Update examples.

Author: afoucret

docs/reference/query-languages/esql/_snippets/functions/description/text_embedding.md

@@ -2,11 +2,11 @@ text_embedding using a row source operator
 required_capability: text_embedding_function
 required_capability: dense_vector_field_type_released
 
-// tag::embedding-eval[]
+// tag::text-embedding-eval[]
 ROW input="Who is Victor Hugo?"
 | EVAL embedding = TEXT_EMBEDDING("Who is Victor Hugo?", "test_dense_inference")
 ;
-// end::embedding-eval[]
+// end::text-embedding-eval[]
 
 input:keyword       | embedding:dense_vector
 Who is Victor Hugo? | [56.0, 50.0, 48.0]
@@ -32,9 +32,11 @@ required_capability: dense_vector_field_type_released
 required_capability: knn_function_v5
 required_capability: semantic_text_field_caps
 
+// tag::text-embedding-knn[]
 FROM semantic_text METADATA _score
 | EVAL query_embedding = TEXT_EMBEDDING("be excellent to each other", "test_dense_inference")
 | WHERE KNN(semantic_text_dense_field, query_embedding)
+// end::text-embedding-knn[]
 | SORT _score DESC
 | LIMIT 10
 | KEEP semantic_text_field, query_embedding
@@ -52,8 +54,10 @@ required_capability: dense_vector_field_type_released
 required_capability: knn_function_v5
 required_capability: semantic_text_field_caps
 
+// tag::text-embedding-knn-inline[]
 FROM semantic_text METADATA _score
 | WHERE KNN(semantic_text_dense_field, TEXT_EMBEDDING("be excellent to each other", "test_dense_inference"))
+// end::text-text-embedding-knn-inline[]
 | SORT _score DESC
 | LIMIT 10
 | KEEP semantic_text_field

docs/reference/query-languages/esql/_snippets/functions/examples/text_embedding.md

@@ -20,7 +20,6 @@
 import org.elasticsearch.xpack.esql.expression.function.FunctionInfo;
 import org.elasticsearch.xpack.esql.expression.function.Param;
 
-import java.io.IOException;
 import java.util.List;
 import java.util.Objects;
 
@@ -40,14 +39,24 @@ public class TextEmbedding extends InferenceFunction<TextEmbedding> {
 
     @FunctionInfo(
         returnType = "dense_vector",
-        description = "Generates dense vector embeddings for text using a specified inference endpoint.",
+        description = "Generates dense vector embeddings from a text using a specified inference endpoint.",
         appliesTo = { @FunctionAppliesTo(version = "9.3", lifeCycle = FunctionAppliesToLifecycle.PREVIEW) },
         preview = true,
         examples = {
             @Example(
                 description = "Generate text embeddings using the 'test_dense_inference' inference endpoint.",
                 file = "text-embedding",
-                tag = "embedding-eval"
+                tag = "text-embedding-eval"
+            ),
+            @Example(
+                description = "Generate text embeddings for use within a KNN search.",
+                file = "text-embedding",
+                tag = "text-embedding-knn"
+            ),
+            @Example(
+                description = "Generate text embeddings inline within a KNN search.",
+                file = "text-embedding",
+                tag = "text-embedding-knn-inline"
             ) }
     )
     public TextEmbedding(
@@ -56,7 +65,7 @@ public TextEmbedding(
         @Param(
             name = InferenceFunction.INFERENCE_ID_PARAMETER_NAME,
             type = { "keyword" },
-            description = "Identifier of the inference endpoint"
+            description = "Identifier of the inference endpoint. The inference endpoint must have the `text_embedding` task type."
         ) Expression inferenceId
     ) {
         super(source, List.of(inputText, inferenceId));
@@ -65,7 +74,7 @@ public TextEmbedding(
     }
 
     @Override
-    public void writeTo(StreamOutput out) throws IOException {
+    public void writeTo(StreamOutput out) {
         throw new UnsupportedOperationException("doesn't escape the node");
     }