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ai-context.js

@@ -24,11 +24,11 @@ async function buildStudentContext(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)
+    // 1. 获取近期成绩 (最近20条)
     const recentScores = await Score.find({ 
         studentNo: student.studentNo, 
         schoolId 
-    }).sort({ _id: -1 }).limit(10);
+    }).sort({ _id: -1 }).limit(20);
 
     // 2. 获取考勤概况
     const attendanceStats = await AttendanceModel.aggregate([
@@ -38,10 +38,6 @@ async function buildStudentContext(username, schoolId) {
     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}
@@ -53,10 +49,7 @@ async function buildStudentContext(username, schoolId) {
 `;
 
     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`;
+        prompt += `- **成绩历史**: ${recentScores.map(s => `${s.courseName}: ${s.score}分 (${s.examName||s.type})`).join('; ')}\n`;
     } else {
         prompt += `- **近期成绩**: 暂无记录\n`;
     }
@@ -67,134 +60,105 @@ async function buildStudentContext(username, schoolId) {
         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)
+ * 构建教师画像上下文 (严格权限版)
+ * 核心逻辑：只查自己教的班级，只查自己教的课（除非是班主任）
  */
 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
+    const homeroomClass = user.homeroomClass; 
     
-    // 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 
+    // 1. 查找所有任课信息
+    const courses = await Course.find({ 
+        schoolId, 
+        $or: [{ teacherId: user._id }, { teacherName: user.trueName || user.username }] 
     });
-    
-    // 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);
+
+    // 2. 构建权限映射
+    // authorizedClasses: Set<string> -> 老师有权限查看的班级列表
+    const authorizedClasses = new Set();
+    // teachingSubjects: Map<className, Set<subjectName>> -> 每个班级教哪些课
+    const teachingSubjects = {};
+
+    if (homeroomClass) authorizedClasses.add(homeroomClass);
+
+    courses.forEach(c => {
+        authorizedClasses.add(c.className);
+        if (!teachingSubjects[c.className]) teachingSubjects[c.className] = new Set();
+        teachingSubjects[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);
+    const classList = Array.from(authorizedClasses);
 
-    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。`;
+    if (classList.length === 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。请告知用户去“班级管理”或“课程安排”绑定。`;
     }
 
+    // 3. 构建详细数据 Prompt
     let prompt = `
 ### 当前用户身份：教师
 - **姓名**: ${user.trueName || username}
-- **负责班级**: ${Array.from(allClasses).join(', ')}
+- **管理权限范围**: [${classList.join(', ')}]
+- **注意**: 你 **绝对不能** 回答关于上述班级以外的任何学生数据。如果用户问其他班级（如“四年级6班”），请明确拒绝，并告知用户系统记录显示他只负责上述班级。
+
+### 详细班级数��
 `;
 
-    // 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]) : [];
+    // 4. 并行获取所有相关班级的学生
+    // 移除 limit，获取全量学生
+    const allStudents = await Student.find({ 
+        schoolId, 
+        className: { $in: classList },
+        status: 'Enrolled'
+    }).sort({ seatNo: 1, studentNo: 1 });
+
+    // 5. 获取这些学生的所有成绩
+    const allStudentNos = allStudents.map(s => s.studentNo);
+    const allScores = await Score.find({ 
+        schoolId, 
+        studentNo: { $in: allStudentNos },
+        status: 'Normal'
+    });
+
+    // 6. 按班级组装数据
+    for (const clsName of classList) {
+        const isHomeroom = clsName === homeroomClass;
+        const subjectsTaught = teachingSubjects[clsName] ? Array.from(teachingSubjects[clsName]) : [];
         
-        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`;
+        prompt += `\n#### 🏫 ${clsName} (${isHomeroom ? '我是班主任' : '我是任课老师'})\n`;
+        if (!isHomeroom && subjectsTaught.length > 0) {
+            prompt += `(非班主任视角：我只教 ${subjectsTaught.join(', ')}，仅展示这些科目的成绩)\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`;
+        const classStudents = allStudents.filter(s => s.className === clsName);
+        
+        if (classStudents.length === 0) {
+            prompt += `- (暂无学生档案)\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);
+        // 组装每个学生的信息
+        const studentLines = classStudents.map(s => {
+            let stuScores = allScores.filter(sc => sc.studentNo === s.studentNo);
             
-            // 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";
+            // 权限过滤：如果不是班主任，过滤掉不教的科目
+            if (!isHomeroom) {
+                stuScores = stuScores.filter(sc => subjectsTaught.includes(sc.courseName));
             }
 
-            // 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 ? "⚠️" : "";
+            // 格式化成绩： [数学:90, 英语:85(期末)]
+            const scoreStrs = stuScores.map(sc => `${sc.courseName}:${sc.score}${sc.examName && sc.examName!=='期末考试' ? '('+sc.examName+')' : ''}`);
+            const scoreText = scoreStrs.length > 0 ? `成绩:[${scoreStrs.join(', ')}]` : "无相关成绩";
 
-            return `- ${flag} **${s.name}**: 考勤[缺${att.absent}/假${att.leave}], 小红花:${s.flowerBalance}, 最新成绩:[${scoreStr}]`;
+            return `- ${s.name}(${s.seatNo || '-'}号): ${scoreText}`;
         });
 
-        // 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`;
-            }
-        }
+        prompt += studentLines.join('\n') + '\n';
     }
 
     return prompt;
@@ -204,36 +168,11 @@ async function buildTeacherContext(username, schoolId) {
  * 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: ${absentNames || '无'}。\n`;
-    }
-
-    return prompt;
+    return `### 当前用户身份：${role === 'PRINCIPAL' ? '校长' : '超级管理员'}\n你拥有最高权限，但请注意保护隐私。`;
 }
 
 /**
- * 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
+ * 主入口
  */
 async function buildUserContext(username, role, schoolId) {
     try {
@@ -248,25 +187,23 @@ async function buildUserContext(username, role, schoolId) {
             roleContext = await buildAdminContext(role, schoolId);
         }
 
-        // 组装最终 System Instruction 片段
         return `
 ---
-【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。
+【系统注入上下文】
+当前时间: ${dateStr}
+以下是当前用户权限范围内的 **全量真实数据**。
 ${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。
+1. **诚实原则**: 数据里有什么就说什么。如果数据里没有（例如学生列表里没这个名字，或者成绩列表为空），直接说“系统里没有记录”。不要编造。
+2. **权限原则**: 严格遵守上述“管理权限范围”。用户问其他班级时，礼貌拒绝。
+3. **数据格式**: 上下文中的数据格式为 "姓名: 成绩:[科目:分数...]"。
+4. **回答风格**: 像一位专业的教务助理，简洁明了。
 ---
 `;
     } catch (e) {
         console.error("Context build failed:", e);
-        return ""; // 失败时降级为空，不影响主流程
+        return ""; 
     }
 }
 

ai-routes.js

@@ -5,7 +5,6 @@ const OpenAI = require('openai');
 const { ConfigModel, User, AIUsageModel, ChatHistoryModel } = require('./models');
 const { buildUserContext } = require('./ai-context');
 
-// ... (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' });
@@ -26,266 +25,6 @@ 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'];
@@ -325,21 +64,29 @@ 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 }); 
 });
 
-// --- PERSISTENT CHAT HISTORY HANDLER ---
-// Instead of relying on client-side 'history', we use MongoDB to ensure cross-device memory.
+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 ---
+const sendSSE = (res, data) => {
+    res.write(`data: ${JSON.stringify(data)}\n\n`);
+};
+
+// --- STANDARD CHAT ROUTE (Context Injection Only) ---
 router.post('/chat', checkAIAccess, async (req, res) => {
-    const { text, audio } = req.body; // Ignore req.body.history for prompt generation
-    
-    // Extract headers for context building
+    const { text, audio } = req.body;
     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');
@@ -349,177 +96,86 @@ router.post('/chat', checkAIAccess, async (req, res) => {
         const user = await User.findOne({ username });
         if (!user) throw new Error('User not found');
 
-        // 1. SAVE USER MSG TO DB
+        // 1. Save User Message
         const userMsgText = text || (audio ? '(Audio Message)' : '');
         if (userMsgText) {
             await ChatHistoryModel.create({ userId: user._id, role: 'user', text: userMsgText });
         }
 
-        // 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);
+        const config = await ConfigModel.findOne({ key: 'main' });
         
-        // 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.
+        // 2. Build Context (The Heavy Lifting)
+        const contextPrompt = await buildUserContext(username, userRole, schoolId);
         
-        const fullContents = [...historyContext];
+        // Setup OpenAI Client
+        const keys = await getKeyPool('openrouter'); 
+        if (keys.length === 0) throw new Error("No API keys available");
         
-        // 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 } }] 
-            });
+        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;
         }
 
-        // --- 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}`;
-        // ---------------------------
+        console.log(`🤖 [Standard Chat] ${modelName} @ ${apiUrl}`);
+        
+        const client = new OpenAI({ 
+            baseURL: apiUrl, 
+            apiKey: keys[0], 
+            defaultHeaders: { "HTTP-Referer": "https://smart.com" } 
+        });
+
+        // 3. Build History
+        const dbHistory = await ChatHistoryModel.find({ userId: user._id }).sort({ timestamp: -1 }).limit(10);
+        let messages = [
+            { role: 'system', content: contextPrompt },
+            ...convertHistoryToOpenAI(dbHistory.reverse())
+        ];
+        if (text) messages.push({ role: 'user', content: text });
+
+        // 4. Stream Response
+        const stream = await client.chat.completions.create({
+            model: modelName,
+            messages: messages,
+            stream: true
+        });
 
-        const answerText = await streamContentWithSmartFallback({
-            contents: fullContents, 
-            config: { systemInstruction: combinedSystemInstruction }
-        }, res);
+        let finalResponseText = "";
 
-        // 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?.content;
+            if (delta) {
+                finalResponseText += delta;
+                sendSSE(res, { type: 'text', content: delta });
+            }
+        }
 
-            // 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`); }
+        // 5. Save Final Answer
+        if (finalResponseText) {
+            await ChatHistoryModel.create({ userId: user._id, role: 'model', text: finalResponseText });
         }
-        res.write('data: [DONE]\n\n'); res.end();
+        recordUsage('chat-response', 'STANDARD');
+
+        sendSSE(res, { type: 'done' });
+        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();
+        console.error("[AI Chat Error]", e);
+        sendSSE(res, { type: 'error', message: e.message });
+        res.end();
     }
 });
 
-// STREAMING ASSESSMENT ENDPOINT
+// ... (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.setHeader('Connection', 'keep-alive');
     res.flushHeaders();
-
-    try {
-        res.write(`data: ${JSON.stringify({ status: 'analyzing' })}\n\n`);
-
-        const evalParts = [{ text: `请作为一名严谨的老师，对学生的回答进行评分。题目是：${question}。` }];
-        if (audio) {
-            evalParts.push({ text: "学生的回答在音频中。" });
-            evalParts.push({ inlineData: { mimeType: 'audio/webm', data: audio } });
-        }
-        
-        // Support multiple images
-        if (images && Array.isArray(images) && images.length > 0) {
-            evalParts.push({ text: "学生的回答写在以下图片中，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)
-        
-        ## 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`); }
-        }
-
-        res.write('data: [DONE]\n\n');
-        res.end();
-
-    } catch (e) {
-        console.error("AI Eval Error:", e);
-        res.write(`data: ${JSON.stringify({ error: true, message: e.message || "Evaluation failed" })}\n\n`);
-        res.end();
-    }
+    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

@@ -34,9 +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' });
-
+    
     const mediaRecorderRef = useRef<MediaRecorder | null>(null);
     const audioChunksRef = useRef<Blob[]>([]);
     const audioContextRef = useRef<AudioContext | null>(null);
@@ -68,8 +67,8 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
 
     // Scroll to bottom
     useEffect(() => {
-        messagesEndRef.current?.scrollIntoView({ behavior: isChatProcessing ? 'auto' : 'smooth', block: 'end' });
-    }, [messages, isChatProcessing, generatingAudioId]);
+        messagesEndRef.current?.scrollIntoView({ behavior: 'smooth', block: 'end' });
+    }, [messages, isChatProcessing]);
 
     const stopPlayback = () => {
         if (currentSourceRef.current) {
@@ -92,30 +91,6 @@ 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 });
@@ -153,7 +128,6 @@ 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 }));
         
@@ -164,12 +138,13 @@ export const ChatPanel: React.FC<ChatPanelProps> = ({ currentUser }) => {
             isAudioMessage: !!audioBase64, 
             timestamp: Date.now() 
         };
+        
         const newAiMsgId = (Date.now() + 1).toString();
         const newAiMsg: AIChatMessage = { 
             id: newAiMsgId, 
             role: 'model', 
             text: '', 
-            timestamp: Date.now() 
+            timestamp: Date.now()
         };
 
         setMessages(prev => [...prev, newUserMsg, newAiMsg]);
@@ -199,6 +174,7 @@ 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() || ''; 
@@ -206,41 +182,26 @@ 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);
-                            
-                            if (data.status === 'tts') {
-                                setGeneratingAudioId(newAiMsgId);
-                            }
-
-                            if (data.text) {
-                                aiTextAccumulated += data.text;
+                            if (data.type === 'text') {
+                                aiTextAccumulated += data.content;
                                 setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: aiTextAccumulated } : 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);
-                            }
-                            if (data.error) {
-                                setGeneratingAudioId(null);
-                                setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: `⚠️ 错误: ${data.message || '未知错误'}` } : m));
+                            else if (data.type === 'done') {
+                                break;
                             }
                         } 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，请重试。' } : m));
+            setMessages(prev => prev.map(m => m.id === newAiMsgId ? { ...m, text: '抱歉，连接断开或发生错误。' } : m));
         } finally { 
             setIsChatProcessing(false); 
-            setGeneratingAudioId(null);
         }
     };
 
@@ -269,19 +230,12 @@ 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-[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>}
-                            
-                            {/* 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>
-                            )}
-
-                            {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 className={`max-w-[85%] flex flex-col items-start ${msg.role === 'user' ? 'items-end' : ''}`}>
+                            <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">思考中...</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>
                         </div>
                     </div>
                 ))}