From e3b8be5a7d3255d80533ce288827f432e0e0d486 Mon Sep 17 00:00:00 2001 From: Yasuo Honda Date: Mon, 25 May 2026 11:01:08 +0900 Subject: [PATCH 1/2] doc: optimize English text for better translation in vector search overview --- ai/concepts/vector-search-overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai/concepts/vector-search-overview.md b/ai/concepts/vector-search-overview.md index 4447d549bd662..3ead9271c10a5 100644 --- a/ai/concepts/vector-search-overview.md +++ b/ai/concepts/vector-search-overview.md @@ -47,7 +47,7 @@ TiDB vector search identifies the top-k nearest neighbor (KNN) vectors by using ![The Schematic TiDB Vector Search](/media/vector-search/embedding-search.png) -As a relational database with integrated vector search capabilities, TiDB enables you to store data and their corresponding vector representations (that is, vector embeddings) together in one database. You can choose any of the following ways for storage: +As a relational database with integrated vector search capabilities, TiDB enables you to store data and their corresponding vector representations (that is, vector embeddings) together in one database. You can store your data in either of the following ways: - Store data and their corresponding vector representations in different columns of the same table. - Store data and their corresponding vector representation in different tables. In this way, you need to use `JOIN` queries to combine the tables when retrieving data. From 33bb46edeec56004e6d4000b46020a877fe8846b Mon Sep 17 00:00:00 2001 From: Yasuo Honda Date: Mon, 25 May 2026 11:04:11 +0900 Subject: [PATCH 2/2] Update ai/concepts/vector-search-overview.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- ai/concepts/vector-search-overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai/concepts/vector-search-overview.md b/ai/concepts/vector-search-overview.md index 3ead9271c10a5..242c625b3ef49 100644 --- a/ai/concepts/vector-search-overview.md +++ b/ai/concepts/vector-search-overview.md @@ -47,7 +47,7 @@ TiDB vector search identifies the top-k nearest neighbor (KNN) vectors by using ![The Schematic TiDB Vector Search](/media/vector-search/embedding-search.png) -As a relational database with integrated vector search capabilities, TiDB enables you to store data and their corresponding vector representations (that is, vector embeddings) together in one database. You can store your data in either of the following ways: +As a relational database with integrated vector search capabilities, TiDB enables you to store data and their corresponding vector representations (vector embeddings) together in one database. You can store your data in either of the following ways: - Store data and their corresponding vector representations in different columns of the same table. - Store data and their corresponding vector representation in different tables. In this way, you need to use `JOIN` queries to combine the tables when retrieving data.