@langchain/openai/dist/chat_models/base.cjs.map

OpenAI integrations for LangChain.js

{"version":3,"file":"base.cjs","names":["BaseChatModel","options: this[\"ParsedCallOptions\"]","fields?: BaseChatOpenAIFields","options?: this[\"ParsedCallOptions\"]","isReasoningModel","reasoning: OpenAIClient.Reasoning | undefined","resFormat?: CallOptions[\"response_format\"]","interopZodResponseFormat","additionalOptions?: this[\"ParsedCallOptions\"]","options: OpenAICoreRequestOptions | undefined","openAIEndpointConfig: OpenAIEndpointConfig","getEndpoint","getHeadersWithUserAgent","OpenAIClient","tool: ChatOpenAIToolType","fields?: { strict?: boolean }","isCustomTool","convertResponsesCustomTool","_convertToOpenAITool","tools: ChatOpenAIToolType[]","kwargs?: Partial<CallOptions>","strict: boolean | undefined","isBuiltInTool","hasProviderToolDefinition","input: BaseLanguageModelInput","options?: CallOptions","messages: BaseMessage[]","messageToOpenAIRole","generations: ChatGeneration[]","functions?: OpenAIClient.Chat.ChatCompletionCreateParams.Function[]","function_call?:\n      | \"none\"\n      | \"auto\"\n      | OpenAIClient.Chat.ChatCompletionFunctionCallOption","formatFunctionDefinitions","input: string | string[]","params?: {\n      model?: OpenAI.ModerationModel;\n      options?: OpenAICoreRequestOptions;\n    }","moderationRequest: OpenAIClient.ModerationCreateParams","wrapOpenAIClientError","PROFILES","config: StructuredOutputMethodOptions<boolean>","outputSchema: InteropZodType<RunOutput> | Record<string, unknown>","config?: StructuredOutputMethodOptions<boolean>","llm: Runnable<BaseLanguageModelInput>","outputParser: Runnable<AIMessageChunk, RunOutput>","getStructuredOutputMethod","StructuredOutputParser","JsonOutputParser","RunnableLambda","aiMessage: AIMessageChunk","JsonOutputKeyToolsParser","openAIFunctionDefinition: FunctionDefinition","RunnablePassthrough","input: any","config","RunnableSequence"],"sources":["../../src/chat_models/base.ts"],"sourcesContent":["import OpenAI, { type ClientOptions, OpenAI as OpenAIClient } from \"openai\";\nimport { AIMessageChunk, type BaseMessage } from \"@langchain/core/messages\";\nimport { type ChatGeneration } from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n  BaseChatModel,\n  type LangSmithParams,\n  type BaseChatModelParams,\n  BaseChatModelCallOptions,\n} from \"@langchain/core/language_models/chat_models\";\nimport {\n  isOpenAITool as isOpenAIFunctionTool,\n  type BaseFunctionCallOptions,\n  type BaseLanguageModelInput,\n  type FunctionDefinition,\n  type StructuredOutputMethodOptions,\n} from \"@langchain/core/language_models/base\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\nimport {\n  Runnable,\n  RunnableLambda,\n  RunnablePassthrough,\n  RunnableSequence,\n} from \"@langchain/core/runnables\";\nimport {\n  JsonOutputParser,\n  StructuredOutputParser,\n} from \"@langchain/core/output_parsers\";\nimport { JsonOutputKeyToolsParser } from \"@langchain/core/output_parsers/openai_tools\";\nimport {\n  getSchemaDescription,\n  InteropZodType,\n  isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport {\n  type OpenAICallOptions,\n  type OpenAIChatInput,\n  type OpenAICoreRequestOptions,\n  type ChatOpenAIResponseFormat,\n  ResponseFormatConfiguration,\n  OpenAIVerbosityParam,\n  type OpenAIApiKey,\n  OpenAICacheRetentionParam,\n} from \"../types.js\";\nimport {\n  type OpenAIEndpointConfig,\n  getEndpoint,\n  getHeadersWithUserAgent,\n} from \"../utils/azure.js\";\nimport {\n  type FunctionDef,\n  formatFunctionDefinitions,\n  OpenAIToolChoice,\n  _convertToOpenAITool,\n  ChatOpenAIToolType,\n  convertResponsesCustomTool,\n  isBuiltInTool,\n  isCustomTool,\n  hasProviderToolDefinition,\n  ResponsesToolChoice,\n} from \"../utils/tools.js\";\nimport {\n  getStructuredOutputMethod,\n  interopZodResponseFormat,\n  _convertOpenAIResponsesUsageToLangChainUsage,\n} from \"../utils/output.js\";\nimport { isReasoningModel, messageToOpenAIRole } from \"../utils/misc.js\";\nimport { wrapOpenAIClientError } from \"../utils/client.js\";\nimport PROFILES from \"./profiles.js\";\n\ninterface OpenAILLMOutput {\n  tokenUsage: {\n    completionTokens?: number;\n    promptTokens?: number;\n    totalTokens?: number;\n  };\n}\n\nexport type { OpenAICallOptions, OpenAIChatInput };\n\nexport interface BaseChatOpenAICallOptions\n  extends BaseChatModelCallOptions,\n    BaseFunctionCallOptions {\n  /**\n   * Additional options to pass to the underlying axios request.\n   */\n  options?: OpenAICoreRequestOptions;\n\n  /**\n   * A list of tools that the model may use to generate responses.\n   * Each tool can be a function, a built-in tool, or a custom tool definition.\n   * If not provided, the model will not use any tools.\n   */\n  tools?: ChatOpenAIToolType[];\n\n  /**\n   * Specifies which tool the model should use to respond.\n   * Can be an {@link OpenAIToolChoice} or a {@link ResponsesToolChoice}.\n   * If not set, the model will decide which tool to use automatically.\n   */\n  // TODO: break OpenAIToolChoice and ResponsesToolChoice into options sub classes\n  tool_choice?: OpenAIToolChoice | ResponsesToolChoice;\n\n  /**\n   * Adds a prompt index to prompts passed to the model to track\n   * what prompt is being used for a given generation.\n   */\n  promptIndex?: number;\n\n  /**\n   * An object specifying the format that the model must output.\n   */\n  response_format?: ChatOpenAIResponseFormat;\n\n  /**\n   * When provided, the completions API will make a best effort to sample\n   * deterministically, such that repeated requests with the same `seed`\n   * and parameters should return the same result.\n   */\n  seed?: number;\n\n  /**\n   * Additional options to pass to streamed completions.\n   * If provided, this takes precedence over \"streamUsage\" set at\n   * initialization time.\n   */\n  stream_options?: OpenAIClient.Chat.ChatCompletionStreamOptions;\n\n  /**\n   * The model may choose to call multiple functions in a single turn. You can\n   * set parallel_tool_calls to false which ensures only one tool is called at most.\n   * [Learn more](https://platform.openai.com/docs/guides/function-calling#parallel-function-calling)\n   */\n  parallel_tool_calls?: boolean;\n\n  /**\n   * If `true`, model output is guaranteed to exactly match the JSON Schema\n   * provided in the tool definition. If `true`, the input schema will also be\n   * validated according to\n   * https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.\n   *\n   * If `false`, input schema will not be validated and model output will not\n   * be validated.\n   *\n   * If `undefined`, `strict` argument will not be passed to the model.\n   */\n  strict?: boolean;\n\n  /**\n   * Output types that you would like the model to generate for this request. Most\n   * models are capable of generating text, which is the default:\n   *\n   * `[\"text\"]`\n   *\n   * The `gpt-4o-audio-preview` model can also be used to\n   * [generate audio](https://platform.openai.com/docs/guides/audio). To request that\n   * this model generate both text and audio responses, you can use:\n   *\n   * `[\"text\", \"audio\"]`\n   */\n  modalities?: Array<OpenAIClient.Chat.ChatCompletionModality>;\n\n  /**\n   * Parameters for audio output. Required when audio output is requested with\n   * `modalities: [\"audio\"]`.\n   * [Learn more](https://platform.openai.com/docs/guides/audio).\n   */\n  audio?: OpenAIClient.Chat.ChatCompletionAudioParam;\n\n  /**\n   * Static predicted output content, such as the content of a text file that is being regenerated.\n   * [Learn more](https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs).\n   */\n  prediction?: OpenAIClient.ChatCompletionPredictionContent;\n\n  /**\n   * Options for reasoning models.\n   *\n   * Note that some options, like reasoning summaries, are only available when using the responses\n   * API. If these options are set, the responses API will be used to fulfill the request.\n   *\n   * These options will be ignored when not using a reasoning model.\n   */\n  reasoning?: OpenAIClient.Reasoning;\n\n  /**\n   * Service tier to use for this request. Can be \"auto\", \"default\", or \"flex\"\n   * Specifies the service tier for prioritization and latency optimization.\n   */\n  service_tier?: OpenAIClient.Chat.ChatCompletionCreateParams[\"service_tier\"];\n\n  /**\n   * Used by OpenAI to cache responses for similar requests to optimize your cache\n   * hit rates. Replaces the `user` field.\n   * [Learn more](https://platform.openai.com/docs/guides/prompt-caching).\n   */\n  promptCacheKey?: string;\n\n  /**\n   * Used by OpenAI to set cache retention time\n   */\n  promptCacheRetention?: OpenAICacheRetentionParam;\n\n  /**\n   * The verbosity of the model's response.\n   */\n  verbosity?: OpenAIVerbosityParam;\n}\n\nexport interface BaseChatOpenAIFields\n  extends Partial<OpenAIChatInput>,\n    BaseChatModelParams {\n  /**\n   * Optional configuration options for the OpenAI client.\n   */\n  configuration?: ClientOptions;\n}\n\n/** @internal */\nexport abstract class BaseChatOpenAI<\n    CallOptions extends BaseChatOpenAICallOptions\n  >\n  extends BaseChatModel<CallOptions, AIMessageChunk>\n  implements Partial<OpenAIChatInput>\n{\n  temperature?: number;\n\n  topP?: number;\n\n  frequencyPenalty?: number;\n\n  presencePenalty?: number;\n\n  n?: number;\n\n  logitBias?: Record<string, number>;\n\n  model = \"gpt-3.5-turbo\";\n\n  modelKwargs?: OpenAIChatInput[\"modelKwargs\"];\n\n  stop?: string[];\n\n  stopSequences?: string[];\n\n  user?: string;\n\n  timeout?: number;\n\n  streaming = false;\n\n  streamUsage = true;\n\n  maxTokens?: number;\n\n  logprobs?: boolean;\n\n  topLogprobs?: number;\n\n  apiKey?: OpenAIApiKey;\n\n  organization?: string;\n\n  __includeRawResponse?: boolean;\n\n  /** @internal */\n  client: OpenAIClient;\n\n  /** @internal */\n  clientConfig: ClientOptions;\n\n  /**\n   * Whether the model supports the `strict` argument when passing in tools.\n   * If `undefined` the `strict` argument will not be passed to OpenAI.\n   */\n  supportsStrictToolCalling?: boolean;\n\n  audio?: OpenAIClient.Chat.ChatCompletionAudioParam;\n\n  modalities?: Array<OpenAIClient.Chat.ChatCompletionModality>;\n\n  reasoning?: OpenAIClient.Reasoning;\n\n  /**\n   * Must be set to `true` in tenancies with Zero Data Retention. Setting to `true` will disable\n   * output storage in the Responses API, but this DOES NOT enable Zero Data Retention in your\n   * OpenAI organization or project. This must be configured directly with OpenAI.\n   *\n   * See:\n   * https://platform.openai.com/docs/guides/your-data\n   * https://platform.openai.com/docs/api-reference/responses/create#responses-create-store\n   *\n   * @default false\n   */\n  zdrEnabled?: boolean | undefined;\n\n  /**\n   * Service tier to use for this request. Can be \"auto\", \"default\", or \"flex\" or \"priority\".\n   * Specifies the service tier for prioritization and latency optimization.\n   */\n  service_tier?: OpenAIClient.Chat.ChatCompletionCreateParams[\"service_tier\"];\n\n  /**\n   * Used by OpenAI to cache responses for similar requests to optimize your cache\n   * hit rates.\n   * [Learn more](https://platform.openai.com/docs/guides/prompt-caching).\n   */\n  promptCacheKey: string;\n\n  /**\n   * Used by OpenAI to set cache retention time\n   */\n  promptCacheRetention?: OpenAICacheRetentionParam;\n\n  /**\n   * The verbosity of the model's response.\n   */\n  verbosity?: OpenAIVerbosityParam;\n\n  protected defaultOptions: CallOptions;\n\n  _llmType() {\n    return \"openai\";\n  }\n\n  static lc_name() {\n    return \"ChatOpenAI\";\n  }\n\n  get callKeys() {\n    return [\n      ...super.callKeys,\n      \"options\",\n      \"function_call\",\n      \"functions\",\n      \"tools\",\n      \"tool_choice\",\n      \"promptIndex\",\n      \"response_format\",\n      \"seed\",\n      \"reasoning\",\n      \"service_tier\",\n    ];\n  }\n\n  lc_serializable = true;\n\n  get lc_secrets(): { [key: string]: string } | undefined {\n    return {\n      apiKey: \"OPENAI_API_KEY\",\n      organization: \"OPENAI_ORGANIZATION\",\n    };\n  }\n\n  get lc_aliases(): Record<string, string> {\n    return {\n      apiKey: \"openai_api_key\",\n      modelName: \"model\",\n    };\n  }\n\n  get lc_serializable_keys(): string[] {\n    return [\n      \"configuration\",\n      \"logprobs\",\n      \"topLogprobs\",\n      \"prefixMessages\",\n      \"supportsStrictToolCalling\",\n      \"modalities\",\n      \"audio\",\n      \"temperature\",\n      \"maxTokens\",\n      \"topP\",\n      \"frequencyPenalty\",\n      \"presencePenalty\",\n      \"n\",\n      \"logitBias\",\n      \"user\",\n      \"streaming\",\n      \"streamUsage\",\n      \"model\",\n      \"modelName\",\n      \"modelKwargs\",\n      \"stop\",\n      \"stopSequences\",\n      \"timeout\",\n      \"apiKey\",\n      \"cache\",\n      \"maxConcurrency\",\n      \"maxRetries\",\n      \"verbose\",\n      \"callbacks\",\n      \"tags\",\n      \"metadata\",\n      \"disableStreaming\",\n      \"zdrEnabled\",\n      \"reasoning\",\n      \"promptCacheKey\",\n      \"promptCacheRetention\",\n      \"verbosity\",\n    ];\n  }\n\n  getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n    const params = this.invocationParams(options);\n    return {\n      ls_provider: \"openai\",\n      ls_model_name: this.model,\n      ls_model_type: \"chat\",\n      ls_temperature: params.temperature ?? undefined,\n      ls_max_tokens: params.max_tokens ?? undefined,\n      ls_stop: options.stop,\n    };\n  }\n\n  /** @ignore */\n  _identifyingParams(): Omit<\n    OpenAIClient.Chat.ChatCompletionCreateParams,\n    \"messages\"\n  > & {\n    model_name: string;\n  } & ClientOptions {\n    return {\n      model_name: this.model,\n      ...this.invocationParams(),\n      ...this.clientConfig,\n    };\n  }\n\n  /**\n   * Get the identifying parameters for the model\n   */\n  identifyingParams() {\n    return this._identifyingParams();\n  }\n\n  constructor(fields?: BaseChatOpenAIFields) {\n    super(fields ?? {});\n\n    const configApiKey =\n      typeof fields?.configuration?.apiKey === \"string\" ||\n      typeof fields?.configuration?.apiKey === \"function\"\n        ? fields?.configuration?.apiKey\n        : undefined;\n    this.apiKey =\n      fields?.apiKey ??\n      configApiKey ??\n      getEnvironmentVariable(\"OPENAI_API_KEY\");\n    this.organization =\n      fields?.configuration?.organization ??\n      getEnvironmentVariable(\"OPENAI_ORGANIZATION\");\n\n    this.model = fields?.model ?? fields?.modelName ?? this.model;\n    this.modelKwargs = fields?.modelKwargs ?? {};\n    this.timeout = fields?.timeout;\n\n    this.temperature = fields?.temperature ?? this.temperature;\n    this.topP = fields?.topP ?? this.topP;\n    this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty;\n    this.presencePenalty = fields?.presencePenalty ?? this.presencePenalty;\n    this.logprobs = fields?.logprobs;\n    this.topLogprobs = fields?.topLogprobs;\n    this.n = fields?.n ?? this.n;\n    this.logitBias = fields?.logitBias;\n    this.stop = fields?.stopSequences ?? fields?.stop;\n    this.stopSequences = this.stop;\n    this.user = fields?.user;\n    this.__includeRawResponse = fields?.__includeRawResponse;\n    this.audio = fields?.audio;\n    this.modalities = fields?.modalities;\n    this.reasoning = fields?.reasoning;\n    this.maxTokens = fields?.maxCompletionTokens ?? fields?.maxTokens;\n    this.promptCacheKey = fields?.promptCacheKey ?? this.promptCacheKey;\n    this.promptCacheRetention =\n      fields?.promptCacheRetention ?? this.promptCacheRetention;\n    this.verbosity = fields?.verbosity ?? this.verbosity;\n\n    this.disableStreaming = fields?.disableStreaming === true;\n    this.streaming = fields?.streaming === true;\n    if (this.disableStreaming) this.streaming = false;\n    // disable streaming in BaseChatModel if explicitly disabled\n    if (fields?.streaming === false) this.disableStreaming = true;\n\n    this.streamUsage = fields?.streamUsage ?? this.streamUsage;\n    if (this.disableStreaming) this.streamUsage = false;\n\n    this.clientConfig = {\n      apiKey: this.apiKey,\n      organization: this.organization,\n      dangerouslyAllowBrowser: true,\n      ...fields?.configuration,\n    };\n\n    // If `supportsStrictToolCalling` is explicitly set, use that value.\n    // Else leave undefined so it's not passed to OpenAI.\n    if (fields?.supportsStrictToolCalling !== undefined) {\n      this.supportsStrictToolCalling = fields.supportsStrictToolCalling;\n    }\n\n    if (fields?.service_tier !== undefined) {\n      this.service_tier = fields.service_tier;\n    }\n\n    this.zdrEnabled = fields?.zdrEnabled ?? false;\n  }\n\n  /**\n   * Returns backwards compatible reasoning parameters from constructor params and call options\n   * @internal\n   */\n  protected _getReasoningParams(\n    options?: this[\"ParsedCallOptions\"]\n  ): OpenAIClient.Reasoning | undefined {\n    if (!isReasoningModel(this.model)) {\n      return;\n    }\n\n    // apply options in reverse order of importance -- newer options supersede older options\n    let reasoning: OpenAIClient.Reasoning | undefined;\n    if (this.reasoning !== undefined) {\n      reasoning = {\n        ...reasoning,\n        ...this.reasoning,\n      };\n    }\n    if (options?.reasoning !== undefined) {\n      reasoning = {\n        ...reasoning,\n        ...options.reasoning,\n      };\n    }\n\n    return reasoning;\n  }\n\n  /**\n   * Returns an openai compatible response format from a set of options\n   * @internal\n   */\n  protected _getResponseFormat(\n    resFormat?: CallOptions[\"response_format\"]\n  ): ResponseFormatConfiguration | undefined {\n    if (\n      resFormat &&\n      resFormat.type === \"json_schema\" &&\n      resFormat.json_schema.schema &&\n      isInteropZodSchema(resFormat.json_schema.schema)\n    ) {\n      return interopZodResponseFormat(\n        resFormat.json_schema.schema,\n        resFormat.json_schema.name,\n        {\n          description: resFormat.json_schema.description,\n        }\n      );\n    }\n    return resFormat as ResponseFormatConfiguration | undefined;\n  }\n\n  protected _combineCallOptions(\n    additionalOptions?: this[\"ParsedCallOptions\"]\n  ): this[\"ParsedCallOptions\"] {\n    return {\n      ...this.defaultOptions,\n      ...(additionalOptions ?? {}),\n    };\n  }\n\n  /** @internal */\n  _getClientOptions(\n    options: OpenAICoreRequestOptions | undefined\n  ): OpenAICoreRequestOptions {\n    if (!this.client) {\n      const openAIEndpointConfig: OpenAIEndpointConfig = {\n        baseURL: this.clientConfig.baseURL,\n      };\n\n      const endpoint = getEndpoint(openAIEndpointConfig);\n      const params = {\n        ...this.clientConfig,\n        baseURL: endpoint,\n        timeout: this.timeout,\n        maxRetries: 0,\n      };\n      if (!params.baseURL) {\n        delete params.baseURL;\n      }\n\n      params.defaultHeaders = getHeadersWithUserAgent(params.defaultHeaders);\n\n      this.client = new OpenAIClient(params);\n    }\n    const requestOptions = {\n      ...this.clientConfig,\n      ...options,\n    } as OpenAICoreRequestOptions;\n    return requestOptions;\n  }\n\n  // TODO: move to completions class\n  protected _convertChatOpenAIToolToCompletionsTool(\n    tool: ChatOpenAIToolType,\n    fields?: { strict?: boolean }\n  ): OpenAIClient.ChatCompletionTool {\n    if (isCustomTool(tool)) {\n      return convertResponsesCustomTool(tool.metadata.customTool);\n    }\n    if (isOpenAIFunctionTool(tool)) {\n      if (fields?.strict !== undefined) {\n        return {\n          ...tool,\n          function: {\n            ...tool.function,\n            strict: fields.strict,\n          },\n        };\n      }\n\n      return tool;\n    }\n    return _convertToOpenAITool(tool, fields);\n  }\n\n  override bindTools(\n    tools: ChatOpenAIToolType[],\n    kwargs?: Partial<CallOptions>\n  ): Runnable<BaseLanguageModelInput, AIMessageChunk, CallOptions> {\n    let strict: boolean | undefined;\n    if (kwargs?.strict !== undefined) {\n      strict = kwargs.strict;\n    } else if (this.supportsStrictToolCalling !== undefined) {\n      strict = this.supportsStrictToolCalling;\n    }\n    return this.withConfig({\n      tools: tools.map((tool) => {\n        // Built-in tools and custom tools pass through as-is\n        if (isBuiltInTool(tool) || isCustomTool(tool)) {\n          return tool;\n        }\n        // Tools with providerToolDefinition (e.g., localShell, shell, computerUse, applyPatch)\n        // should use their provider-specific definition\n        if (hasProviderToolDefinition(tool)) {\n          return tool.extras.providerToolDefinition;\n        }\n        // Regular tools get converted to OpenAI function format\n        return this._convertChatOpenAIToolToCompletionsTool(tool, { strict });\n      }),\n      ...kwargs,\n    } as Partial<CallOptions>);\n  }\n\n  override async stream(input: BaseLanguageModelInput, options?: CallOptions) {\n    return super.stream(\n      input,\n      this._combineCallOptions(options) as CallOptions\n    );\n  }\n\n  override async invoke(input: BaseLanguageModelInput, options?: CallOptions) {\n    return super.invoke(\n      input,\n      this._combineCallOptions(options) as CallOptions\n    );\n  }\n\n  /** @ignore */\n  _combineLLMOutput(...llmOutputs: OpenAILLMOutput[]): OpenAILLMOutput {\n    return llmOutputs.reduce<{\n      [key in keyof OpenAILLMOutput]: Required<OpenAILLMOutput[key]>;\n    }>(\n      (acc, llmOutput) => {\n        if (llmOutput && llmOutput.tokenUsage) {\n          acc.tokenUsage.completionTokens +=\n            llmOutput.tokenUsage.completionTokens ?? 0;\n          acc.tokenUsage.promptTokens += llmOutput.tokenUsage.promptTokens ?? 0;\n          acc.tokenUsage.totalTokens += llmOutput.tokenUsage.totalTokens ?? 0;\n        }\n        return acc;\n      },\n      {\n        tokenUsage: {\n          completionTokens: 0,\n          promptTokens: 0,\n          totalTokens: 0,\n        },\n      }\n    );\n  }\n\n  async getNumTokensFromMessages(messages: BaseMessage[]) {\n    let totalCount = 0;\n    let tokensPerMessage = 0;\n    let tokensPerName = 0;\n\n    // From: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb\n    if (this.model === \"gpt-3.5-turbo-0301\") {\n      tokensPerMessage = 4;\n      tokensPerName = -1;\n    } else {\n      tokensPerMessage = 3;\n      tokensPerName = 1;\n    }\n\n    const countPerMessage = await Promise.all(\n      messages.map(async (message) => {\n        const textCount = await this.getNumTokens(message.content);\n        const roleCount = await this.getNumTokens(messageToOpenAIRole(message));\n        const nameCount =\n          message.name !== undefined\n            ? tokensPerName + (await this.getNumTokens(message.name))\n            : 0;\n        let count = textCount + tokensPerMessage + roleCount + nameCount;\n\n        // From: https://github.com/hmarr/openai-chat-tokens/blob/main/src/index.ts messageTokenEstimate\n        const openAIMessage = message;\n        if (openAIMessage._getType() === \"function\") {\n          count -= 2;\n        }\n        if (openAIMessage.additional_kwargs?.function_call) {\n          count += 3;\n        }\n        if (openAIMessage?.additional_kwargs.function_call?.name) {\n          count += await this.getNumTokens(\n            openAIMessage.additional_kwargs.function_call?.name\n          );\n        }\n        if (openAIMessage.additional_kwargs.function_call?.arguments) {\n          try {\n            count += await this.getNumTokens(\n              // Remove newlines and spaces\n              JSON.stringify(\n                JSON.parse(\n                  openAIMessage.additional_kwargs.function_call?.arguments\n                )\n              )\n            );\n          } catch (error) {\n            console.error(\n              \"Error parsing function arguments\",\n              error,\n              JSON.stringify(openAIMessage.additional_kwargs.function_call)\n            );\n            count += await this.getNumTokens(\n              openAIMessage.additional_kwargs.function_call?.arguments\n            );\n          }\n        }\n\n        totalCount += count;\n        return count;\n      })\n    );\n\n    totalCount += 3; // every reply is primed with <|start|>assistant<|message|>\n\n    return { totalCount, countPerMessage };\n  }\n\n  /** @internal */\n  protected async _getNumTokensFromGenerations(generations: ChatGeneration[]) {\n    const generationUsages = await Promise.all(\n      generations.map(async (generation) => {\n        if (generation.message.additional_kwargs?.function_call) {\n          return (await this.getNumTokensFromMessages([generation.message]))\n            .countPerMessage[0];\n        } else {\n          return await this.getNumTokens(generation.message.content);\n        }\n      })\n    );\n\n    return generationUsages.reduce((a, b) => a + b, 0);\n  }\n\n  /** @internal */\n  protected async _getEstimatedTokenCountFromPrompt(\n    messages: BaseMessage[],\n    functions?: OpenAIClient.Chat.ChatCompletionCreateParams.Function[],\n    function_call?:\n      | \"none\"\n      | \"auto\"\n      | OpenAIClient.Chat.ChatCompletionFunctionCallOption\n  ): Promise<number> {\n    // It appears that if functions are present, the first system message is padded with a trailing newline. This\n    // was inferred by trying lots of combinations of messages and functions and seeing what the token counts were.\n\n    let tokens = (await this.getNumTokensFromMessages(messages)).totalCount;\n\n    // If there are functions, add the function definitions as they count towards token usage\n    if (functions && function_call !== \"auto\") {\n      const promptDefinitions = formatFunctionDefinitions(\n        functions as unknown as FunctionDef[]\n      );\n      tokens += await this.getNumTokens(promptDefinitions);\n      tokens += 9; // Add nine per completion\n    }\n\n    // If there's a system message _and_ functions are present, subtract four tokens. I assume this is because\n    // functions typically add a system message, but reuse the first one if it's already there. This offsets\n    // the extra 9 tokens added by the function definitions.\n    if (functions && messages.find((m) => m._getType() === \"system\")) {\n      tokens -= 4;\n    }\n\n    // If function_call is 'none', add one token.\n    // If it's a FunctionCall object, add 4 + the number of tokens in the function name.\n    // If it's undefined or 'auto', don't add anything.\n    if (function_call === \"none\") {\n      tokens += 1;\n    } else if (typeof function_call === \"object\") {\n      tokens += (await this.getNumTokens(function_call.name)) + 4;\n    }\n\n    return tokens;\n  }\n\n  /**\n   * Moderate content using OpenAI's Moderation API.\n   *\n   * This method checks whether content violates OpenAI's content policy by\n   * analyzing text for categories such as hate, harassment, self-harm,\n   * sexual content, violence, and more.\n   *\n   * @param input - The text or array of texts to moderate\n   * @param params - Optional parameters for the moderation request\n   * @param params.model - The moderation model to use. Defaults to \"omni-moderation-latest\".\n   * @param params.options - Additional options to pass to the underlying request\n   * @returns A promise that resolves to the moderation response containing results for each input\n   *\n   * @example\n   * ```typescript\n   * const model = new ChatOpenAI({ model: \"gpt-4o-mini\" });\n   *\n   * // Moderate a single text\n   * const result = await model.moderateContent(\"This is a test message\");\n   * console.log(result.results[0].flagged); // false\n   * console.log(result.results[0].categories); // { hate: false, harassment: false, ... }\n   *\n   * // Moderate multiple texts\n   * const results = await model.moderateContent([\n   *   \"Hello, how are you?\",\n   *   \"This is inappropriate content\"\n   * ]);\n   * results.results.forEach((result, index) => {\n   *   console.log(`Text ${index + 1} flagged:`, result.flagged);\n   * });\n   *\n   * // Use a specific moderation model\n   * const stableResult = await model.moderateContent(\n   *   \"Test content\",\n   *   { model: \"omni-moderation-latest\" }\n   * );\n   * ```\n   */\n  async moderateContent(\n    input: string | string[],\n    params?: {\n      model?: OpenAI.ModerationModel;\n      options?: OpenAICoreRequestOptions;\n    }\n  ): Promise<OpenAIClient.ModerationCreateResponse> {\n    const clientOptions = this._getClientOptions(params?.options);\n    const moderationModel = params?.model ?? \"omni-moderation-latest\";\n    const moderationRequest: OpenAIClient.ModerationCreateParams = {\n      input,\n      model: moderationModel,\n    };\n\n    return this.caller.call(async () => {\n      try {\n        const response = await this.client.moderations.create(\n          moderationRequest,\n          clientOptions\n        );\n        return response;\n      } catch (e) {\n        const error = wrapOpenAIClientError(e);\n        throw error;\n      }\n    });\n  }\n\n  /**\n   * Return profiling information for the model.\n   *\n   * Provides information about the model's capabilities and constraints,\n   * including token limits, multimodal support, and advanced features like\n   * tool calling and structured output.\n   *\n   * @returns {ModelProfile} An object describing the model's capabilities and constraints\n   *\n   * @example\n   * ```typescript\n   * const model = new ChatOpenAI({ model: \"gpt-4o\" });\n   * const profile = model.profile;\n   * console.log(profile.maxInputTokens); // 128000\n   * console.log(profile.imageInputs); // true\n   * ```\n   */\n  get profile(): ModelProfile {\n    return PROFILES[this.model] ?? {};\n  }\n\n  /** @internal */\n  protected _getStructuredOutputMethod(\n    config: StructuredOutputMethodOptions<boolean>\n  ) {\n    const ensuredConfig = { ...config };\n    if (\n      !this.model.startsWith(\"gpt-3\") &&\n      !this.model.startsWith(\"gpt-4-\") &&\n      this.model !== \"gpt-4\"\n    ) {\n      if (ensuredConfig?.method === undefined) {\n        return \"jsonSchema\";\n      }\n    } else if (ensuredConfig.method === \"jsonSchema\") {\n      console.warn(\n        `[WARNING]: JSON Schema is not supported for model \"${this.model}\". Falling back to tool calling.`\n      );\n    }\n    return ensuredConfig.method;\n  }\n\n  withStructuredOutput<\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // eslint-disable-next-line @typescript-eslint/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<false>\n  ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n  withStructuredOutput<\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // eslint-disable-next-line @typescript-eslint/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<true>\n  ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n  withStructuredOutput<\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    RunOutput extends Record<string, any> = Record<string, any>\n  >(\n    outputSchema:\n      | InteropZodType<RunOutput>\n      // eslint-disable-next-line @typescript-eslint/no-explicit-any\n      | Record<string, any>,\n    config?: StructuredOutputMethodOptions<boolean>\n  ):\n    | Runnable<BaseLanguageModelInput, RunOutput>\n    | Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n  /**\n   * Add structured output to the model.\n   *\n   * The OpenAI model family supports the following structured output methods:\n   * - `jsonSchema`: Use the `response_format` field in the response to return a JSON schema. Only supported with the `gpt-4o-mini`,\n   *   `gpt-4o-mini-2024-07-18`, and `gpt-4o-2024-08-06` model snapshots and later.\n   * - `functionCalling`: Function calling is useful when you are building an application that bridges the models and functionality\n   *   of your application.\n   * - `jsonMode`: JSON mode is a more basic version of the Structured Outputs feature. While JSON mode ensures that model\n   *   output is valid JSON, Structured Outputs reliably matches the model's output to the schema you specify.\n   *   We recommend you use `functionCalling` or `jsonSchema` if it is supported for your use case.\n   *\n   * The default method is `functionCalling`.\n   *\n   * @see https://platform.openai.com/docs/guides/structured-outputs\n   * @param outputSchema - The schema to use for structured output.\n   * @param config - The structured output method options.\n   * @returns The model with structured output.\n   */\n  withStructuredOutput<\n    RunOutput extends Record<string, unknown> = Record<string, unknown>\n  >(\n    outputSchema: InteropZodType<RunOutput> | Record<string, unknown>,\n    config?: StructuredOutputMethodOptions<boolean>\n  ) {\n    let llm: Runnable<BaseLanguageModelInput>;\n    let outputParser: Runnable<AIMessageChunk, RunOutput>;\n\n    const { schema, name, includeRaw } = {\n      ...config,\n      schema: outputSchema,\n 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\"extract\",\n        description: getSchemaDescription(schema),\n        schema,\n        strict: config?.strict,\n      };\n      const asJsonSchema = toJsonSchema(openaiJsonSchemaParams.schema);\n      llm = this.withConfig({\n        outputVersion: \"v0\",\n        response_format: {\n          type: \"json_schema\",\n          json_schema: openaiJsonSchemaParams,\n        },\n        ls_structured_output_format: {\n          kwargs: { method: \"json_schema\" },\n          schema: {\n            title: openaiJsonSchemaParams.name,\n            description: openaiJsonSchemaParams.description,\n            ...asJsonSchema,\n          },\n        },\n      } as Partial<CallOptions>);\n      if (isInteropZodSchema(schema)) {\n        const altParser = StructuredOutputParser.fromZodSchema(schema);\n        outputParser = RunnableLambda.from<AIMessageChunk, RunOutput>(\n          (aiMessage: AIMessageChunk) => {\n            if (\"parsed\" in aiMessage.additional_kwargs) {\n              return aiMessage.additional_kwargs.parsed as RunOutput;\n            }\n            return altParser;\n          }\n        );\n      } else {\n        outputParser = new JsonOutputParser<RunOutput>();\n      }\n    } else {\n      let functionName = name ?? \"extract\";\n      // Is function calling\n      if (isInteropZodSchema(schema)) {\n        const asJsonSchema = toJsonSchema(schema);\n        llm = this.withConfig({\n          outputVersion: \"v0\",\n          tools: [\n            {\n              type: \"function\" as const,\n              function: {\n                name: functionName,\n                description: asJsonSchema.description,\n                parameters: asJsonSchema,\n              },\n            },\n          ],\n          tool_choice: {\n            type: \"function\" as const,\n            function: {\n              name: functionName,\n            },\n          },\n          ls_structured_output_format: {\n            kwargs: { method: \"function_calling\" },\n            schema: { title: functionName, ...asJsonSchema },\n          },\n          // Do not pass `strict` argument to OpenAI if `config.strict` is undefined\n          ...(config?.strict !== undefined ? { strict: config.strict } : {}),\n        } as Partial<CallOptions>);\n        outputParser = new JsonOutputKeyToolsParser({\n          returnSingle: true,\n          keyName: functionName,\n          zodSchema: schema,\n        });\n      } else {\n        let openAIFunctionDefinition: FunctionDefinition;\n        if (\n          typeof schema.name === \"string\" &&\n          typeof schema.parameters === \"object\" &&\n          schema.parameters != null\n        ) {\n          openAIFunctionDefinition = schema as unknown as FunctionDefinition;\n          functionName = schema.name;\n        } else {\n          functionName = (schema.title as string) ?? functionName;\n          openAIFunctionDefinition = {\n            name: functionName,\n            description: (schema.description as string) ?? \"\",\n            parameters: schema,\n          };\n        }\n        const asJsonSchema = toJsonSchema(schema);\n        llm = this.withConfig({\n          outputVersion: \"v0\",\n          tools: [\n            {\n              type: \"function\" as const,\n              function: openAIFunctionDefinition,\n            },\n          ],\n          tool_choice: {\n            type: \"function\" as const,\n            function: {\n              name: functionName,\n            },\n          },\n          ls_structured_output_format: {\n            kwargs: { method: \"function_calling\" },\n            schema: { title: functionName, ...asJsonSchema },\n          },\n          // Do not pass `strict` argument to OpenAI if `config.strict` is undefined\n          ...(config?.strict !== undefined ? { strict: config.strict } : {}),\n        } as Partial<CallOptions>);\n        outputParser = new JsonOutputKeyToolsParser<RunOutput>({\n          returnSingle: true,\n          keyName: functionName,\n        });\n      }\n    }\n\n    if (!includeRaw) {\n      return llm.pipe(outputParser) as Runnable<\n        BaseLanguageModelInput,\n        RunOutput\n      >;\n    }\n\n    const parserAssign = RunnablePassthrough.assign({\n      // eslint-disable-next-line @typescript-eslint/no-explicit-any\n      parsed: (input: any, config) => outputParser.invoke(input.raw, config),\n    });\n    const parserNone = RunnablePassthrough.assign({\n      parsed: () => null,\n    });\n    const parsedWithFallback = parserAssign.withFallbacks({\n      fallbacks: [parserNone],\n    });\n    return RunnableSequence.from<\n      BaseLanguageModelInput,\n      { raw: BaseMessage; parsed: RunOutput }\n    >([{ raw: llm }, parsedWithFallback]);\n  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