diff --git a/docs/reference/search/retriever.asciidoc b/docs/reference/search/retriever.asciidoc
index 6d3a1a36ad407..54836ac33762d 100644
--- a/docs/reference/search/retriever.asciidoc
+++ b/docs/reference/search/retriever.asciidoc
@@ -81,7 +81,43 @@ retrievers) *only* the query element is allowed.
 [[standard-retriever-example]]
 ==== Example
 
-[source,js]
+////
+[source,console]
+----
+PUT /restaurants
+{
+  "mappings": {
+    "properties": {
+      "region": { "type": "keyword" },
+      "year": { "type": "keyword" },
+      "vector": {
+        "type": "dense_vector",
+        "dims": 3
+      }
+    }
+  }
+}
+
+POST /restaurants/_bulk?refresh
+{"index":{}}
+{"region": "Austria", "year": "2019", "vector": [10, 22, 77]}
+{"index":{}}
+{"region": "France", "year": "2019", "vector": [10, 22, 78]}
+{"index":{}}
+{"region": "Austria", "year": "2020", "vector": [10, 22, 79]}
+{"index":{}}
+{"region": "France", "year": "2020", "vector": [10, 22, 80]}
+----
+// TESTSETUP
+
+[source,console]
+--------------------------------------------------
+DELETE /restaurants
+--------------------------------------------------
+// TEARDOWN
+////
+
+[source,console]
 ----
 GET /restaurants/_search
 {
@@ -109,9 +145,8 @@ GET /restaurants/_search
   }
 }
 ----
-// NOTCONSOLE
 <1> Opens the `retriever` object.
-<2> The `standard` retriever is used for definining traditional {es} queries.
+<2> The `standard` retriever is used for defining traditional {es} queries.
 <3> The entry point for defining the search query.
 <4> The `bool` object allows for combining multiple query clauses logically.
 <5> The `should` array indicates conditions under which a document will match. Documents matching these conditions will increase their relevancy score.
@@ -171,9 +206,9 @@ The parameters `query_vector` and `query_vector_builder` cannot be used together
 [[knn-retriever-example]]
 ==== Example
 
-[source,js]
+[source,console]
 ----
-GET my-embeddings/_search
+GET /restaurants/_search
 {
   "retriever": {
     "knn": { <1>
@@ -185,8 +220,7 @@ GET my-embeddings/_search
   }
 }
 ----
-// NOTCONSOLE
-
+// TEST[continued]
 <1> Configuration for k-nearest neighbor (knn) search, which is based on vector similarity.
 <2> Specifies the field name that contains the vectors.
 <3> The query vector against which document vectors are compared in the `knn` search.
@@ -223,7 +257,7 @@ the retriever tree.
 
 A simple hybrid search example (lexical search + dense vector search) combining a `standard` retriever with a `knn` retriever using RRF:
 
-[source,js]
+[source,console]
 ----
 GET /restaurants/_search
 {
@@ -234,7 +268,7 @@ GET /restaurants/_search
           "standard": { <3>
             "query": {
               "multi_match": {
-                "query": "San Francisco",
+                "query": "Austria",
                 "fields": [
                   "city",
                   "region"
@@ -258,7 +292,7 @@ GET /restaurants/_search
   }
 }
 ----
-// NOTCONSOLE
+// TEST[continued]
 <1> Defines a retriever tree with an RRF retriever.
 <2> The sub-retriever array.
 <3> The first sub-retriever is a `standard` retriever.
@@ -272,7 +306,7 @@ GET /restaurants/_search
 
 A more complex hybrid search example (lexical search + ELSER sparse vector search + dense vector search) using RRF:
 
-[source,js]
+[source,console]
 ----
 GET movies/_search
 {
@@ -316,7 +350,7 @@ GET movies/_search
   }
 }
 ----
-// NOTCONSOLE
+// TEST[skip:uses ELSER]
 
 [[text-similarity-reranker-retriever]]
 ==== Text Similarity Re-ranker Retriever
@@ -390,7 +424,7 @@ A text similarity re-ranker retriever is a compound retriever. Child retrievers
 This example enables out-of-the-box semantic search by re-ranking top documents using the Cohere Rerank API. This approach eliminate the need to generate and store embeddings for all indexed documents.
 This requires a <<infer-service-cohere,Cohere Rerank inference endpoint>> using the `rerank` task type.
 
-[source,js]
+[source,console]
 ----
 GET /index/_search
 {
@@ -414,7 +448,7 @@ GET /index/_search
    }
 }
 ----
-// NOTCONSOLE
+// TEST[skip:uses ML]
 
 [discrete]
 [[text-similarity-reranker-retriever-example-eland]]
@@ -452,7 +486,7 @@ eland_import_hub_model \
 +
 . Create an inference endpoint for the `rerank` task
 +
-[source,js]
+[source,console]
 ----
 PUT _inference/rerank/my-msmarco-minilm-model
 {
@@ -464,11 +498,11 @@ PUT _inference/rerank/my-msmarco-minilm-model
   }
 }
 ----
-// NOTCONSOLE
+// TEST[skip:uses ML]
 +
 . Define a `text_similarity_rerank` retriever.
 +
-[source,js]
+[source,console]
 ----
 POST movies/_search
 {
@@ -490,7 +524,7 @@ POST movies/_search
   }
 }
 ----
-// NOTCONSOLE
+// TEST[skip:uses ML]
 +
 This retriever uses a standard `match` query to search the `movie` index for films tagged with the genre "drama".
 It then re-ranks the results based on semantic similarity to the text in the `inference_text` parameter, using the model we uploaded to {es}.