Vector Storage Concepts and Indexing Strategies in Generative AI
Vector Storage Concepts and Indexing Strategies
Storing vectors efficiently is critical for performance. Naive storage leads to slow search times.
1) Vector Indexing
Indexes reduce search complexity from linear scan to near real-time similarity search.
2) Common Indexing Algorithms
- HNSW (Hierarchical Navigable Small World)
- IVF (Inverted File Index)
- Flat Index
3) Trade-Offs
- Accuracy vs speed
- Memory usage vs performance
- Index build time
4) Enterprise Considerations
Index tuning is necessary when scaling beyond millions of vectors.
5) Summary
Efficient indexing determines search speed and scalability.
