[flink] Support distributed vector search

docs/docs/flink/procedures.md

@@ -1177,8 +1177,10 @@ All available procedures are listed below.
             vector_column => 'columnName',<br/>
             query_vector => 'v1,v2,...',<br/>
             top_k => topK,<br/>
-            projection => 'col1,col2',<br/>
-            options => 'key1=value1;key2=value2')<br/>
+            projection => 'col1,col2,__paimon_search_score',<br/>
+            options => 'key1=value1;key2=value2',<br/>
+            `where` => 'predicate',<br/>
+            partitions => 'pt1=v1,pt2=v2;pt1=v3,pt2=v4')<br/>
       </td>
       <td>
          To perform vector similarity search on a table with a global vector index. Returns JSON-serialized rows. Arguments:
@@ -1188,6 +1190,8 @@ All available procedures are listed below.
             <li>top_k(required): the number of nearest neighbors to return.</li>
             <li>projection(optional): comma-separated column names to include in the result. If omitted, all columns are returned.</li>
             <li>options(optional): additional dynamic options of the table.</li>
+            <li>where(optional): a SQL predicate applied before Top-K.</li>
+            <li>partitions(optional): semicolon-separated specs for a partitioned table.</li>
       </td>
       <td>
          CALL sys.vector_search(<br/>
@@ -1200,7 +1204,9 @@ All available procedures are listed below.
             vector_column => 'embedding',<br/>
             query_vector => '1.0,2.0,3.0',<br/>
             top_k => 5,<br/>
-            projection => 'id,name')
+            projection => 'id,name,__paimon_search_score',<br/>
+            options => 'vector-search.distribute.enabled=true;ivf.nprobe=32',<br/>
+            `where` => 'status = ''active''')
       </td>
    </tr>
    </tbody>

docs/docs/multimodal-table/global-index/vector.mdx

@@ -335,7 +335,9 @@ CALL sys.vector_search(
     vector_column => 'embedding',
     query_vector => '1.0,2.0,3.0',
     top_k => 5,
-    projection => 'id,name'
+    projection => 'id,name,__paimon_search_score',
+    options => 'ivf.nprobe=32',
+    `where` => 'id > 100'
 );
 ```
 

docs/docs/primary-key-table/global-index.mdx

@@ -347,8 +347,9 @@ CALL sys.vector_search(
     vector_column => 'embedding',
     query_vector => '0.1,0.2,0.3',
     top_k => 10,
-    projection => 'id,status',
-    options => 'ivf.nprobe=32'
+    projection => 'id,status,__paimon_search_score',
+    options => 'ivf.nprobe=32',
+    `where` => 'status = ''active'''
 );
 ```