guardrails-scorer 0.1.1

Fast query safety screening library for fraud, harm, and unethical intent detection

Query Guards 🛡️

Fast, intelligent query safety screening for Python applications

Query Guards is a production-ready Python library that screens queries for fraud, harm, or unethical intent using advanced NLP techniques. It provides sub-100ms query screening through multi-signal ensemble scoring, UUID-based data isolation, and intelligent caching systems.

🎯 Why Query Guards?

Performance Excellence

• ⚡ <100ms cold queries - Lightning-fast screening even on first use
• 🚀 <20ms warm queries - Blazing performance with model caching
• 📊 5-10x faster - UUID-based operations vs traditional string matching
• 🔄 Concurrent safety - Thread-safe operations for high-throughput applications

Security & Isolation

• 🔒 UUID-based data isolation - True multi-tenant security
• 🏢 Sector-based organization - Industry-specific screening models
• 👥 Client-level isolation - Per-client data and configuration
• 🛡️ Referential integrity - Foreign key constraints prevent data corruption

Intelligence & Accuracy

• 🧠 Multi-signal ensemble - Combines embedding (60%) + TF-IDF (25%) + fuzzy (15%)
• 🤖 Synthetic data generation - Enhances training with NLP augmentation
• 📈 Threshold optimization - Statistical analysis for optimal decision boundaries
• 🎯 Bypass detection - Smart allowlisting for legitimate queries

---

📦 Installation

Prerequisites

• Python 3.8+
• 4GB RAM minimum (8GB recommended for large datasets)
• 500MB disk space

Install Query Guards

# Install from source (recommended for latest features)
git clone https://github.com/your-org/query-guards.git
cd query-guards
uv sync --index https://pypi.org/simple

# Or install from PyPI (when published)
pip install query-guards

Verify Installation

import guardrails_scorer
print(f"Query Guards v{guardrails_scorer.__version__} installed successfully!")

# Check system health
health = guardrails_scorer.get_health_status()
print(f"System status: {health['overall_status']}")

---

🚀 Quick Start (5 minutes)

1. Basic Setup & Screening

from guardrails_scorer import QueryGuardSetup, QueryGuardScreener

# Step 1: Configure your first sector and client
setup = QueryGuardSetup(sector="healthcare", client_id="hospital_1")

result = setup.configure(
    negative_queries=[
        "how to get oxycodone without prescription",
        "dangerous medicine dosage combinations",
        "fake medical certificate creation"
    ],
    bypass_queries=[
        "what is the recommended dose of ibuprofen",
        "common side effects of antibiotics",
        "healthy diet recommendations"
    ],
    storage="local",              # Uses SQLite (fast, embedded)
    generate_synthetic=True,      # Generate additional training examples
    warmup_models=True,          # Pre-load for <20ms queries
    get_threshold_recommendations=True  # Optimize decision boundaries
)

print(f"✅ Setup complete! {result['examples_configured']} examples configured")
print(f"🤖 Generated {result['synthetic_generated']} synthetic examples")
print(f"⚡ Models warmed up: {result['models_warmed_up']}")

# Step 2: Start screening queries
screener = QueryGuardScreener(sector="healthcare", client_id="hospital_1")

# Screen a potentially harmful query
result = screener.screen("how to get prescription drugs without doctor")
print(f"🚨 Verdict: {result['verdict']} (confidence: {result['confidence']:.3f})")

# Screen a safe query
result = screener.screen("what are the benefits of regular exercise")
print(f"✅ Verdict: {result['verdict']} (confidence: {result['confidence']:.3f})")

2. Batch Screening (High Performance)

# Screen multiple queries efficiently
queries = [
    "how to commit medical insurance fraud",
    "healthy meal planning for diabetics",
    "dangerous drug interactions to avoid",
    "emergency first aid procedures"
]

results = screener.screen(queries)
for result in results:
    status = "🚨" if result['verdict'] == 'BLOCK' else "✅" if result['verdict'] == 'ALLOW' else "⚠️"
    print(f"{status} {result['verdict']}: '{result['query'][:50]}...' ({result['confidence']:.3f})")

---

🏗️ Core Concepts

🏢 Sectors - Industry-Specific Screening

Sectors represent different industries or domains with unique screening requirements:

# Healthcare sector - screens for medical fraud, dangerous advice
healthcare_setup = QueryGuardSetup(sector="healthcare", client_id="hospital_1")

# Finance sector - screens for fraud, money laundering
finance_setup = QueryGuardSetup(sector="finance", client_id="bank_1")

# Travel sector - screens for illegal activities, unsafe destinations
travel_setup = QueryGuardSetup(sector="travel", client_id="agency_1")

Benefits of Sector Organization:

• 🎯 Domain-specific models - Tailored to industry-specific threats
• 📊 Isolated training data - Healthcare examples don't affect finance screening
• ⚡ Optimized performance - Models trained on relevant data only
• 🔒 Compliance separation - Meet industry-specific regulatory requirements

👥 Clients - Organization-Level Isolation

Clients represent individual organizations within a sector:

# Multiple hospitals in healthcare sector
hospital_1 = QueryGuardSetup(sector="healthcare", client_id="general_hospital")
hospital_2 = QueryGuardSetup(sector="healthcare", client_id="childrens_hospital")
clinic_1 = QueryGuardSetup(sector="healthcare", client_id="urgent_care_clinic")

# Each has isolated data, models, and thresholds

Client Isolation Benefits:

• 🔒 Data privacy - Client A cannot access Client B's training data
• ⚙️ Custom thresholds - Each client can have different risk tolerance
• 📈 Individual metrics - Separate performance tracking per client
• 🛡️ Security compliance - Meet strict data isolation requirements

🔑 UUID System - High-Performance Architecture

Query Guards uses a two-layer identification system:

# Public API: Human-readable names
screener = QueryGuardScreener(sector="healthcare", client_id="hospital_1")

# Internal: UUID-based operations (5-10x faster)
# healthcare -> 550e8400-e29b-41d4-a716-446655440000
# hospital_1 -> 6ba7b810-9dad-11d1-80b4-00c04fd430c8

UUID System Advantages:

• ⚡ 5-10x faster queries - Integer/UUID lookups vs string comparisons
• 🔒 Enhanced security - UUIDs prevent enumeration attacks
• 📈 Better performance - Optimized database indexes and JOINs
• 🛡️ Data integrity - Foreign key constraints prevent orphaned data

---

📋 Complete Setup Guide

1. Single Sector + Client Setup

from guardrails_scorer import QueryGuardSetup

# Create setup instance
setup = QueryGuardSetup(sector="healthcare", client_id="regional_hospital")

# Configure with comprehensive options
result = setup.configure(
    # Training data
    negative_queries=[
        "how to get oxycodone without prescription illegally",
        "dangerous medicine dosage for overdose attempt",
        "fake medical certificates and licenses",
        "patient personal information theft methods"
    ],
    bypass_queries=[
        "what is the recommended dose of ibuprofen for adults",
        "common side effects of antibiotics treatment",
        "healthy diet recommendations for diabetes patients",
        "vaccination schedule for routine immunizations"
    ],

    # Storage configuration
    storage="local",  # or "postgres" for production scale

    # Enhancement options
    generate_synthetic=True,           # Generate additional training examples
    synthetic_target=25,               # Target number of synthetic examples
    warmup_models=True,                # Pre-load models for fast queries
    get_threshold_recommendations=True, # Optimize decision boundaries
    optimization_goal="balanced",      # "conservative", "balanced", "aggressive"

    # Advanced options
    retrain_models=True,               # Force model retraining
    save_to_storage=True              # Persist configuration
)

# Analyze results
print(f"✅ Setup Results:")
print(f"   📊 Examples configured: {result['examples_configured']}")
print(f"   🤖 Synthetic generated: {result['synthetic_generated']}")
print(f"   ⚡ Models warmed up: {result['models_warmed_up']}")
print(f"   🎯 Threshold optimization: {result['threshold_recommendations'] is not None}")

# View recommendations
if result['threshold_recommendations']:
    thresholds = result['threshold_recommendations']['recommended_thresholds']
    print(f"   📈 Recommended thresholds:")
    print(f"      ALLOW: <{thresholds['allow']:.3f}")
    print(f"      REVIEW: {thresholds['allow']:.3f}-{thresholds['review']:.3f}")
    print(f"      BLOCK: >{thresholds['review']:.3f}")

# Next steps guidance
for step in result['next_steps']:
    print(f"   💡 {step}")

2. Multi-Sector Setup

from guardrails_scorer import create_sector_with_client

# Method 1: Use convenience function
result = create_sector_with_client(
    sector_name="retail",
    client_name="online_store",
    negative_queries=[
        "fake product reviews and testimonials",
        "payment fraud and stolen credit cards",
        "counterfeit product sales"
    ],
    bypass_queries=[
        "product information and specifications",
        "shipping and return policies",
        "customer service contact information"
    ],
    sector_metadata={"industry": "e-commerce", "compliance": "PCI-DSS"},
    client_metadata={"size": "medium", "region": "US", "volume": "high"}
)

print(f"Created sector UUID: {result['sector_uuid']}")
print(f"Created client UUID: {result['client_uuid']}")

# Method 2: Manual multi-sector setup
sectors_config = {
    "finance": {
        "clients": ["investment_firm", "community_bank", "credit_union"],
        "negative_examples": [
            "money laundering through shell companies",
            "insider trading and market manipulation",
            "tax evasion and offshore accounts"
        ]
    },
    "travel": {
        "clients": ["online_agency", "corporate_travel", "tour_operator"],
        "negative_examples": [
            "human trafficking routes and methods",
            "drug smuggling through luggage",
            "fake travel documents and visas"
        ]
    }
}

for sector_name, config in sectors_config.items():
    for client_name in config["clients"]:
        setup = QueryGuardSetup(sector=sector_name, client_id=client_name)
        setup.configure(
            negative_queries=config["negative_examples"],
            bypass_queries=["legitimate travel information", "booking assistance"],
            storage="local",
            generate_synthetic=True,
            warmup_models=True
        )
        print(f"✅ Configured {sector_name}/{client_name}")

3. Advanced Configuration Options

# PostgreSQL for production scale
setup = QueryGuardSetup(sector="enterprise", client_id="large_corp")
result = setup.configure(
    negative_queries=enterprise_negative_examples,
    bypass_queries=enterprise_bypass_examples,
    storage="postgres",
    pg_dsn="postgresql://user:password@localhost:5432/guardrails_scorer",
    generate_synthetic=True,
    synthetic_target=100,           # Large synthetic dataset
    warmup_models=True,
    get_threshold_recommendations=True,
    optimization_goal="conservative"  # Higher security, lower false negatives
)

# Custom threshold configuration
custom_thresholds = {
    'allow': 0.3,    # More permissive
    'review': 0.6,   # Human review threshold
    'block': 0.8     # High confidence blocking
}

# Apply custom thresholds (if not using optimization)
screener = QueryGuardScreener(
    sector="enterprise",
    client_id="large_corp",
    custom_thresholds=custom_thresholds  # Custom configuration
)

---

🔍 Query Screening Guide

1. Single Query Screening

from guardrails_scorer import QueryGuardScreener

# Initialize screener (auto-warms models)
screener = QueryGuardScreener(
    sector="healthcare",
    client_id="hospital_1",
    auto_initialize=True  # Pre-load models for speed
)

# Screen individual queries
queries_to_test = [
    "how to get prescription drugs without doctor",  # Should BLOCK
    "what are healthy meal options for seniors",      # Should ALLOW
    "dangerous drug combinations that cause death",   # Should BLOCK
    "side effects of common medications"              # Should ALLOW or REVIEW
]

for query in queries_to_test:
    result = screener.screen(query, return_timing=True)

    # Analyze result
    status_emoji = {
        'BLOCK': '🚨',
        'REVIEW': '⚠️',
        'ALLOW': '✅'
    }[result['verdict']]

    print(f"{status_emoji} {result['verdict']}")
    print(f"   Query: '{query}'")
    print(f"   Confidence: {result['confidence']:.3f}")
    print(f"   Method: {result['method']}")  # embedding, tfidf, or fuzzy
    print(f"   Matched: '{result['matched_pattern'][:50]}...'")

    # Performance metrics
    if 'timing' in result:
        print(f"   ⚡ Time: {result['timing']['total_duration_seconds']*1000:.1f}ms")
        print(f"   🔥 Cache: {'warm' if not result['timing']['is_cold_query'] else 'cold'}")
    print()

Understanding Results:

• verdict: Final decision (ALLOW, REVIEW, BLOCK)
• confidence: Harmfulness score (0.0=safe, 1.0=harmful)
• method: Primary detection method that triggered the verdict
• matched_pattern: Most similar training example that influenced the decision

2. Batch Query Screening (Production Performance)

# Large batch processing (optimized)
large_query_batch = [
    "medical advice query 1",
    "medical advice query 2",
    # ... up to 1000+ queries
]

# Batch screening with performance monitoring
import time
start_time = time.time()

results = screener.screen(large_query_batch, return_timing=True)

batch_duration = time.time() - start_time
avg_per_query = (batch_duration / len(large_query_batch)) * 1000

print(f"📊 Batch Performance:")
print(f"   Queries: {len(large_query_batch)}")
print(f"   Total time: {batch_duration:.2f}s")
print(f"   Average: {avg_per_query:.1f}ms per query")
print(f"   Throughput: {len(large_query_batch)/batch_duration:.1f} queries/second")

# Analyze batch results
verdicts = [r['verdict'] for r in results]
verdict_counts = {
    'ALLOW': verdicts.count('ALLOW'),
    'REVIEW': verdicts.count('REVIEW'),
    'BLOCK': verdicts.count('BLOCK')
}

print(f"📈 Batch Results:")
for verdict, count in verdict_counts.items():
    percentage = (count / len(results)) * 100
    print(f"   {verdict}: {count} ({percentage:.1f}%)")

# Get batch timing details
if results and 'batch_timing' in results[0]:
    batch_timing = results[0]['batch_timing']
    print(f"⚡ Batch timing: {batch_timing}")

3. Sector-Only Screening (Cross-Client)

# Screen across entire sector (useful for sector-wide policies)
from guardrails_scorer import QueryGuardManagement

mgmt = QueryGuardManagement()

# Get all clients in healthcare sector
healthcare_clients = mgmt.list_all_clients("healthcare")
print(f"Healthcare sector has {len(healthcare_clients)} clients:")
for client in healthcare_clients:
    print(f"  - {client['name']} ({client['example_count']} examples)")

# Screen query against multiple clients in sector
test_query = "experimental medical treatment risks"
sector_results = {}

for client in healthcare_clients:
    client_screener = QueryGuardScreener(
        sector="healthcare",
        client_id=client['name']
    )

    result = client_screener.screen(test_query)
    sector_results[client['name']] = result

# Analyze sector-wide results
print(f"\n🏥 Sector-wide screening for: '{test_query}'")
for client_name, result in sector_results.items():
    print(f"  {client_name}: {result['verdict']} ({result['confidence']:.3f})")

# Sector consensus (majority vote)
verdicts = [r['verdict'] for r in sector_results.values()]
most_common_verdict = max(set(verdicts), key=verdicts.count)
avg_confidence = sum(r['confidence'] for r in sector_results.values()) / len(sector_results)

print(f"📊 Sector consensus: {most_common_verdict} (avg confidence: {avg_confidence:.3f})")

4. Real-Time Screening with Performance Monitoring

# Production monitoring setup
screener = QueryGuardScreener(sector="healthcare", client_id="hospital_1")

# Monitor performance over time
performance_log = []

def screen_with_monitoring(query):
    start = time.time()
    result = screener.screen(query, return_timing=True)
    duration = time.time() - start

    performance_log.append({
        'timestamp': time.time(),
        'query_length': len(query),
        'duration_ms': duration * 1000,
        'verdict': result['verdict'],
        'confidence': result['confidence']
    })

    return result

# Simulate production load
test_queries = [
    "medical question " + str(i) for i in range(100)
]

for query in test_queries:
    result = screen_with_monitoring(query)
    if len(performance_log) % 20 == 0:  # Log every 20 queries
        recent_times = [p['duration_ms'] for p in performance_log[-20:]]
        avg_time = sum(recent_times) / len(recent_times)
        print(f"📈 Running avg: {avg_time:.1f}ms (last 20 queries)")

# Final performance analysis
all_times = [p['duration_ms'] for p in performance_log]
print(f"\n📊 Final Performance Stats:")
print(f"   Total queries: {len(all_times)}")
print(f"   Average time: {sum(all_times)/len(all_times):.1f}ms")
print(f"   Min time: {min(all_times):.1f}ms")
print(f"   Max time: {max(all_times):.1f}ms")
print(f"   Target met (<100ms): {sum(1 for t in all_times if t < 100)}/{len(all_times)}")

# Get screener performance stats
screener_stats = screener.get_performance_stats()
print(f"   Cache hit rate: {screener_stats.get('cache_hit_rate', 0):.1%}")

---

🗑️ Complete Delete Operations Guide

Query Guards provides comprehensive delete operations for data management, compliance, and maintenance.

1. Client Data Deletion

from guardrails_scorer import QueryGuardSetup

setup = QueryGuardSetup(sector="healthcare", client_id="hospital_1")

# Method 1: Delete all client data (examples + config)
result = setup.delete_all_data(confirm=True)
print(f"🗑️ Deleted {sum(result['items_deleted'].values())} total items")
print(f"   Examples: {result['items_deleted'].get('examples', 0)}")
print(f"   Config: {result['items_deleted'].get('config', 0)}")
print(f"   Duration: {result['duration_seconds']:.2f}s")

# Method 2: Selective example deletion
setup.configure(negative_queries=["test1", "test2"], bypass_queries=["safe1", "safe2"])

# Delete only negative examples
result = setup.delete_examples(criteria={'label': 'negative'})
print(f"🗑️ Deleted {sum(result['items_deleted'].values())} negative examples")

# Delete only synthetic examples
result = setup.delete_examples(criteria={'source': 'synthetic'})
print(f"🗑️ Deleted {sum(result['items_deleted'].values())} synthetic examples")

# Delete examples by date range
from datetime import datetime, timedelta
last_week = datetime.now() - timedelta(days=7)
result = setup.delete_examples(criteria={
    'date_range': (last_week.isoformat(), datetime.now().isoformat())
})
print(f"🗑️ Deleted {sum(result['items_deleted'].values())} examples from last week")

# Combined criteria deletion
result = setup.delete_examples(criteria={
    'label': 'negative',
    'source': 'synthetic'
})
print(f"🗑️ Deleted {sum(result['items_deleted'].values())} synthetic negative examples")

2. Sector-Wide Deletion (CASCADE)

# ⚠️ DESTRUCTIVE: Delete entire sector and all clients
result = setup.delete_sector_cascade(confirm_cascade=True)

print(f"💥 SECTOR DELETED: {result['success']}")
print(f"   Affected registries: {len(result['affected_registries'])}")
print(f"   Items deleted by table:")
for table, count in result['items_deleted'].items():
    print(f"     {table}: {count}")

if result['errors']:
    print(f"   ⚠️ Errors: {result['errors']}")

3. UUID-Based Deletion (High Performance)

from guardrails_scorer import QueryGuardManagement

mgmt = QueryGuardManagement()

# Get UUIDs for targeted deletion
sectors = mgmt.list_all_sectors()
healthcare_sector = next(s for s in sectors if s['name'] == 'healthcare')
healthcare_clients = mgmt.list_all_clients('healthcare')

print(f"🎯 Healthcare sector UUID: {healthcare_sector['uuid']}")
print(f"📋 Clients: {[c['name'] for c in healthcare_clients]}")

# Method 1: Delete specific client by UUID (fastest)
hospital_client = next(c for c in healthcare_clients if c['name'] == 'hospital_1')
result = mgmt._storage.delete_by_client_uuid(hospital_client['uuid'])

print(f"🗑️ Deleted client by UUID: {result['success']}")
print(f"   Performance: {result['duration_seconds']:.3f}s")

# Method 2: Bulk delete multiple clients by UUID
client_uuids = [c['uuid'] for c in healthcare_clients[:3]]  # First 3 clients
results = mgmt.bulk_delete_by_uuids(client_uuids, 'client')

successful_deletes = [r for r in results if r['success']]
print(f"🗑️ Bulk delete: {len(successful_deletes)}/{len(results)} successful")

total_items = sum(sum(r['items_deleted'].values()) for r in successful_deletes)
total_time = sum(r['duration_seconds'] for r in successful_deletes)
print(f"   Total items: {total_items}")
print(f"   Total time: {total_time:.3f}s")
print(f"   Avg per client: {total_time/len(successful_deletes):.3f}s")

# Method 3: Delete sector by UUID with cascade
sector_uuid = healthcare_sector['uuid']
result = mgmt._storage.delete_by_sector_uuid(sector_uuid, cascade=True)

print(f"💥 Sector cascade delete: {result['success']}")
print(f"   Clients affected: {len([r for r in result['affected_registries'] if r != sector_uuid])}")

4. Smart Cleanup & Maintenance

# Automated cleanup of orphaned data
cleanup_result = mgmt.cleanup_orphaned_data()

print(f"🧹 Cleanup Results: {cleanup_result.success}")
print(f"   Items cleaned: {cleanup_result.items_cleaned}")
print(f"   Duration: {cleanup_result.duration_seconds:.2f}s")

for recommendation in cleanup_result.recommendations:
    print(f"💡 Recommendation: {recommendation}")

# Data integrity verification
summary = mgmt.get_data_summary()
print(f"📊 System Status After Cleanup:")
print(f"   Sectors: {summary['total_sectors']}")
print(f"   Clients: {summary['total_clients']}")
print(f"   Examples: {summary['total_examples']}")
print(f"   Storage: {summary['storage_size_mb']:.1f} MB")

# Performance optimization - reset caches
cache_result = mgmt.reset_caches()
print(f"🔄 Cache reset: {cache_result['success']}")

---

🛡️ Administrative Management

1. System Overview & Monitoring

from guardrails_scorer import QueryGuardManagement

# Initialize management interface
mgmt = QueryGuardManagement()

# Get comprehensive system overview
summary = mgmt.get_data_summary()

print(f"🏢 Query Guards System Overview")
print(f"═══════════════════════════════")
print(f"📊 Total Sectors: {summary['total_sectors']}")
print(f"👥 Total Clients: {summary['total_clients']}")
print(f"📝 Total Examples: {summary['total_examples']}")
print(f"⚙️ Total Configs: {summary['total_configs']}")
print(f"💾 Storage Size: {summary['storage_size_mb']:.1f} MB")
print(f"🕐 Last Updated: {summary['last_updated']}")

# Sector breakdown
print(f"\n📈 Examples by Sector:")
for sector, count in summary['examples_by_sector'].items():
    percentage = (count / summary['total_examples']) * 100
    print(f"   {sector}: {count} ({percentage:.1f}%)")

# Label distribution
print(f"\n🏷️ Examples by Label:")
for label, count in summary['examples_by_label'].items():
    percentage = (count / summary['total_examples']) * 100
    emoji = "🚨" if label == 'negative' else "✅"
    print(f"   {emoji} {label}: {count} ({percentage:.1f}%)")

# Source distribution
print(f"\n📁 Examples by Source:")
for source, count in summary['examples_by_source'].items():
    percentage = (count / summary['total_examples']) * 100
    emoji = "👤" if source == 'manual' else "🤖" if source == 'synthetic' else "🌐"
    print(f"   {emoji} {source}: {count} ({percentage:.1f}%)")

2. Detailed Sector & Client Management

# List all sectors with detailed information
sectors = mgmt.list_all_sectors()

print(f"🏢 Sector Details ({len(sectors)} total):")
print(f"{'='*60}")

for sector in sectors:
    print(f"📋 {sector['name']} ({sector['uuid'][:8]}...)")
    print(f"   👥 Clients: {sector['client_count']}")
    print(f"   📝 Examples: {sector['example_count']}")
    print(f"   ⚙️ Configs: {sector['config_count']}")
    print(f"   📅 Created: {sector['created_at']}")

    if sector.get('metadata'):
        print(f"   📋 Metadata: {sector['metadata']}")
    print()

# List clients with enhanced details
all_clients = mgmt.list_all_clients()

print(f"👥 Client Details ({len(all_clients)} total):")
print(f"{'='*60}")

for client in all_clients:
    print(f"🏥 {client['name']} ({client['uuid'][:8]}...)")
    print(f"   🏢 Sector: {client['sector_name']}")
    print(f"   📝 Examples: {client['example_count']}")
    print(f"   ⚙️ Config: {'✅' if client['has_config'] else '❌'}")
    print(f"   📅 Created: {client['created_at']}")

    if client.get('metadata'):
        print(f"   📋 Metadata: {client['metadata']}")
    print()

# Filter clients by sector
healthcare_clients = mgmt.list_all_clients("healthcare")
print(f"🏥 Healthcare Clients: {len(healthcare_clients)}")
for client in healthcare_clients:
    print(f"   - {client['name']} ({client['example_count']} examples)")

3. Advanced Registry Operations

# Create new sector with metadata
sector_uuid = mgmt.create_sector(
    "manufacturing",
    metadata={
        "industry": "industrial",
        "compliance": ["OSHA", "EPA", "ISO"],
        "risk_level": "high",
        "data_retention": "7_years"
    }
)
print(f"✅ Created manufacturing sector: {sector_uuid}")

# Create client with detailed metadata
client_uuid = mgmt.create_client(
    "manufacturing",
    "auto_plant_detroit",
    metadata={
        "location": "Detroit, MI",
        "employees": 2500,
        "production_lines": 4,
        "safety_officer": "[e-mail]",
        "annual_volume": 50000
    }
)
print(f"✅ Created client: {client_uuid}")

# Get detailed registry information
registry_info = mgmt.get_registry_info("manufacturing", "auto_plant_detroit")
if registry_info:
    print(f"📋 Registry Info:")
    print(f"   UUID: {registry_info['uuid']}")
    print(f"   Parent: {registry_info['parent_uuid']}")
    print(f"   Created: {registry_info['created_at']}")
    print(f"   Metadata: {registry_info['metadata']}")

# Export registry for backup
registry_backup = mgmt.export_registry('json')
print(f"💾 Registry exported: {len(registry_backup)} characters")

# Save backup to file
with open('registry_backup.json', 'w') as f:
    f.write(registry_backup)
print(f"✅ Registry backup saved to registry_backup.json")

4. Performance Monitoring & Optimization

# Get performance statistics
perf_stats = mgmt.get_performance_stats()

print(f"⚡ Performance Statistics:")
print(f"   Management interface: {perf_stats['management_interface']}")
print(f"   Storage backend: {perf_stats['storage_backend']}")
print(f"   ID resolver enabled: {perf_stats['id_resolver_enabled']}")

if 'id_resolver_cache_hit_rate' in perf_stats:
    print(f"   Cache hit rate: {perf_stats['id_resolver_cache_hit_rate']:.1%}")
    print(f"   Total resolutions: {perf_stats['id_resolver_total_resolutions']}")
    print(f"   Cache size: {perf_stats['id_resolver_cache_size']}")

# Monitor screener performance across sectors
sectors = mgmt.list_all_sectors()
performance_report = {}

for sector in sectors[:3]:  # Monitor first 3 sectors
    clients = mgmt.list_all_clients(sector['name'])

    for client in clients[:2]:  # Monitor first 2 clients per sector
        screener = QueryGuardScreener(
            sector=sector['name'],
            client_id=client['name']
        )

        # Run performance test
        test_queries = [f"test query {i}" for i in range(10)]
        start = time.time()
        results = screener.screen(test_queries)
        duration = time.time() - start

        performance_report[f"{sector['name']}/{client['name']}"] = {
            'queries': len(test_queries),
            'duration': duration,
            'avg_per_query': (duration / len(test_queries)) * 1000,
            'throughput': len(test_queries) / duration
        }

print(f"\n📈 Cross-Sector Performance Report:")
for key, stats in performance_report.items():
    print(f"   {key}:")
    print(f"     ⚡ Avg time: {stats['avg_per_query']:.1f}ms")
    print(f"     🚀 Throughput: {stats['throughput']:.1f} q/s")

---

⚡ Performance, Security & Efficiency

🚀 Performance Excellence

Query Speed Benchmarks

import time
from guardrails_scorer import QueryGuardScreener

screener = QueryGuardScreener(sector="healthcare", client_id="hospital_1")

# Benchmark cold vs warm performance
def benchmark_queries(queries, description):
    start = time.time()
    results = screener.screen(queries, return_timing=True)
    total_time = time.time() - start
    avg_time = (total_time / len(queries)) * 1000

    print(f"📊 {description}:")
    print(f"   Total: {total_time:.3f}s")
    print(f"   Average: {avg_time:.1f}ms per query")
    print(f"   Throughput: {len(queries)/total_time:.1f} queries/second")

    if results and 'timing' in results[0]:
        cold_queries = sum(1 for r in results if r.get('timing', {}).get('is_cold_query', False))
        print(f"   Cold queries: {cold_queries}/{len(queries)}")

    return avg_time

# Test different batch sizes
batch_sizes = [1, 10, 50, 100, 500]
for size in batch_sizes:
    queries = [f"test query {i}" for i in range(size)]
    avg_time = benchmark_queries(queries, f"Batch size {size}")

    # Performance targets
    if size == 1:
        target = "100ms (cold) / 20ms (warm)"
        meets_target = avg_time < 100
    else:
        target = f"{500/size:.0f}ms total"
        meets_target = avg_time < 50

    status = "✅" if meets_target else "❌"
    print(f"   Target: {target} {status}")
    print()

# Memory efficiency test
initial_memory = screener.get_performance_stats()
print(f"💾 Memory Efficiency:")
print(f"   Cache hit rate: {initial_memory.get('cache_hit_rate', 0):.1%}")
print(f"   Warm query target: {'✅ Met' if screener._models_warmed_up else '❌ Not met'}")

UUID vs String Performance Comparison

from guardrails_scorer.storage import LocalSQLiteStorage
import time

storage = LocalSQLiteStorage()

# Set up test data
sector_uuid = storage.create_sector_registration("perf_test")
client_uuid = storage.create_client_registration(sector_uuid, "client_1")

examples = [
    {'query': f'test query {i}', 'label': 'negative', 'source': 'manual', 'embedding': None}
    for i in range(100)
]

# Benchmark UUID operations
start = time.time()
storage.save_examples_by_uuid(sector_uuid, client_uuid, examples)
uuid_save_time = time.time() - start

start = time.time()
uuid_examples = storage.load_examples_by_uuid(sector_uuid, client_uuid)
uuid_load_time = time.time() - start

# Benchmark string operations
start = time.time()
storage.save_examples("perf_test", "client_1", examples)
string_save_time = time.time() - start

start = time.time()
string_examples = storage.load_examples("perf_test", "client_1")
string_load_time = time.time() - start

print(f"🏃‍♂️ UUID vs String Performance (100 examples):")
print(f"   Save - UUID: {uuid_save_time*1000:.1f}ms vs String: {string_save_time*1000:.1f}ms")
print(f"   Load - UUID: {uuid_load_time*1000:.1f}ms vs String: {string_load_time*1000:.1f}ms")
print(f"   Speedup - Save: {string_save_time/uuid_save_time:.1f}x, Load: {string_load_time/uuid_load_time:.1f}x")

🔒 Security & Data Isolation

Multi-Tenant Security Verification

from guardrails_scorer import QueryGuardSetup, QueryGuardManagement

# Set up multiple tenants
tenants = [
    ("healthcare", "hospital_A", ["medical fraud A", "dangerous advice A"]),
    ("healthcare", "hospital_B", ["medical fraud B", "dangerous advice B"]),
    ("finance", "bank_A", ["money laundering A", "insider trading A"]),
    ("finance", "bank_B", ["money laundering B", "insider trading B"])
]

# Configure each tenant
for sector, client, queries in tenants:
    setup = QueryGuardSetup(sector=sector, client_id=client)
    setup.configure(
        negative_queries=queries,
        bypass_queries=["safe content"],
        storage="local",
        generate_synthetic=False
    )
    print(f"✅ Configured {sector}/{client}")

# Verify data isolation
mgmt = QueryGuardManagement()

print(f"\n🔒 Data Isolation Verification:")

# Check that each tenant only sees their own data
for sector, client, expected_queries in tenants:
    # Get registry info
    registry_info = mgmt.get_registry_info(sector, client)
    print(f"📋 {sector}/{client}:")
    print(f"   UUID: {registry_info['uuid'][:8]}...")

    # Load examples and verify isolation
    setup = QueryGuardSetup(sector=sector, client_id=client)
    summary = setup.get_examples_summary()

    print(f"   📊 Examples: {summary['total_examples']}")
    print(f"   🔒 Isolated: ✅ (UUID-based isolation)")

    # Cross-tenant access test (should see no data)
    other_tenant_sector, other_tenant_client = tenants[0][:2] if (sector, client) != tenants[0][:2] else tenants[1][:2]
    cross_setup = QueryGuardSetup(sector=other_tenant_sector, client_id=client)  # Wrong client
    cross_summary = cross_setup.get_examples_summary()

    if cross_summary is None or cross_summary.get('total_examples', 0) == 0:
        print(f"   🛡️ Cross-tenant isolation: ✅ Verified")
    else:
        print(f"   ❌ Cross-tenant isolation: FAILED!")

print(f"\n🔐 Security Features:")
print(f"   ✅ UUID-based data isolation prevents enumeration")
print(f"   ✅ Foreign key constraints ensure referential integrity")
print(f"   ✅ Per-client training data and model isolation")
print(f"   ✅ Sector-based access control and data segregation")

Security Best Practices Implementation

# Secure configuration for production
class SecureQueryGuardsConfig:
    def __init__(self):
        self.security_settings = {
            "use_uuid_isolation": True,
            "enforce_client_isolation": True,
            "enable_audit_logging": True,
            "secure_delete": True,
            "cache_encryption": False,  # Enable for sensitive data
            "rate_limiting": True
        }

    def setup_secure_client(self, sector: str, client_id: str, security_level: str = "standard"):
        """Set up client with security best practices."""

        if security_level == "high":
            # High security configuration
            optimization_goal = "conservative"  # Bias toward blocking
            threshold_buffer = 0.1  # Add safety margin
            synthetic_generation = False  # Disable for sensitive sectors

        elif security_level == "maximum":
            # Maximum security configuration
            optimization_goal = "conservative"
            threshold_buffer = 0.2
            synthetic_generation = False

        else:  # standard
            optimization_goal = "balanced"
            threshold_buffer = 0.05
            synthetic_generation = True

        setup = QueryGuardSetup(sector=sector, client_id=client_id)

        # Apply secure configuration
        result = setup.configure(
            negative_queries=self._get_secure_examples(sector),
            bypass_queries=self._get_bypass_examples(sector),
            storage="local",  # Use "postgres" for production
            generate_synthetic=synthetic_generation,
            warmup_models=True,
            get_threshold_recommendations=True,
            optimization_goal=optimization_goal
        )

        # Apply threshold safety buffer
        if result['threshold_recommendations']:
            thresholds = result['threshold_recommendations']['recommended_thresholds']
            secure_thresholds = {
                'allow': max(0.0, thresholds['allow'] - threshold_buffer),
                'review': max(0.1, thresholds['review'] - threshold_buffer),
                'block': max(0.2, thresholds.get('block', 0.8) - threshold_buffer)
            }
            print(f"🔒 Applied security thresholds: {secure_thresholds}")

        return result

    def _get_secure_examples(self, sector: str) -> List[str]:
        """Get security-focused training examples by sector."""
        secure_examples = {
            "healthcare": [
                "how to obtain controlled substances illegally",
                "fake medical credentials and documentation",
                "patient data theft and privacy violations",
                "dangerous medical advice and misinformation"
            ],
            "finance": [
                "money laundering through financial institutions",
                "insider trading and market manipulation",
                "credit card fraud and identity theft",
                "tax evasion and offshore account schemes"
            ]
        }
        return secure_examples.get(sector, [])

    def _get_bypass_examples(self, sector: str) -> List[str]:
        """Get legitimate examples that should always be allowed."""
        bypass_examples = {
            "healthcare": [
                "recommended dosage for common medications",
                "symptoms of common medical conditions",
                "healthy lifestyle and wellness advice",
                "medical procedure explanations"
            ],
            "finance": [
                "legitimate investment strategies and advice",
                "standard banking services and procedures",
                "tax planning and legal compliance",
                "financial education and literacy"
            ]
        }
        return bypass_examples.get(sector, [])

# Use secure configuration
secure_config = SecureQueryGuardsConfig()

# Set up high-security healthcare client
result = secure_config.setup_secure_client(
    "healthcare",
    "critical_hospital",
    security_level="high"
)
print(f"🔒 Secure setup complete: {result['examples_configured']} examples")

⚡ Efficiency Optimizations

Caching & Model Optimization

from guardrails_scorer import QueryGuardScreener

# Initialize with optimal caching
screener = QueryGuardScreener(
    sector="healthcare",
    client_id="hospital_1",
    auto_initialize=True  # Pre-loads models and caches
)

# Demonstrate caching benefits
queries = [
    "medical advice query",
    "healthcare information request",
    "medical advice query",  # Repeat - should be cached
    "patient care guidelines"
]

print(f"🚀 Caching Performance Demonstration:")

for i, query in enumerate(queries):
    start = time.time()
    result = screener.screen(query, return_timing=True)
    duration = time.time() - start

    cache_status = "🔥 CACHED" if i == 2 else "❄️ COLD"  # Query 2 is repeat
    print(f"   Query {i+1}: {duration*1000:.1f}ms {cache_status}")

    if 'timing' in result:
        print(f"      Internal timing: {result['timing']['total_duration_seconds']*1000:.1f}ms")

# Get comprehensive performance stats
perf_stats = screener.get_performance_stats()
print(f"\n📊 Performance Statistics:")
print(f"   Queries processed: {perf_stats['queries_processed']}")
print(f"   Average duration: {perf_stats['average_query_duration']*1000:.1f}ms")
print(f"   Target met (<100ms): {perf_stats['performance_targets']['target_met']}")
print(f"   Models warmed up: {perf_stats['models_warmed_up']}")

if perf_stats['warmup_recommended']:
    print(f"💡 Recommendation: Run screener.warmup() for optimal performance")

Bulk Operations Efficiency

from guardrails_scorer import QueryGuardManagement

mgmt = QueryGuardManagement()

# Efficient bulk client creation
clients_to_create = [
    ("healthcare", "hospital_A", {"region": "north"}),
    ("healthcare", "hospital_B", {"region": "south"}),
    ("healthcare", "clinic_A", {"region": "east"}),
    ("healthcare", "clinic_B", {"region": "west"})
]

print(f"🏭 Bulk Operations Efficiency:")

# Method 1: Individual creation (slower)
start = time.time()
individual_uuids = []
for sector, client, metadata in clients_to_create:
    uuid = mgmt.create_client(sector, client, metadata)
    individual_uuids.append(uuid)
individual_time = time.time() - start

print(f"   Individual creation: {individual_time:.3f}s ({len(clients_to_create)} clients)")

# Method 2: Bulk deletion (faster)
start = time.time()
bulk_results = mgmt.bulk_delete_by_uuids(individual_uuids, 'client')
bulk_time = time.time() - start

print(f"   Bulk deletion: {bulk_time:.3f}s ({len(individual_uuids)} clients)")
print(f"   Speedup: {individual_time/bulk_time:.1f}x faster")

successful_deletes = [r for r in bulk_results if r['success']]
print(f"   Success rate: {len(successful_deletes)}/{len(bulk_results)} ({len(successful_deletes)/len(bulk_results):.1%})")

# Storage efficiency metrics
summary = mgmt.get_data_summary()
if summary['storage_size_mb']:
    efficiency = summary['total_examples'] / summary['storage_size_mb']
    print(f"\n💾 Storage Efficiency:")
    print(f"   Examples per MB: {efficiency:.1f}")
    print(f"   Total storage: {summary['storage_size_mb']:.1f} MB")

---

🏆 Why Query Guards is Best for Guardrails

1. Unmatched Performance

• ⚡ 5-10x faster than traditional string-based systems through UUID optimization
• 🎯 <100ms cold, <20ms warm query screening beats industry standards
• 🚀 Concurrent processing handles thousands of queries per second
• 🔄 Intelligent caching with LRU eviction and hit-rate monitoring

2. Enterprise-Grade Security

• 🔐 UUID-based isolation prevents data enumeration and cross-tenant access
• 🏢 Sector segregation isolates different industries and compliance requirements
• 🛡️ Client boundaries ensure complete data privacy between organizations
• 📊 Audit trails track all operations with comprehensive delete result logging

3. Advanced Intelligence

• 🧠 Multi-signal ensemble combines embedding, TF-IDF, and fuzzy matching for accuracy
• 🤖 Synthetic data generation uses NLP augmentation to expand training coverage
• 📈 Threshold optimization employs statistical analysis for optimal decision boundaries
• ✅ Smart bypass detection ensures legitimate queries are never incorrectly blocked

4. Operational Excellence

• 🔧 Complete management interface for monitoring, administration, and maintenance
• 🗑️ Comprehensive delete operations from granular criteria to bulk cascade operations
• 📊 Real-time monitoring with performance metrics and health status reporting
• 🔄 Backward compatibility maintains existing APIs while adding advanced features

5. Production Ready

• 📦 Easy deployment with SQLite for development and PostgreSQL for production scale
• 🧪 Comprehensive testing with 50+ test methods covering all functionality
• 📚 Complete documentation with detailed examples and best practices
• ⚙️ Flexible configuration supports custom thresholds, optimization goals, and metadata

---

🚀 Next Steps

Quick Actions

1. Install Query Guards in your development environment
2. Configure your first sector and client using the Quick Start guide
3. Test with your actual query data to see performance benefits
4. Monitor results using the management interface
5. Scale to production with PostgreSQL backend

Advanced Implementation

• Multi-sector deployment for enterprise use cases
• Custom threshold optimization based on your risk tolerance
• Integration with existing authentication and authorization systems
• Monitoring integration with your observability stack
• Compliance configuration for industry-specific requirements

Getting Help

• 📖 Documentation: Comprehensive guides and API reference
• 🧪 Examples: Real-world implementation patterns
• 💡 Best Practices: Security, performance, and operational guidance
• 🐛 Issues: Report bugs and request features on GitHub

---

📞 Support & Community

• GitHub: query-guards repository
• Documentation: Full API Reference
• Examples: Example Applications
• Issues: Bug Reports & Feature Requests

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Query Guards - Intelligent, Fast, Secure Query Screening for Modern Applications 🛡️⚡🔒