feat: adjust prompt; make gpt request can read config from file;add recentMessageQuantityForLlm config;

packages/geek-auto-start-chat-with-boss/default-config-file/boss.json

@@ -11,6 +11,7 @@
     "throttleIntervalMinutes": 10,
     "rechatLimitDay": 21,
     "geminiApiKey": "",
-    "rechatContentSource": 1
+    "rechatContentSource": 1,
+    "recentMessageQuantityForLlm": 8
   }
 }

packages/ui/src/main/flow/READ_NO_REPLY_AUTO_REMINDER/boss-operation.ts

@@ -1,6 +1,7 @@
 import { Page } from 'puppeteer'
-import { sleepWithRandomDelay, sleep } from '@geekgeekrun/utils/sleep.mjs';
+import { sleepWithRandomDelay, sleep } from '@geekgeekrun/utils/sleep.mjs'
 import { completes } from '@geekgeekrun/utils/gpt-request.mjs'
+import { readConfigFile } from '@geekgeekrun/geek-auto-start-chat-with-boss/runtime-file-utils.mjs'
 
 export const sendLookForwardReplyEmotion = async (page: Page) => {
   const emotionEntryButtonProxy = await page.$('.chat-conversation .message-controls .btn-emotion')
@@ -17,20 +18,57 @@ export const sendLookForwardReplyEmotion = async (page: Page) => {
   await lookForwardReplyEmojiProxy!.click()
 }
 
+const resumeContent = ``
+// let _index = 0
+
 export const sendGptContent = async (page: Page, chatRecords) => {
   const chatList = [
     {
       role: 'system',
-      content:
+      content: `
-        '你是一个求职消息发送机器人，正在帮助一位求职者在Boss直聘上寻找一份工作。求职者需要向招聘者聊天，得到招聘者的回复后，方能获得一次投递简历的机会。你需要“从求职者简历中提取到的信息，来生成一个要发送给招聘者的消息”。同时你需要注意，每次发送的内容一定要接续之前发送的内容，最好不要和已发过的内容重复。求职者简历信息如下：' +
+**核心指令：**
-        ``
+你是一个智能求职助手，需要根据用户简历生成30字左右的提醒消息，满足以下要求：
+1. 每次生成需满足：
+   - √ 包含1个核心技能 + 1个成果量化
+   - √ 使用不同句式模板（至少准备5种）
+   - √ 谦虚一些，头衔、工作年限等在历史记录信息中出现一次就好
+   - ✗ 禁止与最近发送的几条相似或雷同
+   - ✗ 禁止使用专业术语堆砌
+   - ✗ 严禁出现简历之外的词语
+
+**简历分析层：**
+请从以下简历内容中提取关键要素：\n${resumeContent}\n
+
+---
+要求提取：
+1. 硬技能：编程语言/技术栈/工具证书等（至少提取5项）
+2. 项目经历与成果：业绩、带量化数据的结果（至少3条）
+3. 软技能：沟通/管理等（至少2项）
+4. 特殊成就：奖项/专利等（可选）
+
+**消息生成层：**
+根据上述要素随机组合生成消息
+
+**质量控制层：**
+每次生成前执行：
+1. 检查历史记录
+2. 确保技能/成果组合未重复
+3. 所生成的新消息，严禁包含最近8条已经发过的内容（包括但不限于职位名称）
+4. 字数严格控制在10-40字
+5. 避免感叹号等激进符号
+6. 减少头衔“资深”、“高级”出现的频率，严禁出现“专家”、“老兵”；减少工作年限“x年”出现的频率
+
+**输出格式：**
+请确保仅回复一句话，以JSON响应，不要包含其他解释或内容；数据结构参考：\`{"response": "这里是将会发送给招聘者的内容"}\``
     }
   ]
   chatList.push({
     role: 'user',
     content:
-      '请帮我写一句开场白。请确保仅响应一句话，以JSON响应；数据结构参考：`{"response": "这里是将会发送给招聘者的内容"}`'
+      '请根据我的简历，帮我写一句谦逊有礼貌的开场白。开头包含“您好”等类似敬语、结尾包含“期待回复”等类似话术。不必包含简历中的具体内容，但需要表达出应聘意向。请确保仅响应一句话，以JSON响应；数据结构参考：`{"response": "这里是将会发送给招聘者的内容"}`'
   })
+  // chatRecords = chatRecords.slice(chatRecords.length - _index)
+  // debugger
   for (const record of chatRecords) {
     const assistantJsonContent = JSON.stringify({
       response: record.text
@@ -42,20 +80,25 @@ export const sendGptContent = async (page: Page, chatRecords) => {
     chatList.push({
       role: 'user',
       content:
-        '请根据接续之前你所回复的内容，根据我的简历，写一句自我介绍，注意尽量不要和之前的聊天内容重复。请确保仅响应一句话，以JSON响应；数据结构参考：`{"response": "这里是将会发送给招聘者的内容"}`'
+        '围绕我简历中关于自我介绍、技术栈、工作经历、项目描述、项目业绩等内容，写一句自我介绍。开头不必包含“您好”、结尾不必包含“期待回复”；务必确保本次所回复的内容不能与之前所回复的内容雷同或相似。请确保仅回复一句话，以JSON响应，不要包含其他解释或内容；数据结构参考：`{"response": "这里是将会发送给招聘者的内容"}`'
     })
   }
-
   console.log(chatList)
-  debugger
+  const llmConfig = await readConfigFile('llm.json')
-  const res = await completes(chatList)
+  const res = await completes(
+    {
+      baseURL: llmConfig.providerCompleteApiUrl,
+      apiKey: llmConfig.providerApiSecret,
+      model: llmConfig.model
+    },
+    chatList
+  )
   console.log(res)
+  // _index++
   let textToSend
   try {
     const rawMarkdownText = res?.message?.content
-    textToSend = JSON.parse(
+    textToSend = JSON.parse(rawMarkdownText.replace(/^```json/m, '').replace(/```$/m, ''))?.response
-      rawMarkdownText.replace(/^```json/m, '').replace(/```$/m, '')
-    )?.response
     if (!textToSend) {
       throw new Error(`empty content. ${err?.message} ${res?.message?.content}`)
     }
@@ -67,12 +110,9 @@ export const sendGptContent = async (page: Page, chatRecords) => {
   await chatInputHandle.click()
   await sleep(500)
   await chatInputHandle.click()
-  await chatInputHandle.type(
+  await chatInputHandle.type(textToSend, {
-    textToSend,
+    delay: 50
-    {
+  })
-      delay: 50
-    }
-  )
   await sleep(1000)
   const sendButtonSelector = `.chat-conversation .message-controls .chat-op .btn-send:not(.disabled)`
   await page.click(sendButtonSelector)

packages/ui/src/main/flow/READ_NO_REPLY_AUTO_REMINDER/index.ts

@@ -21,6 +21,9 @@ import { messageForSaveFilter } from '../../../common/utils/chat-list'
 const throttleIntervalMinutes =
   readConfigFile('boss.json').autoReminder?.throttleIntervalMinutes ?? 10
 const rechatLimitDay = readConfigFile('boss.json').autoReminder?.rechatLimitDay ?? 21
+const recentMessageQuantityForLlm =
+  readConfigFile('boss.json').autoReminder?.recentMessageQuantityForLlm ?? 8
+
 const dbInitPromise = initDb(getPublicDbFilePath())
 
 export const pageMapByName: {
@@ -281,10 +284,10 @@ const mainLoop = async () => {
       // eslint-disable-next-line no-constant-condition
       if (1 + 1 === 2) {
         try {
-          const messageListForGpt = historyMessageList.filter(it => it.bizType !== 101 && it.isSelf)
+          const messageListForGpt = historyMessageList.filter(it => it.bizType !== 101 && it.isSelf).slice(-recentMessageQuantityForLlm)
-          debugger
           await sendGptContent(pageMapByName.boss!, messageListForGpt)
         } catch (err) {
+          console.log(err)
           await sendLookForwardReplyEmotion(pageMapByName.boss!)
         }
       } else {

packages/ui/src/renderer/src/page/LlmConfig/index.vue

@@ -115,20 +115,21 @@ const llmPresetList: {
       providerCompleteApiUrl: 'https://api.deepseek.com'
     }
   },
-  {
+  // TODO:
-    name: '通过 Ollama 部署的 DeepSeek-R1（14B）模型',
+  // {
-    config: {
+  //   name: '通过 Ollama 部署的 DeepSeek-R1（14B）模型',
-      model: 'deepseek-r1:14b',
+  //   config: {
-      providerApiSecret: 'ollama',
+  //     model: 'deepseek-r1:14b',
-      providerCompleteApiUrl: 'http://127.0.0.1:11434'
+  //     providerApiSecret: 'ollama',
-    }
+  //     providerCompleteApiUrl: 'http://127.0.0.1:11434/v1'
-  },
+  //   }
+  // },
   {
     name: '通过 Ollama 部署的 Qwen2.5（7B）模型',
     config: {
       model: 'qwen2.5:7b',
       providerApiSecret: 'ollama',
-      providerCompleteApiUrl: 'http://127.0.0.1:11434'
+      providerCompleteApiUrl: 'http://127.0.0.1:11434/v1'
     }
   }
 ]

packages/ui/src/renderer/src/page/MainLayout/ReadNoReplyReminder.vue

@@ -37,7 +37,7 @@
             RECHAT_CONTENT_SOURCE.GEMINI_WITH_CHAT_CONTEXT
           "
         >
-          <el-form-item prop="geminiApiKey">
+          <el-form-item>
             <div class="flex flex-items-center">
               <el-button size="small" type="primary" @click="handleClickConfigLlm">
                 配置大语言模型
@@ -53,34 +53,21 @@
               </div>
             </div>
           </el-form-item>
+          <el-form-item prop="recentMessageQuantityForLlm">
+            <div>
+              携带最近
+              <el-input-number
+                v-model="formContent.autoReminder.recentMessageQuantityForLlm"
+                class="w-120px"
+                :min="8"
+                :max="20"
+                :precision="0"
+                :step="1"
+              ></el-input-number>
+              次聊天内容作为上下文生成新消息
+            </div>
+          </el-form-item>
         </template>
-        <!-- <el-form-item
-          v-if="
-            formContent.autoReminder.rechatContentSource ===
-            RECHAT_CONTENT_SOURCE.GEMINI_WITH_CHAT_CONTEXT
-          "
-          prop="geminiApiKey"
-        >
-          <div class="flex">
-            Gemini API 密钥&nbsp;<el-button type="text" @click.prevent="goToGeminiNanoApiKeyPage">
-            没有密钥？点击此处申请一个
-            </el-button>
-          </div>
-          <el-input v-model="formContent.autoReminder.geminiApiKey" />
-        </el-form-item>
-        <el-form-item
-          v-if="
-            formContent.autoReminder.rechatContentSource ===
-            RECHAT_CONTENT_SOURCE.GEMINI_WITH_CHAT_CONTEXT
-          "
-          prop="resumeAbstract"
-        >
-          <div class="w-full">
-            <div class="flex">简历、求职期望摘要&nbsp;<el-button type="text">例子</el-button></div>
-            <el-input type="textarea" v-model="formContent.autoReminder.resumeAbstract" />
-          </div>
-          <el-button class="mt-8px">预览Prompt</el-button>
-        </el-form-item> -->
       </div>
       <el-form-item label="跟进间隔（分钟）" prop="throttleIntervalMinutes">
         <el-input-number
@@ -127,9 +114,8 @@ const formContent = ref({
   autoReminder: {
     throttleIntervalMinutes: 10,
     rechatLimitDay: 21,
-    geminiApiKey: '',
     rechatContentSource: 1,
-    resumeAbstract: ''
+    recentMessageQuantityForLlm: 8
   }
 })
 
@@ -150,10 +136,15 @@ electron.ipcRenderer.invoke('fetch-config-file-content').then((res) => {
   const conf = res.config['boss.json']?.autoReminder || {}
   conf.throttleIntervalMinutes = conf.throttleIntervalMinutes ?? 10
   conf.rechatLimitDay = conf.rechatLimitDay ?? 21
-  conf.geminiApiKey = conf.geminiApiKey ?? ''
   conf.rechatContentSource = conf.rechatContentSource ?? 1
-  conf.resumeAbstract = conf.resumeAbstract ?? ''
+  conf.recentMessageQuantityForLlm =
-
+    typeof conf.recentMessageQuantityForLlm === 'number'
+      ? conf.recentMessageQuantityForLlm > 20
+        ? 20
+        : conf.recentMessageQuantityForLlm < 8
+          ? 8
+          : parseInt(conf.recentMessageQuantityForLlm)
+      : 8
   formContent.value.autoReminder = conf
 })
 
@@ -175,10 +166,6 @@ const formRules = {
         cb()
       }
     }
-  },
-  geminiApiKey: {
-    required: true,
-    message: '请输入 Gemini API Key'
   }
 }
 

packages/utils/gpt-request.mjs

@@ -1,19 +1,24 @@
 import OpenAI from "openai";
 
-
+export async function completes(
-const GPT_API_KEY = `sk-40fdef46fee24402bc05311304fce7a1`
+  {
-export async function completes(messages) {
+    baseURL,
+    apiKey,
+    model
+  },
+  messages
+) {
   const openai = new OpenAI({
-    baseURL: 'https://api.deepseek.com',
+    baseURL,
-    apiKey: GPT_API_KEY
+    apiKey,
   });
 
   const completion = await openai.chat.completions.create({
     messages,
-    model: "deepseek-chat",
+    model,
     frequency_penalty: 0,
     max_tokens: 100,
-    temperature: 0.2
+    temperature: 0.1
   });
 
   console.log(completion.choices[0].message.content);