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new eeefb8b58b4 [FLINK-38436][doc] Add Chinese version of vector search
doc (#27232)
eeefb8b58b4 is described below
commit eeefb8b58b40113d47da139b1566b3958f98aef7
Author: Hao Li <[email protected]>
AuthorDate: Thu Nov 13 00:00:29 2025 -0800
[FLINK-38436][doc] Add Chinese version of vector search doc (#27232)
---
docs/content.zh/docs/dev/table/sourcesSinks.md | 15 +++---
.../docs/dev/table/sql/queries/vector-search.md | 63 +++++++++++-----------
2 files changed, 36 insertions(+), 42 deletions(-)
diff --git a/docs/content.zh/docs/dev/table/sourcesSinks.md
b/docs/content.zh/docs/dev/table/sourcesSinks.md
index b5cf452ff23..6b932014205 100644
--- a/docs/content.zh/docs/dev/table/sourcesSinks.md
+++ b/docs/content.zh/docs/dev/table/sourcesSinks.md
@@ -191,20 +191,17 @@ Flink 会对工厂类逐个进行检查，确保其“标识符”是全局唯
#### Vector Search Table Source
-A `VectorSearchTableSource` searches an external storage system using an input
vector and returns the most similar top-K rows during runtime. Users
-can determine which algorithm to use to calculate the similarity between the
input data and data stored in the external system. In general, most
-vector databases support using Euclidean distance or Cosine distance to
calculate similarity.
+在运行期间, `VectorSearchTableSource` 会使用一个输入向量来搜索外部存储系统，并返回最相似的 Top-K
行。用户可以决定使用何种算法来计算输入数据与外部系统中存储的数据之间的相似度。总的来说，大多数向量数据库支持使用欧几里得距离（Euclidean
distance）或余弦距离（Cosine distance）来计算相似度。
-Compared to `ScanTableSource`, the source does not have to read the entire
table and can lazily fetch individual
-values from a (possibly continuously changing) external table when necessary.
+与 `ScanTableSource` 相比，该源无需读取整个表，并可以在需要时从一个(可能在持续变化的)外部表中惰性获取(lazily fetch)单个值。
-Compared to `ScanTableSource`, a `VectorSearchTableSource` currently only
supports emitting insert-only changes.
+与 `ScanTableSource` 相比，`VectorSearchTableSource` 目前仅支持 insert-only 数据流。
-Compared to `LookupTableSource`, a `VectorSearchTableSource` does not use
equality to determine whether a row matches.
+与 `LookupTableSource` 相比，`VectorSearchTableSource` 不会使用等值（equality）来判断行是否匹配。
-Further abilities are not supported. See the documentation of
`org.apache.flink.table.connector.source.VectorSearchTableSource` for more
information.
+目前不支持其他更进一步的功能。更多信息请参阅
`org.apache.flink.table.connector.source.VectorSearchTableSource` 的文档。
-The runtime implementation of a `VectorSearchTableSource` is a `TableFunction`
or `AsyncTableFunction`. The function will be called with the given vector
values during runtime.
+`VectorSearchTableSource` 的运行时实现是一个 `TableFunction` 或
`AsyncTableFunction`。在运行时，算子会根据给定的向量值调用该函数。
<a name="source-abilities"></a>
diff --git a/docs/content.zh/docs/dev/table/sql/queries/vector-search.md
b/docs/content.zh/docs/dev/table/sql/queries/vector-search.md
index 4aec4d507eb..415004cd5dc 100644
--- a/docs/content.zh/docs/dev/table/sql/queries/vector-search.md
+++ b/docs/content.zh/docs/dev/table/sql/queries/vector-search.md
@@ -1,5 +1,5 @@
---
-title: "Vector Search"
+title: "向量搜素"
weight: 7
type: docs
---
@@ -22,22 +22,20 @@ specific language governing permissions and limitations
under the License.
-->
-# Vector Search
+# 向量搜索
{{< label Batch >}} {{< label Streaming >}}
-Flink SQL provides the `VECTOR_SEARCH` table-valued function (TVF) to perform
a vector search in SQL queries. This function allows you to search similar rows
according to the high-dimension vectors.
+Flink SQL 提供了 `VECTOR_SEARCH` 表值函数 (TVF) 来在 SQL 查询中执行向量搜索。该函数允许您根据高维向量搜索相似的行。
-## VECTOR_SEARCH Function
+## VECTOR_SEARCH 函数
-The `VECTOR_SEARCH` uses a processing-time attribute to correlate rows to the
latest version of data in an external table. It's very similar to a lookup join
in Flink SQL, however, the difference is
-`VECTOR_SEARCH` uses the input data vector to compare the similarity with data
in the external table and return the top-k most similar rows.
+`VECTOR_SEARCH` 使用处理时间属性 (processing-time attribute) 将行与外部表中的最新版本数据关联起来。它与
Flink SQL 中的 lookup-join 非常相似，但区别在于 `VECTOR_SEARCH` 使用输入数据向量与外部表中的数据比较相似度，并返回
top-k 个最相似的行。
-### Syntax
+### 语法
```sql
-SELECT *
-FROM input_table, LATERAL TABLE(VECTOR_SEARCH(
+SELECT * FROM input_table, LATERAL TABLE(VECTOR_SEARCH(
TABLE vector_table,
input_table.vector_column,
DESCRIPTOR(index_column),
@@ -46,25 +44,25 @@ FROM input_table, LATERAL TABLE(VECTOR_SEARCH(
))
```
-### Parameters
+### 参数
-- `input_table`: The input table containing the data to be processed
-- `vector_table`: The name of external table that allows searching via vector
-- `vector_column`: The name of the column in the input table, its type should
be FLOAT ARRAY or DOUBLE ARRAY
-- `index_column`: A descriptor specifying which column from the vector table
should be used to compare the similarity with the input data
-- `top_k`: The number of top-k most similar rows to return
-- `config`: (Optional) A map of configuration options for the vector search
+* `input_table`: 包含待处理数据的输入表。
+* `vector_table`: 允许通过向量进行搜索的外部表的名称。
+* `vector_column`: 输入表中的列名，其类型应为 FLOAT ARRAY 或 DOUBLE ARRAY。
+* `index_column`: 一个描述符 (descriptor)，指定应使用向量表 (vector_table) 中的哪一列与输入数据进行相似度比较。
+* `top_k`: 要返回的 top-k 个最相似行的数量。
+* `config`: (可选) 用于向量搜索的配置选项。
-### Configuration Options
+### 配置选项
-The following configuration options can be specified in the config map:
+可以在 config map 中指定以下配置选项：
{{< generated/vector_search_runtime_config_configuration >}}
-### Example
+### 示例
```sql
--- Basic usage
+-- 基本用法
SELECT * FROM
input_table, LATERAL TABLE(VECTOR_SEARCH(
TABLE vector_table,
@@ -73,7 +71,7 @@ input_table, LATERAL TABLE(VECTOR_SEARCH(
10
));
--- With configuration options
+-- 带配置选项
SELECT * FROM
input_table, LATERAL TABLE(VECTOR_SEARCH(
TABLE vector_table,
@@ -83,7 +81,7 @@ input_table, LATERAL TABLE(VECTOR_SEARCH(
MAP['async', 'true', 'timeout', '100s']
));
--- Using named parameters
+-- 使用命名参数
SELECT * FROM
input_table, LATERAL TABLE(VECTOR_SEARCH(
SEARCH_TABLE => TABLE vector_table,
@@ -93,24 +91,23 @@ input_table, LATERAL TABLE(VECTOR_SEARCH(
CONFIG => MAP['async', 'true', 'timeout', '100s']
));
--- Searching with contant value
-SELECT *
-FROM TABLE(VECTOR_SEARCH(
+-- 使用常量值搜索
+SELECT * FROM TABLE(VECTOR_SEARCH(
TABLE vector_table,
ARRAY[10, 20],
DESCRIPTOR(index_column),
- 10,
+ 10
));
```
-### Output
+### 输出
-The output table contains all columns from the input table, the vector search
table columns and a column named `score` to indicate the similarity between the
input row and matched row.
+输出表包含输入表的所有列、向量搜索表 (vector search table) 的列，以及一个名为 `score`
的列，用于表示输入行与匹配行之间的相似度。
-### Notes
+### 注意事项
-1. The implementation of the vector table must implement interface
`org.apache.flink.table.connector.source.VectorSearchTableSource`. Please refer
to [Vector Search Table Source]({{< ref "/docs/dev/table/sourcesSinks"
>}}#vector-search-table-source) for details.
-2. `VECTOR_SEARCH` only supports to consume append-only tables.
-3. `VECTOR_SEARCH` does not require the `LATERAL` keyword when the function
call has no correlation with other tables. For example, if the search column is
a constant or literal value, `LATERAL` can be omitted.
+1. 向量表 (vector table) 的实现必须实现
`org.apache.flink.table.connector.source.VectorSearchTableSource` 接口。详情请参阅
[Vector Search Table Source]({{< ref "/docs/dev/table/sourcesSinks"
>}}#vector-search-table-source)。
+2. `VECTOR_SEARCH` 仅支持读取仅 append-only 表。
+3. 当函数调用与其它表没有关联时，`VECTOR_SEARCH` 不需要 `LATERAL` 关键字。例如，如果搜索列是一个常量或字面值
(literal value)，`LATERAL` 可以被省略。
-{{< top >}}
+{{< top >}}
