feat(group-agent): 大模型监控增加参数

routes/bot/groupAgent/index.ts

@@ -93,7 +93,7 @@ const sendGroupReport = async (
     mentions?: LarkEvent.Mention[]
   }
 ) => {
-  const { larkService, logger, larkCard } = ctx
+  const { larkService, logger, larkCard, requestId } = ctx
   const cardGender = larkCard.child("groupAgent")
   const updateCard = (content: any) =>
     larkService.message.update(messageId, content)
@@ -117,10 +117,14 @@ const sendGroupReport = async (
   logger.debug(`Chat history: ${JSON.stringify(chatHistory)}`)
 
   try {
-    const llmRes = await llm.invoke(functionId, {
+    const llmRes = await llm.invoke(
-      chatHistory: JSON.stringify(chatHistory),
+      functionId,
-      time: new Date().toLocaleString("zh-CN", { timeZone: "Asia/Shanghai" }),
+      {
-    })
+        chatHistory: JSON.stringify(chatHistory),
+        time: new Date().toLocaleString("zh-CN", { timeZone: "Asia/Shanghai" }),
+      },
+      requestId
+    )
     // 记录大模型返回结果后的时间戳
     const processEnd = Date.now()
     // 计算时间差并存储在processingTime变量中，以秒为单位
@@ -173,6 +177,7 @@ const parseGroupAgentQuery = async (
     larkService,
     larkCard,
     logger,
+    requestId,
   } = ctx
   const cardGender = larkCard.child("groupAgent")
 
@@ -199,7 +204,7 @@ const parseGroupAgentQuery = async (
 
   // 使用大模型解析用户输入
   const { chatId, chatName, functionName, functionId, startTime, endTime } =
-    await llm.parseGroupAgentQuery(msgText, groupInfo, functionInfo)
+    await llm.parseGroupAgentQuery(msgText, groupInfo, functionInfo, requestId)
   logger.info(
     `Parsed group agent query: chatId: ${chatId}, chatName: ${chatName}, functionName: ${functionName}, functionId: ${functionId}, startTime: ${startTime}, endTime: ${endTime}`
   )

test/parseGroupAgentQuery.ts

@@ -20,4 +20,6 @@ const functionInfo = JSON.stringify([
 
 const userInput = "你好"
 
-llm.parseGroupAgentQuery(userInput, groupInfo, functionInfo).then(console.log)
+llm
+  .parseGroupAgentQuery(userInput, groupInfo, functionInfo, "localTest")
+  .then(console.log)

utils/llm.ts

@@ -9,11 +9,13 @@ import db from "../db"
  * 获取Langfuse
  * @returns
  */
-const getLangfuse = async () => {
+const getLangfuse = async (name: string, requestId: string) => {
   const langfuseParams = {
     publicKey: await db.appConfig.getLangfusePk(),
     secretKey: await db.appConfig.getLangfuseSk(),
     baseUrl: "http://langfuse.c5-cloudml.xiaomi.srv",
+    sessionId: requestId,
+    name,
   }
   return {
     langfuseHandler: new CallbackHandler(langfuseParams),
@@ -74,14 +76,19 @@ const groupAgentConfig = z.object({
  * @param userInput 用户输入
  * @param groupInfo 群聊信息
  * @param functionInfo 功能信息
+ * @param requestId 请求ID
  * @returns
  */
 const parseGroupAgentQuery = async (
   userInput: string,
   groupInfo: string,
-  functionInfo: string
+  functionInfo: string,
+  requestId: string
 ) => {
-  const { langfuseHandler } = await getLangfuse()
+  const { langfuseHandler } = await getLangfuse(
+    "parseGroupAgentQuery",
+    requestId
+  )
   const model = await getModel("qwen-72b-instruct-int4/v1")
   const structuredLlm = model.withStructuredOutput(groupAgentConfig, {
     name: "groupAgent",
@@ -121,15 +128,17 @@ const parseGroupAgentQuery = async (
  * 调用LLM模型
  * @param promptName 提示Key
  * @param variables 变量
+ * @param requestId 请求ID
  * @param temperature 温度
  * @returns
  */
 const invoke = async (
   promptName: string,
   variables: Record<string, any>,
+  requestId: string,
   temperature = 0
 ) => {
-  const { langfuse, langfuseHandler } = await getLangfuse()
+  const { langfuse, langfuseHandler } = await getLangfuse("invoke", requestId)
   const prompt = await langfuse.getPrompt(promptName)
   const config = prompt.config as { modelName: keyof typeof modelMap }