|
|
| """
|
| Analyze year-based CVE files for RAG suitability
|
| """
|
|
|
| import json
|
| import sys
|
| from pathlib import Path
|
|
|
| def analyze_year_file(file_path: Path):
|
| """Analyze a single year file"""
|
| print(f"\n🔍 Analyzing {file_path.name}...")
|
|
|
| try:
|
|
|
| for encoding in ['utf-8', 'utf-8-sig', 'latin-1']:
|
| try:
|
| with open(file_path, 'r', encoding=encoding) as f:
|
| data = json.load(f)
|
| print(f" ✅ Successfully loaded with {encoding} encoding")
|
| break
|
| except UnicodeDecodeError:
|
| continue
|
| except json.JSONDecodeError as e:
|
| print(f" ❌ JSON decode error with {encoding}: {e}")
|
| continue
|
| else:
|
| print(f" ❌ Failed to load with any encoding")
|
| return None
|
|
|
| if not data:
|
| print(f" ❌ Empty data")
|
| return None
|
|
|
|
|
| sample_doc = data[0]
|
| print(f" 📊 Total documents: {len(data)}")
|
| print(f" 🔑 Document keys: {list(sample_doc.keys())}")
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|
|
|
|
| content = sample_doc.get('content', '')
|
| print(f" 📝 Content length: {len(content)} characters")
|
| print(f" 📝 Content preview: {content[:100]}...")
|
|
|
|
|
| rag_fields = ['id', 'content', 'title']
|
| missing_fields = [field for field in rag_fields if field not in sample_doc]
|
| if missing_fields:
|
| print(f" ⚠️ Missing RAG fields: {missing_fields}")
|
| else:
|
| print(f" ✅ All required RAG fields present")
|
|
|
|
|
| if len(content) < 50:
|
| print(f" ⚠️ Content seems too short for RAG")
|
| elif len(content) > 5000:
|
| print(f" ⚠️ Content might be too long for RAG")
|
| else:
|
| print(f" ✅ Content length suitable for RAG")
|
|
|
|
|
| log4j_count = sum(1 for doc in data if 'log4j' in doc.get('content', '').lower())
|
| cve_44228_count = sum(1 for doc in data if 'CVE-2021-44228' in doc.get('content', ''))
|
|
|
| print(f" 🔍 Log4j mentions: {log4j_count}")
|
| print(f" 🔍 CVE-2021-44228 mentions: {cve_44228_count}")
|
|
|
| return data
|
|
|
| except Exception as e:
|
| print(f" ❌ Error analyzing file: {e}")
|
| return None
|
|
|
| def main():
|
| """Main function"""
|
| print("=== ANALYZING YEAR-BASED FILES FOR RAG SUITABILITY ===")
|
|
|
|
|
| kb_dir = Path("data/knowledge_base")
|
| if not kb_dir.exists():
|
| print("❌ Knowledge base directory not found")
|
| return
|
|
|
|
|
| year_files = list(kb_dir.glob("enhanced_documents_cve_*.json"))
|
| year_files.sort()
|
|
|
| print(f"Found {len(year_files)} year files:")
|
| for file in year_files:
|
| print(f" - {file.name}")
|
|
|
|
|
| recent_years = ['2021', '2022', '2023', '2024']
|
|
|
| for year in recent_years:
|
| file_path = kb_dir / f"enhanced_documents_cve_{year}.json"
|
| if file_path.exists():
|
| data = analyze_year_file(file_path)
|
| if data:
|
|
|
| print(f" 🎯 RAG Suitability Assessment for {year}:")
|
|
|
|
|
| sample_docs = data[:5]
|
| meaningful_content = 0
|
| for doc in sample_docs:
|
| content = doc.get('content', '')
|
| if len(content) > 100 and not content.isspace():
|
| meaningful_content += 1
|
|
|
| if meaningful_content >= 4:
|
| print(f" ✅ Content quality: Good ({meaningful_content}/5 docs have meaningful content)")
|
| else:
|
| print(f" ⚠️ Content quality: Poor ({meaningful_content}/5 docs have meaningful content)")
|
|
|
|
|
| consistent_structure = all(
|
| 'id' in doc and 'content' in doc and 'title' in doc
|
| for doc in sample_docs
|
| )
|
| if consistent_structure:
|
| print(f" ✅ Structure consistency: Good")
|
| else:
|
| print(f" ⚠️ Structure consistency: Poor")
|
|
|
| print(f" 💡 Recommendation: {'Use directly for RAG' if meaningful_content >= 4 and consistent_structure else 'Needs processing for RAG'}")
|
|
|
| if __name__ == "__main__":
|
| main() |
