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

OpenAI integrations for LangChain.js

{"version":3,"file":"responses.cjs","names":["BaseChatOpenAI","options?: this[\"ParsedCallOptions\"]","strict: boolean | undefined","params: ChatResponsesInvocationParams","isBuiltInToolChoice","formatToOpenAIToolChoice","messages: BaseMessage[]","options: this[\"ParsedCallOptions\"]","finalChunk: ChatGenerationChunk | undefined","convertMessagesToResponsesInput","convertResponsesMessageToAIMessage","runManager?: CallbackManagerForLLMRun","convertResponsesDeltaToChatGenerationChunk","request: OpenAIClient.Responses.ResponseCreateParams","requestOptions?: OpenAIClient.RequestOptions","wrapOpenAIClientError","tools: ChatOpenAIToolType[]","fields: { stream?: boolean; strict?: boolean }","reducedTools: ResponsesTool[]","isBuiltInTool","isCustomTool","isOpenAICustomTool","convertCompletionsCustomTool"],"sources":["../../src/chat_models/responses.ts"],"sourcesContent":["import { OpenAI as OpenAIClient } from \"openai\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport { AIMessage, type BaseMessage } from \"@langchain/core/messages\";\nimport { ChatGenerationChunk, type ChatResult } from \"@langchain/core/outputs\";\nimport { isOpenAITool as isOpenAIFunctionTool } from \"@langchain/core/language_models/base\";\nimport { wrapOpenAIClientError } from \"../utils/client.js\";\nimport {\n  ChatOpenAIToolType,\n  convertCompletionsCustomTool,\n  formatToOpenAIToolChoice,\n  isBuiltInTool,\n  isBuiltInToolChoice,\n  isCustomTool,\n  isOpenAICustomTool,\n  ResponsesTool,\n} from \"../utils/tools.js\";\nimport { BaseChatOpenAI, BaseChatOpenAICallOptions } from \"./base.js\";\nimport {\n  convertMessagesToResponsesInput,\n  convertResponsesDeltaToChatGenerationChunk,\n  convertResponsesMessageToAIMessage,\n} from \"../converters/responses.js\";\nimport { OpenAIVerbosityParam } from \"../types.js\";\n\nexport interface ChatOpenAIResponsesCallOptions\n  extends BaseChatOpenAICallOptions {\n  /**\n   * Configuration options for a text response from the model. Can be plain text or\n   * structured JSON data.\n   */\n  text?: OpenAIClient.Responses.ResponseCreateParams[\"text\"];\n\n  /**\n   * The truncation strategy to use for the model response.\n   */\n  truncation?: OpenAIClient.Responses.ResponseCreateParams[\"truncation\"];\n\n  /**\n   * Specify additional output data to include in the model response.\n   */\n  include?: OpenAIClient.Responses.ResponseCreateParams[\"include\"];\n\n  /**\n   * The unique ID of the previous response to the model. Use this to create multi-turn\n   * conversations.\n   */\n  previous_response_id?: OpenAIClient.Responses.ResponseCreateParams[\"previous_response_id\"];\n\n  /**\n   * The verbosity of the model's response.\n   */\n  verbosity?: OpenAIVerbosityParam;\n}\n\nexport type ChatResponsesInvocationParams = Omit<\n  OpenAIClient.Responses.ResponseCreateParams,\n  \"input\"\n>;\n\n/**\n * OpenAI Responses API implementation.\n *\n * Will be exported in a later version of @langchain/openai.\n *\n * @internal\n */\nexport class ChatOpenAIResponses<\n  CallOptions extends ChatOpenAIResponsesCallOptions = ChatOpenAIResponsesCallOptions\n> extends BaseChatOpenAI<CallOptions> {\n  override invocationParams(\n    options?: this[\"ParsedCallOptions\"]\n  ): ChatResponsesInvocationParams {\n    let strict: boolean | undefined;\n    if (options?.strict !== undefined) {\n      strict = options.strict;\n    }\n    if (strict === undefined && this.supportsStrictToolCalling !== undefined) {\n      strict = this.supportsStrictToolCalling;\n    }\n\n    const params: ChatResponsesInvocationParams = {\n      model: this.model,\n      temperature: this.temperature,\n      top_p: this.topP,\n      user: this.user,\n\n      // if include_usage is set or streamUsage then stream must be set to true.\n      stream: this.streaming,\n      previous_response_id: options?.previous_response_id,\n      truncation: options?.truncation,\n      include: options?.include,\n      tools: options?.tools?.length\n        ? this._reduceChatOpenAITools(options.tools, {\n            stream: this.streaming,\n            strict,\n          })\n        : undefined,\n      tool_choice: isBuiltInToolChoice(options?.tool_choice)\n        ? options?.tool_choice\n        : (() => {\n            const formatted = formatToOpenAIToolChoice(options?.tool_choice);\n            if (typeof formatted === \"object\" && \"type\" in formatted) {\n              if (formatted.type === \"function\") {\n                return { type: \"function\", name: formatted.function.name };\n              } else if (formatted.type === \"allowed_tools\") {\n                return {\n                  type: \"allowed_tools\",\n                  mode: formatted.allowed_tools.mode,\n                  tools: formatted.allowed_tools.tools,\n                };\n              } else if (formatted.type === \"custom\") {\n                return {\n                  type: \"custom\",\n                  name: formatted.custom.name,\n                };\n              }\n            }\n            return undefined;\n          })(),\n      text: (() => {\n        if (options?.text) return options.text;\n        const format = this._getResponseFormat(options?.response_format);\n        if (format?.type === \"json_schema\") {\n          if (format.json_schema.schema != null) {\n            return {\n              format: {\n                type: \"json_schema\",\n                schema: format.json_schema.schema,\n                description: format.json_schema.description,\n                name: format.json_schema.name,\n                strict: format.json_schema.strict,\n              },\n              verbosity: options?.verbosity,\n            };\n          }\n          return undefined;\n        }\n        return { format, verbosity: options?.verbosity };\n      })(),\n      parallel_tool_calls: options?.parallel_tool_calls,\n      max_output_tokens: this.maxTokens === -1 ? undefined : this.maxTokens,\n      prompt_cache_key: options?.promptCacheKey ?? this.promptCacheKey,\n      prompt_cache_retention:\n        options?.promptCacheRetention ?? this.promptCacheRetention,\n      ...(this.zdrEnabled ? { store: false } : {}),\n      ...this.modelKwargs,\n    };\n\n    const reasoning = this._getReasoningParams(options);\n\n    if (reasoning !== undefined) {\n      params.reasoning = reasoning;\n    }\n\n    return params;\n  }\n\n  async _generate(\n    messages: BaseMessage[],\n    options: this[\"ParsedCallOptions\"]\n  ): Promise<ChatResult> {\n    const invocationParams = this.invocationParams(options);\n    if (invocationParams.stream) {\n      const stream = this._streamResponseChunks(messages, options);\n      let finalChunk: ChatGenerationChunk | undefined;\n      for await (const chunk of stream) {\n        chunk.message.response_metadata = {\n          ...chunk.generationInfo,\n          ...chunk.message.response_metadata,\n        };\n        finalChunk = finalChunk?.concat(chunk) ?? chunk;\n      }\n\n      return {\n        generations: finalChunk ? [finalChunk] : [],\n        llmOutput: {\n          estimatedTokenUsage: (finalChunk?.message as AIMessage | undefined)\n            ?.usage_metadata,\n        },\n      };\n    } else {\n      const data = await this.completionWithRetry(\n        {\n          input: convertMessagesToResponsesInput({\n            messages,\n            zdrEnabled: this.zdrEnabled ?? false,\n            model: this.model,\n          }),\n          ...invocationParams,\n          stream: false,\n        },\n        { signal: options?.signal, ...options?.options }\n      );\n\n      return {\n        generations: [\n          {\n            text: data.output_text,\n            message: convertResponsesMessageToAIMessage(data),\n          },\n        ],\n        llmOutput: {\n          id: data.id,\n          estimatedTokenUsage: data.usage\n            ? {\n                promptTokens: data.usage.input_tokens,\n                completionTokens: data.usage.output_tokens,\n                totalTokens: data.usage.total_tokens,\n              }\n            : undefined,\n        },\n      };\n    }\n  }\n\n  async *_streamResponseChunks(\n    messages: BaseMessage[],\n    options: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): AsyncGenerator<ChatGenerationChunk> {\n    const streamIterable = await this.completionWithRetry(\n      {\n        ...this.invocationParams(options),\n        input: convertMessagesToResponsesInput({\n          messages,\n          zdrEnabled: this.zdrEnabled ?? false,\n          model: this.model,\n        }),\n        stream: true,\n      },\n      options\n    );\n\n    for await (const data of streamIterable) {\n      const chunk = convertResponsesDeltaToChatGenerationChunk(data);\n      if (chunk == null) continue;\n      yield chunk;\n      await runManager?.handleLLMNewToken(\n        chunk.text || \"\",\n        {\n          prompt: options.promptIndex ?? 0,\n          completion: 0,\n        },\n        undefined,\n        undefined,\n        undefined,\n        { chunk }\n      );\n    }\n  }\n\n  /**\n   * Calls the Responses API with retry logic in case of failures.\n   * @param request The request to send to the OpenAI API.\n   * @param options Optional configuration for the API call.\n   * @returns The response from the OpenAI API.\n   */\n  async completionWithRetry(\n    request: OpenAIClient.Responses.ResponseCreateParamsStreaming,\n    requestOptions?: OpenAIClient.RequestOptions\n  ): Promise<AsyncIterable<OpenAIClient.Responses.ResponseStreamEvent>>;\n\n  async completionWithRetry(\n    request: OpenAIClient.Responses.ResponseCreateParamsNonStreaming,\n    requestOptions?: OpenAIClient.RequestOptions\n  ): Promise<OpenAIClient.Responses.Response>;\n\n  async completionWithRetry(\n    request: OpenAIClient.Responses.ResponseCreateParams,\n    requestOptions?: OpenAIClient.RequestOptions\n  ): Promise<\n    | AsyncIterable<OpenAIClient.Responses.ResponseStreamEvent>\n    | OpenAIClient.Responses.Response\n  > {\n    return this.caller.call(async () => {\n      const clientOptions = this._getClientOptions(requestOptions);\n      try {\n        // use parse if dealing with json_schema\n        if (request.text?.format?.type === \"json_schema\" && !request.stream) {\n          return await this.client.responses.parse(request, clientOptions);\n        }\n        return await this.client.responses.create(request, clientOptions);\n      } catch (e) {\n        const error = wrapOpenAIClientError(e);\n        throw error;\n      }\n    });\n  }\n\n  /** @internal */\n  protected _reduceChatOpenAITools(\n    tools: ChatOpenAIToolType[],\n    fields: { stream?: boolean; strict?: boolean }\n  ): ResponsesTool[] {\n    const reducedTools: ResponsesTool[] = [];\n    for (const tool of tools) {\n      if (isBuiltInTool(tool)) {\n        if (tool.type === \"image_generation\" && fields?.stream) {\n          // OpenAI sends a 400 error if partial_images is not set and we want to stream.\n          // We also set it to 1 since we don't support partial images yet.\n          tool.partial_images = 1;\n        }\n        reducedTools.push(tool);\n      } else if (isCustomTool(tool)) {\n        const customToolData = tool.metadata.customTool;\n        reducedTools.push({\n          type: \"custom\",\n          name: customToolData.name,\n          description: customToolData.description,\n          format: customToolData.format,\n        } as ResponsesTool);\n      } else if (isOpenAIFunctionTool(tool)) {\n        reducedTools.push({\n          type: \"function\",\n          name: tool.function.name,\n          parameters: tool.function.parameters,\n          description: tool.function.description,\n          strict: fields?.strict ?? 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