LangChain.js中OpenAI模型Token使用量及成本追踪实践

import { ChatOpenAI } from 'langchain/chat_models/openai';

// 定义全局变量来累积Token使用量
let totalCompletionTokens = 0; // 累积的完成词Token数
let totalPromptTokens = 0;     // 累积的提示词Token数
let totalExecutionTokens = 0;  // 累积的总Token数

// 初始化ChatOpenAI模型，并配置回调函数
const llm = new ChatOpenAI({
    callbacks: [
      {
        /**
         * 当LLM执行结束时触发的回调函数
         * @param {Object} output - LLM执行的输出，包含tokenUsage信息
         * @param {string} runId - 当前运行的ID
         * @param {string} [parentRunId] - 父运行的ID（如果存在）
         * @param {string[]} [tags] - 与运行相关的标签
         */
        handleLLMEnd: (output, runId, parentRunId, tags) => {
          // 从输出中提取Token使用详情
          const { completionTokens, promptTokens, totalTokens } = output.llmOutput?.tokenUsage;

          // 累加Token数量，确保处理undefined情况
          totalCompletionTokens += completionTokens ?? 0;
          totalPromptTokens += promptTokens ?? 0;
          totalExecutionTokens += totalTokens ?? 0;

          // 可以在此处打印每次运行的Token详情
          console.log(`--- LLM Run ${runId} Ended ---`);
          console.log(`Prompt Tokens: ${promptTokens ?? 0}`);
          console.log(`Completion Tokens: ${completionTokens ?? 0}`);
          console.log(`Total Tokens for this run: ${totalTokens ?? 0}`);
          console.log(`Current Accumulated Prompt Tokens: ${totalPromptTokens}`);
          console.log(`Current Accumulated Completion Tokens: ${totalCompletionTokens}`);
          console.log(`Current Accumulated Total Tokens: ${totalExecutionTokens}`);
        },
      },
    ],
    // 必须使用支持Token计数的模型，例如gpt-3.5-turbo系列
    modelName: 'gpt-3.5-turbo-0613', 
    // 其他OpenAI配置，例如temperature, openAIApiKey等
    temperature: 0.7,
    openAIApiKey: process.env.OPENAI_API_KEY, 
});

// 示例：如何使用配置了回调的LLM实例
async function runExampleChain() {
    console.log("Starting example chain run...");
    const result = await llm.call("What is the capital of France?");
    console.log("LLM Call Result:", result);

    const result2 = await llm.call("What is the highest mountain in the world?");
    console.log("LLM Call Result 2:", result2);

    console.log("\n--- Final Accumulated Token Usage ---");
    console.log(`Total Prompt Tokens Across All Runs: ${totalPromptTokens}`);
    console.log(`Total Completion Tokens Across All Runs: ${totalCompletionTokens}`);
    console.log(`Total Execution Tokens Across All Runs: ${totalExecutionTokens}`);
}

// 调用示例函数
runExampleChain();