What are next-gen indexing methods for vector search?

Next-gen indexing methods for vector search focus on balancing speed, accuracy, and scalability for high-dimensional data. The most widely adopted approaches include graph-based indexes like Hierarchical Navigable Small World (HNSW), quantization techniques such as Product Quantization (PQ), and hybrid methods that combine multiple strategies. These methods address the limitations of traditional approaches like exact k-NN searches, which are computationally expensive for large datasets. Developers often prioritize trade-offs: graph-based methods excel in query speed, quantization reduces memory usage, and hybrid designs aim to optimize both.

One prominent method is HNSW, which constructs a layered graph where each layer represents a subset of data points. Higher layers contain fewer points, enabling fast traversal during searches. For example, a query starts at the top layer, navigates to the nearest neighbor, and refines the search in lower layers. This structure reduces the number of distance computations compared to flat indexes. Another key technique is Product Quantization (PQ), which compresses vectors into smaller codes by splitting them into subvectors and assigning each to a centroid from a pre-trained codebook. Libraries like FAISS use PQ with inverted file indexes (IVF-PQ) to group similar vectors into clusters, allowing efficient coarse-to-fine searches. This reduces memory footprint while maintaining acceptable accuracy, making it practical for billion-scale datasets.

Emerging methods include learned indexes that adapt to data distributions. For instance, DeepHash uses neural networks to map vectors to binary codes optimized for similarity search. DiskANN is another innovation designed for on-disk storage, combining graph-based traversal with compressed vectors to handle memory constraints. Hybrid approaches, such as HNSW combined with PQ, leverage the strengths of multiple techniques—using PQ for compression and HNSW for fast neighbor retrieval. Tools like Milvus and Elasticsearch’s vector search module integrate these methods, offering configurable pipelines. Developers can choose between them based on use cases: HNSW for low-latency applications, PQ for memory efficiency, and learned indexes for domain-specific optimization. These advancements make vector search more accessible for applications like recommendation systems and semantic retrieval.