File size: 19,820 Bytes
519b145
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
# Tối ưu Tốc độ và Độ chính xác Chatbot

Ngày tạo: 2025-01-27

## 1. Phân tích Bottlenecks hiện tại

### 1.1 Intent Classification
**Vấn đề:**
- Loop qua nhiều keywords mỗi lần (fine_keywords: 9 items, fine_single_words: 7 items)
- Tính `_remove_accents()` nhiều lần cho cùng keyword
- Không có compiled regex patterns

**Impact:** ~5-10ms mỗi query

### 1.2 Search Pipeline
**Vấn đề:**
- `list(queryset)` - Load TẤT CẢ objects vào memory trước khi search
- TF-IDF vectorization cho toàn bộ dataset mỗi lần
- Không có early exit khi tìm thấy kết quả tốt
- Query expansion query database mỗi lần

**Impact:** ~100-500ms cho dataset lớn

### 1.3 LLM Generation
**Vấn đề:**
- Prompt được build lại mỗi lần (không cache)
- Không có streaming response
- max_new_tokens=150 (OK) nhưng có thể tối ưu thêm
- Không cache generated responses

**Impact:** ~1-5s cho local model, ~2-10s cho API

### 1.4 Không có Response Caching
**Vấn đề:**
- Cùng query được xử lý lại từ đầu
- Search results không được cache
- Intent classification không được cache

**Impact:** ~100-500ms cho duplicate queries

## 2. Tối ưu Intent Classification

### 2.1 Pre-compile Keyword Patterns

```python
# backend/hue_portal/core/chatbot.py

import re
from functools import lru_cache

class Chatbot:
    def __init__(self):
        self.intent_classifier = None
        self.vectorizer = None
        # Pre-compile keyword patterns
        self._compile_keyword_patterns()
        self._train_classifier()
    
    def _compile_keyword_patterns(self):
        """Pre-compile regex patterns for faster matching."""
        # Fine keywords (multi-word first, then single)
        self.fine_patterns_multi = [
            re.compile(r'\b' + re.escape(kw) + r'\b', re.IGNORECASE)
            for kw in ["mức phạt", "vi phạm", "đèn đỏ", "nồng độ cồn", 
                      "mũ bảo hiểm", "tốc độ", "bằng lái", "vượt đèn"]
        ]
        self.fine_patterns_single = [
            re.compile(r'\b' + re.escape(kw) + r'\b', re.IGNORECASE)
            for kw in ["phạt", "vượt", "đèn", "mức"]
        ]
        
        # Pre-compute accent-free versions
        self.fine_keywords_ascii = [self._remove_accents(kw) for kw in 
                                    ["mức phạt", "vi phạm", "đèn đỏ", ...]]
        
        # Procedure, Office, Advisory patterns...
        # Similar pattern compilation
    
    @lru_cache(maxsize=1000)
    def classify_intent(self, query: str) -> Tuple[str, float]:
        """Cached intent classification."""
        query_lower = query.lower().strip()
        
        # Fast path: Check compiled patterns
        for pattern in self.fine_patterns_multi:
            if pattern.search(query_lower):
                return ("search_fine", 0.95)
        
        # ... rest of logic
```

**Lợi ích:**
- Giảm ~50% thời gian intent classification
- Cache kết quả cho duplicate queries

### 2.2 Early Exit Strategy

```python
def _keyword_based_intent(self, query: str) -> Tuple[str, float]:
    query_lower = query.lower().strip()
    
    # Fast path: Check most common intents first
    # Fine queries are most common → check first
    if any(pattern.search(query_lower) for pattern in self.fine_patterns_multi):
        return ("search_fine", 0.95)
    
    # Early exit for very short queries (likely greeting)
    if len(query.split()) <= 2:
        if any(greeting in query_lower for greeting in ["xin chào", "chào", "hello"]):
            return ("greeting", 0.9)
    
    # ... rest
```

## 3. Tối ưu Search Pipeline

### 3.1 Limit QuerySet trước khi Load

```python
# backend/hue_portal/core/search_ml.py

def search_with_ml(queryset, query, text_fields, top_k=20, min_score=0.1, use_hybrid=True):
    if not query:
        return queryset[:top_k]
    
    # OPTIMIZATION: Limit queryset early for large datasets
    # Only search in first N records if dataset is huge
    MAX_SEARCH_CANDIDATES = 1000
    total_count = queryset.count()
    
    if total_count > MAX_SEARCH_CANDIDATES:
        # Use database-level filtering first
        # Try exact match on primary field first
        primary_field = text_fields[0] if text_fields else None
        if primary_field:
            exact_matches = queryset.filter(
                **{f"{primary_field}__icontains": query}
            )[:top_k * 2]
            
            if exact_matches.count() >= top_k:
                # We have enough exact matches, return them
                return exact_matches[:top_k]
        
        # Limit candidates for ML search
        queryset = queryset[:MAX_SEARCH_CANDIDATES]
    
    # Continue with existing search logic...
```

### 3.2 Cache Search Results

```python
# backend/hue_portal/core/search_ml.py

from functools import lru_cache
import hashlib
import json

def _get_query_hash(query: str, model_name: str, text_fields: tuple) -> str:
    """Generate hash for query caching."""
    key = f"{query}|{model_name}|{':'.join(text_fields)}"
    return hashlib.md5(key.encode()).hexdigest()

# Cache search results for 1 hour
@lru_cache(maxsize=500)
def _cached_search(query_hash: str, queryset_ids: tuple, top_k: int):
    """Cached search results."""
    # This will be called with actual queryset in wrapper
    pass

def search_with_ml(queryset, query, text_fields, top_k=20, min_score=0.1, use_hybrid=True):
    # Check cache first
    query_hash = _get_query_hash(query, queryset.model.__name__, tuple(text_fields))
    
    # Try to get from cache (if queryset hasn't changed)
    # Note: Full caching requires tracking queryset state
    
    # ... existing search logic
```

### 3.3 Optimize TF-IDF Calculation

```python
# Pre-compute TF-IDF vectors for common queries
# Use incremental TF-IDF instead of recalculating

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np

class CachedTfidfVectorizer:
    """TF-IDF vectorizer with caching."""
    
    def __init__(self):
        self.vectorizer = None
        self.doc_vectors = None
        self.doc_ids = None
    
    def fit_transform_cached(self, documents: List[str], doc_ids: List[int]):
        """Fit and cache document vectors."""
        if self.doc_ids == tuple(doc_ids):
            # Same documents, reuse vectors
            return self.doc_vectors
        
        # New documents, recompute
        self.vectorizer = TfidfVectorizer(
            analyzer='word',
            ngram_range=(1, 2),
            min_df=1,
            max_df=0.95,
            lowercase=True
        )
        self.doc_vectors = self.vectorizer.fit_transform(documents)
        self.doc_ids = tuple(doc_ids)
        return self.doc_vectors
```

### 3.4 Early Exit khi có Exact Match

```python
def search_with_ml(queryset, query, text_fields, top_k=20, min_score=0.1, use_hybrid=True):
    # OPTIMIZATION: Check exact matches first (fastest)
    query_normalized = normalize_text(query)
    
    # Try exact match on primary field
    primary_field = text_fields[0] if text_fields else None
    if primary_field:
        exact_qs = queryset.filter(**{f"{primary_field}__iexact": query})
        if exact_qs.exists():
            # Found exact match, return immediately
            return exact_qs[:top_k]
        
        # Try case-insensitive contains (faster than ML)
        contains_qs = queryset.filter(**{f"{primary_field}__icontains": query})
        if contains_qs.count() <= top_k * 2:
            # Small result set, return directly
            return contains_qs[:top_k]
    
    # Only use ML search if no good exact matches
    # ... existing ML search logic
```

## 4. Tối ưu LLM Generation

### 4.1 Prompt Caching

```python
# backend/hue_portal/chatbot/llm_integration.py

from functools import lru_cache
import hashlib

class LLMGenerator:
    def __init__(self, provider: Optional[str] = None):
        self.provider = provider or LLM_PROVIDER
        self.prompt_cache = {}  # Cache prompts by hash
        self.response_cache = {}  # Cache responses
    
    def _get_prompt_hash(self, query: str, documents: List[Any]) -> str:
        """Generate hash for prompt caching."""
        doc_ids = [getattr(doc, 'id', None) for doc in documents[:5]]
        key = f"{query}|{doc_ids}"
        return hashlib.md5(key.encode()).hexdigest()
    
    def generate_answer(self, query: str, context: Optional[List[Dict]], documents: Optional[List[Any]]):
        if not self.is_available():
            return None
        
        # Check cache first
        prompt_hash = self._get_prompt_hash(query, documents or [])
        if prompt_hash in self.response_cache:
            cached_response = self.response_cache[prompt_hash]
            # Check if cache is still valid (e.g., < 1 hour old)
            if cached_response.get('timestamp', 0) > time.time() - 3600:
                return cached_response['response']
        
        # Build prompt (may be cached)
        prompt = self._build_prompt(query, context, documents)
        response = self._generate_from_prompt(prompt, context=context)
        
        # Cache response
        if response:
            self.response_cache[prompt_hash] = {
                'response': response,
                'timestamp': time.time()
            }
        
        return response
```

### 4.2 Optimize Local Model Generation

```python
def _generate_local(self, prompt: str) -> Optional[str]:
    # OPTIMIZATION: Use faster generation parameters
    with torch.no_grad():
        outputs = self.local_model.generate(
            **inputs,
            max_new_tokens=100,  # Reduced from 150
            temperature=0.5,  # Lower for faster generation
            top_p=0.8,  # Lower top_p
            do_sample=False,  # Greedy decoding (faster)
            use_cache=True,
            pad_token_id=self.local_tokenizer.eos_token_id,
            repetition_penalty=1.1,
            # OPTIMIZATION: Early stopping
            eos_token_id=self.local_tokenizer.eos_token_id,
        )
```

### 4.3 Streaming Response (for better UX)

```python
# For API endpoints, support streaming
def generate_answer_streaming(self, query: str, context, documents):
    """Generate answer with streaming for better UX."""
    if self.provider == LLM_PROVIDER_LOCAL:
        # Use generate with stream=True
        for token in self._generate_local_streaming(prompt):
            yield token
    elif self.provider == LLM_PROVIDER_OPENAI:
        # Use OpenAI streaming API
        for chunk in self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}],
            stream=True
        ):
            yield chunk.choices[0].delta.content
```

## 5. Response Caching Strategy

### 5.1 Multi-level Caching

```python
# backend/hue_portal/core/cache_utils.py

from functools import lru_cache
from django.core.cache import cache
import hashlib
import json

class ChatbotCache:
    """Multi-level caching for chatbot responses."""
    
    CACHE_TIMEOUT = 3600  # 1 hour
    
    @staticmethod
    def get_cache_key(query: str, intent: str, session_id: str = None) -> str:
        """Generate cache key."""
        key_parts = [query.lower().strip(), intent]
        if session_id:
            key_parts.append(session_id)
        key_str = "|".join(key_parts)
        return f"chatbot:{hashlib.md5(key_str.encode()).hexdigest()}"
    
    @staticmethod
    def get_cached_response(query: str, intent: str, session_id: str = None):
        """Get cached response."""
        cache_key = ChatbotCache.get_cache_key(query, intent, session_id)
        return cache.get(cache_key)
    
    @staticmethod
    def set_cached_response(query: str, intent: str, response: dict, session_id: str = None):
        """Cache response."""
        cache_key = ChatbotCache.get_cache_key(query, intent, session_id)
        cache.set(cache_key, response, ChatbotCache.CACHE_TIMEOUT)
    
    @staticmethod
    def get_cached_search_results(query: str, model_name: str, text_fields: tuple):
        """Get cached search results."""
        key = f"search:{hashlib.md5(f'{query}|{model_name}|{text_fields}'.encode()).hexdigest()}"
        return cache.get(key)
    
    @staticmethod
    def set_cached_search_results(query: str, model_name: str, text_fields: tuple, results):
        """Cache search results."""
        key = f"search:{hashlib.md5(f'{query}|{model_name}|{text_fields}'.encode()).hexdigest()}"
        cache.set(key, results, ChatbotCache.CACHE_TIMEOUT)
```

### 5.2 Integrate vào Chatbot

```python
# backend/hue_portal/core/chatbot.py

from .cache_utils import ChatbotCache

class Chatbot:
    def generate_response(self, query: str, session_id: str = None) -> Dict[str, Any]:
        query = query.strip()
        
        # Classify intent
        intent, confidence = self.classify_intent(query)
        
        # Check cache first
        cached_response = ChatbotCache.get_cached_response(query, intent, session_id)
        if cached_response:
            return cached_response
        
        # ... existing logic
        
        # Cache response before returning
        response = {
            "message": message,
            "intent": intent,
            "confidence": confidence,
            "results": search_result["results"],
            "count": search_result["count"]
        }
        
        ChatbotCache.set_cached_response(query, intent, response, session_id)
        return response
```

## 6. Tối ưu Query Expansion

### 6.1 Cache Synonyms

```python
# backend/hue_portal/core/search_ml.py

from django.core.cache import cache

@lru_cache(maxsize=1)
def get_all_synonyms():
    """Get all synonyms (cached)."""
    return list(Synonym.objects.all())

def expand_query_with_synonyms(query: str) -> List[str]:
    """Expand query using cached synonyms."""
    query_normalized = normalize_text(query)
    expanded = [query_normalized]
    
    # Use cached synonyms
    synonyms = get_all_synonyms()
    
    for synonym in synonyms:
        keyword = normalize_text(synonym.keyword)
        alias = normalize_text(synonym.alias)
        
        if keyword in query_normalized:
            expanded.append(query_normalized.replace(keyword, alias))
        if alias in query_normalized:
            expanded.append(query_normalized.replace(alias, keyword))
    
    return list(set(expanded))
```

## 7. Database Query Optimization

### 7.1 Use select_related / prefetch_related

```python
# backend/hue_portal/core/chatbot.py

def search_by_intent(self, intent: str, query: str, limit: int = 5):
    if intent == "search_fine":
        qs = Fine.objects.all().select_related('decree')  # If has FK
        # ... rest
    
    elif intent == "search_legal":
        qs = LegalSection.objects.all().select_related('document')
        # ... rest
```

### 7.2 Add Database Indexes

```python
# backend/hue_portal/core/models.py

class Fine(models.Model):
    name = models.CharField(max_length=500, db_index=True)  # Add index
    code = models.CharField(max_length=50, db_index=True)   # Add index
    
    class Meta:
        indexes = [
            models.Index(fields=['name', 'code']),
            models.Index(fields=['min_fine', 'max_fine']),
        ]
```

## 8. Tối ưu Frontend

### 8.1 Debounce Search Input

```typescript
// frontend/src/pages/Chat.tsx

const [input, setInput] = useState('')
const debouncedInput = useDebounce(input, 300)  // Wait 300ms

useEffect(() => {
  if (debouncedInput) {
    // Trigger search suggestions
  }
}, [debouncedInput])
```

### 8.2 Optimistic UI Updates

```typescript
const handleSend = async (messageText?: string) => {
  // Show message immediately (optimistic)
  setMessages(prev => [...prev, {
    role: 'user',
    content: textToSend,
    timestamp: new Date()
  }])
  
  // Then fetch response
  const response = await chat(textToSend, sessionId)
  // Update with actual response
}
```

## 9. Monitoring & Metrics

### 9.1 Add Performance Logging

```python
# backend/hue_portal/chatbot/views.py

import time
from django.utils import timezone

@api_view(["POST"])
def chat(request: Request) -> Response:
    start_time = time.time()
    
    # ... existing logic
    
    # Log performance metrics
    elapsed = time.time() - start_time
    logger.info(f"[PERF] Chat response time: {elapsed:.3f}s | Intent: {intent} | Results: {count}")
    
    # Track slow queries
    if elapsed > 2.0:
        logger.warning(f"[SLOW] Query took {elapsed:.3f}s: {message[:100]}")
    
    return Response(response)
```

### 9.2 Track Cache Hit Rate

```python
class ChatbotCache:
    cache_hits = 0
    cache_misses = 0
    
    @staticmethod
    def get_cached_response(query: str, intent: str, session_id: str = None):
        cached = cache.get(ChatbotCache.get_cache_key(query, intent, session_id))
        if cached:
            ChatbotCache.cache_hits += 1
            return cached
        ChatbotCache.cache_misses += 1
        return None
    
    @staticmethod
    def get_cache_stats():
        total = ChatbotCache.cache_hits + ChatbotCache.cache_misses
        if total == 0:
            return {"hit_rate": 0, "hits": 0, "misses": 0}
        return {
            "hit_rate": ChatbotCache.cache_hits / total,
            "hits": ChatbotCache.cache_hits,
            "misses": ChatbotCache.cache_misses
        }
```

## 10. Expected Performance Improvements

| Optimization | Current | Optimized | Improvement |
|-------------|---------|-----------|-------------|
| Intent Classification | 5-10ms | 1-3ms | **70% faster** |
| Search (small dataset) | 50-100ms | 10-30ms | **70% faster** |
| Search (large dataset) | 200-500ms | 50-150ms | **70% faster** |
| LLM Generation (cached) | 1-5s | 0.01-0.1s | **99% faster** |
| LLM Generation (uncached) | 1-5s | 0.8-4s | **20% faster** |
| Total Response (cached) | 100-500ms | 10-50ms | **90% faster** |
| Total Response (uncached) | 1-6s | 0.5-3s | **50% faster** |

## 11. Implementation Priority

### Phase 1: Quick Wins (1-2 days)
1. ✅ Add response caching (Django cache)
2. ✅ Pre-compile keyword patterns
3. ✅ Cache synonyms
4. ✅ Add database indexes
5. ✅ Early exit for exact matches

### Phase 2: Medium Impact (3-5 days)
1. ✅ Limit QuerySet before loading
2. ✅ Optimize TF-IDF calculation
3. ✅ Prompt caching for LLM
4. ✅ Optimize local model generation
5. ✅ Add performance logging

### Phase 3: Advanced (1-2 weeks)
1. ✅ Streaming responses
2. ✅ Incremental TF-IDF
3. ✅ Advanced caching strategies
4. ✅ Query result pre-computation

## 12. Testing Performance

```python
# backend/scripts/benchmark_chatbot.py

import time
import statistics

def benchmark_chatbot():
    chatbot = get_chatbot()
    test_queries = [
        "Mức phạt vượt đèn đỏ là bao nhiêu?",
        "Thủ tục đăng ký cư trú cần gì?",
        "Địa chỉ công an phường ở đâu?",
        # ... more queries
    ]
    
    times = []
    for query in test_queries:
        start = time.time()
        response = chatbot.generate_response(query)
        elapsed = time.time() - start
        times.append(elapsed)
        print(f"Query: {query[:50]}... | Time: {elapsed:.3f}s")
    
    print(f"\nAverage: {statistics.mean(times):.3f}s")
    print(f"Median: {statistics.median(times):.3f}s")
    print(f"P95: {statistics.quantiles(times, n=20)[18]:.3f}s")
```

## Kết luận

Với các tối ưu trên, chatbot sẽ:
- **Nhanh hơn 50-90%** cho cached queries
- **Nhanh hơn 20-70%** cho uncached queries  
- **Chính xác hơn** với early exit và exact matching
- **Scalable hơn** với database indexes và query limiting