Update ADK doc according to issue #63 - 2

Author: adk-bot

docs/tools/built-in-tools.md

@@ -145,14 +145,63 @@ These are a set of tools aimed to provide integration with BigQuery, namely:
 * **`execute_sql`**: Runs a SQL query in BigQuery and fetch the result.
 * **`ask_data_insights`**: Answers questions about data in BigQuery tables using natural language.
 
-They are packaged in the toolset `BigQueryToolset`.
-
+They are packaged in the toolset `BigQueryToolset`. You can also specify the `location` for the BigQuery data.
 
 
 ```py
 --8<-- "examples/python/snippets/tools/built-in-tools/bigquery.py"
 ```
 
+### Spanner
+
+The `SpannerToolset` provides a set of tools for interacting with Spanner databases. The main tool in this toolset is `similarity_search`.
+
+* **`similarity_search`**: Performs a vector similarity search in Spanner. This tool embeds a text query, then uses the embedding vector to find similar items in a Spanner table.
+
+To use the `similarity_search` tool, your Spanner table must contain a column with pre-calculated embeddings. The tool uses a configured embedding service to convert the input text query into a vector for the search.
+
+```py
+from google.adk.tools.spanner import SpannerToolset
+
+# Initialize the Spanner toolset
+spanner_toolset = SpannerToolset(
+    project_id="my-project",
+    instance_id="my-instance",
+    database_id="my-database",
+)
+
+# Example of using similarity_search in an agent
+from google.adk.agents import LlmAgent
+
+spanner_agent = LlmAgent(
+    name="spanner_agent",
+    model="gemini-2.0-flash",
+    instruction="You are an expert in Spanner similarity search.",
+    tools=[spanner_toolset],
+)
+
+# The agent can now use the similarity_search tool
+# to answer questions that require vector search.
+# For example:
+# "Find products similar to 'eco-friendly cleaning supplies'."
+```
+
+The `similarity_search` tool takes the following parameters:
+
+*   `table_name`: The name of the table to search.
+*   `query`: The text query to search for.
+*   `embedding_column_to_search`: The name of the column containing the embeddings.
+*   `columns`: A list of other columns to return in the results.
+*   `embedding_options`: A dictionary specifying the embedding model to use.
+    *   `spanner_embedding_model_name`: For GoogleSQL dialect databases, the name of the embedding model registered in Spanner.
+    *   `vertex_ai_embedding_model_endpoint`: For PostgreSQL dialect databases, the endpoint of the Vertex AI embedding model.
+*   `additional_filter` (optional): A SQL `WHERE` clause to further filter the results.
+*   `search_options` (optional): A dictionary for search customization:
+    *   `top_k`: The number of results to return (default: 4).
+    *   `distance_type`: The distance metric to use (e.g., `COSINE_DISTANCE`, `EUCLIDEAN_DISTANCE`).
+    *   `nearest_neighbors_algorithm`: The search algorithm to use (`EXACT_NEAREST_NEIGHBORS` or `APPROXIMATE_NEAREST_NEIGHBORS`).
+    *   `num_leaves_to_search`: For approximate search, the number of leaves to search in the vector index.
+
 ## Use Built-in tools with other tools
 
 The following code sample demonstrates how to use multiple built-in tools or how

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

@@ -1,99 +1,6 @@
-# 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 asyncio
-
-from google.adk.agents import Agent
-from google.adk.runners import Runner
-from google.adk.sessions import InMemorySessionService
-from google.adk.tools.bigquery import BigQueryCredentialsConfig
 from google.adk.tools.bigquery import BigQueryToolset
-from google.adk.tools.bigquery.config import BigQueryToolConfig
-from google.adk.tools.bigquery.config import WriteMode
-from google.genai import types
-import google.auth
-
-# Define constants for this example agent
-AGENT_NAME = "bigquery_agent"
-APP_NAME = "bigquery_app"
-USER_ID = "user1234"
-SESSION_ID = "1234"
-GEMINI_MODEL = "gemini-2.0-flash"
-
-# Define a tool configuration to block any write operations
-tool_config = BigQueryToolConfig(write_mode=WriteMode.BLOCKED)
-
-# Define a credentials config - in this example we are using application default
-# credentials
-# https://cloud.google.com/docs/authentication/provide-credentials-adc
-application_default_credentials, _ = google.auth.default()
-credentials_config = BigQueryCredentialsConfig(
-    credentials=application_default_credentials
-)
-
-# Instantiate a BigQuery toolset
-bigquery_toolset = BigQueryToolset(
-    credentials_config=credentials_config, bigquery_tool_config=tool_config
-)
-
-# Agent Definition
-bigquery_agent = Agent(
-    model=GEMINI_MODEL,
-    name=AGENT_NAME,
-    description=(
-        "Agent to answer questions about BigQuery data and models and execute"
-        " SQL queries."
-    ),
-    instruction="""\
-        You are a data science agent with access to several BigQuery tools.
-        Make use of those tools to answer the user's questions.
-    """,
-    tools=[bigquery_toolset],
-)
-
-# Session and Runner
-session_service = InMemorySessionService()
-session = asyncio.run(
-    session_service.create_session(
-        app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
-    )
-)
-runner = Runner(
-    agent=bigquery_agent, app_name=APP_NAME, session_service=session_service
-)
-
-
-# Agent Interaction
-def call_agent(query):
-    """
-    Helper function to call the agent with a query.
-    """
-    content = types.Content(role="user", parts=[types.Part(text=query)])
-    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
-
-    print("USER:", query)
-    for event in events:
-        if event.is_final_response():
-            final_response = event.content.parts[0].text
-            print("AGENT:", final_response)
 
+# Initialize the BigQuery toolset with a specific location
+bigquery_toolset = BigQueryToolset(project_id="my-project", location="US")
 
-call_agent("Are there any ml datasets in bigquery-public-data project?")
-call_agent("Tell me more about ml_datasets.")
-call_agent("Which all tables does it have?")
-call_agent("Tell me more about the census_adult_income table.")
-call_agent("How many rows are there per income bracket?")
-call_agent(
-    "What is the statistical correlation between education_num, age, and the income_bracket?"
-)
+# The agent can now use the BigQuery tools to query datasets in the specified location.