chore: import upstream snapshot with attribution

contributing/samples/integrations/spanner_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 google.adk.agents.llm_agent import LlmAgent
+from google.adk.auth.auth_credential import AuthCredentialTypes
+from google.adk.tools.spanner.settings import Capabilities
+from google.adk.tools.spanner.settings import SpannerToolSettings
+from google.adk.tools.spanner.settings import SpannerVectorStoreSettings
+from google.adk.tools.spanner.spanner_credentials import SpannerCredentialsConfig
+from google.adk.tools.spanner.spanner_toolset import SpannerToolset
+import google.auth
+
+# Define an appropriate credential type
+# Set to None to use the application default credentials (ADC) for a quick
+# development.
+CREDENTIALS_TYPE = None
+
+
+if CREDENTIALS_TYPE == AuthCredentialTypes.OAUTH2:
+  # Initialize the tools to do interactive OAuth
+  # The environment variables OAUTH_CLIENT_ID and OAUTH_CLIENT_SECRET
+  # must be set
+  credentials_config = SpannerCredentialsConfig(
+      client_id=os.getenv("OAUTH_CLIENT_ID"),
+      client_secret=os.getenv("OAUTH_CLIENT_SECRET"),
+      scopes=[
+          "https://www.googleapis.com/auth/spanner.admin",
+          "https://www.googleapis.com/auth/spanner.data",
+      ],
+  )
+elif CREDENTIALS_TYPE == AuthCredentialTypes.SERVICE_ACCOUNT:
+  # Initialize the tools to use the credentials in the service account key.
+  # If this flow is enabled, make sure to replace the file path with your own
+  # service account key file
+  # https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys
+  creds, _ = google.auth.load_credentials_from_file("service_account_key.json")
+  credentials_config = SpannerCredentialsConfig(credentials=creds)
+else:
+  # Initialize the tools to use the application default credentials.
+  # https://cloud.google.com/docs/authentication/provide-credentials-adc
+  application_default_credentials, _ = google.auth.default()
+  credentials_config = SpannerCredentialsConfig(
+      credentials=application_default_credentials
+  )
+
+# Follow the instructions in README.md to set up the example Spanner database.
+# Replace the following settings with your specific Spanner database.
+
+# Define Spanner vector store settings.
+vector_store_settings = SpannerVectorStoreSettings(
+    project_id="<PROJECT_ID>",
+    instance_id="<INSTANCE_ID>",
+    database_id="<DATABASE_ID>",
+    table_name="products",
+    content_column="productDescription",
+    embedding_column="productDescriptionEmbedding",
+    vector_length=768,
+    vertex_ai_embedding_model_name="text-embedding-005",
+    selected_columns=[
+        "productId",
+        "productName",
+        "productDescription",
+    ],
+    nearest_neighbors_algorithm="EXACT_NEAREST_NEIGHBORS",
+    top_k=3,
+    distance_type="COSINE",
+    additional_filter="inventoryCount > 0",
+)
+
+# Define Spanner tool config with the vector store settings.
+tool_settings = SpannerToolSettings(
+    capabilities=[Capabilities.DATA_READ],
+    vector_store_settings=vector_store_settings,
+)
+
+# Get the Spanner toolset with the Spanner tool settings and credentials config.
+# Filter the tools to only include the `vector_store_similarity_search` tool.
+spanner_toolset = SpannerToolset(
+    credentials_config=credentials_config,
+    spanner_tool_settings=tool_settings,
+    # Comment to include all allowed tools.
+    tool_filter=["vector_store_similarity_search"],
+)
+
+
+root_agent = LlmAgent(
+    name="spanner_knowledge_base_agent",
+    description=(
+        "Agent to answer questions about product-specific recommendations."
+    ),
+    instruction="""
+    You are a helpful assistant that answers user questions about product-specific recommendations.
+    1. Always use the `vector_store_similarity_search` tool to find information.
+    2. Directly present all the information results from the `vector_store_similarity_search` tool naturally and well formatted in your response.
+    3. If no information result is returned by the `vector_store_similarity_search` tool, say you don't know.
+    """,
+    # Use the Spanner toolset for vector similarity search.
+    tools=[spanner_toolset],
+)