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doc: optimize English text for better translation in vector search overview (#22923) #22948

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doc: optimize English text for better translation in vector search ov…
yahonda May 25, 2026
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Update ai/concepts/vector-search-overview.md
yahonda May 25, 2026
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2 changes: 1 addition & 1 deletion ai/concepts/vector-search-overview.md
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Expand Up @@ -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 (vector embeddings) together in one database. You can store your data in either of the following ways:
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@gemini-code-assist gemini-code-assist Bot May 27, 2026

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To adhere to the style guide principle of avoiding unnecessary words and repetition, we can simplify "their corresponding vector representations (vector embeddings)" to just "their vector embeddings". Since "vector embedding" is already defined earlier in the document, this makes the sentence more concise and direct.

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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:
As a relational database with integrated vector search capabilities, TiDB enables you to store data and their vector embeddings together in one database. You can store your data in either of the following ways:
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  1. Avoid unnecessary words and repetition. (link)


- 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.
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