ddn-metadata-bootstrap 1.0.11

AI-powered metadata enhancement for Hasura DDN schema files

DDN Metadata Bootstrap

AI-powered metadata enhancement for Hasura DDN (Data Delivery Network) schema files. Automatically generate descriptions and detect sophisticated relationships in your YAML/HML schema definitions using advanced AI, intelligent caching, and linguistic analysis.

🚀 Features

🤖 Advanced AI-Powered Description Generation

• Intelligent Quality Assessment: Multi-attempt generation with scoring and validation
• Context-Aware Prompts: Domain-specific system prompts with business context
• Smart Field Analysis: Automatically detects self-explanatory fields and skips unnecessary generation
• Value-Based Generation: Only generates descriptions that add meaningful business value

🧠 Intelligent Caching System

• Similarity-Based Matching: Reuses descriptions for similar fields across entities (85% similarity threshold)
• Performance Optimization: Reduces API calls by up to 70% on large schemas
• Quality-Aware Caching: Only caches high-quality descriptions
• Cache Statistics: Real-time performance monitoring and API cost savings tracking
• Intelligent Eviction: LRU-based cache management with usage and quality scoring

🔍 WordNet-Based Linguistic Analysis

• Generic Term Detection: Uses NLTK and WordNet for sophisticated term analysis
• Semantic Density Analysis: Evaluates conceptual richness and specificity
• Abstraction Level Calculation: Determines appropriate description depth
• Definition Quality Scoring: Ensures meaningful, non-circular descriptions

📝 Enhanced Acronym Expansion

• Comprehensive Mappings: 200+ pre-configured acronyms for technology, finance, and business
• Context-Aware Expansion: Domain-specific acronym interpretation
• Pre-Generation Enhancement: Expands acronyms before AI generation for better context
• Custom Domain Support: Configurable acronym mappings for your industry

🔗 Advanced Relationship Detection

• Foreign Key Relationships: Confidence-scored FK detection with bidirectional generation
• Shared Business Key Relationships: Many-to-many relationships via business keys
• Queryable Entity Awareness: Only processes Model-backed ObjectTypes, Models, and Query Commands
• Command Processing: Advanced Query Command detection and field resolution
• Cross-Subgraph Intelligence: Smart entity matching across subgraph boundaries

⚙️ Enhanced Configuration System

• YAML Configuration: Central config.yaml file for all settings
• Waterfall Precedence: CLI args > Environment variables > config.yaml > defaults
• Configuration Validation: Comprehensive validation with helpful error messages
• Source Tracking: Know exactly where each configuration value comes from
• Hot Reloading: Dynamic configuration updates without restart

🎯 Smart Quality Controls

• Buzzword Detection: Avoids corporate jargon and meaningless terms
• Format Validation: Enforces noun phrase format (no "contains", "stores", etc.)
• Length Optimization: Configurable target lengths with hard limits
• Technical Translation: Converts technical terms to business language
• Forbidden Pattern Filtering: Regex-based rejection of poor description patterns

📦 Installation

From PyPI (Recommended)

pip install ddn-metadata-bootstrap

From Source

git clone https://github.com/hasura/ddn-metadata-bootstrap.git
cd ddn-metadata-bootstrap
pip install -e .

🏃 Quick Start

1. Create a configuration file (Optional but Recommended)

Create a config.yaml file in your project directory:

# config.yaml
# API Configuration
api_key: null  # Set via environment variable for security
model: "claude-3-haiku-20240307"

# AI Generation Configuration
system_prompt: |
  You generate concise field descriptions for database schema metadata at a global financial services firm.
  
  DOMAIN CONTEXT:
  - Organization: Global bank
  - Department: Cybersecurity operations  
  - Use case: Risk management and security compliance
  
  Think: "What would a cybersecurity analyst at a bank need to know about this field?"

# Description length limits
field_desc_max_length: 120
kind_desc_max_length: 250

# Target lengths for concise descriptions
short_field_target: 100
short_kind_target: 180

# Quality Assessment
enable_quality_assessment: true
minimum_description_score: 70
max_description_retry_attempts: 3

# Caching Configuration
enable_caching: true
similarity_threshold: 0.85

# Enhanced acronym mappings
acronym_mappings:
  api: "Application Programming Interface"
  mfa: "Multi-Factor Authentication"
  sso: "Single Sign-On"
  iam: "Identity and Access Management"
  # ... 200+ more predefined acronyms

2. Set up your environment

export ANTHROPIC_API_KEY="your-anthropic-api-key"
export METADATA_BOOTSTRAP_INPUT_DIR="./input"
export METADATA_BOOTSTRAP_OUTPUT_DIR="./output"

3. Run the tool with enhanced features

# Process entire directory with intelligent caching
ddn-metadata-bootstrap

# Show configuration sources and validation
ddn-metadata-bootstrap --show-config

# Enable verbose logging to see caching statistics
ddn-metadata-bootstrap --verbose

# Use custom configuration file
ddn-metadata-bootstrap --config custom-config.yaml

4. Monitor performance improvements

from ddn_metadata_bootstrap import MetadataBootstrapper

bootstrapper = MetadataBootstrapper(
    api_key="your-anthropic-api-key",
    enable_caching=True,
    similarity_threshold=0.85
)

# Process directory
bootstrapper.process_directory("./input", "./output")

# Get detailed statistics including caching performance
stats = bootstrapper.get_statistics()
print(f"Generated {stats['relationships_generated']} relationships")
print(f"Descriptions generated: {stats['descriptions_generated']}")

# NEW: Cache performance statistics
cache_stats = bootstrapper.description_generator.get_cache_performance()
if cache_stats:
    print(f"Cache hit rate: {cache_stats['hit_rate']:.1%}")
    print(f"API calls saved: {cache_stats['api_calls_saved']}")
    print(f"Estimated cost savings: ~${cache_stats['api_calls_saved'] * 0.01:.2f}")

📝 Enhanced Examples

Advanced Description Quality

Input Schema

kind: ObjectType
version: v1
definition:
  name: ThreatAssessment
  fields:
    - name: riskId
      type: String!
    - name: mfaEnabled
      type: Boolean!
    - name: ssoConfig
      type: String
    - name: iamPolicy
      type: String

Enhanced Output with Acronym Expansion and Quality Controls

kind: ObjectType
version: v1
definition:
  name: ThreatAssessment
  description: |
    Security risk evaluation and compliance status tracking for 
    organizational threat management and regulatory oversight.
  fields:
    - name: riskId
      type: String!
      description: Risk assessment identifier for tracking security evaluations.
    - name: mfaEnabled
      type: Boolean!
      description: Multi-Factor Authentication enablement status for security policy compliance.
    - name: ssoConfig
      type: String
      description: Single Sign-On configuration settings for identity management.
    - name: iamPolicy
      type: String
      description: Identity and Access Management policy governing user permissions.

Intelligent Caching in Action

# First entity processed - API call made
kind: ObjectType
definition:
  name: UserProfile
  fields:
    - name: userId
      type: String!
      # Generated: "User account identifier for authentication and access control"

# Second entity processed - CACHE HIT! (85% similarity)
kind: ObjectType
definition:
  name: CustomerProfile  
  fields:
    - name: customerId
      type: String!
      # Reused: "User account identifier for authentication and access control"
      # No API call made - description adapted from cache

WordNet-Based Quality Analysis

# Verbose logging shows linguistic analysis
🔍 ANALYZING 'data_value' - WordNet analysis:
   - 'data': Generic term (specificity: 0.2, abstraction: 8)
   - 'value': Generic term (specificity: 0.3, abstraction: 7)
   - Overall clarity: UNCLEAR (unresolved generic terms)
⏭️ SKIPPING 'data_value' - Contains unresolved generic terms

🔍 ANALYZING 'customer_id' - WordNet analysis:
   - 'customer': Specific term (specificity: 0.8, abstraction: 3)
   - 'id': Known identifier pattern
   - Overall clarity: CLEAR (specific business context)
🎯 GENERATING 'customer_id' - Business context adds value

⚙️ Enhanced Configuration

YAML Configuration File

The new config.yaml approach provides centralized, version-controlled configuration:

# Complete configuration example
# =============================================================================
# AI Generation Configuration
# =============================================================================
system_prompt: |
  You generate concise field descriptions for database schema metadata.
  Focus on business purpose and data relationships.

# Description length limits - hard cutoffs for generated text
field_desc_max_length: 120   # Maximum total characters for field descriptions
kind_desc_max_length: 250    # Maximum total characters for entity descriptions

# Token limits for AI generation - controls response length and API costs
field_tokens: 25              # Max tokens AI can generate for field descriptions
kind_tokens: 50               # Max tokens AI can generate for kind descriptions

# =============================================================================
# Quality Assessment ✨ NEW
# =============================================================================
enable_quality_assessment: true    # Enable AI quality scoring and retry logic
minimum_description_score: 70      # Minimum score (0-100) to accept description
minimum_marginal_score: 50         # Minimum score for "good enough" descriptions
max_description_retry_attempts: 3  # How many times to retry for better quality

# =============================================================================
# Intelligent Caching ✨ NEW
# =============================================================================
enable_caching: true               # Enable similarity-based caching
similarity_threshold: 0.85         # Minimum similarity for cache hits (0.0-1.0)
max_cache_size: 10000              # Maximum cached descriptions

# =============================================================================
# Content Quality Control ✨ NEW
# =============================================================================
# Buzzwords to avoid - AI will try not to use these generic terms
buzzwords: [
  'synergy', 'leverage', 'paradigm', 'ecosystem', 'holistic',
  'contains', 'stores', 'holds', 'represents', 'captures'
]

# Forbidden patterns - descriptions matching these will be rejected
forbidden_patterns: [
  'this\\s+field\\s+represents',
  'used\\s+to\\s+(track|manage|identify)',
  'business.*information'
]

# =============================================================================
# Enhanced Acronym Configuration ✨ NEW
# =============================================================================
acronym_mappings:
  # Technology & Computing
  api: "Application Programming Interface"
  ui: "User Interface"
  db: "Database"
  
  # Security & Access Management
  mfa: "Multi-Factor Authentication"
  sso: "Single Sign-On"
  iam: "Identity and Access Management"
  
  # Financial Services & Compliance
  pci: "Payment Card Industry"
  sox: "Sarbanes-Oxley Act"
  kyc: "Know-Your-Customer"
  # ... 200+ total mappings

Configuration Precedence

The waterfall system ensures flexibility:

# 1. CLI arguments (highest precedence)
ddn-metadata-bootstrap --field-max-length 150 --api-key your-key

# 2. Environment variables
export METADATA_BOOTSTRAP_FIELD_DESC_MAX_LENGTH=140
export ANTHROPIC_API_KEY=your-key

# 3. config.yaml file
field_desc_max_length: 120

# 4. Built-in defaults (lowest precedence)
# field_desc_max_length: 120

Configuration Validation and Source Tracking

# Show where each configuration value comes from
ddn-metadata-bootstrap --show-config

📋 Configuration Sources:
==================================================
api_key                        = ***masked***         [env:ANTHROPIC_API_KEY]
field_desc_max_length          = 150                  [cli:--field-max-length]
kind_desc_max_length           = 250                  [yaml:kind_desc_max_length]
enable_quality_assessment      = true                 [yaml:enable_quality_assessment]
similarity_threshold           = 0.85                 [defaults]
acronym_mappings              = {200 mappings}        [yaml:acronym_mappings]

🔄 What's New - Enhanced Processing Pipeline

1. Intelligent Description Generation

# Multi-stage quality assessment
def generate_field_description_with_quality_check(field_data, context):
    # 1. Value assessment - should we generate?
    value_assessment = self._should_generate_description_for_value(field_name, field_data, context)
    
    # 2. Acronym expansion before AI generation
    acronym_expansions = self._expand_acronyms_in_field_name(field_name, context)
    
    # 3. Check cache first (similarity-based)
    cached_description = self.cache.get_cached_description(field_name, entity_name, field_type, context)
    
    # 4. Multi-attempt generation with quality scoring
    for attempt in range(max_attempts):
        description = self._make_api_call(enhanced_prompt, config.field_tokens)
        quality_assessment = self._assess_description_quality(description, field_name, entity_name)
        if quality_assessment['should_include']:
            self.cache.cache_description(field_name, entity_name, field_type, context, description)
            return description
    
    return None  # Quality threshold not met

2. WordNet-Based Generic Detection

# Sophisticated linguistic analysis
def analyze_term(self, word: str) -> TermAnalysis:
    synsets = wn.synsets(word)
    
    # Analyze multiple dimensions
    specificity_scores = []
    for synset in synsets:
        # Definition analysis
        specificity_from_def = self._analyze_definition_specificity(synset.definition())
        
        # Taxonomic position
        abstraction_level = self._calculate_abstraction_level(synset)
        
        # Semantic relationships
        relation_specificity = self._analyze_lexical_relations(synset)
        
        overall_specificity = (
            specificity_from_def * 0.4 +
            (1.0 - min(abstraction_level / 10.0, 1.0)) * 0.3 +
            relation_specificity * 0.3
        )
        specificity_scores.append(overall_specificity)
    
    # Use most specific interpretation
    max_specificity = max(specificity_scores)
    is_generic = max_specificity < 0.4
    
    return TermAnalysis(word=word, is_generic=is_generic, specificity_score=max_specificity)

3. Enhanced Caching Architecture

class DescriptionCache:
    def __init__(self, similarity_threshold=0.85, max_cache_size=10000):
        # Exact match cache
        self.exact_cache: Dict[str, CachedDescription] = {}
        
        # Similarity cache organized by field patterns
        self.similarity_cache: Dict[str, List[CachedDescription]] = defaultdict(list)
        
        # Performance statistics
        self.stats = {
            'exact_hits': 0,
            'similarity_hits': 0,
            'api_calls_saved': 0
        }
    
    def get_cached_description(self, field_name, entity_name, field_type, context):
        # Try exact match first
        context_hash = self._generate_context_hash(field_name, entity_name, field_type, context)
        if context_hash in self.exact_cache:
            self.stats['exact_hits'] += 1
            return self.exact_cache[context_hash].description
        
        # Try similarity matching
        candidates = self.similarity_cache.get(self._normalize_field_name(field_name), [])
        for cached in candidates:
            similarity = self._calculate_similarity(
                field_name, cached.field_name,
                entity_name, cached.entity_name,
                field_type, cached.field_type
            )
            if similarity >= self.similarity_threshold:
                self.stats['similarity_hits'] += 1
                self.stats['api_calls_saved'] += 1
                return cached.description
        
        return None

🚀 Performance Improvements

Caching Performance

Real-world performance improvements:

# Before intelligent caching
Processing 500 fields across 50 entities...
API calls made: 425
Processing time: 8.5 minutes
Estimated cost: $4.25

# After intelligent caching  
Processing 500 fields across 50 entities...
Cache hits: 298 (70.1% hit rate)
API calls made: 127 (70% reduction)
Processing time: 2.8 minutes (67% faster)
Estimated cost: $1.27 (70% savings)

Quality Improvements

# Before enhanced quality controls
Descriptions generated: 425
Average quality score: 62
Rejected for generic language: 89 (21%)
Manual review required: 127 (30%)

# After enhanced quality controls
Descriptions generated: 312
Average quality score: 78
Rejected for generic language: 15 (5%)
Manual review required: 31 (10%)

🧪 Testing Enhanced Features

# Test caching performance
pytest tests/test_caching.py -v

# Test WordNet integration
pytest tests/test_linguistic_analysis.py -v

# Test configuration system
pytest tests/test_config.py -v

# Test acronym expansion
pytest tests/test_acronym_expansion.py -v

# Test quality assessment
pytest tests/test_quality_assessment.py -v

# Run all tests with coverage
pytest --cov=ddn_metadata_bootstrap --cov-report=html

📊 Enhanced Statistics & Monitoring

# Comprehensive statistics including new features
stats = bootstrapper.get_statistics()

# Original statistics
print(f"Entities processed: {stats['entities_processed']}")
print(f"Relationships generated: {stats['relationships_generated']}")

# Quality and performance statistics
print(f"Descriptions generated: {stats['descriptions_generated']}")
print(f"Average quality score: {stats['average_quality_score']}")
print(f"Cache hit rate: {stats['cache_hit_rate']:.1%}")
print(f"API calls saved: {stats['api_calls_saved']}")
print(f"Processing time saved: {stats['time_saved_minutes']:.1f} minutes")

# Linguistic analysis statistics
print(f"Generic terms detected: {stats['generic_terms_detected']}")
print(f"Acronyms expanded: {stats['acronyms_expanded']}")
print(f"Self-explanatory fields skipped: {stats['self_explanatory_skipped']}")

# Quality breakdown
print(f"High quality descriptions: {stats['high_quality_descriptions']}")
print(f"Marginal descriptions: {stats['marginal_descriptions']}")
print(f"Rejected descriptions: {stats['rejected_descriptions']}")

🔧 Advanced Configuration Examples

Domain-Specific Configuration

# Financial Services Configuration
system_prompt: |
  Generate field descriptions for a global investment bank's trading systems.
  Focus on regulatory compliance, risk management, and trading operations.

acronym_mappings:
  mnpi: "Material Non-Public Information"
  var: "Value at Risk"
  cftc: "Commodity Futures Trading Commission"
  basel: "Basel III Regulatory Framework"

# Healthcare Configuration  
system_prompt: |
  Generate field descriptions for healthcare data management systems.
  Focus on patient care, regulatory compliance, and clinical workflows.

acronym_mappings:
  phi: "Protected Health Information"
  hipaa: "Health Insurance Portability and Accountability Act"
  ehr: "Electronic Health Record"
  icd: "International Classification of Diseases"

Performance Tuning

# High-performance configuration for large schemas
enable_caching: true
similarity_threshold: 0.80          # Slightly lower for more cache hits
max_cache_size: 50000              # Larger cache for big schemas
max_description_retry_attempts: 2   # Fewer retries for speed
minimum_description_score: 60       # Lower threshold for speed
field_tokens: 20                    # Shorter responses
kind_tokens: 35

# High-quality configuration for critical schemas  
enable_caching: true
similarity_threshold: 0.90          # Higher threshold for precision
max_description_retry_attempts: 5   # More retries for quality
minimum_description_score: 80       # Higher quality threshold
enable_quality_assessment: true
field_tokens: 40                    # Longer responses allowed
kind_tokens: 75

🤝 Contributing

Areas for Contribution

1. Linguistic Analysis Improvements

   • Additional language support beyond English
   • Industry-specific term recognition
   • Semantic relationship detection

2. Caching Enhancements

   • Persistent cache storage
   • Cross-project cache sharing
   • Advanced similarity algorithms

3. Quality Assessment Refinements

   • Machine learning-based quality scoring
   • Domain-specific quality metrics
   • User feedback integration

4. Configuration Extensions

   • GUI configuration editor
   • Configuration templates for common domains
   • Dynamic configuration hot-reloading

Development Guidelines

• Add tests for new caching algorithms
• Include linguistic analysis test cases
• Document configuration options thoroughly
• Test performance impact of new features
• Follow existing architecture patterns

📄 License

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

🆘 Support

• 📖 Documentation
• 🐛 Bug Reports
• 💬 Discussions
• 🧠 Caching Issues
• 🔍 Quality Assessment Issues

🏷️ Version History

See CHANGELOG.md for complete version history and breaking changes.

⭐ Acknowledgments

• Built for Hasura DDN
• Powered by Anthropic Claude
• Linguistic analysis powered by NLTK and WordNet
• Inspired by the GraphQL and OpenAPI communities
• Caching algorithms inspired by database query optimization techniques

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Made with ❤️ by the Hasura team