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105 lines
3.6 KiB
Python
105 lines
3.6 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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"""Tests for simple_prompt_optimizer."""
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from unittest import mock
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from google.adk.agents.llm_agent import Agent
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from google.adk.models.google_llm import LlmResponse
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from google.adk.optimization.data_types import UnstructuredSamplingResult
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from google.adk.optimization.sampler import Sampler
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from google.adk.optimization.simple_prompt_optimizer import SimplePromptOptimizer
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from google.adk.optimization.simple_prompt_optimizer import SimplePromptOptimizerConfig
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from google.genai import types as genai_types
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import pytest
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@pytest.fixture
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def mock_sampler() -> mock.MagicMock:
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sampler = mock.MagicMock(spec=Sampler)
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sampler.get_train_example_ids.return_value = ["1", "2", "3", "4", "5"]
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sampler.get_validation_example_ids.return_value = ["v1", "v2"]
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async def mock_sample_and_score(
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agent: Agent,
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example_set: str,
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batch: list[str] | None = None,
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capture_full_eval_data: bool = False,
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) -> UnstructuredSamplingResult:
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# Determine the actual batch to use
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if batch is None:
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if example_set == "train":
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current_batch = sampler.get_train_example_ids()
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else: # "validation"
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current_batch = sampler.get_validation_example_ids()
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else:
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current_batch = batch
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# Simulate better score for "improved" prompt
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if "IMPROVED" in agent.instruction:
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scores = {uid: 0.9 for uid in current_batch}
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else:
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scores = {uid: 0.5 for uid in current_batch}
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return UnstructuredSamplingResult(scores=scores)
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sampler.sample_and_score.side_effect = mock_sample_and_score
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return sampler
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@pytest.fixture
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def mock_llm_class() -> mock.MagicMock:
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mock_llm = mock.MagicMock()
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async def mock_generate_content_async(*args, **kwargs):
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yield LlmResponse(
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content=genai_types.Content(
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parts=[genai_types.Part(text="IMPROVED PROMPT")]
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)
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)
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mock_llm.generate_content_async.side_effect = mock_generate_content_async
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mock_class = mock.MagicMock(return_value=mock_llm)
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return mock_class
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@mock.patch(
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"google.adk.optimization.simple_prompt_optimizer.LLMRegistry.resolve"
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)
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@pytest.mark.asyncio
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async def test_simple_prompt_optimizer(
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mock_llm_resolve: mock.MagicMock,
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mock_llm_class: mock.MagicMock,
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mock_sampler: mock.MagicMock,
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):
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"""Test the SimplePromptOptimizer."""
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mock_llm_resolve.return_value = mock_llm_class
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config = SimplePromptOptimizerConfig(num_iterations=2, batch_size=2)
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optimizer = SimplePromptOptimizer(config)
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initial_agent = Agent(name="test_agent", instruction="Initial Prompt")
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result = await optimizer.optimize(initial_agent, mock_sampler)
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# Assertions
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assert len(result.optimized_agents) == 1
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optimized_agent = result.optimized_agents[0].optimized_agent
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assert optimized_agent.instruction == "IMPROVED PROMPT"
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assert result.optimized_agents[0].overall_score == 0.9
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# Check mock calls
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assert mock_sampler.get_train_example_ids.call_count == 1
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# 1 initial, 2 iterations, 1 final validation
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assert mock_sampler.sample_and_score.call_count == 4
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assert mock_llm_class.return_value.generate_content_async.call_count == 2
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