65 lines
1.9 KiB
TypeScript
65 lines
1.9 KiB
TypeScript
import { Context } from "../../types"
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import llm from "../../utils/llm"
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import getChatHistory from "./chatHistory"
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const agent = async (ctx: Context.Data) => {
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const {
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logger,
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requestId,
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larkCard,
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larkService,
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larkBody: { messageId, msgText, chatId, mentions },
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} = ctx
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const cardGender = larkCard.child("groupAgent")
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// 回复一个loading的卡片
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const {
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data: { message_id },
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} = await larkService.message.replyCard(
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messageId,
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cardGender.genPendingCard("分析中，请稍等...")
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)
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const updateCard = (content: any) =>
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larkService.message.update(message_id, content)
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// 使用大模型解析用户输入
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const { startTime, endTime } = await llm.timeParser(msgText, requestId)
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logger.info(`Parsed time: startTime: ${startTime}, endTime: ${endTime}`)
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// 更新卡片
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updateCard(cardGender.genPendingCard("正在爬楼中，请稍等..."))
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// 获取聊天记录
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const chatHistory = await getChatHistory(ctx, {
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chatId,
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startTime,
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endTime,
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mentions,
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})
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// 如果没有聊天记录，返回错误信息
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if (chatHistory.length === 0) {
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logger.info("No chat history found")
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return await updateCard(cardGender.genErrorCard("未找到聊天记录"))
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}
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logger.debug(`Chat history: ${JSON.stringify(chatHistory)}`)
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// 调用大模型
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try {
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const llmRes = await llm.invoke(
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"groupAgent",
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{
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userInput: msgText,
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chatHistory: JSON.stringify(chatHistory),
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time: new Date().toLocaleString("zh-CN", { timeZone: "Asia/Shanghai" }),
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},
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requestId
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)
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logger.info(
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`LLM invoked successfully, see detail: http://langfuse.ai.srv/project/cm1j2tkj9001gukrgdvc1swuw/sessions/${requestId}`
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)
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await updateCard(cardGender.genSuccessCard(llmRes))
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} catch (error: any) {
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logger.error(`Failed to invoke llm: ${error.message}`)
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await updateCard(cardGender.genErrorCard("LLM调用失败: " + error.message))
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}
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}
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export default agent
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