midLLMOps
How does vector database indexing work in LLMOps systems?
Updated May 16, 2026
Short answer
Vector databases index embeddings using approximate nearest neighbor (ANN) algorithms for fast similarity search.
Deep explanation
Vector databases store embeddings and use ANN algorithms like HNSW or IVF to retrieve semantically similar vectors efficiently. Instead of scanning all data, they build graph or cluster-based indexes to reduce search complexity from O(n) to near-logarithmic time.
Real-world example
Enterprise search retrieving relevant documents from millions of internal files.
Common mistakes
- Using brute-force search on large embedding datasets.
Follow-up questions
- What is HNSW?
- Why is ANN used instead of exact search?