update 2.0

thisIsAnVariableOfACoder · thisIsAnVariableOfACoder · commit 593dc3d525dc · 2026-02-10T21:58:52.000+07:00
diff --git a/backend/.env b/backend/.env
@@ -0,0 +1,6 @@
+# NVIDIA Nim Configuration
+OPENAI_API_KEY=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
+OPENAI_API_URL=https://integrate.api.nvidia.com/v1/chat/completions
+OPENAI_MODEL=mistralai/mistral-nemo-12b-instruct
+LLM_RERANK=1
+LLM_TIMEOUT_MS=15000
diff --git a/backend/.env.example b/backend/.env.example
@@ -0,0 +1,6 @@
+# NVIDIA Nim Configuration
+OPENAI_API_KEY=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
+OPENAI_API_URL=https://integrate.api.nvidia.com/v1/chat/completions
+OPENAI_MODEL=mistralai/mistral-nemo-12b-instruct
+LLM_RERANK=1
+LLM_TIMEOUT_MS=15000
diff --git a/backend/database/career_advisor.db b/backend/database/career_advisor.db
diff --git a/backend/routes/chatbot.js b/backend/routes/chatbot.js
@@ -10,11 +10,12 @@ const {
     getRefinementProgress
 } = require('../services/questionEngine');
 const { matchCareerAsync } = require('../services/matcher');
+const llmScorer = require('../services/llmScorer');
 
 /**
  * Start a new conversation
  */
-router.post('/start', (req, res) => {
+router.post('/start', async (req, res) => {
     try {
         const { conversationId, userType = 'high_school' } = req.body;
 
@@ -23,14 +24,35 @@ router.post('/start', (req, res) => {
         }
 
         const state = getConversationState(conversationId, userType);
-        const firstQuestion = getNextQuestion(conversationId, userType);
+
+        let firstQuestion = null;
+        if (llmScorer.isEnabled()) {
+            const result = await llmScorer.generateAgentQuestion({
+                profile: { user_type: userType },
+                history: []
+            });
+            if (result && result.question) {
+                firstQuestion = {
+                    id: 'ai_1',
+                    text: result.question,
+                    type: 'multiple_choice',
+                    options: result.options || ["Có", "Không", "Khác..."],
+                    is_ai: true
+                };
+            }
+        }
+
+        if (!firstQuestion) {
+            firstQuestion = getNextQuestion(conversationId, userType);
+        }
 
         res.json({
             success: true,
             conversationId,
             mode: 'initial',
             question: firstQuestion,
-            canRefine: false
+            canRefine: false,
+            isAiAgent: llmScorer.isEnabled()
         });
     } catch (error) {
         console.error('Error starting conversation:', error);
@@ -41,7 +63,7 @@ router.post('/start', (req, res) => {
 /**
  * Answer a question
  */
-router.post('/answer', (req, res) => {
+router.post('/answer', async (req, res) => {
     try {
         const { conversationId, questionId, answer } = req.body;
 
@@ -56,10 +78,28 @@ router.post('/answer', (req, res) => {
         const state = getConversationState(conversationId);
         let nextQuestion = null;
 
-        if (state.mode === 'initial') {
+        if (llmScorer.isEnabled()) {
+            const history = state.answers.map(a => ({
+                question: a.question,
+                answer: a.answer
+            }));
+            const result = await llmScorer.generateAgentQuestion({
+                profile: { user_type: state.userType },
+                history
+            });
+
+            if (result && result.question && result.question !== "DONE") {
+                nextQuestion = {
+                    id: `ai_${state.answers.length + 1}`,
+                    text: result.question,
+                    type: 'multiple_choice',
+                    options: result.options || ["Có", "Không", "Khác..."],
+                    is_ai: true
+                };
+            }
+        } else if (state.mode === 'initial') {
             nextQuestion = getNextQuestion(conversationId, state.userType);
         }
-        // In refinement mode, frontend manages question flow
 
         const progress = getRefinementProgress(conversationId);
 
@@ -68,8 +108,8 @@ router.post('/answer', (req, res) => {
             recorded: true,
             nextQuestion,
             progress,
-            canRefine: canStartRefinement(conversationId),
-            done: !nextQuestion && state.mode === 'initial'
+            canRefine: !llmScorer.isEnabled() && canStartRefinement(conversationId),
+            done: !nextQuestion
         });
     } catch (error) {
         console.error('Error recording answer:', error);
@@ -88,7 +128,32 @@ router.post('/recommendations', async (req, res) => {
             return res.status(400).json({ error: 'conversationId is required' });
         }
 
-        const result = getCareerRecommendations(conversationId);
+        const state = getConversationState(conversationId);
+        let result = null;
+
+        if (llmScorer.isEnabled()) {
+            const history = state.answers.map(a => ({
+                question: a.question,
+                answer: a.answer
+            }));
+            const aiResult = await llmScorer.generateAgentRecommendations({
+                profile: { user_type: state.userType },
+                history
+            });
+
+            if (aiResult?.recommendations) {
+                result = {
+                    recommendations: aiResult.recommendations,
+                    totalAnswers: state.answers.length,
+                    isAiAgent: true
+                };
+            }
+        }
+
+        if (!result) {
+            // Fallback to database-driven logic if AI fails
+            result = getCareerRecommendations(conversationId);
+        }
 
         res.json({
             success: true,
diff --git a/backend/services/llmScorer.js b/backend/services/llmScorer.js
@@ -1,9 +1,9 @@
 const https = require('https');
 
 const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
-const OPENAI_MODEL = process.env.OPENAI_MODEL || 'gpt-4.1-mini';
-const OPENAI_API_URL = process.env.OPENAI_API_URL || 'https://api.openai.com/v1/chat/completions';
-const LLM_TIMEOUT_MS = Number(process.env.LLM_TIMEOUT_MS || 8000);
+const OPENAI_MODEL = process.env.OPENAI_MODEL || 'mistralai/mistral-nemo-12b-instruct';
+const OPENAI_API_URL = process.env.OPENAI_API_URL || 'https://integrate.api.nvidia.com/v1/chat/completions';
+const LLM_TIMEOUT_MS = Number(process.env.LLM_TIMEOUT_MS || 15000);
 const LLM_ENABLED = process.env.LLM_RERANK === '1';
 
 function isEnabled() {
@@ -65,6 +65,7 @@ function mergeScores(base, llmScores) {
     if (!item.career_name) continue;
     map.set(item.career_name, clampScore(item.match_score));
   }
+
   let merged = base.map((item) => {
     const score = map.has(item.career_name)
       ? map.get(item.career_name)
@@ -75,8 +76,28 @@ function mergeScores(base, llmScores) {
       confidence: score >= 70 ? 'high' : score >= 50 ? 'medium' : 'low'
     };
   });
-  merged = merged.sort((a, b) => b.match_score - a.match_score).slice(0, 10);
-  return ensureUniqueScores(merged);
+
+  // Sort by new score
+  merged = merged.sort((a, b) => b.match_score - a.match_score);
+
+  // Recalculate probabilities (Softmax-like or simple normalization)
+  const topK = merged.slice(0, 10);
+  const maxScore = topK[0]?.match_score || 0;
+  const expScores = topK.map(item => ({
+    ...item,
+    exp: Math.exp((item.match_score - maxScore) / 10) // Temperature = 10 for AI scores
+  }));
+  const sumExp = expScores.reduce((sum, item) => sum + item.exp, 0);
+
+  const final = expScores.map(item => {
+    const { exp, ...rest } = item;
+    return {
+      ...rest,
+      probability: sumExp > 0 ? exp / sumExp : 1 / topK.length
+    };
+  });
+
+  return ensureUniqueScores(final);
 }
 
 function safeProfile(profile) {
@@ -132,4 +153,79 @@ function postJson(url, payload, timeoutMs) {
   });
 }
 
-module.exports = { scoreCareersWithLLM, mergeScores, isEnabled };
+async function generateAgentQuestion({ profile, history }) {
+  if (!isEnabled()) return null;
+
+  const system = [
+    'Bạn là chuyên gia hướng nghiệp thông minh.',
+    'Dựa trên lịch sử hội thoại và hồ sơ, hãy đặt MỘT câu hỏi tiếp theo để hiểu rõ hơn về người dùng.',
+    'Câu hỏi phải giúp thu hẹp các lựa chọn nghề nghiệp.',
+    'Câu hỏi nên ngắn gọn, tự nhiên, thân thiện.',
+    'Nếu đã đủ thông tin, hãy trả về chuỗi "DONE".',
+    'Trả về JSON: {"question": "...", "options": ["Có", "Không", "Khác..."], "reasoning": "Tại sao đặt câu hỏi này"}'
+  ].join(' ');
+
+  const payload = {
+    model: OPENAI_MODEL,
+    temperature: 0.7,
+    response_format: { type: 'json_object' },
+    messages: [
+      { role: 'system', content: system },
+      {
+        role: 'user',
+        content: JSON.stringify({ profile: safeProfile(profile), history })
+      }
+    ]
+  };
+
+  const data = await postJson(OPENAI_API_URL, payload, LLM_TIMEOUT_MS);
+  const content = data?.choices?.[0]?.message?.content;
+  if (!content) return null;
+  try {
+    return JSON.parse(content);
+  } catch {
+    return null;
+  }
+}
+
+async function generateAgentRecommendations({ profile, history }) {
+  if (!isEnabled()) return null;
+
+  const system = [
+    'Bạn là chuyên gia hướng nghiệp cấp cao.',
+    'Hãy đề xuất top 5-10 nghề nghiệp phù hợp nhất dựa trên lịch sử hội thoại.',
+    'KHÔNG giới hạn trong bất kỳ danh sách nào, hãy dùng kiến thức rộng lớn của bạn.',
+    'Với mỗi nghề, cung cấp: tên nghề, match_score (0-100), xác suất (0-1), lý do cụ thể.',
+    'Trả về JSON: {"recommendations": [{"career_name": "...", "match_score": 85, "probability": 0.4, "reasons": ["..."]}]}'
+  ].join(' ');
+
+  const payload = {
+    model: OPENAI_MODEL,
+    temperature: 0.5,
+    response_format: { type: 'json_object' },
+    messages: [
+      { role: 'system', content: system },
+      {
+        role: 'user',
+        content: JSON.stringify({ profile: safeProfile(profile), history })
+      }
+    ]
+  };
+
+  const data = await postJson(OPENAI_API_URL, payload, LLM_TIMEOUT_MS * 2);
+  const content = data?.choices?.[0]?.message?.content;
+  if (!content) return null;
+  try {
+    return JSON.parse(content);
+  } catch {
+    return null;
+  }
+}
+
+module.exports = {
+  scoreCareersWithLLM,
+  mergeScores,
+  isEnabled,
+  generateAgentQuestion,
+  generateAgentRecommendations
+};
