Indexes in vector stores are built to enable efficient similarity searches by organizing high-dimensional vectors into structures optimized for fast querying. The process typically starts with converting raw data (like text or images) into vector embeddings using machine learning models. Once vectors are generated, the system selects an indexing algorithm based on trade-offs between search speed, accuracy, and memory usage. Common methods include Hierarchical Navigable Small World (HNSW) graphs, Inverted File Index with Product Quantization (IVF-PQ), or tree-based structures. For example, HNSW builds a multi-layered graph where each layer connects vectors to their nearest neighbors, allowing queries to “navigate” the graph quickly. IVF-PQ divides vectors into clusters and compresses them using quantization, reducing memory overhead while maintaining reasonable accuracy. Libraries like FAISS or systems like Milvus implement these methods, letting developers choose parameters (e.g., number of clusters in IVF) to balance performance for their use case.
Maintaining indexes involves handling updates, rebalancing structures, and optimizing resource usage. When new vectors are added, the index must be updated without disrupting existing queries. Some systems, like HNSW, allow incremental additions by inserting vectors into the graph layers, but this can lead to degraded performance over time. Others, like IVF, might require periodic re-clustering if new data shifts the distribution of vectors significantly. Deletions are more complex: since many indexes rely on precomputed relationships (like cluster assignments), removing vectors often requires marking them as inactive or rebuilding parts of the index. Maintenance tasks like re-indexing or tuning parameters (e.g., increasing the number of clusters) are often scheduled during low-traffic periods to minimize downtime. For instance, a vector store might run background jobs to rebalance an HNSW graph or retrain IVF clusters after a batch of updates, ensuring the index remains efficient.
Performance tuning and resource management are critical for long-term maintenance. Developers must monitor metrics like query latency, recall rates, and memory usage to decide when to optimize. For example, an HNSW index with too many layers might consume excessive memory, requiring adjustments to the graph’s connection density. Distributed systems like Milvus address scalability by splitting data across nodes, but this introduces challenges in synchronizing indexes and handling cross-node queries. Versioning is another consideration: some teams maintain multiple index versions to enable rollbacks if updates introduce errors. Finally, resource constraints often dictate choices—disk-based indexes save memory but slow down queries, while in-memory indexes prioritize speed at higher costs. By combining periodic maintenance, parameter tuning, and infrastructure scaling, developers ensure the vector store remains responsive and accurate as data evolves.