Vector Indexes — Semantic Search, ANN & Exact kNN

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• Vector Indexes — Semantic Search, ANN & Exact kNN

  6 studies
  1. 1.Vector Indexes — Semantic Search, ANN, and When Exact kNN WinsSemantic search turns text, images, or events into vectors and answers what is closest to the query vector. Exact kNN scans every vector: perfect recall, zero build cost, trivial filters, and cost linear in N. ANN indexes give up a little recall for sub-linear queries. This hub is the decision map across HNSW, IVF, PQ, LSH, ScaNN, and DiskANN, including when brute force is the right answer.
  2. 2.HNSW — Layers, Greedy Search, M and efHNSW is a stack of proximity graphs. Layer 0 holds every vector, and each higher layer keeps an exponentially smaller sample. A query greedily walks the sparse top layers, then runs a beam of width efSearch on layer 0. M, efConstruction, and efSearch are the three knobs that set memory, graph quality, and the recall versus latency dial.
  3. 3.IVF, PQ, and ScaNN — Partition, Quantize, Re-rankIVF partitions space with k-means and scans only the nprobe cells nearest the query. PQ compresses each vector into a few bytes of codebook ids and scores them with table lookups. Together, with an exact re-rank, that is the classic recipe from hundreds of millions to billions of vectors. ScaNN spends its quantization error where inner-product rank actually moves.
  4. 4.Filtered and Hybrid Search — Metadata, BM25, and Broken RecallReal queries are nearest vectors for this tenant, in stock, that this user may see, and often matching an exact error code. Filters fight ANN structures: post-filtering returns too few hits, a matching-only walk strands, and a filter-aware walk gets expensive when the predicate is selective. Hybrid search adds a BM25 leg and fuses ranks so one score scale cannot silently win.
  5. 5.Recall, Latency, and Memory — Tuning and EvaluationEvery ANN index trades recall, latency, and memory, with build time as the fourth wheel. The first artifact is an evaluation harness: frozen real queries, exact ground truth, recall at k plus tail recall, p50 and p99, and bytes per replica. Sweep efSearch, nprobe, quantization, and re-rank depth, then pick the cheapest point that meets the target.
  6. 6.Sharding, Updates, and Stale Embeddings — Production Vector SearchA vector index that works on a laptop fails in production in predictable ways. It outgrows one node, scatter-gather inflates p99, tombstones erode recall, and embeddings go stale when the text or the model changes. Mixing two embedding models fails silently: no error, just garbage neighbors. Hash-shard, return full k per shard, and cut over models with a second index and an alias.