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Update ADK doc according to issue #744 - 5 #755

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241 changes: 9 additions & 232 deletions docs/tools/built-in-tools.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,35 +27,15 @@ The `google_search` tool allows the agent to perform web searches using Google S
When you use grounding with Google Search, and you receive Search suggestions in your response, you must display the Search suggestions in production and in your applications.
For more information on grounding with Google Search, see Grounding with Google Search documentation for [Google AI Studio](https://ai.google.dev/gemini-api/docs/grounding/search-suggestions) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-search-suggestions). The UI code (HTML) is returned in the Gemini response as `renderedContent`, and you will need to show the HTML in your app, in accordance with the policy.

=== "Python"

```py
--8<-- "examples/python/snippets/tools/built-in-tools/google_search.py"
```

=== "Java"

```java
--8<-- "examples/java/snippets/src/main/java/tools/GoogleSearchAgentApp.java:full_code"
```

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BREAKING CHANGE: removed existing content without reason

### Code Execution

The `built_in_code_execution` tool enables the agent to execute code,
specifically when using Gemini 2 models. This allows the model to perform tasks
like calculations, data manipulation, or running small scripts.

=== "Python"

```py
--8<-- "examples/python/snippets/tools/built-in-tools/code_execution.py"
```

=== "Java"

```java
--8<-- "examples/java/snippets/src/main/java/tools/CodeExecutionAgentApp.java:full_code"
```

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### GKE Code Executor

Expand Down Expand Up @@ -100,30 +80,7 @@ For a complete, ready-to-use configuration, see the
sample. For more information on deploying ADK workflows to GKE, see
[Deploy to Google Kubernetes Engine (GKE)](/adk-docs/deploy/gke/).

=== "Python"

```python
from google.adk.agents import LlmAgent
from google.adk.code_executors import GkeCodeExecutor

# Initialize the executor, targeting the namespace where its ServiceAccount
# has the required RBAC permissions.
# This example also sets a custom timeout and resource limits.
gke_executor = GkeCodeExecutor(
namespace="agent-sandbox",
timeout_seconds=600,
cpu_limit="1000m", # 1 CPU core
mem_limit="1Gi",
)

# The agent now uses this executor for any code it generates.
gke_agent = LlmAgent(
name="gke_coding_agent",
model="gemini-2.0-flash",
instruction="You are a helpful AI agent that writes and executes Python code.",
code_executor=gke_executor,
)
```


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#### Configuration parameters

Expand All @@ -149,11 +106,7 @@ 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"
```

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### Vertex AI Search

Expand All @@ -163,9 +116,6 @@ documents, company policies, knowledge bases). This built-in tool requires you
to provide the specific data store ID during configuration. For further details of the tool, see [Understanding Vertex AI Search grounding](../grounding/vertex_ai_search_grounding.md).


```py
--8<-- "examples/python/snippets/tools/built-in-tools/vertexai_search.py"
```

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### BigQuery
Expand All @@ -176,17 +126,16 @@ These are a set of tools aimed to provide integration with BigQuery, namely:
* **`get_dataset_info`**: Fetches metadata about a BigQuery dataset.
* **`list_table_ids`**: Fetches table ids present in a BigQuery dataset.
* **`get_table_info`**: Fetches metadata about a BigQuery table.
* **`execute_sql`**: Runs a SQL query in BigQuery and fetch the result.
* **`execute_sql`**: Runs a SQL query in BigQuery and fetch the result. This tool now includes a `dry_run` parameter. If set to `True`, the query will be validated by BigQuery but not actually executed, returning information about the query's validity and potential cost.
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possibly usable content: review and incorporate if possible

* **`forecast`**: Runs a BigQuery AI time series forecast using the `AI.FORECAST` function.
* **`ask_data_insights`**: Answers questions about data in BigQuery tables using natural language.
* **`analyze_contribution`**: Runs a BigQuery ML contribution analysis using `ML.CREATE_MODEL` and `ML.GET_INSIGHTS` to identify the dimensions that contribute most significantly to a given metric.
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possibly usable content: review and incorporate if possible.


They are packaged in the toolset `BigQueryToolset`.



```py
--8<-- "examples/python/snippets/tools/built-in-tools/bigquery.py"
```


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### Spanner
Expand All @@ -205,9 +154,7 @@ They are packaged in the toolset `SpannerToolset`.



```py
--8<-- "examples/python/snippets/tools/built-in-tools/spanner.py"
```


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### Bigtable
Expand All @@ -224,105 +171,14 @@ They are packaged in the toolset `BigtableToolset`.



```py
--8<-- "examples/python/snippets/tools/built-in-tools/bigtable.py"
```


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BREAKING CHANGE: removed existing content without reason

## Use Built-in tools with other tools

The following code sample demonstrates how to use multiple built-in tools or how
to use built-in tools with other tools by using multiple agents:

=== "Python"

```py
from google.adk.tools.agent_tool import AgentTool
from google.adk.agents import Agent
from google.adk.tools import google_search
from google.adk.code_executors import BuiltInCodeExecutor


search_agent = Agent(
model='gemini-2.0-flash',
name='SearchAgent',
instruction="""
You're a specialist in Google Search
""",
tools=[google_search],
)
coding_agent = Agent(
model='gemini-2.0-flash',
name='CodeAgent',
instruction="""
You're a specialist in Code Execution
""",
code_executor=BuiltInCodeExecutor(),
)
root_agent = Agent(
name="RootAgent",
model="gemini-2.0-flash",
description="Root Agent",
tools=[AgentTool(agent=search_agent), AgentTool(agent=coding_agent)],
)
```

=== "Java"

```java
import com.google.adk.agents.BaseAgent;
import com.google.adk.agents.LlmAgent;
import com.google.adk.tools.AgentTool;
import com.google.adk.tools.BuiltInCodeExecutionTool;
import com.google.adk.tools.GoogleSearchTool;
import com.google.common.collect.ImmutableList;

public class NestedAgentApp {

private static final String MODEL_ID = "gemini-2.0-flash";

public static void main(String[] args) {

// Define the SearchAgent
LlmAgent searchAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("SearchAgent")
.instruction("You're a specialist in Google Search")
.tools(new GoogleSearchTool()) // Instantiate GoogleSearchTool
.build();


// Define the CodingAgent
LlmAgent codingAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("CodeAgent")
.instruction("You're a specialist in Code Execution")
.tools(new BuiltInCodeExecutionTool()) // Instantiate BuiltInCodeExecutionTool
.build();

// Define the RootAgent, which uses AgentTool.create() to wrap SearchAgent and CodingAgent
BaseAgent rootAgent =
LlmAgent.builder()
.name("RootAgent")
.model(MODEL_ID)
.description("Root Agent")
.tools(
AgentTool.create(searchAgent), // Use create method
AgentTool.create(codingAgent) // Use create method
)
.build();

// Note: This sample only demonstrates the agent definitions.
// To run these agents, you'd need to integrate them with a Runner and SessionService,
// similar to the previous examples.
System.out.println("Agents defined successfully:");
System.out.println(" Root Agent: " + rootAgent.name());
System.out.println(" Search Agent (nested): " + searchAgent.name());
System.out.println(" Code Agent (nested): " + codingAgent.name());
}
}
```


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### Limitations
Expand All @@ -335,29 +191,8 @@ to use built-in tools with other tools by using multiple agents:
For example, the following approach that uses ***a built-in tool along with
other tools*** within a single agent is **not** currently supported:

=== "Python"

```py
root_agent = Agent(
name="RootAgent",
model="gemini-2.0-flash",
description="Root Agent",
tools=[custom_function],
code_executor=BuiltInCodeExecutor() # <-- not supported when used with tools
)
```

=== "Java"

```java
LlmAgent searchAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("SearchAgent")
.instruction("You're a specialist in Google Search")
.tools(new GoogleSearchTool(), new YourCustomTool()) // <-- not supported
.build();
```



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!!! warning

Expand All @@ -366,61 +201,3 @@ to use built-in tools with other tools by using multiple agents:
For example, the following approach that uses built-in tools within sub-agents
is **not** currently supported:

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=== "Python"

```py
search_agent = Agent(
model='gemini-2.0-flash',
name='SearchAgent',
instruction="""
You're a specialist in Google Search
""",
tools=[google_search],
)
coding_agent = Agent(
model='gemini-2.0-flash',
name='CodeAgent',
instruction="""
You're a specialist in Code Execution
""",
code_executor=BuiltInCodeExecutor(),
)
root_agent = Agent(
name="RootAgent",
model="gemini-2.0-flash",
description="Root Agent",
sub_agents=[
search_agent,
coding_agent
],
)
```

=== "Java"

```java
LlmAgent searchAgent =
LlmAgent.builder()
.model("gemini-2.0-flash")
.name("SearchAgent")
.instruction("You're a specialist in Google Search")
.tools(new GoogleSearchTool())
.build();

LlmAgent codingAgent =
LlmAgent.builder()
.model("gemini-2.0-flash")
.name("CodeAgent")
.instruction("You're a specialist in Code Execution")
.tools(new BuiltInCodeExecutionTool())
.build();


LlmAgent rootAgent =
LlmAgent.builder()
.name("RootAgent")
.model("gemini-2.0-flash")
.description("Root Agent")
.subAgents(searchAgent, codingAgent) // Not supported, as the sub agents use built in tools.
.build();
```