Skip to content

Vector Index Optimization

Jump to bottom
Temp edited this page Sep 23, 2025 · 1 revision

Vector Index Optimization

Last Updated: September 23, 2025 1:48 PM EST

The SQLite MCP Server provides advanced vector index optimization capabilities for high-performance approximate nearest neighbor (ANN) search, enabling efficient similarity search over large embedding datasets.

🔧 Available Vector Optimization Tools

Tool Description
create_ann_index Create approximate nearest neighbor indexes for fast similarity search
optimize_vector_storage Optimize embedding storage with compression and clustering
vector_clustering Perform k-means clustering on embedding vectors
benchmark_vector_search Benchmark and compare vector search performance

⚡ Approximate Nearest Neighbor (ANN) Search

Create ANN Indexes

// Create ANN index for fast similarity search
create_ann_index({
  "table_name": "document_embeddings",
  "vector_column": "embedding",
  "index_type": "hnsw",  // hnsw, lsh, quantized
  "dimensions": 1536,
  "max_connections": 16,
  "ef_construction": 200
})

// Create LSH index for high-dimensional vectors
create_ann_index({
  "table_name": "image_embeddings", 
  "vector_column": "embedding",
  "index_type": "lsh",
  "dimensions": 2048,
  "hash_tables": 10,
  "hash_bits": 12
})

ANN Search Performance

// Fast approximate search with ANN index
semantic_search({
  "table_name": "document_embeddings",
  "query_embedding": query_vector,
  "use_ann_index": true,
  "ef_search": 100,  // HNSW search parameter
  "limit": 50,
  "approximate": true
})

// Compare exact vs approximate results
benchmark_vector_search({
  "table_name": "document_embeddings",
  "query_embedding": query_vector,
  "test_exact": true,
  "test_approximate": true,
  "measure_recall": true
})

🗜️ Vector Storage Optimization

Compression Techniques

// Optimize storage with vector quantization
optimize_vector_storage({
  "table_name": "embeddings",
  "vector_column": "embedding",
  "compression": "quantized",
  "quantization_bits": 8,  // 8-bit quantization
  "preserve_original": false
})

// Product quantization for high compression
optimize_vector_storage({
  "table_name": "large_embeddings",
  "vector_column": "embedding", 
  "compression": "product_quantized",
  "subvectors": 8,
  "codebook_size": 256
})

Storage Format Optimization

// Binary storage optimization
optimize_vector_storage({
  "table_name": "embeddings",
  "vector_column": "embedding",
  "storage_format": "binary",
  "normalize_vectors": true,
  "remove_duplicates": true,
  "cluster_similar": true
})

📊 Vector Clustering

K-Means Clustering

// Cluster embeddings for better organization
vector_clustering({
  "table_name": "document_embeddings",
  "vector_column": "embedding",
  "num_clusters": 50,
  "algorithm": "kmeans",
  "max_iterations": 100,
  "store_centroids": true
})

// Hierarchical clustering for nested organization
vector_clustering({
  "table_name": "product_embeddings",
  "vector_column": "embedding",
  "algorithm": "hierarchical",
  "linkage": "ward",
  "distance_threshold": 0.5,
  "create_hierarchy_table": true
})

Cluster-Based Search

// Search within specific clusters
semantic_search({
  "table_name": "document_embeddings",
  "query_embedding": query_vector,
  "cluster_filter": [1, 3, 7],  // Search only these clusters
  "limit": 20,
  "expand_search": true  // Expand to nearby clusters if needed
})

📈 Performance Benchmarking

Search Performance Analysis

// Comprehensive performance benchmark
benchmark_vector_search({
  "table_name": "embeddings",
  "query_embeddings": test_queries,
  "test_configurations": [
    {"method": "exact", "name": "Exact Search"},
    {"method": "hnsw", "ef_search": 50, "name": "HNSW-50"},
    {"method": "hnsw", "ef_search": 100, "name": "HNSW-100"},
    {"method": "lsh", "hash_tables": 10, "name": "LSH-10"}
  ],
  "metrics": ["latency", "recall", "throughput"]
})

Index Optimization Tuning

// Tune HNSW parameters for optimal performance
create_ann_index({
  "table_name": "embeddings",
  "vector_column": "embedding",
  "index_type": "hnsw",
  "dimensions": 1536,
  "optimization_target": "recall",  // recall, speed, balanced
  "auto_tune": true,
  "validation_queries": sample_queries
})

💡 Real-World Optimization Scenarios

Large-Scale Document Search

// 1. Create optimized storage for millions of documents
optimize_vector_storage({
  "table_name": "document_embeddings",
  "vector_column": "embedding",
  "compression": "quantized",
  "quantization_bits": 8,
  "cluster_similar": true
});

// 2. Build HNSW index for fast search
create_ann_index({
  "table_name": "document_embeddings",
  "vector_column": "embedding_compressed",
  "index_type": "hnsw",
  "max_connections": 32,
  "ef_construction": 400
});

// 3. Benchmark performance
benchmark_vector_search({
  "table_name": "document_embeddings",
  "sample_size": 1000,
  "target_recall": 0.95,
  "max_latency_ms": 50
});

E-commerce Product Recommendations

// 1. Cluster products by category and features
vector_clustering({
  "table_name": "product_embeddings",
  "vector_column": "embedding",
  "num_clusters": 100,
  "algorithm": "kmeans",
  "metadata_columns": ["category", "price_range"]
});

// 2. Create category-specific indexes
create_ann_index({
  "table_name": "product_embeddings",
  "vector_column": "embedding",
  "index_type": "hnsw",
  "partition_by": "cluster_id",
  "per_partition_optimization": true
});

// 3. Optimized recommendation search
semantic_search({
  "table_name": "product_embeddings",
  "query_embedding": user_preference_vector,
  "use_ann_index": true,
  "cluster_filter": relevant_clusters,
  "limit": 20,
  "diversify_results": true
});

Real-time Similarity Search

// 1. Optimize for low-latency search
optimize_vector_storage({
  "table_name": "realtime_embeddings",
  "vector_column": "embedding",
  "storage_format": "binary",
  "memory_mapped": true,
  "cache_frequently_accessed": true
});

// 2. Create speed-optimized index
create_ann_index({
  "table_name": "realtime_embeddings",
  "vector_column": "embedding",
  "index_type": "lsh",
  "optimization_target": "speed",
  "max_latency_ms": 10
});

// 3. Monitor performance
benchmark_vector_search({
  "table_name": "realtime_embeddings",
  "continuous_monitoring": true,
  "alert_on_degradation": true,
  "target_p95_latency": 15
});

🎯 Best Practices

1. Choose the Right Index Type

// HNSW for high recall requirements
create_ann_index({
  "index_type": "hnsw",
  "optimization_target": "recall"  // Best for accuracy
});

// LSH for speed-critical applications  
create_ann_index({
  "index_type": "lsh",
  "optimization_target": "speed"   // Best for low latency
});

// Quantized for memory-constrained environments
create_ann_index({
  "index_type": "quantized",
  "optimization_target": "memory"  // Best for large datasets
});

2. Optimize Storage Based on Use Case

// Read-heavy workloads: Prioritize compression
optimize_vector_storage({
  "compression": "quantized",
  "quantization_bits": 8,
  "optimize_for": "read_performance"
});

// Write-heavy workloads: Balance compression and write speed
optimize_vector_storage({
  "compression": "light",
  "optimize_for": "write_performance",
  "batch_updates": true
});

3. Monitor and Tune Performance

// Regular performance monitoring
benchmark_vector_search({
  "schedule": "daily",
  "performance_regression_alert": true,
  "auto_reoptimize": true
});

// A/B test different configurations
benchmark_vector_search({
  "compare_configurations": [
    {"name": "current", "config": current_config},
    {"name": "optimized", "config": new_config}
  ],
  "statistical_significance": true
});

4. Scale Appropriately

// Partition large datasets
create_ann_index({
  "table_name": "large_embeddings",
  "partition_strategy": "hash",
  "partitions": 16,
  "parallel_indexing": true
});

// Use clustering for better locality
vector_clustering({
  "num_clusters": Math.ceil(total_vectors / 10000),
  "rebalance_periodically": true
});

⚠️ Performance Considerations

Memory Usage

  • HNSW indexes require significant memory but provide best recall
  • LSH indexes are memory-efficient but may have lower recall
  • Quantized storage reduces memory by 4-8x with minimal accuracy loss

Search Latency

  • Exact search: O(n) - slow but perfect recall
  • HNSW search: O(log n) - fast with high recall
  • LSH search: O(1) - fastest but lower recall
  • Clustered search: O(k + log n/k) - balanced approach

Index Building Time

  • HNSW: Slower to build, faster to search
  • LSH: Fast to build, variable search performance
  • Clustering: Moderate build time, enables fast filtered search

📚 Related Pages

⚡ Optimization Tip: Vector index optimization is about balancing speed, accuracy, and memory usage. Start with exact search to establish baseline performance, then experiment with ANN methods based on your specific requirements.

Clone this wiki locally