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ai-context.js

@@ -1,5 +1,8 @@
 
-const { User, Student, School } = require('./models');
+const { 
+    User, Student, Score, AttendanceModel, ClassModel, 
+    LeaveRequestModel, TodoModel, School, Course 
+} = require('./models');
 
 /**
  * 格式化当前日期
@@ -11,50 +14,259 @@ const getCurrentDateInfo = () => {
 };
 
 /**
- * 构建用户上下文 - Agentic版 (精简)
- * 既然 AI 现在有了 query_database 工具，我们不需要把所有数据都塞进 System Prompt。
- * 我们只需要告诉它：“你是谁”，“用户是谁”，以及“你有查库的能力”。
+ * 构建学生画像上下文 (学生视角)
+ */
+async function buildStudentContext(username, schoolId) {
+    const student = await Student.findOne({ 
+        $or: [{ studentNo: username }, { name: username }],
+        schoolId 
+    });
+
+    if (!student) return "无法找到该学生的详细档案。";
+
+    // 1. 获取近期成绩 (最近10条，让AI掌握更多趋势)
+    const recentScores = await Score.find({ 
+        studentNo: student.studentNo, 
+        schoolId 
+    }).sort({ _id: -1 }).limit(10);
+
+    // 2. 获取考勤概况
+    const attendanceStats = await AttendanceModel.aggregate([
+        { $match: { studentId: student._id.toString() } },
+        { $group: { _id: "$status", count: { $sum: 1 } } }
+    ]);
+    const absentCount = attendanceStats.find(a => a._id === 'Absent')?.count || 0;
+    const leaveCount = attendanceStats.find(a => a._id === 'Leave')?.count || 0;
+
+    // 3. 获取待办事项
+    const user = await User.findOne({ username, schoolId });
+    const todos = user ? await TodoModel.find({ userId: user._id, isCompleted: false }).limit(5) : [];
+
+    let prompt = `
+### 当前用户身份：学生 (个人视图)
+- **姓名**: ${student.name}
+- **班级**: ${student.className}
+- **学号**: ${student.studentNo}
+- **积分(小红花)**: ${student.flowerBalance} 🌺
+
+### 个人学习数据
+`;
+
+    if (recentScores.length > 0) {
+        prompt += `- **近期成绩历史**: ${recentScores.map(s => `${s.courseName}: ${s.score} (${s.type || '考试'})`).join('; ')}\n`;
+        // 计算简单平均分
+        const avg = (recentScores.reduce((acc, s) => acc + s.score, 0) / recentScores.length).toFixed(1);
+        prompt += `- **近期平均分**: ${avg}\n`;
+    } else {
+        prompt += `- **近期成绩**: 暂无记录\n`;
+    }
+
+    if (absentCount > 0 || leaveCount > 0) {
+        prompt += `- **考勤异常**: 本学期缺勤 ${absentCount} 次，请假 ${leaveCount} 次。\n`;
+    } else {
+        prompt += `- **考勤状况**: 全勤，表现极佳。\n`;
+    }
+
+    if (todos.length > 0) {
+        prompt += `- **未完成待办**: ${todos.map(t => t.content).join('; ')}\n`;
+    }
+
+    return prompt;
+}
+
+/**
+ * 构建教师画像上下文 (增强版 - 智能区分班主任与科任视角)
+ */
+async function buildTeacherContext(username, schoolId) {
+    const user = await User.findOne({ username, schoolId });
+    if (!user) return "无法找到该教师档案。";
+
+    // 1. 确定老师的身份范围
+    const homeroomClassName = user.homeroomClass; // 班主任班级
+    
+    // 查找该老师任教的所有课程 (找出任教的其他班级)
+    const teachingCourses = await Course.find({ 
+        $or: [{ teacherId: user._id }, { teacherName: user.trueName || user.username }],
+        schoolId 
+    });
+    
+    // 构建任教班级 -> 科目列表的映射 (e.g., "三年级(2)班": ["数学", "科学"])
+    const teachingMap = {};
+    teachingCourses.forEach(c => {
+        if (!teachingMap[c.className]) teachingMap[c.className] = new Set();
+        teachingMap[c.className].add(c.courseName);
+    });
+
+    // 合并所有相关班级 (班主任班级 + 任课班级)
+    const allClasses = new Set(Object.keys(teachingMap));
+    if (homeroomClassName) allClasses.add(homeroomClassName);
+
+    if (allClasses.size === 0) {
+        return `### 当前用户身份：教师\n- **姓名**: ${user.trueName || username}\n- **状态**: 暂未绑定任何班级或课程数据。`;
+    }
+
+    let prompt = `
+### 当前用户身份：教师
+- **姓名**: ${user.trueName || username}
+- **负责班级**: ${Array.from(allClasses).join(', ')}
+`;
+
+    // 2. 遍历所有相关班级，构建详细数据
+    for (const className of allClasses) {
+        const isHomeroom = className === homeroomClassName;
+        const subjectsTaught = teachingMap[className] ? Array.from(teachingMap[className]) : [];
+        
+        prompt += `\n#### 🏫 班级: ${className} (${isHomeroom ? '我是班主任' : '我是任课老师'})\n`;
+        if (!isHomeroom) {
+            prompt += `(非班主任视角：仅展示我任教的科目 [${subjectsTaught.join(', ')}] 的数据)\n`;
+        }
+
+        // 2.1 获取该班学生
+        const students = await Student.find({ className, schoolId });
+        if (students.length === 0) {
+            prompt += `- 暂无学生数据\n`;
+            continue;
+        }
+        
+        const studentNos = students.map(s => s.studentNo);
+        const studentIds = students.map(s => s._id.toString());
+
+        // 2.2 获取考勤 (全班)
+        const attendanceRaw = await AttendanceModel.aggregate([
+            { $match: { studentId: { $in: studentIds }, status: { $in: ['Absent', 'Leave'] } } },
+            { $group: { _id: "$studentId", absent: { $sum: { $cond: [{ $eq: ["$status", "Absent"] }, 1, 0] } }, leave: { $sum: { $cond: [{ $eq: ["$status", "Leave"] }, 1, 0] } } } }
+        ]);
+        const attendanceMap = {};
+        attendanceRaw.forEach(a => attendanceMap[a._id] = a);
+
+        // 2.3 获取成绩 (按需获取)
+        // 查询该班级学生的所有成绩
+        // 为了性能，还是查出来再内存过滤，比多次DB查询快
+        const allScores = await Score.find({ 
+            schoolId, 
+            studentNo: { $in: studentNos }
+        }).sort({ _id: -1 }); // 最新的在前
+
+        // 构建每个学生的成绩摘要
+        const studentDetails = students.map(s => {
+            const att = attendanceMap[s._id.toString()] || { absent: 0, leave: 0 };
+            
+            // 筛选该学生的成绩
+            let myScores = allScores.filter(sc => sc.studentNo === s.studentNo);
+
+            // 【关键逻辑】过滤显示哪些科目
+            if (!isHomeroom) {
+                // 如果不是班主任，只保留我教的科目的成绩
+                myScores = myScores.filter(sc => subjectsTaught.includes(sc.courseName));
+            }
+
+            // 【聚合逻辑】每个科目只取最近一次成绩 (去重)
+            const latestSubjectScores = {};
+            myScores.forEach(sc => {
+                if (!latestSubjectScores[sc.courseName]) {
+                    latestSubjectScores[sc.courseName] = sc;
+                }
+            });
+
+            const finalScores = Object.values(latestSubjectScores);
+            
+            // 格式化成绩字符串
+            let scoreStr = "";
+            if (finalScores.length > 0) {
+                scoreStr = finalScores.map(sc => `${sc.courseName}:${sc.score}`).join(', ');
+            } else {
+                scoreStr = "无相关成绩";
+            }
+
+            // 标记异常 (缺勤多 或 有不及格)
+            const hasIssue = att.absent > 0 || finalScores.some(sc => sc.score < 60);
+            const flag = hasIssue ? "⚠️" : "";
+
+            return `- ${flag} **${s.name}**: 考勤[缺${att.absent}/假${att.leave}], 小红花:${s.flowerBalance}, 最新成绩:[${scoreStr}]`;
+        });
+
+        // 将学生列表加入 Prompt (限制长度，如果班级人太多，可能需要截断，但Gemini窗口大，通常没事)
+        prompt += studentDetails.join('\n') + '\n';
+
+        // 2.4 如果是班主任，额外显示待办
+        if (isHomeroom) {
+            const pendingLeaves = await LeaveRequestModel.countDocuments({ className, schoolId, status: 'Pending' });
+            if (pendingLeaves > 0) {
+                prompt += `> 🔴 班务提醒: 有 ${pendingLeaves} 条请假申请待审批。\n`;
+            }
+        }
+    }
+
+    return prompt;
+}
+
+/**
+ * 构建管理员/校长画像上下文
+ */
+async function buildAdminContext(role, schoolId) {
+    let prompt = `### 当前用户身份：${role === 'PRINCIPAL' ? '校长' : '超级管理员'}\n`;
+    
+    if (role === 'PRINCIPAL' && schoolId) {
+        const school = await School.findById(schoolId);
+        const totalStudents = await Student.countDocuments({ schoolId });
+        const totalTeachers = await User.countDocuments({ schoolId, role: 'TEACHER' });
+        
+        // 今日缺勤详细名单
+        const today = new Date().toISOString().split('T')[0];
+        const absences = await AttendanceModel.find({ schoolId, date: today, status: { $in: ['Absent', 'Leave'] } });
+        const absentNames = absences.map(a => `${a.studentName}(${a.className})`).join(', ');
+
+        // 全校均分
+        const recentScores = await Score.find({ schoolId }).sort({_id:-1}).limit(100);
+        let avgScore = 0;
+        if (recentScores.length) avgScore = (recentScores.reduce((a,b)=>a+b.score,0)/recentScores.length).toFixed(1);
+
+        prompt += `- **学校**: ${school ? school.name : '未知'}\n`;
+        prompt += `- **宏观数据**: 教师 ${totalTeachers} 人，学生 ${totalStudents} 人，近期全校抽样平均分 ${avgScore}。\n`;
+        prompt += `- **今日出勤**: 缺勤/请假 ${absences.length} 人。名单: ${absentNames || '无'}。\n`;
+    }
+
+    return prompt;
+}
+
+/**
+ * 主入口：构建用户上下文 Prompt
+ * @param {string} username - 请求头中的用户名
+ * @param {string} role - 请求头中的角色
+ * @param {string} schoolId - 请求头中的学校ID
  */
 async function buildUserContext(username, role, schoolId) {
     try {
         const dateStr = getCurrentDateInfo();
-        let userProfile = "";
+        let roleContext = "";
 
-        // 基础用户信息
         if (role === 'STUDENT') {
-            const student = await Student.findOne({ 
-                $or: [{ studentNo: username }, { name: username }],
-                schoolId 
-            });
-            if (student) {
-                userProfile = `用户是学生：${student.name} (班级: ${student.className}, 学号: ${student.studentNo})`;
-            }
+            roleContext = await buildStudentContext(username, schoolId);
         } else if (role === 'TEACHER') {
-            const user = await User.findOne({ username, schoolId });
-            if (user) {
-                userProfile = `用户是教师：${user.trueName || username} (任教: ${user.teachingSubject || '无'}, 班主任: ${user.homeroomClass || '否'})`;
-            }
-        } else {
-            userProfile = `用户是管理员/校长。`;
+            roleContext = await buildTeacherContext(username, schoolId);
+        } else if (role === 'ADMIN' || role === 'PRINCIPAL') {
+            roleContext = await buildAdminContext(role, schoolId);
         }
 
+        // 组装最终 System Instruction 片段
         return `
 ---
-【系统信息】
-当前时间: ${dateStr}
-${userProfile}
-
-【能力说明】
-1. 你是一个拥有“本地数据库查询权限”的智能助教。
-2. 工具执行环境：**工具是在用户的本地服务器上执行的**。你可以通过 Function Calling 获取内网数据，**不需要**公网访问权限。
-3. 如果用户问“张三考了多少分”或“我们班谁考勤不好”，请**务必**大胆调用 \`query_database\` 工具。
-4. 不要回答“我无法访问数据库”，因为工具会帮你完成访问并将结果传回给你。
-5. 如果查询结果返回 JSON 数据，请将其整理为通俗易懂的自然语言回答用户。
+【上下文注入信息 (Context Injection) - 绝密】
+当前系统时间: ${dateStr}
+以下是当前用户的核心数据和其管辖范围内的详细档案。
+${roleContext}
+
+【AI 行为准则】
+1. 你拥有上述所有数据的“上帝视角”。
+2. **班主任视角**: 当用户是班主任时，你通过上下文已知晓全班所有科目的成绩。如果问“王五偏科吗”，请对比他的各科成绩作答。
+3. **任课老师视角**: 当用户非班主任时，你只能看到他所教科目的成绩。如果问“李华其他课怎么样”，请诚实回答“我只能看到您任教科目的数据，无法评价其他科目”。
+4. 回答要具体。不要说“他成绩一般”，要说“他最近数学考了60分，英语考了85分”。
+5. 数据格式说明: [科目:分数] 代表该科目最近一次录入的成绩。
 ---
 `;
     } catch (e) {
         console.error("Context build failed:", e);
-        return "";
+        return ""; // 失败时降级为空，不影响主流程
     }
 }
 

ai-routes.js

@@ -4,8 +4,8 @@ const router = express.Router();
 const OpenAI = require('openai');
 const { ConfigModel, User, AIUsageModel, ChatHistoryModel } = require('./models');
 const { buildUserContext } = require('./ai-context');
-const { mongoTools, getOpenAITools, executeMongoTool } = require('./ai-tools');
 
+// ... (Key Management, Usage Tracking, Helpers remain same)
 // Fetch keys from DB + merge with ENV variables
 async function getKeyPool(type) {
     const config = await ConfigModel.findOne({ key: 'main' });
@@ -26,6 +26,266 @@ async function recordUsage(model, provider) {
     } catch (e) { console.error("Failed to record AI usage stats:", e); }
 }
 
+const wait = (ms) => new Promise(resolve => setTimeout(resolve, ms));
+async function callAIWithRetry(aiModelCall, retries = 1) {
+    for (let i = 0; i < retries; i++) {
+        try { return await aiModelCall(); }
+        catch (e) {
+            if (e.status === 400 || e.status === 401 || e.status === 403) throw e;
+            if (i < retries - 1) { await wait(1000 * Math.pow(2, i)); continue; }
+            throw e;
+        }
+    }
+}
+
+function convertGeminiToOpenAI(baseParams) {
+    const messages = [];
+    if (baseParams.config?.systemInstruction) messages.push({ role: 'system', content: baseParams.config.systemInstruction });
+    
+    let contents = baseParams.contents;
+    if (contents && !Array.isArray(contents)) {
+        contents = [contents];
+    }
+
+    if (contents && Array.isArray(contents)) {
+        contents.forEach(content => {
+            let role = (content.role === 'model' || content.role === 'assistant') ? 'assistant' : 'user';
+            const messageContent = [];
+            if (content.parts) {
+                content.parts.forEach(p => {
+                    if (p.text) messageContent.push({ type: 'text', text: p.text });
+                    else if (p.inlineData && p.inlineData.mimeType.startsWith('image/')) {
+                        messageContent.push({ type: 'image_url', image_url: { url: `data:${p.inlineData.mimeType};base64,${p.inlineData.data}` } });
+                    }
+                });
+            }
+            if (messageContent.length > 0) {
+                if (messageContent.length === 1 && messageContent[0].type === 'text') {
+                    messages.push({ role: role, content: messageContent[0].text });
+                } else {
+                    messages.push({ role: role, content: messageContent });
+                }
+            }
+        });
+    }
+    return messages;
+}
+
+const PROVIDERS = { GEMINI: 'GEMINI', OPENROUTER: 'OPENROUTER', GEMMA: 'GEMMA' };
+const DEFAULT_OPENROUTER_MODELS = ['qwen/qwen3-coder:free', 'openai/gpt-oss-120b:free', 'qwen/qwen3-235b-a22b:free', 'tngtech/deepseek-r1t-chimera:free'];
+
+// Runtime override logic
+let runtimeProviderOrder = [];
+
+function deprioritizeProvider(providerName) {
+    if (runtimeProviderOrder.length > 0 && runtimeProviderOrder[runtimeProviderOrder.length - 1] === providerName) return;
+    console.log(`[AI System] ⚠️ Deprioritizing ${providerName} due to errors. Moving to end of queue.`);
+    runtimeProviderOrder = runtimeProviderOrder.filter(p => p !== providerName).concat(providerName);
+    console.log(`[AI System] 🔄 New Priority Order: ${runtimeProviderOrder.join(' -> ')}`);
+}
+
+function isQuotaError(e) {
+    const msg = (e.message || '').toLowerCase();
+    return e.status === 429 || e.status === 503 || msg.includes('quota') || msg.includes('overloaded') || msg.includes('resource_exhausted') || msg.includes('rate limit') || msg.includes('credits');
+}
+
+// Streaming Helpers
+async function streamGemini(baseParams, res) {
+    const { GoogleGenAI } = await import("@google/genai");
+    const models = ['gemini-2.5-flash', 'gemini-2.5-flash-lite'];
+    const keys = await getKeyPool('gemini');
+    if (keys.length === 0) throw new Error("No Gemini API keys");
+    
+    for (const apiKey of keys) {
+        const client = new GoogleGenAI({ apiKey });
+        for (const modelName of models) {
+            try {
+                console.log(`[AI] 🚀 Attempting Gemini Model: ${modelName} (Key ends with ...${apiKey.slice(-4)})`);
+                const result = await client.models.generateContentStream({ ...baseParams, model: modelName });
+                
+                let hasStarted = false;
+                let fullText = "";
+                
+                for await (const chunk of result) {
+                    if (!hasStarted) {
+                        console.log(`[AI] ✅ Connected to Gemini: ${modelName}`);
+                        recordUsage(modelName, PROVIDERS.GEMINI);
+                        hasStarted = true;
+                    }
+                    if (chunk.text) {
+                        fullText += chunk.text;
+                        res.write(`data: ${JSON.stringify({ text: chunk.text })}\n\n`);
+                        if (res.flush) res.flush();
+                    }
+                }
+                return fullText;
+            } catch (e) {
+                console.warn(`[AI] ⚠️ Gemini ${modelName} Error: ${e.message}`);
+                if (isQuotaError(e)) {
+                    console.log(`[AI] 🔄 Quota exceeded for ${modelName}, trying next...`);
+                    continue; 
+                } 
+                throw e; 
+            }
+        }
+    }
+    throw new Error("Gemini streaming failed (All keys/models exhausted)");
+}
+
+async function streamOpenRouter(baseParams, res) {
+    const config = await ConfigModel.findOne({ key: 'main' });
+    const models = (config && config.openRouterModels?.length) ? config.openRouterModels.map(m => m.id) : DEFAULT_OPENROUTER_MODELS;
+    const messages = convertGeminiToOpenAI(baseParams);
+    const keys = await getKeyPool('openrouter');
+    if (keys.length === 0) throw new Error("No OpenRouter API keys");
+    
+    if (messages.length === 0) {
+        throw new Error("Conversion resulted in empty messages array. Check input format.");
+    }
+
+    for (const apiKey of keys) {
+        for (const modelName of models) {
+            const modelConfig = config?.openRouterModels?.find(m => m.id === modelName);
+            const baseURL = modelConfig?.apiUrl ? modelConfig.apiUrl : "https://openrouter.ai/api/v1";
+            const providerLabel = modelConfig?.apiUrl ? 'Custom API' : 'OpenRouter';
+            
+            const client = new OpenAI({ baseURL, apiKey, defaultHeaders: { "HTTP-Referer": "https://smart.com", "X-Title": "Smart School" } });
+            
+            // --- DOUBAO OPTIMIZATION (Context Caching) ---
+            const extraBody = {};
+            if (modelName.toLowerCase().includes('doubao')) {
+                console.log(`[AI] 💡 Activating Doubao Prefix Caching for ${modelName}`);
+                // Doubao-specific caching parameter
+                extraBody.caching = { type: "enabled", prefix: true };
+                // Disable thinking to save tokens/time if not needed (optional based on user pref, but here we prioritize speed for chat)
+                extraBody.thinking = { type: "disabled" };
+            }
+            // ---------------------------------------------
+
+            try {
+                console.log(`[AI] 🚀 Attempting ${providerLabel} Model: ${modelName} (URL: ${baseURL})`);
+                
+                const stream = await client.chat.completions.create({ 
+                    model: modelName, 
+                    messages, 
+                    stream: true,
+                    ...extraBody 
+                });
+                
+                console.log(`[AI] ✅ Connected to ${providerLabel}: ${modelName}`);
+                recordUsage(modelName, PROVIDERS.OPENROUTER);
+                
+                let fullText = '';
+                for await (const chunk of stream) {
+                    const text = chunk.choices[0]?.delta?.content || '';
+                    if (text) {
+                        fullText += text;
+                        res.write(`data: ${JSON.stringify({ text: text })}\n\n`);
+                        if (res.flush) res.flush();
+                    }
+                }
+                return fullText;
+            } catch (e) { 
+                console.warn(`[AI] ⚠️ ${providerLabel} ${modelName} Error: ${e.message}`);
+                if (isQuotaError(e)) {
+                    console.log(`[AI] 🔄 Rate limit/Quota for ${modelName}, switching...`);
+                    break;
+                }
+            }
+        }
+    }
+    throw new Error("OpenRouter/Custom stream failed (All models exhausted)");
+}
+
+async function streamGemma(baseParams, res) {
+    const { GoogleGenAI } = await import("@google/genai");
+    const models = ['gemma-3-27b-it', 'gemma-3-12b-it'];
+    const keys = await getKeyPool('gemini'); 
+    if (keys.length === 0) throw new Error("No keys for Gemma");
+    
+    for (const apiKey of keys) {
+        const client = new GoogleGenAI({ apiKey });
+        for (const modelName of models) {
+            try {
+                console.log(`[AI] 🚀 Attempting Gemma Model: ${modelName}`);
+                const result = await client.models.generateContentStream({ ...baseParams, model: modelName });
+                
+                let hasStarted = false;
+                let fullText = "";
+                for await (const chunk of result) {
+                    if (!hasStarted) {
+                        console.log(`[AI] ✅ Connected to Gemma: ${modelName}`);
+                        recordUsage(modelName, PROVIDERS.GEMMA);
+                        hasStarted = true;
+                    }
+                    if (chunk.text) {
+                        fullText += chunk.text;
+                        res.write(`data: ${JSON.stringify({ text: chunk.text })}\n\n`);
+                        if (res.flush) res.flush();
+                    }
+                }
+                return fullText;
+            } catch (e) { 
+                console.warn(`[AI] ⚠️ Gemma ${modelName} Error: ${e.message}`);
+                if (isQuotaError(e)) continue; 
+            }
+        }
+    }
+    throw new Error("Gemma stream failed");
+}
+
+async function streamContentWithSmartFallback(baseParams, res) {
+    let hasAudio = false;
+    const contentsArray = Array.isArray(baseParams.contents) ? baseParams.contents : [baseParams.contents];
+    
+    contentsArray.forEach(c => {
+        if (c && c.parts) {
+            c.parts.forEach(p => { if (p.inlineData && p.inlineData.mimeType.startsWith('audio/')) hasAudio = true; });
+        }
+    });
+    
+    if (hasAudio) {
+        try { 
+            console.log(`[AI] 🎤 Audio detected, forcing Gemini provider.`);
+            return await streamGemini(baseParams, res); 
+        } catch(e) { 
+            console.error(`[AI] ❌ Audio Processing Failed: ${e.message}`);
+            deprioritizeProvider(PROVIDERS.GEMINI); 
+            throw new Error('QUOTA_EXCEEDED_AUDIO'); 
+        }
+    }
+    
+    const config = await ConfigModel.findOne({ key: 'main' });
+    const configuredOrder = config?.aiProviderOrder && config.aiProviderOrder.length > 0 
+        ? config.aiProviderOrder 
+        : [PROVIDERS.GEMINI, PROVIDERS.OPENROUTER, PROVIDERS.GEMMA];
+
+    const runtimeSet = new Set(runtimeProviderOrder);
+    if (runtimeProviderOrder.length === 0 || runtimeProviderOrder.length !== configuredOrder.length || !configuredOrder.every(p => runtimeSet.has(p))) {
+        runtimeProviderOrder = [...configuredOrder];
+    }
+
+    let finalError = null;
+    for (const provider of runtimeProviderOrder) {
+        try {
+            console.log(`[AI] 👉 Trying Provider: ${provider}...`);
+            if (provider === PROVIDERS.GEMINI) return await streamGemini(baseParams, res);
+            else if (provider === PROVIDERS.OPENROUTER) return await streamOpenRouter(baseParams, res);
+            else if (provider === PROVIDERS.GEMMA) return await streamGemma(baseParams, res);
+        } catch (e) {
+            console.error(`[AI] ❌ Provider ${provider} Failed: ${e.message}`);
+            finalError = e;
+            if (isQuotaError(e)) { 
+                console.log(`[AI] 📉 Quota/Rate Limit detected. Switching provider...`);
+                deprioritizeProvider(provider); 
+                continue; 
+            }
+            continue; 
+        }
+    }
+    throw finalError || new Error('All streaming models unavailable.');
+}
+
 const checkAIAccess = async (req, res, next) => {
     const username = req.headers['x-user-username'];
     const role = req.headers['x-user-role'];
@@ -65,30 +325,21 @@ router.get('/stats', checkAIAccess, async (req, res) => {
 });
 
 router.post('/reset-pool', checkAIAccess, (req, res) => { 
+    runtimeProviderOrder = []; 
+    console.log('[AI] 🔄 Provider priority pool reset.');
     res.json({ success: true }); 
 });
 
-function convertHistoryToOpenAI(history) {
-    return history.map(msg => ({
-        role: msg.role === 'model' ? 'assistant' : 'user',
-        content: msg.parts ? msg.parts.map(p => p.text).join('') : (msg.text || '')
-    }));
-}
-
-// --- SSE Protocol Helper ---
-// Sends structured events to client: { type: 'text'|'thought'|'done'|'error', content?: string }
-const sendSSE = (res, data) => {
-    res.write(`data: ${JSON.stringify(data)}\n\n`);
-};
-
-// --- REAL STREAMING CHAT ROUTE ---
+// --- PERSISTENT CHAT HISTORY HANDLER ---
+// Instead of relying on client-side 'history', we use MongoDB to ensure cross-device memory.
 router.post('/chat', checkAIAccess, async (req, res) => {
-    const { text, audio } = req.body;
+    const { text, audio } = req.body; // Ignore req.body.history for prompt generation
+    
+    // Extract headers for context building
     const username = req.headers['x-user-username'];
     const userRole = req.headers['x-user-role'];
     const schoolId = req.headers['x-school-id'];
 
-    // SSE Setup
     res.setHeader('Content-Type', 'text/event-stream');
     res.setHeader('Cache-Control', 'no-cache');
     res.setHeader('Connection', 'keep-alive');
@@ -98,164 +349,177 @@ router.post('/chat', checkAIAccess, async (req, res) => {
         const user = await User.findOne({ username });
         if (!user) throw new Error('User not found');
 
-        // 1. Save User Message
+        // 1. SAVE USER MSG TO DB
         const userMsgText = text || (audio ? '(Audio Message)' : '');
         if (userMsgText) {
             await ChatHistoryModel.create({ userId: user._id, role: 'user', text: userMsgText });
         }
 
-        const config = await ConfigModel.findOne({ key: 'main' });
-        const contextPrompt = await buildUserContext(username, userRole, schoolId);
+        // 2. FETCH HISTORY FROM DB (Long-term Memory)
+        // Retrieve last 30 messages for context
+        const dbHistory = await ChatHistoryModel.find({ userId: user._id })
+            .sort({ timestamp: -1 })
+            .limit(30);
+        
+        // Re-order for API (oldest first)
+        const historyContext = dbHistory.reverse().map(msg => ({
+            role: msg.role === 'user' ? 'user' : 'model',
+            parts: [{ text: msg.text }]
+        }));
+
+        // 3. PREPARE REQUEST
+        // The last user message is already in DB and retrieved in historyContext.
+        // We need to separate "history" from "current message" for some APIs, 
+        // but Google/OpenAI handle a list of messages fine.
+        // However, standard pattern is: History + Current.
+        // Since we fetched ALL (including current), we just pass historyContext as contents.
+        // NOTE: If audio is present, we must append it specifically as the "current" part 
+        // because DB only stores text representation for now.
         
-        // Setup OpenAI Client (Used for both Doubao and OpenRouter)
-        const keys = await getKeyPool('openrouter'); 
-        if (keys.length === 0) throw new Error("No API keys available");
+        const fullContents = [...historyContext];
         
-        let modelName = 'qwen/qwen3-coder:free';
-        let apiUrl = 'https://openrouter.ai/api/v1'; 
-        if (config?.openRouterModels && config.openRouterModels.length > 0) {
-            const m = config.openRouterModels[0];
-            modelName = m.id;
-            if (m.apiUrl) apiUrl = m.apiUrl;
+        // If this request has audio, append it as a new part (since DB load only has text placeholder)
+        // We replace the last 'user' text message with the audio payload for the AI model
+        if (audio) {
+            // Remove the text placeholder we just loaded
+            if (fullContents.length > 0 && fullContents[fullContents.length - 1].role === 'user') {
+                fullContents.pop();
+            }
+            fullContents.push({ 
+                role: 'user', 
+                parts: [{ inlineData: { mimeType: 'audio/webm', data: audio } }] 
+            });
         }
 
-        console.log(`🤖 [Streaming Agent] ${modelName} @ ${apiUrl}`);
-        
-        const client = new OpenAI({ 
-            baseURL: apiUrl, 
-            apiKey: keys[0], 
-            defaultHeaders: { "HTTP-Referer": "https://smart.com" } 
-        });
+        // --- NEW: Inject Context ---
+        const contextPrompt = await buildUserContext(username, userRole, schoolId);
+        const baseSystemInstruction = "你是一位友善、耐心且知识渊博的中小学AI助教。请用简洁、鼓励性的语言回答学生的问题。回复支持 Markdown 格式。";
+        const combinedSystemInstruction = `${baseSystemInstruction}\n${contextPrompt}`;
+        // ---------------------------
 
-        // 2. Build History
-        const dbHistory = await ChatHistoryModel.find({ userId: user._id }).sort({ timestamp: -1 }).limit(10);
-        let messages = [
-            { role: 'system', content: `${contextPrompt}\n\n重要：如果用户查询具体数据，请使用 query_database 工具。` },
-            ...convertHistoryToOpenAI(dbHistory.reverse())
-        ];
-        if (text) messages.push({ role: 'user', content: text });
-
-        // 3. Recursive Agent Loop
-        let finalResponseText = "";
-        let turnCount = 0;
-        const MAX_TURNS = 5;
-
-        // Loop handles: LLM -> Tool Call -> Tool Result -> LLM -> Answer
-        while (turnCount < MAX_TURNS) {
-            
-            // Start Stream for this turn
-            const stream = await client.chat.completions.create({
-                model: modelName,
-                messages: messages,
-                tools: getOpenAITools(), 
-                tool_choice: "auto",
-                stream: true // Enable REAL streaming
-            });
+        const answerText = await streamContentWithSmartFallback({
+            contents: fullContents, 
+            config: { systemInstruction: combinedSystemInstruction }
+        }, res);
 
-            let toolCallBuffer = []; // To accumulate tool call chunks
-            let currentContent = "";
+        // 4. SAVE AI RESPONSE TO DB
+        if (answerText) {
+            await ChatHistoryModel.create({ userId: user._id, role: 'model', text: answerText });
 
-            for await (const chunk of stream) {
-                const delta = chunk.choices[0]?.delta;
-                
-                // A. Handle Text Content
-                if (delta?.content) {
-                    currentContent += delta.content;
-                    finalResponseText += delta.content;
-                    // Directly stream text to client
-                    sendSSE(res, { type: 'text', content: delta.content });
+            // Signal that text generation is done and TTS is starting
+            res.write(`data: ${JSON.stringify({ status: 'tts' })}\n\n`);
+            try {
+                const { GoogleGenAI } = await import("@google/genai");
+                const keys = await getKeyPool('gemini');
+                let audioBytes = null;
+                for (const apiKey of keys) {
+                    try {
+                        const client = new GoogleGenAI({ apiKey });
+                        const ttsResponse = await client.models.generateContent({
+                            model: "gemini-2.5-flash-preview-tts", 
+                            contents: [{ parts: [{ text: answerText }] }],
+                            config: { responseModalities: ['AUDIO'], speechConfig: { voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Kore' } } } }
+                        });
+                        audioBytes = ttsResponse.candidates?.[0]?.content?.parts?.[0]?.inlineData?.data;
+                        if (audioBytes) break;
+                    } catch(e) { if (isQuotaError(e)) continue; break; }
                 }
+                if (audioBytes) res.write(`data: ${JSON.stringify({ audio: audioBytes })}\n\n`);
+                else res.write(`data: ${JSON.stringify({ ttsSkipped: true })}\n\n`);
+            } catch (ttsError) { res.write(`data: ${JSON.stringify({ ttsSkipped: true })}\n\n`); }
+        }
+        res.write('data: [DONE]\n\n'); res.end();
+    } catch (e) {
+        console.error("[AI Chat Route Error]", e);
+        res.write(`data: ${JSON.stringify({ error: true, message: e.message })}\n\n`); res.end();
+    }
+});
 
-                // B. Handle Tool Calls (Accumulate args)
-                if (delta?.tool_calls) {
-                    const toolCalls = delta.tool_calls;
-                    for (const toolCall of toolCalls) {
-                        const index = toolCall.index;
-                        if (!toolCallBuffer[index]) {
-                            toolCallBuffer[index] = { 
-                                id: toolCall.id,
-                                name: toolCall.function?.name || "", 
-                                arguments: "" 
-                            };
-                        }
-                        if (toolCall.function?.name) toolCallBuffer[index].name = toolCall.function.name;
-                        if (toolCall.function?.arguments) toolCallBuffer[index].arguments += toolCall.function.arguments;
-                    }
-                }
-            }
+// STREAMING ASSESSMENT ENDPOINT
+router.post('/evaluate', checkAIAccess, async (req, res) => {
+    const { question, audio, image, images } = req.body;
+    res.setHeader('Content-Type', 'text/event-stream');
+    res.setHeader('Cache-Control', 'no-cache');
+    res.setHeader('Connection', 'keep-alive');
+    res.flushHeaders();
 
-            // End of stream for this turn.
-            // Check if we have tool calls to execute.
-            if (toolCallBuffer.length > 0) {
-                // Add the assistant's "intent" message to history
-                // Note: We reconstruct the message object as if it wasn't streamed
-                messages.push({
-                    role: 'assistant',
-                    content: currentContent || null, // Content might be null if only calling tools
-                    tool_calls: toolCallBuffer.map(tc => ({
-                        id: tc.id || `call_${Date.now()}`,
-                        type: 'function',
-                        function: { name: tc.name, arguments: tc.arguments }
-                    }))
-                });
+    try {
+        res.write(`data: ${JSON.stringify({ status: 'analyzing' })}\n\n`);
 
-                // Notify Frontend: Tool Execution Started
-                sendSSE(res, { type: 'thought', content: `🤔 正在调用工具: ${toolCallBuffer.map(t => t.name).join(', ')} ...` });
-
-                // Execute Tools
-                for (const toolCall of toolCallBuffer) {
-                    const toolResult = await executeMongoTool({ 
-                        name: toolCall.name, 
-                        args: undefined, 
-                        arguments: toolCall.arguments 
-                    }, user, userRole, schoolId);
-
-                    // Add result to history
-                    messages.push({
-                        role: "tool",
-                        tool_call_id: toolCall.id || `call_${Date.now()}`,
-                        content: JSON.stringify(toolResult)
-                    });
-
-                    // Notify Frontend: Tool Result
-                    const shortResult = JSON.stringify(toolResult).substring(0, 50) + "...";
-                    sendSSE(res, { type: 'thought', content: `✅ 工具执行完成: ${shortResult}` });
-                }
-                
-                // Continue loop to let LLM generate answer based on tool result
-                turnCount++;
-            } else {
-                // No tool calls, we are done.
-                break;
-            }
+        const evalParts = [{ text: `请作为一名严谨的老师，对学生的回答进行评分。题目是：${question}。` }];
+        if (audio) {
+            evalParts.push({ text: "学生的回答在音频中。" });
+            evalParts.push({ inlineData: { mimeType: 'audio/webm', data: audio } });
         }
-
-        // 4. Save Final Answer (Prevent Empty Text Error)
-        if (finalResponseText && finalResponseText.trim().length > 0) {
-            await ChatHistoryModel.create({ userId: user._id, role: 'model', text: finalResponseText });
+        
+        // Support multiple images
+        if (images && Array.isArray(images) && images.length > 0) {
+            evalParts.push({ text: "学生的回答写���以下图片中，请识别所有图片中的文字内容并进行批改：" });
+            images.forEach(img => {
+                if(img) evalParts.push({ inlineData: { mimeType: 'image/jpeg', data: img } });
+            });
+        } else if (image) {
+            // Legacy single image support
+            evalParts.push({ text: "学生的回答写在图片中，请识别图片中的文字内容并进行批改。" });
+            evalParts.push({ inlineData: { mimeType: 'image/jpeg', data: image } });
         }
+
+        // Force structured markdown output for streaming parsing
+        evalParts.push({ text: `请分析：1. 内容准确性 2. 表达/书写规范。
+        必须严格按照以下格式输出(不要使用Markdown代码块包裹):
+        
+        ## Transcription
+        (在此处输出识别到的学生回答内容，如果是图片则为识别的文字)
+        
+        ## Feedback
+        (在此处输出简短的鼓励性评语和建议)
         
-        recordUsage('agent-response', 'AGENT');
+        ## Score
+        (在此处仅输出一个0-100的数字)` });
+
+        // Stream Text
+        const fullText = await streamContentWithSmartFallback({
+            // CRITICAL FIX: Pass as array of objects for OpenRouter compatibility
+            contents: [{ role: 'user', parts: evalParts }],
+            // NO JSON MODE to allow progressive text streaming
+        }, res);
+
+        // Extract Feedback for TTS
+        const feedbackMatch = fullText.match(/## Feedback\s+([\s\S]*?)(?=## Score|$)/i);
+        const feedbackText = feedbackMatch ? feedbackMatch[1].trim() : "";
+
+        // Generate TTS if feedback exists
+        if (feedbackText) {
+            res.write(`data: ${JSON.stringify({ status: 'tts' })}\n\n`);
+            try {
+                const { GoogleGenAI } = await import("@google/genai");
+                const keys = await getKeyPool('gemini');
+                let feedbackAudio = null;
+                for (const apiKey of keys) {
+                    try {
+                        const client = new GoogleGenAI({ apiKey });
+                        const ttsResponse = await client.models.generateContent({
+                            model: "gemini-2.5-flash-preview-tts",
+                            contents: [{ parts: [{ text: feedbackText }] }],
+                            config: { responseModalities: ['AUDIO'], speechConfig: { voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Kore' } } } }
+                        });
+                        feedbackAudio = ttsResponse.candidates?.[0]?.content?.parts?.[0]?.inlineData?.data;
+                        if (feedbackAudio) break;
+                    } catch(e) { if (isQuotaError(e)) continue; break; }
+                }
+                if (feedbackAudio) res.write(`data: ${JSON.stringify({ audio: feedbackAudio })}\n\n`);
+                else res.write(`data: ${JSON.stringify({ ttsSkipped: true })}\n\n`);
+            } catch (ttsErr) { res.write(`data: ${JSON.stringify({ ttsSkipped: true })}\n\n`); }
+        }
 
-        // 5. Send Done Signal
-        sendSSE(res, { type: 'done' });
+        res.write('data: [DONE]\n\n');
         res.end();
 
     } catch (e) {
-        console.error("[AI Chat Error]", e);
-        sendSSE(res, { type: 'error', message: e.message });
+        console.error("AI Eval Error:", e);
+        res.write(`data: ${JSON.stringify({ error: true, message: e.message || "Evaluation failed" })}\n\n`);
         res.end();
     }
 });
 
-// ... (Rest of the file: evaluate route, export)
-router.post('/evaluate', checkAIAccess, async (req, res) => {
-    const { question, audio, image, images } = req.body;
-    res.setHeader('Content-Type', 'text/event-stream');
-    res.setHeader('Cache-Control', 'no-cache');
-    res.flushHeaders();
-    res.write(`data: ${JSON.stringify({ error: true, message: "Use Gemeni provider for multimodel evaluation" })}\n\n`);
-    res.end();
-});
-
 module.exports = router;

components/ai/ChatPanel.tsx

@@ -1,7 +1,7 @@
 
 import React, { useState, useRef, useEffect } from 'react';
 import { AIChatMessage, User } from '../../types';
-import { Bot, Mic, Square, Volume2, Send, Sparkles, Loader2, StopCircle, Trash2, BrainCircuit, ChevronDown, ChevronRight } from 'lucide-react';
+import { Bot, Mic, Square, Volume2, Send, Sparkles, Loader2, StopCircle, Trash2 } from 'lucide-react';
 import ReactMarkdown from 'react-markdown';
 import remarkGfm from 'remark-gfm';
 import { blobToBase64, base64ToUint8Array, decodePCM, cleanTextForTTS } from '../../utils/mediaHelpers';
@@ -34,10 +34,8 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
     const [inputMode, setInputMode] = useState<'text' | 'audio'>('text');
     const [isChatProcessing, setIsChatProcessing] = useState(false);
     const [isChatRecording, setIsChatRecording] = useState(false);
+    const [generatingAudioId, setGeneratingAudioId] = useState<string | null>(null);
     const [toast, setToast] = useState<ToastState>({ show: false, message: '', type: 'success' });
-    
-    // State to toggle thoughts visibility per message
-    const [expandedThoughts, setExpandedThoughts] = useState<Record<string, boolean>>({});
 
     const mediaRecorderRef = useRef<MediaRecorder | null>(null);
     const audioChunksRef = useRef<Blob[]>([]);
@@ -70,8 +68,8 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
 
     // Scroll to bottom
     useEffect(() => {
-        messagesEndRef.current?.scrollIntoView({ behavior: 'smooth', block: 'end' });
-    }, [messages, isChatProcessing]);
+        messagesEndRef.current?.scrollIntoView({ behavior: isChatProcessing ? 'auto' : 'smooth', block: 'end' });
+    }, [messages, isChatProcessing, generatingAudioId]);
 
     const stopPlayback = () => {
         if (currentSourceRef.current) {
@@ -94,6 +92,30 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
         window.speechSynthesis.speak(utterance);
     };
 
+    const playPCMAudio = async (base64Audio: string) => {
+        stopPlayback();
+        try {
+            if (!audioContextRef.current) {
+                // @ts-ignore
+                const AudioCtor = window.AudioContext || window.webkitAudioContext;
+                audioContextRef.current = new AudioCtor();
+            }
+            if (audioContextRef.current?.state === 'suspended') {
+                await audioContextRef.current.resume();
+            }
+            const bytes = base64ToUint8Array(base64Audio);
+            const audioBuffer = decodePCM(bytes, audioContextRef.current!);
+            const source = audioContextRef.current!.createBufferSource();
+            source.buffer = audioBuffer;
+            source.connect(audioContextRef.current!.destination);
+            source.start(0);
+            currentSourceRef.current = source;
+        } catch (e) {
+            console.error("Audio playback error", e);
+            setToast({ show: true, message: '语音播放失败', type: 'error' });
+        }
+    };
+
     const startRecording = async () => {
         try {
             const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
@@ -131,6 +153,7 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
     const handleChatSubmit = async (text?: string, audioBase64?: string) => {
         if (!text && !audioBase64) return;
         stopPlayback();
+        setGeneratingAudioId(null);
         
         const historyPayload = messages.filter(m => m.id !== 'welcome').map(m => ({ role: m.role, text: m.text }));
         
@@ -141,21 +164,15 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
             isAudioMessage: !!audioBase64, 
             timestamp: Date.now() 
         };
-        
         const newAiMsgId = (Date.now() + 1).toString();
-        // Init with empty thoughts array
         const newAiMsg: AIChatMessage = { 
             id: newAiMsgId, 
             role: 'model', 
             text: '', 
-            timestamp: Date.now(),
-            thoughts: [] 
+            timestamp: Date.now() 
         };
 
         setMessages(prev => [...prev, newUserMsg, newAiMsg]);
-        // Auto-expand thoughts for new message
-        setExpandedThoughts(prev => ({...prev, [newAiMsgId]: true}));
-        
         setTextInput('');
         setIsChatProcessing(true);
 
@@ -182,7 +199,6 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
             while (true) {
                 const { done, value } = await reader.read();
                 if (done) break;
-                
                 buffer += decoder.decode(value, { stream: true });
                 const parts = buffer.split('\n\n');
                 buffer = parts.pop() || ''; 
@@ -190,44 +206,44 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
                 for (const line of parts) {
                     if (line.startsWith('data: ')) {
                         const jsonStr = line.replace('data: ', '').trim();
+                        if (jsonStr === '[DONE]') break;
                         try {
                             const data = JSON.parse(jsonStr);
                             
-                            // REAL STREAMING HANDLING
-                            if (data.type === 'text') {
-                                aiTextAccumulated += data.content;
+                            if (data.status === 'tts') {
+                                setGeneratingAudioId(newAiMsgId);
+                            }
+
+                            if (data.text) {
+                                aiTextAccumulated += data.text;
                                 setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: aiTextAccumulated } : m));
-                            } 
-                            else if (data.type === 'thought') {
-                                setMessages(prev => prev.map(m => {
-                                    if (m.id === newAiMsgId) {
-                                        const oldThoughts = m.thoughts || [];
-                                        return { ...m, thoughts: [...oldThoughts, data.content] };
-                                    }
-                                    return m;
-                                }));
                             }
-                            else if (data.type === 'error') {
-                                setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: `⚠️ 错误: ${data.message}` } : m));
+                            if (data.audio) {
+                                setGeneratingAudioId(null);
+                                setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, audio: data.audio } : m));
+                                playPCMAudio(data.audio);
+                            }
+                            if (data.ttsSkipped) {
+                                setGeneratingAudioId(null);
+                                setToast({ show: true, message: 'AI 语音额度已用尽，已切换至本地语音播报', type: 'error' });
+                                speakWithBrowser(aiTextAccumulated);
                             }
-                            else if (data.type === 'done') {
-                                break;
+                            if (data.error) {
+                                setGeneratingAudioId(null);
+                                setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: `⚠️ 错误: ${data.message || '未知错误'}` } : m));
                             }
                         } catch (e) {}
                     }
                 }
             }
         } catch (error: any) {
-            setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: '抱歉，连接断开或发生错误。' } : m));
+            setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: '抱歉，连接断开或发生错误，请重试。' } : m));
         } finally { 
             setIsChatProcessing(false); 
+            setGeneratingAudioId(null);
         }
     };
 
-    const toggleThoughts = (msgId: string) => {
-        setExpandedThoughts(prev => ({...prev, [msgId]: !prev[msgId]}));
-    };
-
     const clearHistory = () => {
         setMessages([{ 
             id: 'welcome', 
@@ -253,39 +269,19 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
                         <div className={`w-10 h-10 rounded-full flex items-center justify-center shrink-0 ${msg.role === 'model' ? 'bg-blue-100 text-blue-600' : 'bg-gray-200 text-gray-600'}`}>
                             {msg.role === 'model' ? <Sparkles size={20}/> : <Bot size={20}/>}
                         </div>
-                        <div className={`max-w-[85%] flex flex-col items-start ${msg.role === 'user' ? 'items-end' : ''}`}>
+                        <div className={`max-w-[80%] p-3 rounded-2xl text-sm overflow-hidden ${msg.role === 'user' ? 'bg-blue-600 text-white rounded-tr-none' : 'bg-white border border-gray-200 text-gray-800 rounded-tl-none shadow-sm'}`}>
+                            <div className="markdown-body"><ReactMarkdown remarkPlugins={[remarkGfm]}>{msg.text || ''}</ReactMarkdown></div>
+                            {msg.role === 'model' && !msg.text && isChatProcessing && <div className="flex items-center gap-2 text-gray-400 py-1"><Loader2 className="animate-spin" size={14}/><span className="text-xs">思考中...</span></div>}
                             
-                            {/* Chain of Thought / Tool Logs */}
-                            {msg.thoughts && msg.thoughts.length > 0 && (
-                                <div className="mb-2 w-full max-w-md">
-                                    <div 
-                                        onClick={() => toggleThoughts(msg.id)}
-                                        className="flex items-center gap-2 text-xs text-gray-500 bg-gray-50 border border-gray-200 rounded-lg px-3 py-1.5 cursor-pointer hover:bg-gray-100 transition-colors w-fit"
-                                    >
-                                        <BrainCircuit size={14} className={isChatProcessing && msg.id === messages[messages.length-1].id ? "animate-pulse text-purple-500" : "text-gray-400"}/>
-                                        <span>{isChatProcessing && msg.id === messages[messages.length-1].id ? '深度思考 & 工具调用中...' : '思维链 / 系统日志'}</span>
-                                        {expandedThoughts[msg.id] ? <ChevronDown size={14}/> : <ChevronRight size={14}/>}
-                                    </div>
-                                    
-                                    {expandedThoughts[msg.id] && (
-                                        <div className="mt-1 bg-gray-50 border border-gray-100 rounded-lg p-3 text-xs font-mono text-gray-600 space-y-1 animate-in slide-in-from-top-1">
-                                            {msg.thoughts.map((t, idx) => (
-                                                <div key={idx} className="flex gap-2 border-l-2 border-gray-200 pl-2">
-                                                    <span className="text-gray-400 select-none">[{idx+1}]</span>
-                                                    <span className="whitespace-pre-wrap">{t}</span>
-                                                </div>
-                                            ))}
-                                        </div>
-                                    )}
+                            {/* Audio Generating Indicator */}
+                            {msg.id === generatingAudioId && (
+                                <div className="flex items-center gap-2 text-purple-600 py-2 animate-pulse mt-1 border-t border-purple-100 pt-2">
+                                    <Loader2 className="animate-spin" size={14}/>
+                                    <span className="text-xs font-bold">正在生成语音回复...</span>
                                 </div>
                             )}
 
-                            {/* Message Bubble */}
-                            <div className={`p-3 rounded-2xl text-sm overflow-hidden shadow-sm ${msg.role === 'user' ? 'bg-blue-600 text-white rounded-tr-none' : 'bg-white border border-gray-200 text-gray-800 rounded-tl-none'}`}>
-                                <div className="markdown-body"><ReactMarkdown remarkPlugins={[remarkGfm]}>{msg.text || ''}</ReactMarkdown></div>
-                                {msg.role === 'model' && !msg.text && isChatProcessing && <div className="flex items-center gap-2 text-gray-400 py-1"><Loader2 className="animate-spin" size={14}/><span className="text-xs">组织语言中...</span></div>}
-                                {(msg.role === 'model' && msg.text && !isChatProcessing) && (<button onClick={() => speakWithBrowser(msg.text!)} className="mt-2 flex items-center gap-2 text-xs bg-gray-50 text-gray-600 px-3 py-1.5 rounded-full hover:bg-gray-100 border border-gray-200 transition-colors w-fit"><Volume2 size={14}/> 朗读</button>)}
-                            </div>
+                            {msg.audio ? (<button onClick={() => playPCMAudio(msg.audio!)} className="mt-2 flex items-center gap-2 text-xs bg-blue-50 text-blue-600 px-3 py-1.5 rounded-full hover:bg-blue-100 border border-blue-100 transition-colors w-fit"><Volume2 size={14}/> 播放语音 (AI)</button>) : (msg.role === 'model' && msg.text && !isChatProcessing && !generatingAudioId) && (<button onClick={() => speakWithBrowser(msg.text!)} className="mt-2 flex items-center gap-2 text-xs bg-gray-50 text-gray-600 px-3 py-1.5 rounded-full hover:bg-gray-100 border border-gray-200 transition-colors w-fit"><Volume2 size={14}/> 朗读 (本地)</button>)}
                         </div>
                     </div>
                 ))}

types.ts

@@ -388,5 +388,4 @@ export interface AIChatMessage {
   audio?: string;
   isAudioMessage?: boolean;
   timestamp: number;
-  thoughts?: string[]; // Chain of Thought / Tool execution logs
 }