Vector Similarity Search

Overview

Vector similarity search finds vectors closest to a query vector in high-dimensional space, enabling semantic understanding in AI applications.

Process

1. Query: Input text/image/audio
2. Embed: Convert to vector
3. Search: Find nearest vectors
4. Rank: Order by similarity
5. Return: Top-k results

Distance Metrics

• Cosine Similarity: Direction-based
• Euclidean (L2): Straight-line distance
• Dot Product: Inner product
• Manhattan (L1): Grid-based distance

Search Methods

Exact Search

• Checks all vectors
• Perfect accuracy
• O(N) complexity
• Only for small datasets

Approximate (ANN)

• Uses index structures
• 90-99% recall
• Sub-linear time
• Scales to billions

Applications

• Semantic Search: Find similar documents
• Recommendations: Similar items/users
• Image Search: Visual similarity
• RAG: Context retrieval
• Anomaly Detection: Outlier finding
• Deduplication: Finding duplicates

Performance Factors

• Index type (HNSW, IVF, etc.)
• Vector dimensions
• Dataset size
• Similarity metric
• Hardware (CPU/GPU)

Optimization

• Quantization for compression
• Efficient indexes
• Caching strategies
• Batch processing
• GPU acceleration

Pricing

Core database operation, costs included in vector DB usage.