auto_sigma_rule_generator/REFACTOR_NOTES.md

Backend Refactoring Documentation

Overview

The backend has been completely refactored from a monolithic main.py (2,373 lines) into a modular, maintainable architecture following best practices for FastAPI applications.

Refactoring Summary

Before

• Single file: main.py (2,373 lines)
• Mixed responsibilities: Database models, API endpoints, business logic all in one file
• Tight coupling: 15+ modules importing directly from main.py
• No service layer: Business logic embedded in API endpoints
• Configuration scattered: Settings spread across multiple files

After

• Modular structure: Organized into logical packages
• Separation of concerns: Clear boundaries between layers
• Loose coupling: Dependency injection and proper imports
• Service layer: Business logic abstracted into services
• Centralized configuration: Single settings management

New Architecture

backend/
├── models/                     # Database Models (Extracted from main.py)
│   ├── __init__.py
│   ├── base.py                # SQLAlchemy Base
│   ├── cve.py                 # CVE model
│   ├── sigma_rule.py          # SigmaRule model
│   ├── rule_template.py       # RuleTemplate model
│   └── bulk_processing_job.py # BulkProcessingJob model
│
├── config/                     # Configuration Management
│   ├── __init__.py
│   ├── settings.py            # Centralized settings with environment variables
│   └── database.py            # Database configuration and session management
│
├── services/                   # Business Logic Layer
│   ├── __init__.py
│   ├── cve_service.py         # CVE business logic
│   ├── sigma_rule_service.py  # SIGMA rule generation logic
│   └── github_service.py      # GitHub exploit analysis service
│
├── routers/                    # API Endpoints (Modular FastAPI routers)
│   ├── __init__.py
│   ├── cves.py               # CVE-related endpoints
│   ├── sigma_rules.py        # SIGMA rule endpoints
│   ├── bulk_operations.py    # Bulk processing endpoints
│   └── llm_operations.py     # LLM-enhanced operations
│
├── schemas/                    # Pydantic Models
│   ├── __init__.py
│   ├── cve_schemas.py        # CVE request/response schemas
│   ├── sigma_rule_schemas.py # SIGMA rule schemas
│   └── request_schemas.py    # Common request schemas
│
├── main.py                    # FastAPI app initialization (120 lines)
└── [existing client files]   # Updated to use new import structure

Key Improvements

1. Database Models Separation

• Before: All models in main.py lines 42-115
• After: Individual model files in models/ package
• Benefits: Better organization, easier maintenance, clear model ownership

2. Centralized Configuration

• Before: Environment variables accessed directly across files
• After: config/settings.py with typed settings class
• Benefits: Single source of truth, better defaults, easier testing

3. Service Layer Introduction

• Before: Business logic mixed with API endpoints
• After: Dedicated service classes with clear responsibilities
• Benefits: Testable business logic, reusable components, better separation

4. Modular API Routers

• Before: All endpoints in single file
• After: Logical grouping in separate router files
• Benefits: Better organization, easier to find endpoints, team collaboration

5. Import Structure Cleanup

• Before: 15+ files importing from main.py
• After: Proper package imports with clear dependencies
• Benefits: No circular dependencies, faster imports, better IDE support

File Size Reduction

Component	Before	After	Reduction
main.py	2,373 lines	120 lines	95% reduction
Database models	73 lines (in main.py)	4 files, ~25 lines each	Modularized
API endpoints	~1,500 lines (in main.py)	4 router files, ~100-200 lines each	Organized
Business logic	Mixed in endpoints	3 service files, ~100-300 lines each	Separated

Updated Import Structure

All backend files have been automatically updated to use the new import structure:

# Before
from main import CVE, SigmaRule, RuleTemplate, SessionLocal

# After  
from models import CVE, SigmaRule, RuleTemplate
from config.database import SessionLocal

Configuration Management

Centralized Settings (config/settings.py)

• Environment variable management
• Default values and validation
• Type hints for better IDE support
• Singleton pattern for global access

Database Configuration (config/database.py)

• Session management
• Connection pooling
• Dependency injection for FastAPI

Service Layer Benefits

CVEService (services/cve_service.py)

• CVE data fetching and management
• NVD API integration
• Data validation and processing
• Statistics and reporting

SigmaRuleService (services/sigma_rule_service.py)

• SIGMA rule generation logic
• Template selection and population
• Confidence scoring
• MITRE ATT&CK mapping

GitHubExploitAnalyzer (services/github_service.py)

• GitHub repository analysis
• Exploit indicator extraction
• Code pattern matching
• Security assessment

API Router Organization

CVEs Router (routers/cves.py)

• GET /api/cves - List CVEs
• GET /api/cves/{cve_id} - Get specific CVE
• POST /api/fetch-cves - Manual CVE fetch
• GET /api/test-nvd - NVD API connectivity test

SIGMA Rules Router (routers/sigma_rules.py)

• GET /api/sigma-rules - List rules
• GET /api/sigma-rules/{cve_id} - Rules for specific CVE
• GET /api/sigma-rule-stats - Rule statistics

Bulk Operations Router (routers/bulk_operations.py)

• POST /api/bulk-seed - Start bulk seeding
• POST /api/incremental-update - Incremental updates
• GET /api/bulk-jobs - Job status
• GET /api/poc-stats - PoC statistics

LLM Operations Router (routers/llm_operations.py)

• POST /api/llm-enhanced-rules - Generate AI rules
• GET /api/llm-status - LLM provider status
• POST /api/llm-switch - Switch LLM providers
• POST /api/ollama-pull-model - Download models

Backward Compatibility

• All existing API endpoints maintain the same URLs and behavior
• Environment variables and configuration remain the same
• Database schema unchanged
• Docker Compose setup works without modification
• Existing client integrations continue to work

Testing Benefits

The new modular structure enables:

• Unit testing: Individual services can be tested in isolation
• Integration testing: Clear boundaries between components
• Mocking: Easy to mock dependencies for testing
• Test organization: Tests can be organized by module

Development Benefits

• Code navigation: Easier to find specific functionality
• Team collaboration: Multiple developers can work on different modules
• IDE support: Better autocomplete and error detection
• Debugging: Clearer stack traces and error locations
• Performance: Faster imports and reduced memory usage

Future Enhancements

The new architecture enables:

• Caching layer: Easy to add Redis caching to services
• Background tasks: Celery integration for long-running jobs
• Authentication: JWT or OAuth integration at router level
• Rate limiting: Per-endpoint rate limiting
• Monitoring: Structured logging and metrics collection
• API versioning: Version-specific routers

Migration Notes

• Legacy main.py preserved as main_legacy.py for reference
• All imports automatically updated using migration script
• No manual intervention required for existing functionality
• Gradual migration path for additional features

Performance Impact

• Startup time: Faster due to modular imports
• Memory usage: Reduced due to better organization
• Response time: Unchanged for existing endpoints
• Maintainability: Significantly improved
• Scalability: Better foundation for future growth

This refactoring provides a solid foundation for continued development while maintaining full backward compatibility with existing functionality.