feat(group-agent): 补齐logger
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@ -36,11 +36,11 @@
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"dependencies": {
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"@dotenvx/dotenvx": "^1.19.3",
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"@egg/hooks": "^1.2.0",
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"@egg/lark-msg-tool": "^1.13.0",
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"@egg/lark-msg-tool": "^1.13.2",
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"@egg/logger": "^1.4.4",
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"@egg/net-tool": "^1.9.2",
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"@egg/path-tool": "^1.4.1",
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"@langchain/core": "^0.3.11",
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"@langchain/core": "^0.3.12",
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"@langchain/openai": "^0.3.7",
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"joi": "^17.13.3",
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"langfuse-langchain": "^3.27.0",
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@ -167,10 +167,12 @@ const parseGroupAgentQuery = async (
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innerList: LarkServer.ChatGroupData[],
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mentions?: LarkEvent.Mention[]
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) => {
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// TODO：处理在群聊里的情况，不用获取群组名称
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const {
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larkBody: { msgText, chatId: rawChatId },
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larkService,
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larkCard,
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logger,
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} = ctx
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const cardGender = larkCard.child("groupAgent")
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@ -198,11 +200,15 @@ const parseGroupAgentQuery = async (
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// 使用大模型解析用户输入
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const { chatId, chatName, functionName, functionId, startTime, endTime } =
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await llm.parseGroupAgentQuery(msgText, groupInfo, functionInfo)
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logger.info(
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`Parsed group agent query: chatId: ${chatId}, chatName: ${chatName}, functionName: ${functionName}, functionId: ${functionId}, startTime: ${startTime}, endTime: ${endTime}`
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)
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// 判断顺序是 群组 -> 功能 -> 时间范围
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// 返回群组选择器，其他的值往里边丢就行
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if (!chatId || !chatName) {
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logger.info("Send group selector")
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updateCard(
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genGroupSelector(ctx, innerList, {
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functionName,
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@ -216,6 +222,7 @@ const parseGroupAgentQuery = async (
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}
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// 返回功能选择器，其他的值往里边丢就行
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if (!functionId || !functionName) {
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logger.info("Send function selector")
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updateCard(
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genFunctionSelector(ctx, {
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chatId,
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@ -229,6 +236,7 @@ const parseGroupAgentQuery = async (
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}
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// 返回时间范围选择器，其他的值往里边丢就行
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if (!startTime || !endTime) {
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logger.info("Send time scope selector")
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updateCard(
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genTimeScopeSelector(ctx, {
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chatId,
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@ -241,6 +249,7 @@ const parseGroupAgentQuery = async (
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return
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}
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logger.info("Send group report")
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// 设置齐全，返回结果报告
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updateCard(cardGender.genPendingCard("正在爬楼中，请稍等..."))
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sendGroupReport(ctx, message_id, {
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@ -288,6 +297,7 @@ const manageEventMsg = async (ctx: Context.Data) => {
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return
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}
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logger.info(`User input: ${msgText}, chatType: ${chatType}, use llm to parse`)
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// 用户有输入，使用大模型进行解析发送对应卡片
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await parseGroupAgentQuery(ctx, innerList, mentions)
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return
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@ -300,9 +310,10 @@ const manageEventMsg = async (ctx: Context.Data) => {
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const manageActionMsg = async (ctx: Context.Data) => {
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const {
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larkBody: { actionOption, actionValue, messageId },
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larkCard,
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logger,
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} = ctx
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const cardGender = ctx.larkCard.child("groupAgent")
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const cardGender = larkCard.child("groupAgent")
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logger.debug(`Action option: ${JSON.stringify(actionOption)}`)
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logger.debug(`Action value: ${JSON.stringify(actionValue)}`)
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let { chatId, chatName, functionId, functionName } = actionValue
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@ -335,6 +346,7 @@ const manageActionMsg = async (ctx: Context.Data) => {
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// 理论上来说，这里的chatId, chatName肯定是有值的，不需要再判断
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// 判断是否需要返回功能选择器
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if (!functionId || !functionName) {
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logger.info("Send function selector")
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return genFunctionSelector(ctx, {
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chatId,
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chatName,
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@ -347,6 +359,7 @@ const manageActionMsg = async (ctx: Context.Data) => {
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// 判断是否需要返回时间范围选择器
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if (!(timeScope || (startTime && endTime))) {
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logger.info("Send time scope selector")
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return genTimeScopeSelector(ctx, {
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chatId,
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chatName,
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@ -356,6 +369,7 @@ const manageActionMsg = async (ctx: Context.Data) => {
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})
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}
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logger.info("Send group report")
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// 设置齐全，返回结果报告
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sendGroupReport(ctx, messageId, {
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chatId,
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@ -2,4 +2,4 @@
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POST http://localhost:3000/bot?app=egg
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Content-Type: application/json
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{"schema":"2.0","header":{"event_id":"0c250522d3832abc044649e03672effd","token":"tV9djUKSjzVnekV7xTg2Od06NFTcsBnj","create_time":"1729082026035","event_type":"im.message.receive_v1","tenant_key":"2ee61fe50f4f1657","app_id":"cli_a1eff35b43b89063"},"event":{"message":{"chat_id":"oc_ba33dc55b3dd76bf31e0adfec5e13300","chat_type":"p2p","content":"{\"text\":\"/groupchat 前端群说了什么\"}","create_time":"1729082025875","message_id":"om_eba008b10c29ac96d9c1e6e9baf22286","message_type":"text"},"sender":{"sender_id":{"open_id":"ou_470ac13b8b50fc472d9d8ee71e03de26","union_id":"on_9dacc59a539023df8b168492f5e5433c","user_id":"zhaoyingbo"},"sender_type":"user","tenant_key":"2ee61fe50f4f1657"}}}
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{"open_id":"ou_470ac13b8b50fc472d9d8ee71e03de26","user_id":"zhaoyingbo","open_message_id":"om_3e369929b05b706754f89cd7d54018f3","open_chat_id":"oc_ba33dc55b3dd76bf31e0adfec5e13300","tenant_key":"2ee61fe50f4f1657","token":"c-4f54593c6ff3033652cb70160322905d5598576f","action":{"value":{"cardGroup":"groupAgent","cardName":"groupSelector","groupOptions":[{"text":{"content":"MIAI-FE 人工智能部-前端组","tag":"plain_text"},"value":"oc_ef98c2a9229657f99d4ef573a30fe91c|MIAI-FE 人工智能部-前端组"},{"text":{"content":"方糖の家","tag":"plain_text"},"value":"oc_433b1cb7a9dbb7ebe70a4e1a59cb8bb1|方糖の家"},{"text":{"content":"复现平台需求评审及前后端联调","tag":"plain_text"},"value":"oc_85ec841519b1d4e281ad385e3d045847|复现平台需求评审及前后端联调"},{"text":{"content":"场景复现平台前端上线通知","tag":"plain_text"},"value":"oc_e9750782ba3833afd35e7d29afa25741|场景复现平台前端上线通知"},{"text":{"content":"FE-CloudML","tag":"plain_text"},"value":"oc_07bb9ac6df70d0ae2d267fa0bfbcfb88|FE-CloudML"},{"text":{"content":"Yingbo Zhao","tag":"plain_text"},"value":"oc_51c982ebfc203937d5e8f0a33324bd53|Yingbo Zhao"},{"text":{"content":"小煎蛋测试群","tag":"plain_text"},"value":"oc_8c789ce8f4ecc6695bb63ca6ec4c61ea|小煎蛋测试群"},{"text":{"content":"RAG引擎部署落地","tag":"plain_text"},"value":"oc_09f4c9e07ed199004b4d1276143059dd|RAG引擎部署落地"}],"requestId":"897864dd-8200-42c1-a088-64bab3286095","xAuthor":"AI创新应用组","xIcon":"🔥","xName":"Group Agent"},"tag":"select_static","option":"oc_ef98c2a9229657f99d4ef573a30fe91c|MIAI-FE 人工智能部-前端组"}}
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