File size: 4,408 Bytes
8f05ad1 | 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 | #!/usr/bin/env python3
"""
Quick test script to verify enhancement modules work.
Run this before the full chat to check all modules are functioning.
"""
import sys
from pathlib import Path
# Add src to path
sys.path.insert(0, str(Path(__file__).parent / "src"))
print("=" * 50)
print("Testing Stack 2.9 Enhancement Modules")
print("=" * 50)
# Test 1: Config
print("\n[1] Testing Configuration...")
from enhancements import get_config, EnhancementConfig
config = get_config()
print(f" ✓ Config loaded: NLP={config.nlp.use_bert_embeddings}, RAG={config.knowledge_graph.rag_enabled}")
# Test 2: NLP Modules
print("\n[2] Testing NLP Modules...")
from enhancements.nlp import IntentDetector, EntityRecognizer
# Test Intent Detection
intent_detector = IntentDetector()
test_intents = [
"Write a function to calculate fibonacci",
"Help me debug this error",
"Explain what is Python",
"Hello there!",
]
print(" Intent Detection:")
for text in test_intents:
result = intent_detector.detect_intent(text)
print(f" '{text[:30]}...' → {result['intent']} ({result['confidence']:.2f})")
# Test Entity Recognition
entity_recognizer = EntityRecognizer()
test_entities = [
"My email is test@example.com and I live in New York",
"Visit https://github.com for code",
"Call me at 555-123-4567",
]
print(" Entity Recognition:")
for text in test_entities:
entities = entity_recognizer.recognize_entities(text)
print(f" '{text[:30]}...' → {[e['type'] for e in entities]}")
# Test 3: Knowledge Graph
print("\n[3] Testing Knowledge Graph...")
from enhancements.knowledge_graph import KnowledgeGraph, RAGEngine
kg = KnowledgeGraph()
kg.add_entity("Python", "language", {"version": "3.11"})
kg.add_entity("Stack2.9", "ai_assistant", {"version": "2.9"})
kg.add_relationship("Stack2.9", "Python", "uses")
print(f" ✓ Knowledge Graph: {kg.get_stats()}")
# Test RAG
rag = RAGEngine()
rag.add_document("doc1", "Python is a programming language.")
rag.add_document("doc2", "Stack 2.9 is an AI coding assistant.")
results = rag.retrieve("Tell me about Python")
print(f" ✓ RAG Retrieval: {len(results)} docs found")
# Test 4: Emotional Intelligence
print("\n[4] Testing Emotional Intelligence...")
from enhancements.emotional_intelligence import SentimentAnalyzer, EmpathyEngine
sentiment = SentimentAnalyzer()
test_sentiments = [
"This is amazing! I love it!",
"I'm frustrated with this problem",
"Can you help me?",
]
print(" Sentiment Analysis:")
for text in test_sentiments:
result = sentiment.analyze_sentiment(text)
print(f" '{text[:30]}...' → {result['sentiment']} ({result['emotion_tone']})")
empathy = EmpathyEngine()
test_response = "Here's your code:"
empathetic = empathy.generate_empathetic_response(
"I'm having trouble with my code",
test_response
)
print(f" ✓ Empathy Engine: Modified response with prefix")
# Test 5: Collaboration
print("\n[5] Testing Collaboration...")
from enhancements.collaboration import ConversationStateManager, MCPIntegration
conv_mgr = ConversationStateManager()
session_id = conv_mgr.create_session()
conv_mgr.add_message("user", "Hello AI!")
conv_mgr.add_message("assistant", "Hello! How can I help?")
history = conv_mgr.get_conversation_history()
print(f" ✓ Conversation Manager: {len(history)} messages in session")
mcp = MCPIntegration()
tools = mcp.list_tools()
print(f" ✓ MCP Integration: {len(tools)} tools registered")
# Test 6: Learning
print("\n[6] Testing Learning System...")
from enhancements.learning import FeedbackCollector, PerformanceMonitor
feedback = FeedbackCollector(storage_path="data/test_feedback")
fb_id = feedback.add_thumbs_up("Test message", "Test response")
stats = feedback.get_statistics()
print(f" ✓ Feedback Collector: {stats['total']} entries")
perf = PerformanceMonitor(storage_path="data/test_performance")
perf.record_response_time(0.5)
perf.record_successful_interaction()
summary = perf.get_summary()
print(f" ✓ Performance Monitor: avg response {summary['average_response_time']:.2f}s")
# Summary
print("\n" + "=" * 50)
print("All Enhancement Modules Tested Successfully!")
print("=" * 50)
print("\nTo run the enhanced chat:")
print(" python enhanced_chat.py")
print("\nOptions:")
print(" --no-bert Disable BERT embeddings")
print(" --no-rag Disable RAG")
print(" --no-empathy Disable emotional intelligence") |