From 6315d514719d95f473095cb6e568b8b76130b4cf Mon Sep 17 00:00:00 2001 From: Filatenkov Artur <58165623+FArthur-cmd@users.noreply.github.com> Date: Wed, 20 Jul 2022 15:36:07 +0300 Subject: [PATCH] Update annindexes.md --- docs/en/engines/table-engines/mergetree-family/annindexes.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/engines/table-engines/mergetree-family/annindexes.md b/docs/en/engines/table-engines/mergetree-family/annindexes.md index 624d27f41b7..c651ee16c4f 100644 --- a/docs/en/engines/table-engines/mergetree-family/annindexes.md +++ b/docs/en/engines/table-engines/mergetree-family/annindexes.md @@ -1,6 +1,6 @@ # Approximate Nearest Neighbor Search Indexes [experimental] {#table_engines-ANNIndex} -The main task that indexes is to quickly find nearest neighbors for multidimensional data. An example of such a problem can be finding similar pictures (texts) for a given picture (text). That problem can be reduced to finding the nearest [embeddings](https://cloud.google.com/architecture/overview-extracting-and-serving-feature-embeddings-for-machine-learning). They can be created from data using [UDF](../../../sql-reference/functions/index.md#executable-user-defined-functions). +The main task that indexes achieve is to quickly find nearest neighbors for multidimensional data. An example of such a problem can be finding similar pictures (texts) for a given picture (text). That problem can be reduced to finding the nearest [embeddings](https://cloud.google.com/architecture/overview-extracting-and-serving-feature-embeddings-for-machine-learning). They can be created from data using [UDF](../../../sql-reference/functions/index.md#executable-user-defined-functions). The next query finds the closest neighbors in N-dimensional space using the L2 (Euclidean) distance: ``` sql @@ -117,4 +117,4 @@ FROM table_name [WHERE ...] ORDER BY L2Distance(Column, Point) LIMIT N SETTING ann_index_select_query_params=`k_search=100` -``` \ No newline at end of file +```