Vector Similarity Search & Indexing Methods

The document discusses various indexing methods for efficient vector similarity search on large datasets. It introduces graph-based indexes that use approximate nearest neighbor algorithms based on navigable small-world graphs. Space partition indexes are also discussed, including inverted multi-indexes and optimizations for billion-scale approximate nearest neighbors. Product quantization encoding is covered as another indexing approach. The document concludes by proposing a layered framework that decomposes vector search into space partitioning, candidate filtering, and result validation layers to balance accuracy, speed and system requirements.