mirror of
https://github.com/encounter/adk-python.git
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George WealeandCopybara-Service
2367901ec5
Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 858763407
489 lines
14 KiB
Python
489 lines
14 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Unit tests for BaseLlmFlow toolset integration."""
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from unittest import mock
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from unittest.mock import AsyncMock
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from google.adk.agents.llm_agent import Agent
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from google.adk.events.event import Event
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from google.adk.flows.llm_flows.base_llm_flow import BaseLlmFlow
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from google.adk.models.google_llm import Gemini
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from google.adk.models.llm_request import LlmRequest
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from google.adk.models.llm_response import LlmResponse
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from google.adk.plugins.base_plugin import BasePlugin
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from google.adk.tools.base_toolset import BaseToolset
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from google.adk.tools.google_search_tool import GoogleSearchTool
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from google.genai import types
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import pytest
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from ... import testing_utils
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google_search = GoogleSearchTool(bypass_multi_tools_limit=True)
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class BaseLlmFlowForTesting(BaseLlmFlow):
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"""Test implementation of BaseLlmFlow for testing purposes."""
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pass
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@pytest.mark.asyncio
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async def test_preprocess_calls_toolset_process_llm_request():
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"""Test that _preprocess_async calls process_llm_request on toolsets."""
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# Create a mock toolset that tracks if process_llm_request was called
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class _MockToolset(BaseToolset):
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def __init__(self):
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super().__init__()
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self.process_llm_request_called = False
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self.process_llm_request = AsyncMock(side_effect=self._track_call)
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async def _track_call(self, **kwargs):
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self.process_llm_request_called = True
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async def get_tools(self, readonly_context=None):
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return []
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async def close(self):
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pass
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mock_toolset = _MockToolset()
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# Create a mock model that returns a simple response
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mock_response = LlmResponse(
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content=types.Content(
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role='model', parts=[types.Part.from_text(text='Test response')]
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),
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partial=False,
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)
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mock_model = testing_utils.MockModel.create(responses=[mock_response])
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# Create agent with the mock toolset
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agent = Agent(name='test_agent', model=mock_model, tools=[mock_toolset])
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, user_content='test message'
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)
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flow = BaseLlmFlowForTesting()
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# Call _preprocess_async
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llm_request = LlmRequest()
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events = []
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async for event in flow._preprocess_async(invocation_context, llm_request):
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events.append(event)
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# Verify that process_llm_request was called on the toolset
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assert mock_toolset.process_llm_request_called
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@pytest.mark.asyncio
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async def test_preprocess_handles_mixed_tools_and_toolsets():
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"""Test that _preprocess_async properly handles both tools and toolsets."""
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from google.adk.tools.base_tool import BaseTool
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# Create a mock tool
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class _MockTool(BaseTool):
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def __init__(self):
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super().__init__(name='mock_tool', description='Mock tool')
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self.process_llm_request_called = False
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self.process_llm_request = AsyncMock(side_effect=self._track_call)
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async def _track_call(self, **kwargs):
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self.process_llm_request_called = True
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async def call(self, **kwargs):
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return 'mock result'
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# Create a mock toolset
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class _MockToolset(BaseToolset):
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def __init__(self):
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super().__init__()
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self.process_llm_request_called = False
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self.process_llm_request = AsyncMock(side_effect=self._track_call)
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async def _track_call(self, **kwargs):
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self.process_llm_request_called = True
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async def get_tools(self, readonly_context=None):
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return []
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async def close(self):
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pass
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def _test_function():
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"""Test function tool."""
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return 'function result'
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mock_tool = _MockTool()
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mock_toolset = _MockToolset()
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# Create agent with mixed tools and toolsets
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agent = Agent(
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name='test_agent', tools=[mock_tool, _test_function, mock_toolset]
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)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, user_content='test message'
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)
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flow = BaseLlmFlowForTesting()
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# Call _preprocess_async
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llm_request = LlmRequest()
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events = []
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async for event in flow._preprocess_async(invocation_context, llm_request):
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events.append(event)
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# Verify that process_llm_request was called on both tools and toolsets
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assert mock_tool.process_llm_request_called
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assert mock_toolset.process_llm_request_called
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# TODO(b/448114567): Remove the following test_preprocess_with_google_search
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# tests once the workaround is no longer needed.
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@pytest.mark.asyncio
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async def test_preprocess_with_google_search_only():
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"""Test _preprocess_async with only the google_search tool."""
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agent = Agent(name='test_agent', model='gemini-pro', tools=[google_search])
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, user_content='test message'
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)
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flow = BaseLlmFlowForTesting()
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llm_request = LlmRequest(model='gemini-pro')
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async for _ in flow._preprocess_async(invocation_context, llm_request):
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pass
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assert len(llm_request.config.tools) == 1
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assert llm_request.config.tools[0].google_search is not None
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@pytest.mark.asyncio
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async def test_preprocess_with_google_search_workaround():
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"""Test _preprocess_async with google_search and another tool."""
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def _my_tool(sides: int) -> int:
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"""A simple tool."""
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return sides
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agent = Agent(
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name='test_agent', model='gemini-pro', tools=[_my_tool, google_search]
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)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, user_content='test message'
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)
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flow = BaseLlmFlowForTesting()
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llm_request = LlmRequest(model='gemini-pro')
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async for _ in flow._preprocess_async(invocation_context, llm_request):
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pass
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assert len(llm_request.config.tools) == 1
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declarations = llm_request.config.tools[0].function_declarations
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assert len(declarations) == 2
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assert {d.name for d in declarations} == {'_my_tool', 'google_search_agent'}
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@pytest.mark.asyncio
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async def test_preprocess_calls_convert_tool_union_to_tools():
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"""Test that _preprocess_async calls _convert_tool_union_to_tools."""
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class _MockTool:
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process_llm_request = AsyncMock()
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mock_tool_instance = _MockTool()
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def _my_tool(sides: int) -> int:
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"""A simple tool."""
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return sides
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with mock.patch(
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'google.adk.agents.llm_agent._convert_tool_union_to_tools',
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new_callable=AsyncMock,
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) as mock_convert:
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mock_convert.return_value = [mock_tool_instance]
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model = Gemini(model='gemini-2')
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agent = Agent(
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name='test_agent', model=model, tools=[_my_tool, google_search]
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)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, user_content='test message'
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)
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flow = BaseLlmFlowForTesting()
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llm_request = LlmRequest(model='gemini-2')
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async for _ in flow._preprocess_async(invocation_context, llm_request):
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pass
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mock_convert.assert_called_with(
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google_search,
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mock.ANY, # ReadonlyContext(invocation_context)
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model,
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True, # multiple_tools
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)
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# TODO(b/448114567): Remove the following
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# test_handle_after_model_callback_grounding tests once the workaround
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# is no longer needed.
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def dummy_tool():
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pass
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@pytest.mark.parametrize(
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'tools, state_metadata, expect_metadata',
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[
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([], None, False),
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([google_search, dummy_tool], {'foo': 'bar'}, True),
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([dummy_tool], {'foo': 'bar'}, False),
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([google_search, dummy_tool], None, False),
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],
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ids=[
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'no_search_no_grounding',
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'with_search_with_grounding',
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'no_search_with_grounding',
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'with_search_no_grounding',
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],
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)
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@pytest.mark.asyncio
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async def test_handle_after_model_callback_grounding_with_no_callbacks(
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tools, state_metadata, expect_metadata
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):
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"""Test handling grounding metadata when there are no callbacks."""
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agent = Agent(name='test_agent', tools=tools)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent
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)
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if state_metadata:
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invocation_context.session.state['temp:_adk_grounding_metadata'] = (
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state_metadata
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)
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llm_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='response')])
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)
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event = Event(
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id=Event.new_id(),
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invocation_id=invocation_context.invocation_id,
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author=agent.name,
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)
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flow = BaseLlmFlowForTesting()
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result = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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if expect_metadata:
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llm_response.grounding_metadata = state_metadata
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assert result == llm_response
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else:
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assert result is None
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@pytest.mark.parametrize(
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'tools, state_metadata, expect_metadata',
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[
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([], None, False),
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([google_search, dummy_tool], {'foo': 'bar'}, True),
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([dummy_tool], {'foo': 'bar'}, False),
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([google_search, dummy_tool], None, False),
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],
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ids=[
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'no_search_no_grounding',
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'with_search_with_grounding',
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'no_search_with_grounding',
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'with_search_no_grounding',
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],
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)
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@pytest.mark.asyncio
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async def test_handle_after_model_callback_grounding_with_callback_override(
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tools, state_metadata, expect_metadata
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):
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"""Test handling grounding metadata when there is a callback override."""
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agent_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='agent')])
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)
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agent_callback = AsyncMock(return_value=agent_response)
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agent = Agent(
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name='test_agent', tools=tools, after_model_callback=[agent_callback]
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)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent
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)
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if state_metadata:
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invocation_context.session.state['temp:_adk_grounding_metadata'] = (
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state_metadata
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)
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llm_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='response')])
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)
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event = Event(
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id=Event.new_id(),
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invocation_id=invocation_context.invocation_id,
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author=agent.name,
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)
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flow = BaseLlmFlowForTesting()
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result = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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if expect_metadata:
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agent_response.grounding_metadata = state_metadata
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assert result == agent_response
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agent_callback.assert_called_once()
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@pytest.mark.parametrize(
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'tools, state_metadata, expect_metadata',
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[
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([], None, False),
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([google_search, dummy_tool], {'foo': 'bar'}, True),
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([dummy_tool], {'foo': 'bar'}, False),
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([google_search, dummy_tool], None, False),
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],
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ids=[
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'no_search_no_grounding',
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'with_search_with_grounding',
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'no_search_with_grounding',
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'with_search_no_grounding',
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],
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)
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@pytest.mark.asyncio
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async def test_handle_after_model_callback_grounding_with_plugin_override(
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tools, state_metadata, expect_metadata
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):
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"""Test handling grounding metadata when there is a plugin override."""
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plugin_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='plugin')])
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)
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class _MockPlugin(BasePlugin):
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def __init__(self):
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super().__init__(name='mock_plugin')
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after_model_callback = AsyncMock(return_value=plugin_response)
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plugin = _MockPlugin()
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agent = Agent(name='test_agent', tools=tools)
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent, plugins=[plugin]
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)
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if state_metadata:
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invocation_context.session.state['temp:_adk_grounding_metadata'] = (
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state_metadata
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)
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llm_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='response')])
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)
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event = Event(
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id=Event.new_id(),
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invocation_id=invocation_context.invocation_id,
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author=agent.name,
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)
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flow = BaseLlmFlowForTesting()
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result = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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if expect_metadata:
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plugin_response.grounding_metadata = state_metadata
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assert result == plugin_response
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plugin.after_model_callback.assert_called_once()
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@pytest.mark.asyncio
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async def test_handle_after_model_callback_caches_canonical_tools():
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"""Test that canonical_tools is only called once per invocation_context."""
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canonical_tools_call_count = 0
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async def mock_canonical_tools(self, readonly_context=None):
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nonlocal canonical_tools_call_count
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canonical_tools_call_count += 1
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from google.adk.tools.base_tool import BaseTool
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class MockGoogleSearchTool(BaseTool):
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def __init__(self):
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super().__init__(name='google_search_agent', description='Mock search')
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async def call(self, **kwargs):
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return 'mock result'
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return [MockGoogleSearchTool()]
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agent = Agent(name='test_agent', tools=[google_search, dummy_tool])
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with mock.patch.object(
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type(agent), 'canonical_tools', new=mock_canonical_tools
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):
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invocation_context = await testing_utils.create_invocation_context(
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agent=agent
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)
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assert invocation_context.canonical_tools_cache is None
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invocation_context.session.state['temp:_adk_grounding_metadata'] = {
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'foo': 'bar'
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}
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llm_response = LlmResponse(
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content=types.Content(parts=[types.Part.from_text(text='response')])
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)
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event = Event(
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id=Event.new_id(),
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invocation_id=invocation_context.invocation_id,
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author=agent.name,
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)
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flow = BaseLlmFlowForTesting()
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# Call _handle_after_model_callback multiple times with the same context
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result1 = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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result2 = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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result3 = await flow._handle_after_model_callback(
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invocation_context, llm_response, event
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)
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assert canonical_tools_call_count == 1, (
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'canonical_tools should be called once, but was called '
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f'{canonical_tools_call_count} times'
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)
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assert invocation_context.canonical_tools_cache is not None
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assert len(invocation_context.canonical_tools_cache) == 1
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assert (
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invocation_context.canonical_tools_cache[0].name
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== 'google_search_agent'
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)
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assert result1.grounding_metadata == {'foo': 'bar'}
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assert result2.grounding_metadata == {'foo': 'bar'}
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assert result3.grounding_metadata == {'foo': 'bar'}
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