test_fixes.py•6.89 kB
#!/usr/bin/env python3
"""
Test script to verify the insight engine fixes and add sample data.
"""
import asyncio
import sys
import os
# Add the src directory to the Python path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
from synapse_mcp.core.knowledge_graph import KnowledgeGraph
from synapse_mcp.zettelkasten.insight_engine import InsightEngine
async def test_insight_engine():
"""Test the insight engine with sample data."""
# Initialize knowledge graph
kg = KnowledgeGraph()
await kg.connect()
print("📊 Adding sample data to knowledge graph...")
# Add some sample entities and relationships
sample_data = {
'entities': [
{
'id': 'python',
'name': 'Python',
'type': 'Programming Language',
'confidence': 1.0,
'source': 'test_data',
'properties': {
'original_label': 'Python',
'start_char': 0,
'end_char': 6
}
},
{
'id': 'machine_learning',
'name': 'Machine Learning',
'type': 'Technology',
'confidence': 1.0,
'source': 'test_data',
'properties': {
'original_label': 'Machine Learning',
'start_char': 0,
'end_char': 16
}
},
{
'id': 'neural_networks',
'name': 'Neural Networks',
'type': 'Algorithm',
'confidence': 1.0,
'source': 'test_data',
'properties': {
'original_label': 'Neural Networks',
'start_char': 0,
'end_char': 15
}
},
{
'id': 'tensorflow',
'name': 'TensorFlow',
'type': 'Library',
'confidence': 1.0,
'source': 'test_data',
'properties': {
'original_label': 'TensorFlow',
'start_char': 0,
'end_char': 10
}
}
],
'relationships': [
{
'source_id': 'tensorflow',
'target_id': 'python',
'type': 'IMPLEMENTED_IN',
'confidence': 0.95,
'source': 'test_data',
'properties': {
'predicate': 'implemented in',
'source_span': 'TensorFlow is implemented in Python'
}
},
{
'source_id': 'tensorflow',
'target_id': 'machine_learning',
'type': 'USED_FOR',
'confidence': 0.9,
'source': 'test_data',
'properties': {
'predicate': 'used for',
'source_span': 'TensorFlow is used for machine learning'
}
},
{
'source_id': 'neural_networks',
'target_id': 'machine_learning',
'type': 'PART_OF',
'confidence': 0.85,
'source': 'test_data',
'properties': {
'predicate': 'part of',
'source_span': 'Neural networks are part of machine learning'
}
}
],
'facts': [
{
'id': 'fact_1',
'content': 'TensorFlow is a popular machine learning framework implemented in Python.',
'logical_form': 'POPULAR(tensorflow) ∧ ML_FRAMEWORK(tensorflow) ∧ IMPLEMENTED_IN(tensorflow, python)',
'confidence': 0.9,
'source': 'test_data',
'entities': ['tensorflow', 'python', 'machine_learning'],
'metadata': {
'extraction_method': 'manual',
'entities': ['tensorflow', 'python', 'machine_learning']
}
},
{
'id': 'fact_2',
'content': 'Neural networks are a fundamental component of modern machine learning systems.',
'logical_form': 'FUNDAMENTAL(neural_networks) ∧ COMPONENT_OF(neural_networks, machine_learning)',
'confidence': 0.95,
'source': 'test_data',
'entities': ['neural_networks', 'machine_learning'],
'metadata': {
'extraction_method': 'manual',
'entities': ['neural_networks', 'machine_learning']
}
}
]
}
# Store the sample data
stats = await kg.store_processed_data(sample_data)
print(f"✅ Stored: {stats['entities_count']} entities, {stats['relationships_count']} relationships, {stats['facts_count']} facts")
# Initialize insight engine
print("🧠 Initializing insight engine...")
insight_engine = InsightEngine(kg, None) # No Montague parser needed for this test
await insight_engine.initialize()
# Test insight generation
print("🔍 Generating insights...")
try:
insights = await insight_engine.generate_insights(confidence_threshold=0.5)
print(f"✅ Generated {len(insights)} insights successfully!")
for i, insight in enumerate(insights, 1):
print(f"\n📝 Insight {i}:")
print(f" Title: {insight['title']}")
print(f" Confidence: {insight['confidence']:.2f}")
print(f" Pattern Type: {insight['pattern_type']}")
print(f" Content Preview: {insight['content'][:100]}...")
except Exception as e:
print(f"❌ Error generating insights: {e}")
import traceback
traceback.print_exc()
# Test knowledge query
print("\n🔍 Testing knowledge queries...")
try:
query_results = await kg.query_semantic("TensorFlow Python machine learning")
print(f"✅ Query returned {len(query_results)} results")
for result in query_results[:2]: # Show first 2 results
print(f" - {result['statement'][:80]}...")
except Exception as e:
print(f"❌ Error querying knowledge: {e}")
# Get statistics
print("\n📊 Knowledge Graph Statistics:")
try:
stats = await kg.get_statistics()
for key, value in stats.items():
print(f" {key}: {value}")
except Exception as e:
print(f"❌ Error getting statistics: {e}")
# Cleanup
await insight_engine.cleanup()
await kg.close()
print("\n✅ Test completed!")
if __name__ == "__main__":
asyncio.run(test_insight_engine())