What is Vector Database
Vector Database - Embedding storage and retrieval
A specialized database system designed to store and efficiently retrieve high-dimensional vector embeddings for multimodal data.
How It Works
Vector databases store high-dimensional vectors (embeddings) and provide efficient similarity search capabilities. They use specialized indexing structures like HNSW or IVF to enable fast approximate nearest neighbor search.
Technical Details
Implements approximate nearest neighbor (ANN) algorithms, dimensionality reduction techniques, and clustering methods to optimize vector storage and retrieval. Often includes support for metadata filtering and hybrid search capabilities.
Best Practices
- Choose appropriate index types for your use case
- Optimize vector dimensions and precision
- Implement efficient update strategies
- Use appropriate similarity metrics
- Consider scalability requirements
Common Pitfalls
- Poor index configuration
- Ignoring the curse of dimensionality
- Inefficient update strategies
- Inappropriate similarity metrics
- Inadequate scalability planning
Advanced Tips
- Use hybrid search capabilities
- Implement efficient update strategies
- Consider distributed architectures
- Optimize for specific use cases
- Monitor and tune performance