119 lines
3.1 KiB
TypeScript
119 lines
3.1 KiB
TypeScript
import { PromptTemplate } from "@langchain/core/prompts"
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import { z } from "zod"
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import { adjustTimeRange, getTimeRange } from "../time"
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import { getLangfuse, getModel } from "./base"
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/**
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* 解析timeParser用户输入
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* 如果解析失败，则返回过去三天的时间范围
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* @param userInput 用户输入
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* @param requestId 请求ID
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* @returns
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*/
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const timeParser = async (userInput: string, requestId: string) => {
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const { langfuseHandler } = await getLangfuse(
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"parseGroupAgentQuery",
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requestId
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)
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const model = await getModel()
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const structuredLlm = model.withStructuredOutput(
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z.object({
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s: z.string().describe("开始时间，格式为 YYYY-MM-DD HH:mm:ss"),
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e: z.string().describe("结束时间，格式为 YYYY-MM-DD HH:mm:ss"),
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}),
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{
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name: "timeParser",
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}
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)
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const invokeParser = async () => {
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try {
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return await structuredLlm.invoke(
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`
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当前时间为：${new Date().toLocaleString("zh-CN", { timeZone: "Asia/Shanghai" })}
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请解析以下用户输入，提取出开始时间和结束时间，默认为过去三天
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除非用户指定到了分钟，开始时间为当天的 00:00:00，结束时间为当天的 23:59:59
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请将解析结果严格按照以下格式返回：
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\`\`\`ts
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interface GroupAgent {
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s: string // 开始时间，格式为 YYYY-MM-DD HH:mm:ss
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e: string // 结束时间，格式为 YYYY-MM-DD HH:mm:ss
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}
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\`\`\`
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用户输入：
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\`\`\`
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${userInput.replaceAll("`", " ")}
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\`\`\`
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`,
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{
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callbacks: [langfuseHandler],
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}
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)
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} catch {
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// 如果解析失败，则返回空字符串
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return { s: "", e: "" }
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}
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}
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const validateResult = (result: any) => {
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const regex = /^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}$/
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return regex.test(result.s) && regex.test(result.e)
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}
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let result
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let attempts = 0
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do {
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result = await invokeParser()
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attempts++
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} while (!validateResult(result) && attempts < 3)
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// 如果解析失败，则返回过去三天的时间范围
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if (!validateResult(result)) {
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return getTimeRange("threeDays")
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}
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// 解析成功，调整时间范围
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return adjustTimeRange(result.s, result.e)
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}
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/**
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* 调用LLM模型
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* @param promptName 提示Key
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* @param variables 变量
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* @param requestId 请求ID
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* @param temperature 温度
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* @returns
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*/
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const invoke = async (
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promptName: string,
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variables: Record<string, any>,
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requestId: string,
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temperature = 0
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) => {
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const { langfuse, langfuseHandler } = await getLangfuse("invoke", requestId)
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const prompt = await langfuse.getPrompt(promptName)
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const langchainTextPrompt = PromptTemplate.fromTemplate(
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prompt.getLangchainPrompt()
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).withConfig({
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metadata: { langfusePrompt: prompt },
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})
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const chain = langchainTextPrompt.pipe(await getModel(temperature))
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const { content } = await chain.invoke(variables, {
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callbacks: [langfuseHandler],
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})
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return content
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}
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const llm = {
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timeParser,
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invoke,
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}
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export default llm
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