Skip to main content

Auto-Complete Guide

The Auto-Complete Engine provides intelligent suggestions as users type, improving search efficiency and discoverability. This guide covers configuration, usage, and advanced features.

Overview​

The Auto-Complete Engine uses multiple data sources to provide relevant suggestions:

  • Query History: Past searches from the current user
  • Popular Queries: Frequently searched terms across all users
  • Domain Terms: Specialized vocabulary from your knowledge base
  • Spelling Corrections: Automatic correction of common typos
  • Synonyms: Alternative terms with similar meanings

Basic Usage​

1. Initialize the Engine​

from search_interface.autocomplete import AutoCompleteEngine, AutoCompleteConfig

# Create configuration
config = AutoCompleteConfig(
    use_query_history=True,
    use_popular_queries=True,
    use_domain_terms=True,
    max_suggestions=10,
    min_query_length=2
)

# Initialize engine
engine = AutoCompleteEngine(config)

2. Get Suggestions​

# Get suggestions for a partial query
partial_query = "python"
suggestions = await engine.get_suggestions(partial_query, user_id="user123")

print("Auto-complete suggestions:")
for suggestion in suggestions:
    print(f"- {suggestion.text} (confidence: {suggestion.confidence:.2f})")

3. Track Selections​

# Track when user selects a suggestion
selected_suggestion = suggestions[0]
await engine.track_selection(selected_suggestion, user_id="user123")

Configuration Options​

AutoCompleteConfig​

config = AutoCompleteConfig(
    # Data sources
    use_query_history=True,          # Include user's search history
    use_popular_queries=True,        # Include popular queries
    use_domain_terms=True,           # Include domain-specific terms
    use_spelling_corrections=True,   # Include spelling corrections
    use_synonyms=True,               # Include synonym suggestions
    
    # Suggestion limits
    max_suggestions=10,              # Maximum number of suggestions
    min_query_length=2,              # Minimum query length for suggestions
    max_query_length=100,            # Maximum query length
    
    # Scoring weights
    history_weight=0.4,              # Weight for query history
    popularity_weight=0.3,           # Weight for popular queries
    domain_weight=0.2,               # Weight for domain terms
    spelling_weight=0.1,             # Weight for spelling corrections
    
    # Performance settings
    cache_enabled=True,              # Enable caching
    cache_ttl_seconds=300,           # Cache time-to-live
    debounce_delay_ms=200,           # Delay before processing
    
    # Personalization
    personalization_enabled=True,    # Enable personalized suggestions
    learning_rate=0.1,               # Learning rate for personalization
    history_retention_days=90,       # Days to retain query history
)

Advanced Features​

1. Custom Domain Terms​

# Add custom domain terms
domain_terms = [
    "machine learning",
    "neural networks",
    "deep learning",
    "natural language processing",
    "computer vision"
]

await engine.add_domain_terms(domain_terms)

# Get suggestions with domain terms
suggestions = await engine.get_suggestions("machine", user_id="user123")

2. Spelling Corrections​

# Enable spelling corrections
config = AutoCompleteConfig(
    use_spelling_corrections=True,
    spelling_confidence_threshold=0.7
)

engine = AutoCompleteEngine(config)

# Get suggestions with spelling corrections
suggestions = await engine.get_suggestions("pythn", user_id="user123")
# Will suggest "python" as a spelling correction

3. Synonym Suggestions​

# Add synonym mappings
synonyms = {
    "python": ["py", "python3", "python programming"],
    "tutorial": ["guide", "how-to", "learn"],
    "beginner": ["starter", "intro", "basics"]
}

await engine.add_synonyms(synonyms)

# Get suggestions with synonyms
suggestions = await engine.get_suggestions("py", user_id="user123")
# Will suggest "python" and related terms

4. Contextual Suggestions​

# Get suggestions with context
context = {
    "category": "programming",
    "language": "python",
    "difficulty": "beginner"
}

suggestions = await engine.get_suggestions(
    "tutorial", 
    user_id="user123",
    context=context
)

Performance Optimization​

1. Caching​

# Enable caching for better performance
config = AutoCompleteConfig(
    cache_enabled=True,
    cache_ttl_seconds=300,  # 5 minutes
    cache_max_size=1000     # Maximum cached entries
)

engine = AutoCompleteEngine(config)

2. Debouncing​

# Configure debouncing to reduce API calls
config = AutoCompleteConfig(
    debounce_delay_ms=200,  # Wait 200ms after user stops typing
    max_concurrent_requests=5
)

engine = AutoCompleteEngine(config)

3. Batch Processing​

# Process multiple queries in batch
queries = ["python", "java", "javascript"]
batch_suggestions = await engine.get_batch_suggestions(queries, user_id="user123")

for query, suggestions in batch_suggestions.items():
    print(f"Query: {query}")
    print(f"Suggestions: {[s.text for s in suggestions]}")

Integration Examples​

1. Real-time Search Interface​

import asyncio
from typing import List

class SearchInterface:
    def __init__(self):
        self.autocomplete_engine = AutoCompleteEngine(AutoCompleteConfig())
        self.pending_requests = {}
    
    async def on_query_change(self, query: str, user_id: str):
        """Handle query changes with debouncing."""
        # Cancel previous request
        if query in self.pending_requests:
            self.pending_requests[query].cancel()
        
        # Create new request
        task = asyncio.create_task(
            self._get_suggestions_after_delay(query, user_id)
        )
        self.pending_requests[query] = task
        
        try:
            suggestions = await task
            return suggestions
        except asyncio.CancelledError:
            return []
        finally:
            self.pending_requests.pop(query, None)
    
    async def _get_suggestions_after_delay(self, query: str, user_id: str):
        """Get suggestions after delay."""
        await asyncio.sleep(0.2)  # 200ms delay
        return await self.autocomplete_engine.get_suggestions(query, user_id)

2. Mobile App Integration​

class MobileSearchInterface:
    def __init__(self):
        self.autocomplete_engine = AutoCompleteEngine(AutoCompleteConfig())
        self.cache = {}
    
    async def get_suggestions(self, query: str, user_id: str):
        """Get suggestions with mobile-specific optimizations."""
        # Check cache first
        cache_key = f"{user_id}:{query}"
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        # Get suggestions
        suggestions = await self.autocomplete_engine.get_suggestions(query, user_id)
        
        # Cache for mobile performance
        self.cache[cache_key] = suggestions
        return suggestions

3. Voice Search Integration​

class VoiceSearchInterface:
    def __init__(self):
        self.autocomplete_engine = AutoCompleteEngine(AutoCompleteConfig())
    
    async def process_voice_input(self, transcribed_text: str, user_id: str):
        """Process voice input with auto-complete."""
        # Get suggestions for transcribed text
        suggestions = await self.autocomplete_engine.get_suggestions(
            transcribed_text, user_id
        )
        
        # If confidence is low, suggest corrections
        if suggestions and suggestions[0].confidence < 0.8:
            return {
                "original": transcribed_text,
                "suggestions": suggestions,
                "needs_confirmation": True
            }
        
        return {
            "original": transcribed_text,
            "suggestions": suggestions,
            "needs_confirmation": False
        }

Analytics and Monitoring​

1. Track Suggestion Performance​

# Track suggestion metrics
await engine.track_suggestion_metrics(
    query="python",
    suggestions_shown=5,
    suggestions_clicked=2,
    user_id="user123"
)

2. Monitor Performance​

# Get performance metrics
metrics = await engine.get_performance_metrics()

print("Auto-complete performance:")
print(f"Average response time: {metrics['avg_response_time_ms']}ms")
print(f"Cache hit rate: {metrics['cache_hit_rate']:.2%}")
print(f"Total suggestions: {metrics['total_suggestions']}")

3. A/B Testing​

# Test different configurations
config_a = AutoCompleteConfig(max_suggestions=5)
config_b = AutoCompleteConfig(max_suggestions=10)

# Run A/B test
test_results = await engine.run_ab_test(
    config_a, config_b, 
    test_duration_days=7
)

print("A/B test results:")
print(f"Config A click rate: {test_results['config_a']['click_rate']:.2%}")
print(f"Config B click rate: {test_results['config_b']['click_rate']:.2%}")

Troubleshooting​

Common Issues​

  1. Slow Response Times

    • Enable caching
    • Reduce max_suggestions
    • Increase debounce_delay_ms

  2. Irrelevant Suggestions

    • Adjust scoring weights
    • Add more domain terms
    • Improve query history quality

  3. Memory Usage

    • Reduce cache_max_size
    • Decrease history_retention_days
    • Use batch processing

Debug Mode​

# Enable debug mode for troubleshooting
config = AutoCompleteConfig(debug=True)
engine = AutoCompleteEngine(config)

# Get detailed debug information
suggestions = await engine.get_suggestions("python", user_id="user123")
print(f"Debug info: {suggestions[0].debug_info}")

Best Practices​

  1. Start Simple: Begin with basic configuration and add features gradually
  2. Monitor Performance: Track response times and user engagement
  3. Test Regularly: Use A/B testing to optimize suggestions
  4. Update Domain Terms: Keep domain vocabulary current
  5. Handle Errors: Implement proper error handling and fallbacks
  6. Cache Strategically: Use caching to improve performance
  7. Personalize Gradually: Start with basic personalization and enhance over time

Next Steps​