMessageChunk; parsed: RunOutput }\n  >;\n\n  withStructuredOutput<\n    // oxlint-disable-next-line typescript/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>,\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // oxlint-disable-next-line typescript/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<boolean>\n  ):\n    | Runnable<BaseLanguageModelInput, RunOutput>\n    | Runnable<\n        BaseLanguageModelInput,\n        { raw: AIMessage | AIMessageChunk; parsed: RunOutput }\n      > {\n    if (config?.strict) {\n      throw new Error(`\"strict\" mode is not supported for this model.`);\n    }\n\n    // oxlint-disable-next-line typescript/no-explicit-any\n    const schema: InteropZodType<RunOutput> | Record<string, any> =\n      outputSchema;\n    const name = config?.name;\n    const description =\n      getSchemaDescription(schema) ?? \"A function available to call.\";\n    const method = config?.method;\n    const includeRaw = config?.includeRaw;\n\n    if (method === \"jsonMode\") {\n      throw new Error(\n        `ChatAlibabaTongyi only supports \"functionCalling\" for structured output.`\n      );\n    }\n\n    let functionName = name ?? \"extract\";\n    const outputFormatSchema = isInteropZodSchema(schema)\n      ? toJsonSchema(schema)\n      : schema;\n    let tools: ToolDefinition[];\n    if (isInteropZodSchema(schema)) {\n      tools = [\n        {\n          type: \"function\",\n          function: {\n            name: functionName,\n            description,\n            parameters: outputFormatSchema,\n          },\n        },\n      ];\n    } else {\n      if (\"name\" in schema && typeof schema.name === \"string\") {\n        functionName = schema.name;\n      }\n      tools = [\n        {\n          type: \"function\",\n          function: {\n            name: functionName,\n            description,\n            parameters: schema,\n          },\n        },\n      ];\n    }\n\n    const llm = this.bindTools(tools).withConfig({\n      tool_choice: {\n        type: \"function\",\n        function: {\n          name: functionName,\n        },\n      },\n      ls_structured_output_format: {\n        kwargs: { method: \"functionCalling\" },\n        schema: outputFormatSchema,\n      },\n    });\n    const outputParser = RunnableLambda.from<\n      AIMessage | AIMessageChunk,\n      RunOutput\n    >((input) => {\n      const toolCalls = input.tool_calls;\n      if (!toolCalls || toolCalls.length === 0) {\n        throw new Error(\"No tool calls found in the response.\");\n      }\n      const toolCall = toolCalls.find((tc) => tc.name === functionName);\n      if (!toolCall) {\n        throw new Error(`No tool call found with name ${functionName}.`);\n      }\n      return toolCall.args as RunOutput;\n    });\n\n    if (!includeRaw) {\n      return llm.pipe(outputParser).withConfig({\n        runName: \"StructuredOutput\",\n      }) as Runnable<BaseLanguageModelInput, RunOutput>;\n    }\n\n    const parserAssign = RunnablePassthrough.assign({\n      // oxlint-disable-next-line typescript/no-explicit-any\n      parsed: (input: any, cfg) => outputParser.invoke(input.raw, cfg),\n    });\n    const parserNone = RunnablePassthrough.assign({\n      parsed: () => null,\n    });\n    const parsedWithFallback = parserAssign.withFallbacks({\n      fallbacks: [parserNone],\n    });\n\n    return RunnableSequence.from<\n      BaseLanguageModelInput,\n      { raw: AIMessage | AIMessageChunk; parsed: RunOutput }\n    >([\n      {\n        raw: llm,\n      },\n      parsedWithFallback,\n    ]).withConfig({\n      runName: \"StructuredOutputRunnable\",\n    });\n  }\n\n  /** @ignore */\n  async _generate(\n    messages: BaseMessage[],\n    options?: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): Promise<ChatResult> {\n    const parameters = this.invocationParams(options);\n    const messagesMapped = convertMessagesToTongyiParams(messages);\n\n    const data = parameters.stream\n      ? await new Promise<ChatCompletionResponse>((resolve, reject) => {\n          let response: ChatCompletionResponse | undefined;\n          let concatenatedText = \"\";\n          let mergedToolCalls: TongyiToolCall[] = [];\n          const streamedToolCallState = new Map<string, TongyiToolCall>();\n          let rejected = false;\n          let resolved = false;\n          this.completionWithRetry(\n            {\n              model: this.model,\n              parameters,\n              input: {\n                messages: messagesMapped,\n              },\n            },\n            true,\n            options?.signal,\n            (event) => {\n              const data: ChatCompletionResponse = JSON.parse(event.data);\n              if (data?.code) {\n                if (rejected) {\n                  return;\n                }\n                rejected = true;\n                reject(new Error(data?.message));\n                return;\n              }\n\n              const { text, finishReason, rawToolCalls } =\n                extractOutputFromStreamChunk(data.output);\n              concatenatedText += text;\n              mergedToolCalls = applyToolCallDeltas(\n                streamedToolCallState,\n                rawToolCalls\n              );\n\n              if (!response) {\n                response = {\n                  ...data,\n                  output: {\n                    ...(data.output ?? {}),\n                    text: concatenatedText,\n                    finish_reason: finishReason,\n                    choices: [\n                      {\n                        finish_reason: finishReason,\n                        message: {\n                          role: \"assistant\",\n                          content: concatenatedText,\n                          tool_calls: mergedToolCalls,\n                        },\n                      },\n                    ],\n                  },\n                };\n              } else {\n                response.output = {\n                  ...(response.output ?? {}),\n                  text: concatenatedText,\n                  finish_reason: finishReason ?? response.output?.finish_reason,\n                  choices: [\n                    {\n                      finish_reason:\n                        finishReason ?? response.output?.finish_reason,\n                      message: {\n                        role: \"assistant\",\n                        content: concatenatedText,\n                        tool_calls: mergedToolCalls,\n                      },\n                    },\n                  ],\n                };\n                response.usage = data.usage;\n              }\n\n              // eslint-disable-next-line no-void\n              void runManager?.handleLLMNewToken(text ?? \"\");\n              if (finishReason && finishReason !== \"null\") {\n                if (resolved || rejected) {\n                  return;\n                }\n                resolved = true;\n                resolve(\n                  response ?? {\n                    ...data,\n                    output: {\n                      ...(data.output ?? {}),\n                      text: concatenatedText,\n                      finish_reason: finishReason,\n                    },\n                  }\n                );\n              }\n            }\n          )\n            .then(() => {\n              if (resolved || rejected) {\n                return;\n              }\n              resolved = true;\n              resolve(\n                response ?? {\n                  usage: {\n                    input_tokens: 0,\n                    output_tokens: 0,\n                    total_tokens: 0,\n                  },\n                  output: {\n                    text: concatenatedText,\n                    finish_reason: \"null\",\n                    choices: [\n                      {\n                        finish_reason: \"null\",\n                        message: {\n                          role: \"assistant\",\n                          content: concatenatedText,\n                          tool_calls: mergedToolCalls,\n                        },\n                      },\n                    ],\n                  },\n                }\n              );\n            })\n            .catch((error) => {\n              if (!rejected) {\n                rejected = true;\n                reject(error);\n              }\n            });\n        })\n      : await this.completionWithRetry(\n          {\n            model: this.model,\n            parameters,\n            input: {\n              messages: messagesMapped,\n            },\n          },\n          false,\n          options?.signal\n        ).then<ChatCompletionResponse>((data) => {\n          if (data?.code) {\n            throw new Error(data?.message);\n          }\n\n          return data;\n        });\n\n    const {\n      input_tokens = 0,\n      output_tokens = 0,\n      total_tokens = 0,\n    } = data.usage ?? {};\n    const usageMetadata: UsageMetadata = {\n      input_tokens,\n      output_tokens,\n      total_tokens,\n    };\n    const { text, finishReason, rawToolCalls } = extractOutputMessage(\n      data.output\n    );\n    const requestId = data.request_id ?? data.requestId;\n    const { toolCalls, invalidToolCalls } = parseRawToolCalls(rawToolCalls);\n    const isToolResponse = rawToolCalls.length > 0;\n    const message = isToolResponse\n      ? new AIMessage({\n          content: text,\n          additional_kwargs: {\n            tool_calls: convertRawToolCallsToOpenAIToolCalls(rawToolCalls),\n          },\n          tool_calls: toolCalls,\n          invalid_tool_calls: invalidToolCalls,\n          usage_metadata: usageMetadata,\n          response_metadata: {\n            model_provider: \"alibaba_tongyi\",\n            model: this.model,\n            request_id: requestId,\n            ...(finishReason ? { finish_reason: finishReason } : {}),\n          },\n        })\n      : new AIMessage({\n          content: text,\n          usage_metadata: usageMetadata,\n          response_metadata: {\n            model_provider: \"alibaba_tongyi\",\n            model: this.model,\n            request_id: requestId,\n            ...(finishReason ? { finish_reason: finishReason } : {}),\n          },\n        });\n\n    return {\n      generations: [\n        {\n          text,\n          message,\n          generationInfo: finishReason\n            ? { finish_reason: finishReason }\n            : undefined,\n        },\n      ],\n      llmOutput: {\n        tokenUsage: {\n          promptTokens: input_tokens,\n          completionTokens: output_tokens,\n          totalTokens: total_tokens,\n        },\n      },\n    };\n  }\n\n  /** @ignore */\n  async completionWithRetry(\n    request: ChatCompletionRequest,\n    stream: boolean,\n    signal?: AbortSignal,\n    onmessage?: (event: MessageEvent) => void\n  ) {\n    const makeCompletionRequest = async () => {\n      const response = await fetch(this.apiUrl, {\n        method: \"POST\",\n        headers: {\n          // This integration uses the native DashScope endpoint\n          // (`/api/v1/services/aigc/text-generation/generation`), not the OpenAI-compatible\n          // endpoint (`/compatible-mode/v1/chat/completions`). For native streaming, DashScope\n          // expects `X-DashScope-SSE: enable` to emit SSE `data:` chunks our parser consumes.\n          ...(stream\n            ? {\n                Accept: \"text/event-stream\",\n                \"X-DashScope-SSE\": \"enable\",\n              }\n            : {}),\n          Authorization: `Bearer ${this.alibabaApiKey}`,\n          \"Content-Type\": \"application/json\",\n        },\n        body: JSON.stringify(request),\n        signal,\n      });\n\n      if (!stream) {\n        return response.json();\n      }\n\n      if (response.body) {\n        // response will not be a stream if an error occurred\n        if (\n          !response.headers.get(\"content-type\")?.startsWith(\"text/event-stream\")\n        ) {\n          onmessage?.(\n            new MessageEvent(\"message\", {\n              data: await response.text(),\n            })\n          );\n          return;\n        }\n        const reader = response.body.getReader();\n        const decoder = new TextDecoder(\"utf-8\");\n        let data = \"\";\n        let continueReading = true;\n        while (continueReading) {\n          const { done, value } = await reader.read();\n          if (done) {\n            continueReading = false;\n            break;\n          }\n          data += decoder.decode(value);\n          let continueProcessing = true;\n          while (continueProcessing) {\n            const newlineIndex = data.indexOf(\"\\n\");\n            if (newlineIndex === -1) {\n              continueProcessing = false;\n              break;\n            }\n            const line = data.slice(0, newlineIndex);\n            data = data.slice(newlineIndex + 1);\n            if (line.startsWith(\"data:\")) {\n              const event = new MessageEvent(\"message\", {\n                data: line.slice(\"data:\".length).trim(),\n              });\n              onmessage?.(event);\n            }\n          }\n        }\n      }\n    };\n\n    return this.caller.call(makeCompletionRequest);\n  }\n\n  async *_streamResponseChunks(\n    messages: BaseMessage[],\n    options?: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): AsyncGenerator<ChatGenerationChunk> {\n    const parameters = {\n      ...this.invocationParams(options),\n      stream: true,\n      incremental_output: true,\n    };\n\n    const messagesMapped = convertMessagesToTongyiParams(messages);\n\n    const stream = await this.caller.call(async () =>\n      this.createTongyiStream(\n        {\n          model: this.model,\n          parameters,\n          input: {\n            messages: messagesMapped,\n          },\n        },\n        options?.signal\n      )\n    );\n\n    for await (const chunk of stream) {\n      /* if some error occurs:\n         {\n          \"code\": \"DataInspectionFailed\",\n          \"message\": \"Output data may contain inappropriate content.\",\n          \"request_id\": \"43d18007-5aa5-9d18-b3b3-a55aba9ce8cb\"\n        }\n      */\n      if (!chunk.output && chunk.code) {\n        throw new Error(JSON.stringify(chunk));\n      }\n      const { text, finishReason, rawToolCalls } = extractOutputFromStreamChunk(\n        chunk.output\n      );\n      const toolCallChunks = convertRawToolCallsToToolCallChunks(rawToolCalls);\n      const requestId = chunk.request_id ?? chunk.requestId;\n      const usageMetadata: UsageMetadata | undefined = chunk.usage\n        ? {\n            input_tokens: chunk.usage.input_tokens ?? 0,\n            output_tokens: chunk.usage.output_tokens ?? 0,\n            total_tokens: chunk.usage.total_tokens ?? 0,\n          }\n        : undefined;\n      yield new ChatGenerationChunk({\n        text,\n        message: new AIMessageChunk({\n          content: text,\n          tool_call_chunks: toolCallChunks,\n          usage_metadata: usageMetadata,\n          response_metadata: {\n            model_provider: \"alibaba_tongyi\",\n            model: this.model,\n            request_id: requestId,\n            ...(finishReason ? { finish_reason: finishReason } : {}),\n          },\n        }),\n        generationInfo:\n          finishReason === \"stop\" || finishReason === \"tool_calls\"\n            ? {\n                finish_reason: finishReason,\n                request_id: requestId,\n                usage: chunk.usage,\n              }\n            : undefined,\n      });\n      await runManager?.handleLLMNewToken(text);\n    }\n  }\n\n  private async *createTongyiStream(\n    request: ChatCompletionRequest,\n    signal?: AbortSignal\n  ) {\n    const response = await fetch(this.apiUrl, {\n      method: \"POST\",\n      headers: {\n        Authorization: `Bearer ${this.alibabaApiKey}`,\n        // Native DashScope stream() path: keep SSE framing explicit on the\n        // `/api/v1/services/aigc/text-generation/generation` endpoint.\n        Accept: \"text/event-stream\",\n        \"X-DashScope-SSE\": \"enable\",\n        \"Content-Type\": \"application/json\",\n      },\n      body: JSON.stringify(request),\n      signal,\n    });\n\n    if (!response.ok) {\n      let error;\n      const responseText = await response.text();\n      try {\n        const json = JSON.parse(responseText);\n        error = new Error(\n          `Tongyi call failed with status code ${response.status}: ${json.error}`\n        );\n      } catch {\n        error = new Error(\n          `Tongyi call failed with status code ${response.status}: ${responseText}`\n        );\n      }\n      // oxlint-disable-next-line typescript/no-explicit-any\n      (error as any).response = response;\n      throw error;\n    }\n    if (!response.body) {\n      throw new Error(\n        \"Could not begin Tongyi stream. Please check the given URL and try again.\"\n      );\n    }\n    const stream = IterableReadableStream.fromReadableStream(response.body);\n    const decoder = new TextDecoder();\n    let extra = \"\";\n    for await (const chunk of stream) {\n      const decoded = extra + decoder.decode(chunk);\n      const lines = decoded.split(\"\\n\");\n      extra = lines.pop() || \"\";\n      for (const line of lines) {\n        if (!line.startsWith(\"data:\")) {\n          continue;\n        }\n        try {\n          yield JSON.parse(line.slice(\"data:\".length).trim());\n        } catch {\n          console.warn(`Received a non-JSON parseable chunk: ${line}`);\n        }\n      }\n    }\n  }\n\n  _llmType(): string {\n    return \"alibaba_tongyi\";\n  }\n\n  /** @ignore */\n  _combineLLMOutput() {\n    return [];\n  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