From 00148dfa1fdd866cefe449b199c47d43f9bb5639 Mon Sep 17 00:00:00 2001 From: Andrew Kane Date: Mon, 10 Apr 2023 21:12:25 -0700 Subject: [PATCH] Improved indexing docs [skip ci] --- README.md | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index ea09d3b..f3d24c5 100644 --- a/README.md +++ b/README.md @@ -156,14 +156,8 @@ You can add an index to use approximate nearest neighbor search, which trades so Three keys to achieving good recall are: 1. Create the index *after* the table has some data -2. Choose an appropriate number of lists (lower is better for recall, higher is better for speed) - -A good place to start is: - -- `rows / 1000` for up to 1M rows -- `sqrt(rows)` for over 1M rows - -3. Choose an appropriate [number of probes](#query-options) when querying +2. Choose an appropriate number of lists (a good place to start is `rows / 1000` for up to 1M rows and `sqrt(rows)` for over 1M rows) +3. When querying, specify an appropriate number of [probes](#query-options) (higher is better for recall, lower is better for speed) Add an index for each distance function you want to use.