fix(docs): Add rag engine to adk built in tool doc

docs/tools/built-in-tools.md

@@ -58,6 +58,19 @@ like calculations, data manipulation, or running small scripts.
     --8<-- "examples/java/snippets/src/main/java/tools/CodeExecutionAgentApp.java:full_code"
     ```
 
+### Vertex AI RAG Engine
+
+The `vertex_ai_rag_retrieval` tool allows the agent to perform private data retrieval using Vertex
+AI RAG Engine.
+
+When you use grounding with Vertex AI RAG Engine, you need to prepare a RAG corpus before hand.
+Please refer to the [RAG ADK agent sample](https://github.com/google/adk-samples/blob/main/python/agents/RAG/rag/shared_libraries/prepare_corpus_and_data.py) or [Vertex AI RAG Engine page](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-quickstart) for setting it up.
+
+=== "Python"
+
+    ```py
+    --8<-- "examples/python/snippets/tools/built-in-tools/vertexai_rag_engine.py"
+    ```
 
 ### Vertex AI Search
 

examples/python/snippets/tools/built-in-tools/vertexai_rag_engine.py

@@ -0,0 +1,50 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from google.adk.agents import Agent
+from google.adk.tools.retrieval.vertex_ai_rag_retrieval import VertexAiRagRetrieval
+from vertexai.preview import rag
+
+from dotenv import load_dotenv
+from .prompts import return_instructions_root
+
+load_dotenv()
+
+ask_vertex_retrieval = VertexAiRagRetrieval(
+    name='retrieve_rag_documentation',
+    description=(
+        'Use this tool to retrieve documentation and reference materials for the question from the RAG corpus,'
+    ),
+    rag_resources=[
+        rag.RagResource(
+            # please fill in your own rag corpus
+            # here is a sample rag corpus for testing purpose
+            # e.g. projects/123/locations/us-central1/ragCorpora/456
+            rag_corpus=os.environ.get("RAG_CORPUS")
+        )
+    ],
+    similarity_top_k=10,
+    vector_distance_threshold=0.6,
+)
+
+root_agent = Agent(
+    model='gemini-2.0-flash-001',
+    name='ask_rag_agent',
+    instruction=return_instructions_root(),
+    tools=[
+        ask_vertex_retrieval,
+    ]
+)