From ce9506db7124e1dcedac12eeda8ced129e80ee4f Mon Sep 17 00:00:00 2001 From: Keming Date: Tue, 14 Jan 2025 00:53:13 +0800 Subject: [PATCH] docs(hnsw): fix markdown list style (#2015) --- docs/src/concepts/index_hnsw.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/concepts/index_hnsw.md b/docs/src/concepts/index_hnsw.md index 0e9930b9..93e1f1b7 100644 --- a/docs/src/concepts/index_hnsw.md +++ b/docs/src/concepts/index_hnsw.md @@ -7,7 +7,7 @@ Approximate Nearest Neighbor (ANN) search is a method for finding data points ne There are three main types of ANN search algorithms: * **Tree-based search algorithms**: Use a tree structure to organize and store data points. -* * **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice. +* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice. * **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex. HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below.