Files
adk-python/tests/integration/conftest.py
T
Douglas Reid 2b5acb98f5 feat(models): add support for gemma model via gemini api
Merge https://github.com/google/adk-python/pull/2857

Adds support for invoking Gemma models via the Gemini API endpoint. To support agentic function, callbacks are added which can extract and transform function calls and responses into user and model messages in the history.

This change is intended to allow developers to explore the use of Gemma models for agentic purposes without requiring local deployment of the models. This should ease the burden of experimentation and testing for developers.

A basic "hello world" style agent example is provided to demonstrate proper functioning of Gemma 3 models inside an Agent container, using the dice roll + prime check framework of similar examples for other models.

## Testing

### Testing Plan
- add and run integration and unit tests
- manual run of example `multi_tool_agent` from quickstart using new `Gemma` model
- manual run of `hello_world_gemma` agent

### Automated Test Results:
| Test Command | Results |
|----------------|---------|
| pytest ./tests/unittests | 4386 passed, 2849 warnings in 58.43s |
| pytest ./tests/unittests/models/test_google_llm.py | 100 passed, 4 warnings in 5.83s |
| pytest ./tests/integration/models/test_google_llm.py | 5 passed, 2 warnings in 3.73s |

### Manual Testing

Here is a log of `multi_tool_agent` run with locally-built wheel and using Gemma model.
```
❯ adk run multi_tool_agent
Log setup complete: /var/folders/bg/_133c0ds2kb7cn699cpmmh_h0061bp/T/agents_log/agent.20250904_152617.log
To access latest log: tail -F /var/folders/bg/_133c0ds2kb7cn699cpmmh_h0061bp/T/agents_log/agent.latest.log
/Users/<redacted>/venvs/adk-quickstart/lib/python3.11/site-packages/google/adk/cli/cli.py:143: UserWarning: [EXPERIMENTAL] InMemoryCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
  credential_service = InMemoryCredentialService()
/Users/<redacted>/venvs/adk-quickstart/lib/python3.11/site-packages/google/adk/auth/credential_service/in_memory_credential_service.py:33: UserWarning: [EXPERIMENTAL] BaseCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
  super().__init__()
Running agent weather_time_agent, type exit to exit.
[user]: what's the weather like today?
[weather_time_agent]: Which city are you asking about?

[user]: new york
[weather_time_agent]: OK. The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees Fahrenheit).
```

And here is a snippet of a log generated with DEBUG level logging of the `hello_world_gemma` sample. It demonstrates how function calls are extracted and inserted based on Gemma model interactions:

```
...
2025-09-04 15:32:41,708 - DEBUG - google_llm.py:138 -
LLM Request:
-----------------------------------------------------------
System Instruction:
None
-----------------------------------------------------------
Contents:
{"parts":[{"text":"\n      You roll dice and answer questions about the outcome of the dice rolls.\n      You can roll dice of different sizes...\n"}],"role":"user"}
{"parts":[{"text":"Hi, introduce yourself."}],"role":"user"}
{"parts":[{"text":"Hello! I am data_processing_agent, a hello world agent that can roll many-sided dice and check if numbers are prime. I'm ready to assist you with those tasks. Let's begin!\n\n\n\n"}],"role":"model"}
{"parts":[{"text":"Roll a die with 100 sides and check if it is prime"}],"role":"user"}
{"parts":[{"text":"{\"args\":{\"sides\":100},\"name\":\"roll_die\"}"}],"role":"model"}
{"parts":[{"text":"Invoking tool `roll_die` produced: `{\"result\": 82}`."}],"role":"user"}
{"parts":[{"text":"{\"args\":{\"nums\":[82]},\"name\":\"check_prime\"}"}],"role":"model"}
{"parts":[{"text":"Invoking tool `check_prime` produced: `{\"result\": \"No prime numbers found.\"}`."}],"role":"user"}
{"parts":[{"text":"The die roll was 82, and it is not a prime number.\n\n\n\n"}],"role":"model"}
{"parts":[{"text":"Roll it again."}],"role":"user"}
-----------------------------------------------------------
Functions:

-----------------------------------------------------------

2025-09-04 15:32:41,708 - INFO - models.py:8165 - AFC is enabled with max remote calls: 10.
2025-09-04 15:32:42,693 - INFO - google_llm.py:180 - Response received from the model.
2025-09-04 15:32:42,693 - DEBUG - google_llm.py:181 -
LLM Response:
-----------------------------------------------------------
Text:
{"args":{"sides":100},"name":"roll_die"}
-----------------------------------------------------------
...
```
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2857 from douglas-reid:add-gemma-via-api e6d015f6a9ccbcf20ef7a7af8e4bbe1e9a5936b6
PiperOrigin-RevId: 816451001
2025-10-07 17:38:35 -07:00

120 lines
3.7 KiB
Python

# 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 logging
import os
from typing import Literal
import warnings
from dotenv import load_dotenv
from google.adk import Agent
from pytest import fixture
from pytest import FixtureRequest
from pytest import hookimpl
from pytest import Metafunc
from .utils import TestRunner
logger = logging.getLogger('google_adk.' + __name__)
def load_env_for_tests():
dotenv_path = os.path.join(os.path.dirname(__file__), '.env')
if not os.path.exists(dotenv_path):
warnings.warn(
f'Missing .env file at {dotenv_path}. See dotenv.sample for an example.'
)
else:
load_dotenv(dotenv_path, override=True, verbose=True)
if 'GOOGLE_API_KEY' not in os.environ:
warnings.warn(
'Missing GOOGLE_API_KEY in the environment variables. GOOGLE_AI backend'
' integration tests will fail.'
)
for env_var in [
'GOOGLE_CLOUD_PROJECT',
'GOOGLE_CLOUD_LOCATION',
]:
if env_var not in os.environ:
warnings.warn(
f'Missing {env_var} in the environment variables. Vertex backend'
' integration tests will fail.'
)
load_env_for_tests()
BackendType = Literal['GOOGLE_AI', 'VERTEX']
@fixture
def agent_runner(request: FixtureRequest) -> TestRunner:
assert isinstance(request.param, dict)
if 'agent' in request.param:
assert isinstance(request.param['agent'], Agent)
return TestRunner(request.param['agent'])
elif 'agent_name' in request.param:
assert isinstance(request.param['agent_name'], str)
return TestRunner.from_agent_name(request.param['agent_name'])
raise NotImplementedError('Must provide agent or agent_name.')
@fixture(autouse=True)
def llm_backend(request: FixtureRequest):
# Set backend environment value.
original_val = os.environ.get('GOOGLE_GENAI_USE_VERTEXAI')
backend_type = request.param
if backend_type == 'GOOGLE_AI':
os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = '0'
else:
os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = '1'
yield # Run the test
# Restore the environment
if original_val is None:
os.environ.pop('GOOGLE_GENAI_USE_VERTEXAI', None)
else:
os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = original_val
@hookimpl(tryfirst=True)
def pytest_generate_tests(metafunc: Metafunc):
if llm_backend.__name__ in metafunc.fixturenames:
if not _is_explicitly_marked(llm_backend.__name__, metafunc):
test_backend = os.environ.get('TEST_BACKEND', 'BOTH')
if test_backend == 'GOOGLE_AI_ONLY':
metafunc.parametrize(llm_backend.__name__, ['GOOGLE_AI'], indirect=True)
elif test_backend == 'VERTEX_ONLY':
metafunc.parametrize(llm_backend.__name__, ['VERTEX'], indirect=True)
elif test_backend == 'BOTH':
metafunc.parametrize(
llm_backend.__name__, ['GOOGLE_AI', 'VERTEX'], indirect=True
)
else:
raise ValueError(
f'Invalid TEST_BACKEND value: {test_backend}, should be one of'
' [GOOGLE_AI_ONLY, VERTEX_ONLY, BOTH]'
)
def _is_explicitly_marked(mark_name: str, metafunc: Metafunc) -> bool:
if hasattr(metafunc.function, 'pytestmark'):
for mark in metafunc.function.pytestmark:
if mark.name == 'parametrize' and mark_name in mark.args[0]:
return True
return False