chore: import upstream snapshot with attribution

contributing/samples/integrations/rag_agent/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2026 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.
+
+from . import agent

contributing/samples/integrations/rag_agent/agent.py

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+# Copyright 2026 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 dotenv import load_dotenv
+from google.adk.agents.llm_agent import Agent
+from google.adk.tools.retrieval.vertex_ai_rag_retrieval import VertexAiRagRetrieval
+from vertexai.preview import rag
+
+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
+            # e.g. projects/123/locations/us-central1/ragCorpora/456
+            rag_corpus=os.environ.get("RAG_CORPUS"),
+        )
+    ],
+    similarity_top_k=1,
+    vector_distance_threshold=0.6,
+)
+
+root_agent = Agent(
+    name="root_agent",
+    instruction=(
+        "You are an AI assistant with access to specialized corpus of"
+        " documents. Your role is to provide accurate and concise answers to"
+        " questions based on documents that are retrievable using"
+        " ask_vertex_retrieval."
+    ),
+    tools=[ask_vertex_retrieval],
+)