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34367da | 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 | #!/usr/bin/env python3
"""Check Clone Profile i Neo4j"""
from neo4j import GraphDatabase
driver = GraphDatabase.driver(
'neo4j+s://054eff27.databases.neo4j.io',
auth=('neo4j', 'Qrt37mkb0xBZ7_ts5tG1J70K2mVDGPMF2L7Njlm7cg8')
)
with driver.session() as s:
# Clone Profile
result = s.run('''
MATCH (p:CloneProfile)
RETURN p.name as name, p.role as role, p.totalMessages as msgs,
p.formalityScore as formality, p.technicalDensity as tech,
p.personalityTraits as traits, p.expertiseAreas as expertise
''')
print("=" * 60)
print("🧬 CLONE PROFILE I NEO4J")
print("=" * 60)
for r in result:
print(f"Navn: {r['name']}")
print(f"Rolle: {r['role']}")
print(f"Beskeder analyseret: {r['msgs']}")
print(f"Formalitet: {round(r['formality'] * 100)}%")
print(f"Teknisk densitet: {round(r['tech'] * 100)}%")
print()
print("Personlighedstræk:")
for trait in r['traits']:
print(f" • {trait}")
print()
print("Ekspertiseområder:")
for exp in r['expertise']:
print(f" 🎯 {exp}")
# Knowledge domains with relationships
print()
print("=" * 60)
print("🧠 KNOWLEDGE DOMAINS")
print("=" * 60)
result = s.run('''
MATCH (p:CloneProfile)-[r:HAS_EXPERTISE]->(d:KnowledgeDomain)
RETURN d.name as domain, r.confidence as conf, r.messageCount as count
ORDER BY r.confidence DESC LIMIT 8
''')
for r in result:
bar = "█" * int(r['conf'] * 10)
print(f" {r['domain']:25} {bar} {round(r['conf'] * 100)}%")
# Common phrases
print()
print("=" * 60)
print("💬 COMMON PHRASES (cleaned)")
print("=" * 60)
result = s.run('''
MATCH (p:CloneProfile)-[r:USES_PHRASE]->(ph:CommonPhrase)
WHERE NOT ph.phrase CONTAINS 'http'
AND NOT ph.phrase CONTAINS 'mailto'
AND NOT ph.phrase CONTAINS 'urldefense'
RETURN ph.phrase as phrase, r.frequency as freq
ORDER BY r.frequency DESC LIMIT 10
''')
for r in result:
print(f" \"{r['phrase']}\" ({r['freq']}x)")
# Topics (cleaned)
print()
print("=" * 60)
print("🏷️ TOP TOPICS (cleaned)")
print("=" * 60)
result = s.run('''
MATCH (p:CloneProfile)-[r:DISCUSSES]->(t:CloneTopic)
WHERE NOT t.name CONTAINS 'http'
AND NOT t.name CONTAINS 'mailto'
AND NOT t.name CONTAINS 'urldefense'
AND NOT t.name CONTAINS 'gnpmbas'
AND NOT t.name CONTAINS 'safelink'
AND size(t.name) > 3
RETURN t.name as topic, r.frequency as freq
ORDER BY r.frequency DESC LIMIT 20
''')
for r in result:
print(f" {r['topic']}: {r['freq']}")
# Total stats
print()
print("=" * 60)
print("📊 TOTAL GRAPH STATS")
print("=" * 60)
result = s.run('''
MATCH (p:CloneProfile)
OPTIONAL MATCH (p)-[:HAS_EXPERTISE]->(d:KnowledgeDomain)
OPTIONAL MATCH (p)-[:USES_PHRASE]->(ph:CommonPhrase)
OPTIONAL MATCH (p)-[:DISCUSSES]->(t:CloneTopic)
RETURN count(DISTINCT d) as domains, count(DISTINCT ph) as phrases, count(DISTINCT t) as topics
''')
for r in result:
print(f" Knowledge Domains: {r['domains']}")
print(f" Common Phrases: {r['phrases']}")
print(f" Topics: {r['topics']}")
driver.close()
print()
print("✅ Clone Profile verificeret!")
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