What is Indexing?
Indexing is the process of organizing data into specialized data structures that enable efficient search and retrieval operations. In the context of vector databases and AI systems, indexing involves creating structures that allow fast similarity search across large collections of embeddings, balancing the trade-offs between search speed, accuracy, and memory consumption. Proper indexing is crucial for making retrieval systems performant at scale.
Different indexing strategies serve different needs. Flat indexes store all vectors and compute exact distances during search, providing perfect accuracy but slow performance on large datasets. Approximate nearest neighbor (ANN) indexes like IVF, HNSW, or LSH partition or organize the vector space to enable much faster search by exploring only a subset of vectors, trading some accuracy for dramatic speed improvements. The choice of index type depends on dataset size, accuracy requirements, and computational resources.
In RAG systems and agent memory architectures, the indexing strategy significantly impacts user experience and system capabilities. Poor indexing can make retrieval too slow for interactive applications or limit the size of knowledge bases that can be supported. Modern vector databases abstract much of the indexing complexity, automatically selecting and tuning indexes based on data characteristics and query patterns, but understanding indexing principles remains important for optimizing performance and diagnosing retrieval issues.