Allow google search tool to set different model

[lwangverizon]

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Link to Issue or Description of Change

1. Link to an existing issue (if applicable):

Allow google search tool to set different model #4135

2. Or, if no issue exists, describe the change:

If applicable, please follow the issue templates to provide as much detail as
possible.

Problem:
Currently, the Google Search tool inherits and uses the same LLM model set from the parent agent for processing and summarizing search results. This creates a limitation for users who wish to decouple the agent's reasoning model from the model used for search summarization (e.g., for cost optimization or using a lightweight model for simpler summarization tasks).

Solution:
I have updated the Google Search tool to accept an optional LLM model parameter.
Custom Model: Users can now explicitly specify which model should be used for processing search results.
Default Behavior: If no model is specified, the tool defaults to the parent agent's model, ensuring backward compatibility.

    # If a custom model is specified, use it instead of the original model
    if self.model is not None:
      llm_request.model = self.model

Testing Plan

Added a new test case test_process_llm_request_with_custom_model in [test_google_search_tool.py] that verifies:

When a custom model parameter is provided to GoogleSearchTool, it overrides the model from the incoming llm_request during process_llm_request
The tool correctly uses the custom model for LLM calls while maintaining other request parameters

Unit Tests:

• I have added or updated unit tests for my change.
• All unit tests pass locally.

(base) wanglu2:adk-python/ (feature/allow-google-search-tool-set-different-llm✗) $ uv run pytest ./tests/unittests/tools/test_google_search_tool.py [22:07:32]
======================================================================== test session starts ========================================================================
platform darwin -- Python 3.13.1, pytest-9.0.2, pluggy-1.6.0
rootdir: /Users/wanglu2/Documents/Git/adk-python
configfile: pyproject.toml
plugins: mock-3.15.1, anyio-4.12.0, xdist-3.8.0, asyncio-1.3.0, langsmith-0.6.0
asyncio: mode=Mode.AUTO, debug=False, asyncio_default_fixture_loop_scope=function, asyncio_default_test_loop_scope=function
collected 21 items

tests/unittests/tools/test_google_search_tool.py ..................... [100%]

======================================================================== 21 passed in 7.91s =========================================================================

Checklist

• I have read the CONTRIBUTING.md document.
• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have added tests that prove my fix is effective or that my feature works.
• New and existing unit tests pass locally with my changes.
• I have manually tested my changes end-to-end.
• Any dependent changes have been merged and published in downstream modules.

Additional context

[gemini-code-assist]

Summary of Changes

Hello @lwangverizon, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the GoogleSearchTool by introducing an optional parameter that allows users to specify a dedicated Large Language Model for processing search results. This provides greater control over LLM usage, enabling scenarios where a different model might be preferred for search summarization, such as for cost efficiency or specific task requirements, while ensuring existing functionality remains unchanged by defaulting to the agent's model if no custom model is provided.

Highlights

• Custom LLM Model for Google Search Tool: The GoogleSearchTool now accepts an optional model parameter during initialization, allowing users to specify a distinct LLM for processing and summarizing search results, independent of the main agent's model.
• Decoupling LLM Usage: This change provides flexibility for users to optimize costs or use lightweight models for search summarization by decoupling the LLM used for search from the primary agent's reasoning model.
• Backward Compatibility: The tool maintains backward compatibility; if no custom model is explicitly provided, it defaults to using the parent agent's LLM model as before.
• New Unit Tests: New unit tests have been added to verify that the custom model parameter correctly overrides the LLM request model and that the default behavior is preserved when no custom model is specified.

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[gemini-code-assist]

Code Review

This pull request effectively adds the capability to specify a custom model for the GoogleSearchTool, decoupling it from the parent agent's model. The implementation is straightforward and correct, and the new functionality is well-covered by unit tests. My review includes a couple of suggestions for improvement: one regarding a potentially unused parameter in the GoogleSearchTool's initializer, and another to refactor the new tests to reduce code duplication and improve maintainability.

[gemini-code-assist]

The bypass_multi_tools_limit parameter appears to be unused within the GoogleSearchTool class. The logic in process_llm_request for Gemini 1.x models unconditionally raises a ValueError if other tools are present, and this check does not consult bypass_multi_tools_limit. For Gemini 2.x+ models, multiple tools are supported by default, making the flag seem redundant there as well.

If this parameter is obsolete, consider removing it and the corresponding instance attribute self.bypass_multi_tools_limit to improve code clarity. If it has a purpose that is not immediately apparent, adding a more detailed explanation in the docstring would be helpful.

[lwangverizon]

i will let ADK team decide if this can be removed

[ankursharmas]

This will actually override the model to which Agent talks to.