How does vector search index data?

Vector search indexes data by organizing high-dimensional vectors into structures that enable efficient similarity comparisons. The core idea is to map vectors into an index that groups similar items together, reducing the need for exhaustive comparisons. Most methods use clustering, tree-based hierarchies, or graph-based navigation to achieve this[1][4][8]. For example, in IVFFlat (Inverted File with Flat Compression), vectors are first clustered into groups using algorithms like k-means. During a search, the system identifies the nearest cluster centroids to the query vector and only compares vectors within those clusters, significantly reducing computation[8]. Similarly, HNSW (Hierarchical Navigable Small World) builds a multi-layered graph where each layer allows “jumps” to neighboring nodes, enabling logarithmic-time search complexity[4].

Specific techniques vary based on trade-offs between speed and accuracy. Product Quantization (PQ) splits vectors into subvectors and compresses them into compact codes, allowing approximate distance calculations with lower memory usage[4][8]. Platforms like Milvus combine these methods: users can choose between exact search (e.g., IndexFlatL2, which computes all pairwise distances) or approximate methods like IndexIVFFlat for faster results[5][8]. For instance, Alibaba’s Proxima optimizes both recall rate and latency by refining clustering and search path selection[2]. Practical implementations also involve steps like vector normalization, dimensionality reduction, and hardware acceleration (e.g., GPU support in Faiss)[8].

Deployment considerations include balancing index construction time, memory usage, and query performance. In Elasticsearch, vector fields are defined as dense_vector, and searches use scripted similarity metrics like cosine distance[10]. For dynamic data, systems like MongoDB’s RAG use incremental indexing to update vectors without full rebuilds[7]. Developers must configure parameters such as cluster counts (nlist in IVFFlat) or graph connections (efConstruction in HNSW) based on dataset size and accuracy requirements[4][8]. Open-source tools like Faiss and Milvus provide APIs to streamline these optimizations[5][8].