Similarity Search and Embedding Storage

Similarity Search and Embedding Storage solutions enable efficient storage, indexing, and retrieval of vector embeddings to find similar items in large datasets.

Key Capabilities Used

• Pattern Discovery
• Classification

Overview

Modern similarity search solutions provide:

• Vector embedding generation and storage
• Efficient indexing algorithms (e.g., HNSW, IVF)
• Approximate nearest neighbor (ANN) search
• Scalable vector database infrastructure
• Low-latency querying

Common Use Cases

• Semantic search engines
• Recommendation systems
• Content deduplication
• Image and audio similarity matching
• Document retrieval
• Product matching

Implementation Tools

For implementing similarity search, consider these tools:

• Vector Databases
• Vector Similarity Search
• Search Engines

Best Practices

• Choose appropriate embedding dimensions
• Select indexing algorithms based on dataset size
• Balance accuracy vs. speed tradeoffs
• Implement proper data preprocessing
• Consider scaling requirements early