80 lines
2.4 KiB
TypeScript
80 lines
2.4 KiB
TypeScript
import { Context } from "../../types"
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import llm from "../../utils/llm"
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import { cleanLLMRes } from "../../utils/llm/base"
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import getChatHistory from "./chatHistory"
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const agent = async (ctx: Context) => {
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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: { message },
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appInfo,
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larkBody: { messageId, msgText, chatId, mentions, rawMsgText, openId },
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} = ctx
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const cardGender = larkCard.child("groupAgent")
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const loadingMessageId = await message.updateOrReply(
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cardGender.genPendingCard("正在分析时间区间，请稍等...")
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)
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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("解析的时间范围", { startTime, endTime })
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// 更新卡片
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await message.updateOrReply(
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cardGender.genPendingCard("正在爬楼中，请稍等...")
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)
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// 获取聊天记录
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const { messages: chatHistory, mentions: historyMentions } =
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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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senderOpenId: openId,
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excludedMessageIds: [loadingMessageId, messageId],
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excludeMentions: [appInfo.appName],
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})
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logger.debug("获取的聊天记录", { count: chatHistory.length })
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// 根据Mention，拼装原始消息
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let userInput = rawMsgText.trim()
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for (const mention of mentions ?? []) {
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if (mention.name !== appInfo.appName) {
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userInput = userInput.replace(mention.key, `@${mention.name}`)
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} else {
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userInput = userInput.replace(mention.key, "")
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}
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}
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// 调用大模型
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try {
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await message.updateOrReply(
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cardGender.genPendingCard("LLM输出中，请稍等...")
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)
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const llmRes = (await llm.invoke(
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"groupAgent",
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{
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userName: historyMentions.get(openId || "") ?? "用户",
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userInput,
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chatHistory: JSON.stringify(chatHistory),
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time: new Date().toLocaleString("zh-CN", { timeZone: "Asia/Shanghai" }),
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},
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requestId
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)) as string
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logger.info("LLM调用成功", {
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requestId,
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url: `http://langfuse.ai.srv/project/cm1j2tkj9001gukrgdvc1swuw/sessions/${requestId}`,
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})
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const cleanedLlmRes = cleanLLMRes(llmRes)
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await message.updateOrReply(cardGender.genSuccessCard(cleanedLlmRes))
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} catch (error: any) {
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logger.error("LLM调用失败", { error: error.message })
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await message.updateOrReply(
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cardGender.genErrorCard("LLM调用失败: " + error.message)
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)
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}
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}
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export default agent
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