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2 changes: 1 addition & 1 deletion ai/integrations/vector-search-integrate-with-langchain.md
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Expand Up @@ -15,7 +15,7 @@ This tutorial demonstrates how to integrate [TiDB Vector Search](/ai/concepts/ve

> **Tip**
>
> You can view the complete [sample code](https://docs.langchain.com/oss/python/integrations/vectorstores/tidb_vector) in LangChain documentation.
> You can view the complete sample code in <place-holder-url>.

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TBD


## Prerequisites

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2 changes: 1 addition & 1 deletion ticdc/ticdc-debezium.md
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Expand Up @@ -781,7 +781,7 @@ The key fields of the preceding JSON data are explained as follows:

### Data type mapping

The data format mapping in the TiCDC Debezium message basically follows the [Debezium data type mapping rules](https://debezium.io/documentation/reference/2.4/connectors/mysql.html#mysql-data-types), which is generally consistent with the native message of the Debezium Connector for MySQL. However, for some data types, the following differences exist between TiCDC Debezium and Debezium Connector messages:
The data format mapping in the TiCDC Debezium message basically follows the [Debezium data type mapping rules](https://debezium.io/documentation/reference/stable/connectors/mysql.html#mysql-data-types), which is generally consistent with the native message of the Debezium Connector for MySQL. However, for some data types, the following differences exist between TiCDC Debezium and Debezium Connector messages:
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- Currently, TiDB does not support spatial data types, including GEOMETRY, LINESTRING, POLYGON, MULTIPOINT, MULTILINESTRING, MULTIPOLYGON, and GEOMETRYCOLLECTION.

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