xml-analysis-framework 1.2.3

XML document analysis and preprocessing framework designed for AI/ML data pipelines

XML Analysis Framework

A production-ready XML document analysis and preprocessing framework with 29 specialized handlers designed for AI/ML data pipelines. Transform any XML document into structured, AI-ready data and optimized chunks with 100% success rate across 71 diverse test files.

🚀 Quick Start

Simple API - Get Started in Seconds

import xml_analysis_framework as xaf

# 🎯 One-line analysis with specialized handlers
result = xaf.analyze("path/to/file.xml")
print(f"Document type: {result['document_type'].type_name}")
print(f"Handler used: {result['handler_used']}")

# 📊 Basic schema analysis  
schema = xaf.analyze_schema("path/to/file.xml")
print(f"Elements: {schema.total_elements}, Depth: {schema.max_depth}")

# ✂️ Smart chunking for AI/ML
chunks = xaf.chunk("path/to/file.xml", strategy="auto")
print(f"Created {len(chunks)} optimized chunks")

Advanced Usage

import xml_analysis_framework as xaf

# Enhanced analysis with full results
analysis = xaf.analyze_enhanced("document.xml")
doc_type = analysis['document_type']
specialized = analysis['analysis']

print(f"Type: {doc_type.type_name} (confidence: {doc_type.confidence:.2f})")
print(f"AI use cases: {len(specialized.ai_use_cases)}")
print(f"Quality score: {specialized.quality_metrics.get('completeness_score')}")

# Different chunking strategies
hierarchical_chunks = xaf.chunk("document.xml", strategy="hierarchical")
sliding_chunks = xaf.chunk("document.xml", strategy="sliding_window") 
content_chunks = xaf.chunk("document.xml", strategy="content_aware")

# Process chunks
for chunk in hierarchical_chunks:
    print(f"Chunk {chunk.chunk_id}: {len(chunk.content)} chars")
    print(f"Type: {chunk.chunk_type}, Elements: {len(chunk.elements_included)}")

Expert Usage - Direct Class Access

# For advanced customization, use the classes directly
from xml_analysis_framework import XMLDocumentAnalyzer, ChunkingOrchestrator

analyzer = XMLDocumentAnalyzer(max_file_size_mb=500)
orchestrator = ChunkingOrchestrator(max_file_size_mb=1000)

# Custom analysis
result = analyzer.analyze_document("file.xml")

# Custom chunking with config
from xml_analysis_framework import XMLChunkingStrategy
config = XMLChunkingStrategy.ChunkingConfig(max_chunk_size=2000)
chunks = orchestrator.chunk_document("file.xml", result, config=config)

🎯 Key Features

1. 🏆 Production Proven Results

• 100% Success Rate: All 71 test files processed successfully
• 2,752 Chunks Generated: Average 38.8 optimized chunks per file
• 54 Document Types Detected: Comprehensive XML format coverage
• Minimal Dependencies: Only defusedxml for security + Python stdlib

2. 🧠 29 Specialized XML Handlers

Enterprise-grade document intelligence:

• Security & Compliance: SCAP, SAML, SOAP (90-100% confidence)
• DevOps & Build: Maven POM, Ant, Ivy, Spring, Log4j (95-100% confidence)
• Content & Documentation: RSS/Atom, DocBook, XHTML, SVG
• Enterprise Systems: ServiceNow, Hibernate, Struts configurations
• Data & APIs: GPX, KML, GraphML, WADL/WSDL, XML Schemas

3. ⚡ Intelligent Processing Pipeline

• Smart Document Detection: Confidence scoring with graceful fallbacks
• Semantic Chunking: Document-type-aware optimal segmentation
• Token Optimization: LLM context window optimized chunks
• Quality Assessment: Automated data quality metrics

4. 🤖 AI-Ready Integration

• Vector Store Ready: Structured embeddings with rich metadata
• Graph Database Compatible: Relationship and dependency mapping
• LLM Agent Optimized: Context-aware, actionable insights
• Complete AI Workflows: See AI Integration Guide

📋 Supported Document Types (29 Handlers)

Category	Handlers	Confidence	Use Cases
🔐 Security & Compliance	SCAP, SAML, SOAP	90-100%	Vulnerability assessment, compliance monitoring, security posture analysis
⚙️ DevOps & Build Tools	Maven POM, Ant, Ivy	95-100%	Dependency analysis, build optimization, technical debt assessment
🏢 Enterprise Configuration	Spring, Hibernate, Struts, Log4j	95-100%	Configuration validation, security scanning, modernization planning
📄 Content & Documentation	RSS, DocBook, XHTML, SVG	90-100%	Content intelligence, documentation search, knowledge management
🗂️ Enterprise Systems	ServiceNow, XML Sitemap	95-100%	Incident analysis, process automation, system integration
🌍 Geospatial & Data	GPX, KML, GraphML	85-95%	Route optimization, geographic analysis, network intelligence
🔌 API & Integration	WADL, WSDL, XLIFF	90-95%	Service discovery, integration planning, translation workflows
📐 Schemas & Standards	XML Schema (XSD)	100%	Schema validation, data modeling, API documentation

🏗️ Architecture

xml-analysis-framework/
├── README.md                    # Project overview
├── LICENSE                      # MIT license
├── requirements.txt            # Dependencies (Python stdlib only)
├── setup.py                    # Package installation
├── .gitignore                  # Git ignore patterns
├── .github/workflows/          # CI/CD pipelines
│
├── src/                        # Source code
│   ├── core/                   # Core framework
│   │   ├── analyzer.py         # Main analysis engine
│   │   ├── schema_analyzer.py  # XML schema analysis
│   │   └── chunking.py         # Chunking strategies
│   ├── handlers/              # 28 specialized handlers
│   └── utils/                 # Utility functions
│
├── tests/                      # Comprehensive test suite
│   ├── unit/                  # Handler unit tests (16 files)
│   ├── integration/           # Integration tests (11 files)
│   ├── comprehensive/         # Full system tests (4 files)
│   └── run_all_tests.py      # Master test runner
│
├── examples/                   # Usage examples
│   ├── basic_analysis.py      # Simple analysis
│   └── enhanced_analysis.py   # Full featured analysis
│
├── scripts/                    # Utility scripts
│   ├── collect_test_files.py  # Test data collection
│   └── debug/                 # Debug utilities
│
├── docs/                       # Documentation
│   ├── architecture/          # Design documents
│   ├── guides/                # User guides
│   └── api/                   # API documentation
│
├── sample_data/               # Test XML files (99+ examples)
│   ├── test_files/           # Real-world examples
│   └── test_files_synthetic/ # Generated test cases
│
└── artifacts/                 # Build artifacts, results
    ├── analysis_results/     # JSON analysis outputs
    └── reports/             # Generated reports

🔒 Security

XML Security Protection

This framework uses defusedxml to protect against common XML security vulnerabilities:

• XXE (XML External Entity) attacks: Prevents reading local files or making network requests
• Billion Laughs attack: Prevents exponential entity expansion DoS attacks
• DTD retrieval: Blocks external DTD fetching to prevent data exfiltration

Security Features

# All XML parsing is automatically protected
from core.analyzer import XMLDocumentAnalyzer

analyzer = XMLDocumentAnalyzer()
# Safe parsing - malicious XML will be rejected
result = analyzer.analyze_document("potentially_malicious.xml")

if result.get('security_issue'):
    print(f"Security threat detected: {result['error']}")

Best Practices

1. Always use the framework's parsers - Never use xml.etree.ElementTree directly
2. Validate file sizes - Set reasonable limits for your use case
3. Sanitize file paths - Ensure input paths are properly validated
4. Monitor for security exceptions - Log and alert on security-blocked parsing attempts

File Size Limits

The framework includes built-in file size limits to prevent memory exhaustion:

# Built-in size limits in analyzer and chunking
from core.analyzer import XMLDocumentAnalyzer
from core.chunking import ChunkingOrchestrator

# Create analyzer with 50MB limit
analyzer = XMLDocumentAnalyzer(max_file_size_mb=50.0)

# Create chunking orchestrator with 100MB limit  
orchestrator = ChunkingOrchestrator(max_file_size_mb=100.0)

# Utility functions for easy setup
from utils import create_analyzer_with_limits, FileSizeLimits

# Use predefined limits
analyzer = create_analyzer_with_limits(FileSizeLimits.PRODUCTION_MEDIUM)  # 50MB
safe_result = safe_analyze_document("file.xml", FileSizeLimits.REAL_TIME)  # 5MB

🔧 Installation

# Install from source
git clone https://github.com/redhat-ai-americas/xml-analysis-framework.git
cd xml-analysis-framework
pip install -e .

# Or install development dependencies
pip install -e .[dev]

Dependencies

• defusedxml (0.7.1+): For secure XML parsing protection
• Python standard library (3.8+) for all other functionality

📖 Usage Examples

Basic Analysis

from core.schema_analyzer import XMLSchemaAnalyzer

analyzer = XMLSchemaAnalyzer()
schema = analyzer.analyze_file('document.xml')

# Access schema properties
print(f"Root element: {schema.root_element}")
print(f"Total elements: {schema.total_elements}")
print(f"Namespaces: {schema.namespaces}")

Enhanced Analysis with Specialized Handlers

from core.analyzer import XMLDocumentAnalyzer

analyzer = XMLDocumentAnalyzer()
result = analyzer.analyze_document('maven-project.xml')

print(f"Document Type: {result['document_type'].type_name}")
print(f"Confidence: {result['confidence']:.2f}")
print(f"Handler Used: {result['handler_used']}")
print(f"AI Use Cases: {result['analysis'].ai_use_cases}")

Safe Analysis with File Validation

from utils import safe_analyze_document, FileSizeLimits

# Safe analysis with comprehensive validation
result = safe_analyze_document(
    'document.xml', 
    max_size_mb=FileSizeLimits.PRODUCTION_MEDIUM
)

if result.get('error'):
    print(f"Analysis failed: {result['error']}")
else:
    print(f"Success: {result['document_type'].type_name}")

Intelligent Chunking

from core.chunking import ChunkingOrchestrator, XMLChunkingStrategy

orchestrator = ChunkingOrchestrator()
chunks = orchestrator.chunk_document(
    'large_document.xml',
    specialized_analysis={},  # Analysis result from XMLDocumentAnalyzer
    strategy='auto'
)

# Token estimation
token_estimator = XMLChunkingStrategy()
for chunk in chunks:
    token_count = token_estimator.estimate_tokens(chunk.content)
    print(f"Chunk {chunk.chunk_id}: ~{token_count} tokens")

🧪 Testing & Validation

Production-Tested Performance

• ✅ 100% Success Rate: All 71 XML files processed successfully
• ✅ 2,752 Chunks Generated: Optimal segmentation across diverse document types
• ✅ 54 Document Types: Comprehensive coverage from ServiceNow to SCAP to Maven
• ✅ Secure by Default: Protected against XXE and billion laughs attacks

Test Coverage

# Run comprehensive end-to-end test
python test_end_to_end_workflow.py

# Run individual component tests  
python test_all_chunking.py        # Chunking strategies
python test_servicenow_analysis.py # ServiceNow handler validation
python test_scap_analysis.py       # Security document analysis

Real-World Test Data

• Enterprise Systems: ServiceNow incident exports (8 files)
• Security Documents: SCAP/XCCDF compliance reports (4 files)
• Build Configurations: Maven, Ant, Ivy projects (12 files)
• Enterprise Config: Spring, Hibernate, Log4j (15 files)
• Content & APIs: DocBook, RSS, WADL, Sitemaps (32 files)

🤖 AI Integration & Use Cases

AI Workflow Overview

graph LR
    A[XML Documents] --> B[XML Analysis Framework]
    B --> C[Document Analysis<br/>29 Specialized Handlers]
    B --> D[Smart Chunking<br/>Token-Optimized]
    B --> E[AI-Ready Output<br/>Structured JSON]
  
    E --> F[Vector Store<br/>Semantic Search]
    E --> G[Graph Database<br/>Relationships]
    E --> H[LLM Agent<br/>Intelligence]
  
    F --> I[Security Intelligence]
    G --> J[DevOps Automation] 
    H --> K[Knowledge Management]
  
    style B fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    style E fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    style I fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px
    style J fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px
    style K fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px

See Complete AI Integration Guide for detailed workflows, implementation examples, and advanced use cases.

🔐 Security Intelligence Applications

• SCAP Compliance Monitoring: Automated vulnerability assessment and risk scoring
• SAML Security Analysis: Authentication flow security validation and threat detection
• Log4j Vulnerability Detection: CVE scanning and automated remediation guidance
• SOAP Security Assessment: Web service configuration security review

⚙️ DevOps & Configuration Intelligence

• Dependency Risk Analysis: Maven/Ant/Ivy vulnerability scanning and upgrade planning
• Configuration Drift Detection: Spring/Hibernate consistency monitoring
• Build Optimization: Performance analysis and security hardening recommendations
• Technical Debt Assessment: Legacy system modernization planning

🏢 Enterprise System Intelligence

• ServiceNow Process Mining: Incident pattern analysis and workflow optimization
• Cross-System Correlation: Configuration impact analysis and change management
• Compliance Automation: Regulatory requirement mapping and validation

📚 Knowledge Management Applications

• Technical Documentation Search: Semantic search across DocBook, API documentation
• Content Intelligence: RSS/Atom trend analysis and topic extraction
• API Discovery: WADL/WSDL service catalog and integration recommendations

🔬 Production Metrics & Performance

Framework Statistics

• ✅ 100% Success Rate: 71/71 files processed without errors
• 📊 2,752 Chunks Generated: Optimal 38.8 avg chunks per document
• 🎯 54 Document Types: Comprehensive XML format coverage
• ⚡ High Performance: 0.015s average processing time per document
• 🔒 Secure Parsing: defusedxml protection against XML attacks

Handler Confidence Levels

• 100% Confidence: XML Schema (XSD), Maven POM, Log4j, RSS/Atom, Sitemaps
• 95% Confidence: ServiceNow, Apache Ant, Ivy, Spring, Hibernate, SAML, SOAP
• 90% Confidence: SCAP/XCCDF, DocBook, WADL/WSDL
• Intelligent Fallback: Generic XML handler for unknown formats

🚀 Extending the Framework

Adding New Handlers

from core.analyzer import XMLHandler, SpecializedAnalysis, DocumentTypeInfo

class CustomHandler(XMLHandler):
    def can_handle(self, root, namespaces):
        return root.tag == 'custom-format', 1.0
  
    def detect_type(self, root, namespaces):
        return DocumentTypeInfo(
            type_name="Custom Format",
            confidence=1.0,
            version="1.0"
        )
  
    def analyze(self, root, file_path):
        return SpecializedAnalysis(
            document_type="Custom Format",
            key_findings={"custom_data": "value"},
            ai_use_cases=["Custom AI application"],
            structured_data={"extracted": "data"}
        )

Custom Chunking Strategies

from core.chunking import XMLChunkingStrategy, ChunkingOrchestrator

class CustomChunking(XMLChunkingStrategy):
    def chunk_document(self, file_path, specialized_analysis=None):
        # Custom chunking logic
        return chunks

# Register custom strategy
orchestrator = ChunkingOrchestrator()
orchestrator.strategies['custom'] = CustomChunking

📊 Real Production Output Examples

ServiceNow Incident Analysis

{
  "document_summary": {
    "document_type": "ServiceNow Incident",
    "type_confidence": 0.95,
    "handler_used": "ServiceNowHandler",
    "file_size_mb": 0.029
  },
  "key_insights": {
    "data_highlights": {
      "state": "7", "priority": "4", "impact": "3",
      "assignment_group": "REDACTED_GROUP",
      "resolution_time": "240 days, 0:45:51",
      "journal_analysis": {
        "total_entries": 9,
        "unique_contributors": 1
      }
    },
    "ai_applications": [
      "Incident pattern analysis",
      "Resolution time prediction", 
      "Workload optimization"
    ]
  },
  "structured_content": {
    "chunking_strategy": "content_aware_medium",
    "total_chunks": 75,
    "quality_metrics": {
      "overall_readiness": 0.87
    }
  }
}

Log4j Security Analysis

{
  "document_summary": {
    "document_type": "Log4j Configuration",
    "type_confidence": 1.0,
    "handler_used": "Log4jConfigHandler"
  },
  "key_insights": {
    "data_highlights": {
      "security_concerns": {
        "security_risks": ["External socket appender detected"],
        "log4shell_vulnerable": false,
        "external_connections": [{"host": "log-server.example.com"}]
      },
      "performance": {
        "async_appenders": 1,
        "performance_risks": ["Location info impacts performance"]
      }
    },
    "ai_applications": [
      "Vulnerability assessment",
      "Performance optimization",
      "Security hardening"
    ]
  },
  "structured_content": {
    "total_chunks": 19,
    "chunking_strategy": "hierarchical_small"
  }
}

🤝 Contributing

We welcome contributions! Whether you're adding new XML handlers, improving chunking algorithms, or enhancing AI integrations, your contributions help make XML analysis more accessible and powerful.

Priority contribution areas:

• 🎯 New XML format handlers (ERP, CRM, healthcare, government)
• ⚡ Enhanced chunking algorithms and strategies
• 🚀 Performance optimizations for large files
• 🤖 Advanced AI/ML integration examples
• 📝 Documentation and usage examples

👉 See CONTRIBUTING.md for complete guidelines, development setup, and submission process.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Designed as part of the AI Building Blocks initiative
• Built for the modern AI/ML ecosystem
• Community-driven XML format support