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
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:
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