mirror of
https://github.com/encounter/adk-python.git
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Luis PabonandCopybara-Service
409df1378f
Merge https://github.com/google/adk-python/pull/3170 Addresses Feature Request: #3116 This PR adds a `speech_config` to the **LLM Agent configuration** for the **live use case**. When an **asynchronous LLM** call is made to the **Gemini Live API**, it prioritizes the most specific agent configuration's speech_config. If that is null, it then uses the run configuration's speech_config. Unit tests have been added to verify this behavior. COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3170 from qyuo:bidi_agent_speech_config af1bd277d4f95c4a7d9aa0b16828ba3de826ce08 PiperOrigin-RevId: 822305427
2047 lines
67 KiB
Python
2047 lines
67 KiB
Python
# Copyright 2025 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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import os
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import sys
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from typing import Optional
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from unittest import mock
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from unittest.mock import AsyncMock
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from google.adk import version as adk_version
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from google.adk.agents.context_cache_config import ContextCacheConfig
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from google.adk.models.cache_metadata import CacheMetadata
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from google.adk.models.gemini_llm_connection import GeminiLlmConnection
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from google.adk.models.google_llm import _AGENT_ENGINE_TELEMETRY_ENV_VARIABLE_NAME
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from google.adk.models.google_llm import _AGENT_ENGINE_TELEMETRY_TAG
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from google.adk.models.google_llm import _build_request_log
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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.utils.variant_utils import GoogleLLMVariant
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from google.genai import types
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from google.genai.types import Content
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from google.genai.types import Part
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import pytest
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class MockAsyncIterator:
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"""Mock for async iterator."""
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def __init__(self, seq):
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self.iter = iter(seq)
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def __aiter__(self):
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return self
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async def __anext__(self):
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try:
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return next(self.iter)
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except StopIteration as exc:
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raise StopAsyncIteration from exc
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async def aclose(self):
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pass
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@pytest.fixture
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def generate_content_response():
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return types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model",
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parts=[Part.from_text(text="Hello, how can I help you?")],
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),
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finish_reason=types.FinishReason.STOP,
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)
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]
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)
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@pytest.fixture
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def gemini_llm():
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return Gemini(model="gemini-1.5-flash")
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@pytest.fixture
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def llm_request():
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return LlmRequest(
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model="gemini-1.5-flash",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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config=types.GenerateContentConfig(
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temperature=0.1,
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response_modalities=[types.Modality.TEXT],
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system_instruction="You are a helpful assistant",
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),
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)
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@pytest.fixture
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def cache_metadata():
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import time
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return CacheMetadata(
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cache_name="projects/test/locations/us-central1/cachedContents/test123",
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expire_time=time.time() + 3600,
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fingerprint="test_fingerprint",
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invocations_used=2,
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contents_count=3,
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created_at=time.time() - 600,
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)
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@pytest.fixture
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def llm_request_with_cache(cache_metadata):
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return LlmRequest(
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model="gemini-1.5-flash",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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config=types.GenerateContentConfig(
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temperature=0.1,
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response_modalities=[types.Modality.TEXT],
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system_instruction="You are a helpful assistant",
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),
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cache_config=ContextCacheConfig(
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cache_intervals=10, ttl_seconds=3600, min_tokens=100
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),
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cache_metadata=cache_metadata,
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)
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@pytest.fixture
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def llm_request_with_computer_use():
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return LlmRequest(
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model="gemini-1.5-flash",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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config=types.GenerateContentConfig(
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temperature=0.1,
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response_modalities=[types.Modality.TEXT],
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system_instruction="You are a helpful assistant",
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tools=[
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types.Tool(
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computer_use=types.ComputerUse(
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environment=types.Environment.ENVIRONMENT_BROWSER
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)
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)
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],
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),
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)
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@pytest.fixture
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def mock_os_environ():
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initial_env = os.environ.copy()
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with mock.patch.dict(os.environ, initial_env, clear=False) as m:
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yield m
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def test_supported_models():
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models = Gemini.supported_models()
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assert len(models) == 4
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assert models[0] == r"gemini-.*"
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assert models[1] == r"model-optimizer-.*"
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assert models[2] == r"projects\/.+\/locations\/.+\/endpoints\/.+"
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assert (
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models[3]
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== r"projects\/.+\/locations\/.+\/publishers\/google\/models\/gemini.+"
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)
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def test_client_version_header():
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model = Gemini(model="gemini-1.5-flash")
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client = model.api_client
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# Check that ADK version and Python version are present in headers
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adk_version_string = f"google-adk/{adk_version.__version__}"
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python_version_string = f"gl-python/{sys.version.split()[0]}"
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x_goog_api_client_header = client._api_client._http_options.headers[
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"x-goog-api-client"
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]
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user_agent_header = client._api_client._http_options.headers["user-agent"]
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# Verify ADK version is present
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assert adk_version_string in x_goog_api_client_header
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assert adk_version_string in user_agent_header
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# Verify Python version is present
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assert python_version_string in x_goog_api_client_header
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assert python_version_string in user_agent_header
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# Verify some Google SDK version is present (could be genai-sdk or vertex-genai-modules)
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assert any(
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sdk in x_goog_api_client_header
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for sdk in ["google-genai-sdk/", "vertex-genai-modules/"]
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)
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assert any(
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sdk in user_agent_header
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for sdk in ["google-genai-sdk/", "vertex-genai-modules/"]
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)
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def test_client_version_header_with_agent_engine(mock_os_environ):
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os.environ[_AGENT_ENGINE_TELEMETRY_ENV_VARIABLE_NAME] = "my_test_project"
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model = Gemini(model="gemini-1.5-flash")
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client = model.api_client
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# Check that ADK version with telemetry tag and Python version are present in headers
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adk_version_with_telemetry = (
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f"google-adk/{adk_version.__version__}+{_AGENT_ENGINE_TELEMETRY_TAG}"
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)
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python_version_string = f"gl-python/{sys.version.split()[0]}"
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x_goog_api_client_header = client._api_client._http_options.headers[
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"x-goog-api-client"
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]
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user_agent_header = client._api_client._http_options.headers["user-agent"]
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# Verify ADK version with telemetry tag is present
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assert adk_version_with_telemetry in x_goog_api_client_header
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assert adk_version_with_telemetry in user_agent_header
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# Verify Python version is present
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assert python_version_string in x_goog_api_client_header
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assert python_version_string in user_agent_header
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# Verify some Google SDK version is present (could be genai-sdk or vertex-genai-modules)
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assert any(
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sdk in x_goog_api_client_header
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for sdk in ["google-genai-sdk/", "vertex-genai-modules/"]
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)
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assert any(
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sdk in user_agent_header
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for sdk in ["google-genai-sdk/", "vertex-genai-modules/"]
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)
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def test_maybe_append_user_content(gemini_llm, llm_request):
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# Test with user content already present
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gemini_llm._maybe_append_user_content(llm_request)
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assert len(llm_request.contents) == 1
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# Test with model content as the last message
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llm_request.contents.append(
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Content(role="model", parts=[Part.from_text(text="Response")])
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)
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gemini_llm._maybe_append_user_content(llm_request)
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assert len(llm_request.contents) == 3
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assert llm_request.contents[-1].role == "user"
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assert "Continue processing" in llm_request.contents[-1].parts[0].text
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@pytest.mark.asyncio
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async def test_generate_content_async(
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gemini_llm, llm_request, generate_content_response
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):
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with mock.patch.object(gemini_llm, "api_client") as mock_client:
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# Create a mock coroutine that returns the generate_content_response
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async def mock_coro():
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return generate_content_response
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# Assign the coroutine to the mocked method
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mock_client.aio.models.generate_content.return_value = mock_coro()
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responses = [
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resp
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async for resp in gemini_llm.generate_content_async(
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llm_request, stream=False
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)
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]
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assert len(responses) == 1
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assert isinstance(responses[0], LlmResponse)
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assert responses[0].content.parts[0].text == "Hello, how can I help you?"
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mock_client.aio.models.generate_content.assert_called_once()
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@pytest.mark.asyncio
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async def test_generate_content_async_stream(gemini_llm, llm_request):
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with mock.patch.object(gemini_llm, "api_client") as mock_client:
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mock_responses = [
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model", parts=[Part.from_text(text="Hello")]
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),
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finish_reason=None,
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)
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]
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),
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model", parts=[Part.from_text(text=", how")]
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),
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finish_reason=None,
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)
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]
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),
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model",
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parts=[Part.from_text(text=" can I help you?")],
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),
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finish_reason=types.FinishReason.STOP,
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)
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]
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),
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]
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# Create a mock coroutine that returns the MockAsyncIterator
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async def mock_coro():
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return MockAsyncIterator(mock_responses)
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# Set the mock to return the coroutine
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mock_client.aio.models.generate_content_stream.return_value = mock_coro()
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responses = [
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resp
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async for resp in gemini_llm.generate_content_async(
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llm_request, stream=True
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)
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]
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# Assertions remain the same
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assert len(responses) == 4
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assert responses[0].partial is True
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assert responses[1].partial is True
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assert responses[2].partial is True
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assert responses[3].content.parts[0].text == "Hello, how can I help you?"
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mock_client.aio.models.generate_content_stream.assert_called_once()
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@pytest.mark.asyncio
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async def test_generate_content_async_stream_preserves_thinking_and_text_parts(
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gemini_llm, llm_request
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):
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with mock.patch.object(gemini_llm, "api_client") as mock_client:
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response1 = types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model",
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parts=[Part(text="Think1", thought=True)],
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),
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finish_reason=None,
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)
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]
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)
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response2 = types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model",
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parts=[Part(text="Think2", thought=True)],
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),
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finish_reason=None,
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)
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]
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)
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response3 = types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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role="model",
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parts=[Part.from_text(text="Answer.")],
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),
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finish_reason=types.FinishReason.STOP,
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)
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]
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)
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async def mock_coro():
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return MockAsyncIterator([response1, response2, response3])
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mock_client.aio.models.generate_content_stream.return_value = mock_coro()
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responses = [
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resp
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async for resp in gemini_llm.generate_content_async(
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llm_request, stream=True
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)
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]
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assert len(responses) == 4
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assert responses[0].partial is True
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assert responses[1].partial is True
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assert responses[2].partial is True
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assert responses[3].content.parts[0].text == "Think1Think2"
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assert responses[3].content.parts[0].thought is True
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assert responses[3].content.parts[1].text == "Answer."
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mock_client.aio.models.generate_content_stream.assert_called_once()
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@pytest.mark.asyncio
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async def test_connect(gemini_llm, llm_request):
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# Create a mock connection
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mock_connection = mock.MagicMock(spec=GeminiLlmConnection)
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# Create a mock context manager
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class MockContextManager:
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async def __aenter__(self):
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return mock_connection
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async def __aexit__(self, *args):
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pass
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# Mock the connect method at the class level
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with mock.patch(
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"google.adk.models.google_llm.Gemini.connect",
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return_value=MockContextManager(),
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):
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async with gemini_llm.connect(llm_request) as connection:
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assert connection is mock_connection
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@pytest.mark.asyncio
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async def test_generate_content_async_with_custom_headers(
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gemini_llm, llm_request, generate_content_response
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):
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"""Test that tracking headers are updated when custom headers are provided."""
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# Add custom headers to the request config
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custom_headers = {"custom-header": "custom-value"}
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for key in gemini_llm._tracking_headers:
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custom_headers[key] = "custom " + gemini_llm._tracking_headers[key]
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llm_request.config.http_options = types.HttpOptions(headers=custom_headers)
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with mock.patch.object(gemini_llm, "api_client") as mock_client:
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# Create a mock coroutine that returns the generate_content_response
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async def mock_coro():
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return generate_content_response
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mock_client.aio.models.generate_content.return_value = mock_coro()
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responses = [
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resp
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async for resp in gemini_llm.generate_content_async(
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llm_request, stream=False
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)
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]
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|
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# Verify that the config passed to generate_content contains merged headers
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mock_client.aio.models.generate_content.assert_called_once()
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call_args = mock_client.aio.models.generate_content.call_args
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config_arg = call_args.kwargs["config"]
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for key, value in config_arg.http_options.headers.items():
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if key in gemini_llm._tracking_headers:
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assert value == gemini_llm._tracking_headers[key] + " custom"
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else:
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assert value == custom_headers[key]
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|
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assert len(responses) == 1
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assert isinstance(responses[0], LlmResponse)
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|
|
|
|
|
@pytest.mark.asyncio
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|
async def test_generate_content_async_stream_with_custom_headers(
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gemini_llm, llm_request
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):
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"""Test that tracking headers are updated when custom headers are provided in streaming mode."""
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# Add custom headers to the request config
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custom_headers = {"custom-header": "custom-value"}
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llm_request.config.http_options = types.HttpOptions(headers=custom_headers)
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|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
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mock_responses = [
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types.GenerateContentResponse(
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|
candidates=[
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|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
)
|
|
]
|
|
|
|
async def mock_coro():
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|
return MockAsyncIterator(mock_responses)
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|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
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|
|
|
responses = [
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resp
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async for resp in gemini_llm.generate_content_async(
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llm_request, stream=True
|
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)
|
|
]
|
|
|
|
# Verify that the config passed to generate_content_stream contains merged headers
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|
mock_client.aio.models.generate_content_stream.assert_called_once()
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|
call_args = mock_client.aio.models.generate_content_stream.call_args
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config_arg = call_args.kwargs["config"]
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|
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expected_headers = custom_headers.copy()
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expected_headers.update(gemini_llm._tracking_headers)
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assert config_arg.http_options.headers == expected_headers
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|
|
|
assert len(responses) == 2
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|
|
|
|
@pytest.mark.parametrize("stream", [True, False])
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|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_patches_tracking_headers(
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|
stream, gemini_llm, llm_request, generate_content_response
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|
):
|
|
"""Tests that tracking headers are added to the request config."""
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|
# Set the request's config.http_options to None.
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|
llm_request.config.http_options = None
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|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
if stream:
|
|
# Create a mock coroutine that returns the mock_responses.
|
|
async def mock_coro():
|
|
return MockAsyncIterator([generate_content_response])
|
|
|
|
# Mock for streaming response.
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
else:
|
|
# Create a mock coroutine that returns the generate_content_response.
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
# Mock for non-streaming response.
|
|
mock_client.aio.models.generate_content.return_value = mock_coro()
|
|
|
|
# Call the generate_content_async method.
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=stream
|
|
)
|
|
]
|
|
|
|
# Assert that the config passed to the generate_content or
|
|
# generate_content_stream method contains the tracking headers.
|
|
if stream:
|
|
mock_client.aio.models.generate_content_stream.assert_called_once()
|
|
call_args = mock_client.aio.models.generate_content_stream.call_args
|
|
else:
|
|
mock_client.aio.models.generate_content.assert_called_once()
|
|
call_args = mock_client.aio.models.generate_content.call_args
|
|
|
|
final_config = call_args.kwargs["config"]
|
|
|
|
assert final_config is not None
|
|
assert final_config.http_options is not None
|
|
assert (
|
|
final_config.http_options.headers["x-goog-api-client"]
|
|
== gemini_llm._tracking_headers["x-goog-api-client"]
|
|
)
|
|
|
|
assert len(responses) == 2 if stream else 1
|
|
|
|
|
|
def test_live_api_version_vertex_ai(gemini_llm):
|
|
"""Test that _live_api_version returns 'v1beta1' for Vertex AI backend."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.VERTEX_AI
|
|
):
|
|
assert gemini_llm._live_api_version == "v1beta1"
|
|
|
|
|
|
def test_live_api_version_gemini_api(gemini_llm):
|
|
"""Test that _live_api_version returns 'v1alpha' for Gemini API backend."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.GEMINI_API
|
|
):
|
|
assert gemini_llm._live_api_version == "v1alpha"
|
|
|
|
|
|
def test_live_api_client_properties(gemini_llm):
|
|
"""Test that _live_api_client is properly configured with tracking headers and API version."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.VERTEX_AI
|
|
):
|
|
client = gemini_llm._live_api_client
|
|
|
|
# Verify that the client has the correct headers and API version
|
|
http_options = client._api_client._http_options
|
|
assert http_options.api_version == "v1beta1"
|
|
|
|
# Check that tracking headers are included
|
|
tracking_headers = gemini_llm._tracking_headers
|
|
for key, value in tracking_headers.items():
|
|
assert key in http_options.headers
|
|
assert value in http_options.headers[key]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_with_custom_headers(gemini_llm, llm_request):
|
|
"""Test that connect method updates tracking headers and API version when custom headers are provided."""
|
|
# Setup request with live connect config and custom headers
|
|
custom_headers = {"custom-live-header": "live-value"}
|
|
llm_request.live_connect_config = types.LiveConnectConfig(
|
|
http_options=types.HttpOptions(headers=custom_headers)
|
|
)
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
# Mock the _live_api_client to return a mock client
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
# Create a mock context manager
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Verify that the connect method was called with the right config
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify that tracking headers were merged with custom headers
|
|
expected_headers = custom_headers.copy()
|
|
expected_headers.update(gemini_llm._tracking_headers)
|
|
assert config_arg.http_options.headers == expected_headers
|
|
|
|
# Verify that API version was set
|
|
assert config_arg.http_options.api_version == gemini_llm._live_api_version
|
|
|
|
# Verify that system instruction and tools were set
|
|
assert config_arg.system_instruction is not None
|
|
assert config_arg.tools == llm_request.config.tools
|
|
|
|
# Verify connection is properly wrapped
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_without_custom_headers(gemini_llm, llm_request):
|
|
"""Test that connect method works properly when no custom headers are provided."""
|
|
# Setup request with live connect config but no custom headers
|
|
llm_request.live_connect_config = types.LiveConnectConfig()
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Verify that the connect method was called with the right config
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify that http_options remains None since no custom headers were provided
|
|
assert config_arg.http_options is None
|
|
|
|
# Verify that system instruction and tools were still set
|
|
assert config_arg.system_instruction is not None
|
|
assert config_arg.tools == llm_request.config.tools
|
|
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
(
|
|
"api_backend, "
|
|
"expected_file_display_name, "
|
|
"expected_inline_display_name, "
|
|
"expected_labels"
|
|
),
|
|
[
|
|
(
|
|
GoogleLLMVariant.GEMINI_API,
|
|
None,
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
GoogleLLMVariant.VERTEX_AI,
|
|
"My Test PDF",
|
|
"My Test Image",
|
|
{"key": "value"},
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.asyncio
|
|
async def test_preprocess_request_handles_backend_specific_fields(
|
|
gemini_llm: Gemini,
|
|
api_backend: GoogleLLMVariant,
|
|
expected_file_display_name: Optional[str],
|
|
expected_inline_display_name: Optional[str],
|
|
expected_labels: Optional[str],
|
|
):
|
|
"""Tests that _preprocess_request correctly sanitizes fields based on the API backend.
|
|
|
|
- For GEMINI_API, it should remove 'display_name' from file/inline data
|
|
and remove 'labels' from the config.
|
|
- For VERTEX_AI, it should leave these fields untouched.
|
|
"""
|
|
# Arrange: Create a request with fields that need to be preprocessed.
|
|
llm_request_with_files = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
Content(
|
|
role="user",
|
|
parts=[
|
|
Part(
|
|
file_data=types.FileData(
|
|
file_uri="gs://bucket/file.pdf",
|
|
mime_type="application/pdf",
|
|
display_name="My Test PDF",
|
|
)
|
|
),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
data=b"some_bytes",
|
|
mime_type="image/png",
|
|
display_name="My Test Image",
|
|
)
|
|
),
|
|
],
|
|
)
|
|
],
|
|
config=types.GenerateContentConfig(labels={"key": "value"}),
|
|
)
|
|
|
|
# Mock the _api_backend property to control the test scenario
|
|
with mock.patch.object(
|
|
Gemini, "_api_backend", new_callable=mock.PropertyMock
|
|
) as mock_backend:
|
|
mock_backend.return_value = api_backend
|
|
|
|
# Act: Run the preprocessing method
|
|
await gemini_llm._preprocess_request(llm_request_with_files)
|
|
|
|
# Assert: Check if the fields were correctly processed
|
|
file_part = llm_request_with_files.contents[0].parts[0]
|
|
inline_part = llm_request_with_files.contents[0].parts[1]
|
|
|
|
assert file_part.file_data.display_name == expected_file_display_name
|
|
assert inline_part.inline_data.display_name == expected_inline_display_name
|
|
assert llm_request_with_files.config.labels == expected_labels
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_aggregated_content_regardless_of_finish_reason():
|
|
"""Test that aggregated content is generated regardless of finish_reason."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Test with different finish reasons
|
|
test_cases = [
|
|
types.FinishReason.MAX_TOKENS,
|
|
types.FinishReason.SAFETY,
|
|
types.FinishReason.RECITATION,
|
|
types.FinishReason.OTHER,
|
|
]
|
|
|
|
for finish_reason in test_cases:
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" world")]
|
|
),
|
|
finish_reason=finish_reason,
|
|
finish_message=f"Finished with {finish_reason}",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with error info
|
|
final_response = responses[2]
|
|
assert final_response.content.parts[0].text == "Hello world"
|
|
# After the code changes, error_code and error_message are set for non-STOP finish reasons
|
|
assert final_response.error_code == finish_reason
|
|
assert final_response.error_message == f"Finished with {finish_reason}"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_with_thought_and_text_error_handling():
|
|
"""Test that aggregated content with thought and text preserves error information."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part(text="Think1", thought=True)]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Answer")]
|
|
),
|
|
finish_reason=types.FinishReason.MAX_TOKENS,
|
|
finish_message="Maximum tokens reached",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with both thought and text
|
|
final_response = responses[2]
|
|
assert len(final_response.content.parts) == 2
|
|
assert final_response.content.parts[0].text == "Think1"
|
|
assert final_response.content.parts[0].thought is True
|
|
assert final_response.content.parts[1].text == "Answer"
|
|
# After the code changes, error_code and error_message are set for non-STOP finish reasons
|
|
assert final_response.error_code == types.FinishReason.MAX_TOKENS
|
|
assert final_response.error_message == "Maximum tokens reached"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_error_info_none_for_stop_finish_reason():
|
|
"""Test that error_code and error_message are None when finish_reason is STOP."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" world")]
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
finish_message="Successfully completed",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with error info None for STOP finish reason
|
|
final_response = responses[2]
|
|
assert final_response.content.parts[0].text == "Hello world"
|
|
assert final_response.error_code is None
|
|
assert final_response.error_message is None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_error_info_set_for_non_stop_finish_reason():
|
|
"""Test that error_code and error_message are set for non-STOP finish reasons."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" world")]
|
|
),
|
|
finish_reason=types.FinishReason.MAX_TOKENS,
|
|
finish_message="Maximum tokens reached",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with error info set for non-STOP finish reason
|
|
final_response = responses[2]
|
|
assert final_response.content.parts[0].text == "Hello world"
|
|
assert final_response.error_code == types.FinishReason.MAX_TOKENS
|
|
assert final_response.error_message == "Maximum tokens reached"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_no_aggregated_content_without_text():
|
|
"""Test that no aggregated content is generated when there's no accumulated text."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Mock response with no text content
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="test", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have only 1 response (no aggregated content generated)
|
|
assert len(responses) == 1
|
|
# Verify it's a function call, not text
|
|
assert responses[0].content.parts[0].function_call is not None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_mixed_text_function_call_text():
|
|
"""Test streaming with pattern: [text, function_call, text] to verify proper aggregation."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create responses with pattern: text -> function_call -> text
|
|
mock_responses = [
|
|
# First text chunk
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="First text")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call interrupts the text flow
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="test_func", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# More text after function call
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text=" second text")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have multiple responses:
|
|
# 1. Partial text "First text"
|
|
# 2. Aggregated "First text" when function call interrupts
|
|
# 3. Function call
|
|
# 4. Partial text " second text"
|
|
# 5. Final aggregated " second text"
|
|
assert len(responses) == 5
|
|
|
|
# First partial text
|
|
assert responses[0].partial is True
|
|
assert responses[0].content.parts[0].text == "First text"
|
|
|
|
# Aggregated first text (when function call interrupts)
|
|
assert responses[1].content.parts[0].text == "First text"
|
|
assert (
|
|
responses[1].partial is None
|
|
) # Aggregated responses don't have partial flag
|
|
|
|
# Function call
|
|
assert responses[2].content.parts[0].function_call is not None
|
|
assert responses[2].content.parts[0].function_call.name == "test_func"
|
|
|
|
# Second partial text
|
|
assert responses[3].partial is True
|
|
assert responses[3].content.parts[0].text == " second text"
|
|
|
|
# Final aggregated text with error info
|
|
assert responses[4].content.parts[0].text == " second text"
|
|
assert (
|
|
responses[4].error_code is None
|
|
) # STOP finish reason should have None error_code
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_multiple_text_parts_in_single_response():
|
|
"""Test streaming with multiple text parts in a single response."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create a response with multiple text parts
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part.from_text(text="First part"),
|
|
Part.from_text(text=" second part"),
|
|
],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should handle only the first text part in current implementation
|
|
# Note: This test documents current behavior - the implementation only
|
|
# looks at parts[0].text, so it would only process "First part"
|
|
assert len(responses) >= 1
|
|
assert responses[0].content.parts[0].text == "First part"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_complex_mixed_thought_text_function():
|
|
"""Test complex streaming with thought, text, and function calls mixed."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Complex pattern: thought -> text -> function_call -> thought -> text
|
|
mock_responses = [
|
|
# Thought
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="Thinking...", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Regular text
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text="Here's my answer")],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="lookup", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# More thought
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="More thinking...", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Final text
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text=" and conclusion")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should properly separate thought and regular text across aggregations
|
|
assert len(responses) > 5 # Multiple partial + aggregated responses
|
|
|
|
# Verify we get both thought and regular text parts in aggregated responses
|
|
aggregated_responses = [
|
|
r
|
|
for r in responses
|
|
if r.partial is None and r.content and len(r.content.parts) > 1
|
|
]
|
|
assert (
|
|
len(aggregated_responses) > 0
|
|
) # Should have at least one aggregated response with multiple parts
|
|
|
|
# Final aggregated response should have both thought and text
|
|
final_response = responses[-1]
|
|
assert (
|
|
final_response.error_code is None
|
|
) # STOP finish reason should have None error_code
|
|
assert len(final_response.content.parts) == 2 # thought part + text part
|
|
assert final_response.content.parts[0].thought is True
|
|
assert "More thinking..." in final_response.content.parts[0].text
|
|
assert final_response.content.parts[1].text == " and conclusion"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_two_separate_text_aggregations():
|
|
"""Test that [text, function_call, text] results in two separate text aggregations."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create responses: multiple text chunks -> function_call -> multiple text chunks
|
|
mock_responses = [
|
|
# First text accumulation (multiple chunks)
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="First")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" chunk")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call interrupts
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="divide", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Second text accumulation (multiple chunks)
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Second")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" chunk")]
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Find the aggregated text responses (non-partial, text-only)
|
|
aggregated_text_responses = [
|
|
r
|
|
for r in responses
|
|
if (
|
|
r.partial is None
|
|
and r.content
|
|
and r.content.parts
|
|
and r.content.parts[0].text
|
|
and not r.content.parts[0].function_call
|
|
)
|
|
]
|
|
|
|
# Should have two separate text aggregations: "First chunk" and "Second chunk"
|
|
assert len(aggregated_text_responses) >= 2
|
|
|
|
# First aggregation should contain "First chunk"
|
|
first_aggregation = aggregated_text_responses[0]
|
|
assert first_aggregation.content.parts[0].text == "First chunk"
|
|
|
|
# Final aggregation should contain "Second chunk" and have error info
|
|
final_aggregation = aggregated_text_responses[-1]
|
|
assert final_aggregation.content.parts[0].text == "Second chunk"
|
|
assert (
|
|
final_aggregation.error_code is None
|
|
) # STOP finish reason should have None error_code
|
|
|
|
# Verify the function call is preserved between aggregations
|
|
function_call_responses = [
|
|
r
|
|
for r in responses
|
|
if (r.content and r.content.parts and r.content.parts[0].function_call)
|
|
]
|
|
assert len(function_call_responses) == 1
|
|
assert (
|
|
function_call_responses[0].content.parts[0].function_call.name
|
|
== "divide"
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_computer_use_removes_system_instruction():
|
|
"""Test that system instruction is set to None when computer use is configured."""
|
|
llm = Gemini()
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
types.Content(role="user", parts=[types.Part.from_text(text="Hello")])
|
|
],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="You are a helpful assistant",
|
|
tools=[
|
|
types.Tool(
|
|
computer_use=types.ComputerUse(
|
|
environment=types.Environment.ENVIRONMENT_BROWSER
|
|
)
|
|
)
|
|
],
|
|
),
|
|
)
|
|
|
|
await llm._preprocess_request(llm_request)
|
|
|
|
# System instruction should be set to None when computer use is configured
|
|
assert llm_request.config.system_instruction is None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_computer_use_preserves_system_instruction_when_no_computer_use():
|
|
"""Test that system instruction is preserved when computer use is not configured."""
|
|
llm = Gemini()
|
|
|
|
original_instruction = "You are a helpful assistant"
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
types.Content(role="user", parts=[types.Part.from_text(text="Hello")])
|
|
],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction=original_instruction,
|
|
tools=[
|
|
types.Tool(
|
|
function_declarations=[
|
|
types.FunctionDeclaration(name="test", description="test")
|
|
]
|
|
)
|
|
],
|
|
),
|
|
)
|
|
|
|
await llm._preprocess_request(llm_request)
|
|
|
|
# System instruction should be preserved when no computer use
|
|
assert llm_request.config.system_instruction == original_instruction
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_computer_use_with_no_config():
|
|
"""Test that preprocessing works when config is None."""
|
|
llm = Gemini()
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
types.Content(role="user", parts=[types.Part.from_text(text="Hello")])
|
|
],
|
|
)
|
|
|
|
# Should not raise an exception
|
|
await llm._preprocess_request(llm_request)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_computer_use_with_no_tools():
|
|
"""Test that preprocessing works when config.tools is None."""
|
|
llm = Gemini()
|
|
|
|
original_instruction = "You are a helpful assistant"
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
types.Content(role="user", parts=[types.Part.from_text(text="Hello")])
|
|
],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction=original_instruction,
|
|
tools=None,
|
|
),
|
|
)
|
|
|
|
await llm._preprocess_request(llm_request)
|
|
|
|
# System instruction should be preserved when no tools
|
|
assert llm_request.config.system_instruction == original_instruction
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_adapt_computer_use_tool_wait():
|
|
"""Test that _adapt_computer_use_tool correctly adapts wait to wait_5_seconds."""
|
|
from google.adk.tools.computer_use.computer_use_tool import ComputerUseTool
|
|
|
|
llm = Gemini()
|
|
|
|
# Create a mock wait tool
|
|
mock_wait_func = AsyncMock()
|
|
mock_wait_func.return_value = "mock_result"
|
|
|
|
original_wait_tool = ComputerUseTool(
|
|
func=mock_wait_func,
|
|
screen_size=(1920, 1080),
|
|
virtual_screen_size=(1000, 1000),
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
config=types.GenerateContentConfig(),
|
|
)
|
|
|
|
# Add wait to tools_dict
|
|
llm_request.tools_dict["wait"] = original_wait_tool
|
|
|
|
# Call the adaptation method (now async)
|
|
await llm._adapt_computer_use_tool(llm_request)
|
|
|
|
# Verify wait was removed and wait_5_seconds was added
|
|
assert "wait" not in llm_request.tools_dict
|
|
assert "wait_5_seconds" in llm_request.tools_dict
|
|
|
|
# Verify the new tool has correct properties
|
|
wait_5_seconds_tool = llm_request.tools_dict["wait_5_seconds"]
|
|
assert isinstance(wait_5_seconds_tool, ComputerUseTool)
|
|
assert wait_5_seconds_tool._screen_size == (1920, 1080)
|
|
assert wait_5_seconds_tool._coordinate_space == (1000, 1000)
|
|
|
|
# Verify calling the new tool calls the original with 5 seconds
|
|
result = await wait_5_seconds_tool.func()
|
|
assert result == "mock_result"
|
|
mock_wait_func.assert_awaited_once_with(5)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_adapt_computer_use_tool_no_wait():
|
|
"""Test that _adapt_computer_use_tool does nothing when wait is not present."""
|
|
llm = Gemini()
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
config=types.GenerateContentConfig(),
|
|
)
|
|
|
|
# Don't add any tools
|
|
original_tools_dict = llm_request.tools_dict.copy()
|
|
|
|
# Call the adaptation method (now async)
|
|
await llm._adapt_computer_use_tool(llm_request)
|
|
|
|
# Verify tools_dict is unchanged
|
|
assert llm_request.tools_dict == original_tools_dict
|
|
assert "wait_5_seconds" not in llm_request.tools_dict
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_cache_metadata_integration(
|
|
gemini_llm, llm_request_with_cache, cache_metadata
|
|
):
|
|
"""Test integration between Google LLM and cache manager with proper parameter order.
|
|
|
|
This test specifically validates that the cache manager's populate_cache_metadata_in_response
|
|
method is called with the correct parameter order: (llm_response, cache_metadata).
|
|
|
|
This test would have caught the parameter order bug where cache_metadata and llm_response
|
|
were passed in the wrong order, causing 'CacheMetadata' object has no attribute 'usage_metadata' errors.
|
|
"""
|
|
|
|
# Create a mock response with usage metadata including cached tokens
|
|
generate_content_response = types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text="Hello, how can I help you?")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
],
|
|
usage_metadata=types.GenerateContentResponseUsageMetadata(
|
|
prompt_token_count=1500,
|
|
candidates_token_count=150,
|
|
cached_content_token_count=800, # This is the key field that was always 0 due to the bug
|
|
total_token_count=1650,
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create a mock coroutine that returns the generate_content_response
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.aio.models.generate_content.return_value = mock_coro()
|
|
|
|
# Mock the cache manager module to verify correct method call
|
|
with mock.patch(
|
|
"google.adk.models.gemini_context_cache_manager.GeminiContextCacheManager"
|
|
) as MockCacheManagerClass:
|
|
mock_cache_manager = MockCacheManagerClass.return_value
|
|
# Configure cache manager to handle context caching
|
|
mock_cache_manager.handle_context_caching = AsyncMock(
|
|
return_value=cache_metadata
|
|
)
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request_with_cache, stream=False
|
|
)
|
|
]
|
|
|
|
# Verify the response was processed
|
|
assert len(responses) == 1
|
|
response = responses[0]
|
|
assert isinstance(response, LlmResponse)
|
|
assert response.content.parts[0].text == "Hello, how can I help you?"
|
|
|
|
# CRITICAL TEST: Verify populate_cache_metadata_in_response was called with correct parameter order
|
|
mock_cache_manager.populate_cache_metadata_in_response.assert_called_once()
|
|
call_args = (
|
|
mock_cache_manager.populate_cache_metadata_in_response.call_args
|
|
)
|
|
|
|
# The first argument should be the LlmResponse (not CacheMetadata)
|
|
first_arg = call_args[0][0] # First positional argument
|
|
second_arg = call_args[0][1] # Second positional argument
|
|
|
|
# Verify correct parameter order: (llm_response, cache_metadata)
|
|
assert isinstance(first_arg, LlmResponse), (
|
|
f"First parameter should be LlmResponse, got {type(first_arg)}. "
|
|
"This indicates parameters are in wrong order."
|
|
)
|
|
assert isinstance(second_arg, CacheMetadata), (
|
|
f"Second parameter should be CacheMetadata, got {type(second_arg)}. "
|
|
"This indicates parameters are in wrong order."
|
|
)
|
|
|
|
# Verify the LlmResponse has the expected usage metadata
|
|
assert first_arg.usage_metadata is not None
|
|
assert first_arg.usage_metadata.cached_content_token_count == 800
|
|
assert first_arg.usage_metadata.prompt_token_count == 1500
|
|
assert first_arg.usage_metadata.candidates_token_count == 150
|
|
|
|
# Verify cache metadata is preserved
|
|
assert second_arg.cache_name == cache_metadata.cache_name
|
|
assert second_arg.invocations_used == cache_metadata.invocations_used
|
|
|
|
|
|
def test_build_request_log_with_config_multiple_tool_types():
|
|
"""Test that _build_request_log includes config with multiple tool types."""
|
|
func_decl = types.FunctionDeclaration(
|
|
name="test_function",
|
|
description="A test function",
|
|
parameters={"type": "object", "properties": {}},
|
|
)
|
|
|
|
tool = types.Tool(
|
|
function_declarations=[func_decl],
|
|
google_search=types.GoogleSearch(),
|
|
code_execution=types.ToolCodeExecution(),
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.7,
|
|
max_output_tokens=500,
|
|
system_instruction="You are a helpful assistant",
|
|
tools=[tool],
|
|
),
|
|
)
|
|
|
|
log_output = _build_request_log(llm_request)
|
|
|
|
# Verify config section exists
|
|
assert "Config:" in log_output
|
|
|
|
# Verify config contains expected fields (using Python dict format with single quotes)
|
|
assert "'temperature': 0.7" in log_output
|
|
assert "'max_output_tokens': 500" in log_output
|
|
|
|
# Verify config contains other tool types (not function_declarations)
|
|
assert "'google_search'" in log_output
|
|
assert "'code_execution'" in log_output
|
|
|
|
# Verify function_declarations is NOT in config section
|
|
# (it should only be in the Functions section)
|
|
config_section = log_output.split("Functions:")[0]
|
|
assert "'function_declarations'" not in config_section
|
|
|
|
# Verify function is in Functions section
|
|
assert "Functions:" in log_output
|
|
assert "test_function" in log_output
|
|
|
|
# Verify system instruction is NOT in config section
|
|
assert (
|
|
"'system_instruction'"
|
|
not in log_output.split("Contents:")[0].split("Config:")[1]
|
|
)
|
|
|
|
|
|
def test_build_request_log_function_declarations_in_second_tool():
|
|
"""Test that function_declarations in non-first tool are handled correctly."""
|
|
func_decl = types.FunctionDeclaration(
|
|
name="my_function",
|
|
description="A test function",
|
|
parameters={"type": "object", "properties": {}},
|
|
)
|
|
|
|
# First tool has only google_search
|
|
tool1 = types.Tool(google_search=types.GoogleSearch())
|
|
|
|
# Second tool has function_declarations
|
|
tool2 = types.Tool(
|
|
function_declarations=[func_decl],
|
|
code_execution=types.ToolCodeExecution(),
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.5,
|
|
system_instruction="You are a helpful assistant",
|
|
tools=[tool1, tool2],
|
|
),
|
|
)
|
|
|
|
log_output = _build_request_log(llm_request)
|
|
|
|
# Verify function is in Functions section
|
|
assert "Functions:" in log_output
|
|
assert "my_function" in log_output
|
|
|
|
# Verify function_declarations is NOT in config section
|
|
config_section = log_output.split("Functions:")[0]
|
|
assert "'function_declarations'" not in config_section
|
|
|
|
# Verify both tools are in config but without function_declarations (Python dict format)
|
|
assert "'google_search'" in log_output
|
|
assert "'code_execution'" in log_output
|
|
|
|
# Verify config has the expected structure without parsing
|
|
config_section = log_output.split("Config:")[1].split("---")[0]
|
|
# Should have 2 tools (two dict entries in the tools list)
|
|
assert config_section.count("'google_search'") == 1
|
|
assert config_section.count("'code_execution'") == 1
|
|
# Function declarations should NOT be in config section
|
|
assert "'function_declarations'" not in config_section
|
|
|
|
|
|
def test_build_request_log_fallback_to_repr_on_all_failures(monkeypatch):
|
|
"""Test that _build_request_log falls back to repr() if model_dump fails."""
|
|
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.7,
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
# Mock model_dump at class level to raise exception
|
|
def mock_model_dump(*args, **kwargs):
|
|
raise Exception("dump failed")
|
|
|
|
monkeypatch.setattr(
|
|
types.GenerateContentConfig, "model_dump", mock_model_dump
|
|
)
|
|
|
|
log_output = _build_request_log(llm_request)
|
|
|
|
# Should still succeed using repr()
|
|
assert "Config:" in log_output
|
|
assert "GenerateContentConfig" in log_output
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_uses_gemini_speech_config_when_request_is_none(
|
|
gemini_llm, llm_request
|
|
):
|
|
"""Tests that Gemini's speech_config is used when live_connect_config's is None."""
|
|
# Arrange: Set a speech_config on the Gemini instance with the voice "Kore"
|
|
gemini_llm.speech_config = types.SpeechConfig(
|
|
voice_config=types.VoiceConfig(
|
|
prebuilt_voice_config=types.PrebuiltVoiceConfig(
|
|
voice_name="Kore",
|
|
)
|
|
)
|
|
)
|
|
llm_request.live_connect_config = (
|
|
types.LiveConnectConfig()
|
|
) # speech_config is None
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
# Act
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Assert
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify the speech_config from the Gemini instance was used
|
|
assert config_arg.speech_config is not None
|
|
assert (
|
|
config_arg.speech_config.voice_config.prebuilt_voice_config.voice_name
|
|
== "Kore"
|
|
)
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_uses_request_speech_config_when_gemini_is_none(
|
|
gemini_llm, llm_request
|
|
):
|
|
"""Tests that request's speech_config is used when Gemini's is None."""
|
|
# Arrange: Set a speech_config on the request instance with the voice "Kore"
|
|
gemini_llm.speech_config = None
|
|
request_speech_config = types.SpeechConfig(
|
|
voice_config=types.VoiceConfig(
|
|
prebuilt_voice_config=types.PrebuiltVoiceConfig(
|
|
voice_name="Kore",
|
|
)
|
|
)
|
|
)
|
|
llm_request.live_connect_config = types.LiveConnectConfig(
|
|
speech_config=request_speech_config
|
|
)
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
# Act
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Assert
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify the speech_config from the request instance was used
|
|
assert config_arg.speech_config is not None
|
|
assert (
|
|
config_arg.speech_config.voice_config.prebuilt_voice_config.voice_name
|
|
== "Kore"
|
|
)
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_request_gemini_config_overrides_speech_config(
|
|
gemini_llm, llm_request
|
|
):
|
|
"""Tests that live_connect_config's speech_config is preserved even if Gemini has one."""
|
|
# Arrange: Set different speech_configs on both the Gemini instance ("Puck") and the request ("Zephyr")
|
|
gemini_llm.speech_config = types.SpeechConfig(
|
|
voice_config=types.VoiceConfig(
|
|
prebuilt_voice_config=types.PrebuiltVoiceConfig(
|
|
voice_name="Puck",
|
|
)
|
|
)
|
|
)
|
|
request_speech_config = types.SpeechConfig(
|
|
voice_config=types.VoiceConfig(
|
|
prebuilt_voice_config=types.PrebuiltVoiceConfig(
|
|
voice_name="Zephyr",
|
|
)
|
|
)
|
|
)
|
|
llm_request.live_connect_config = types.LiveConnectConfig(
|
|
speech_config=request_speech_config
|
|
)
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
# Act
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Assert
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify the speech_config from the request ("Zephyr") was overwritten by Gemini's speech_config ("Puck")
|
|
assert config_arg.speech_config is not None
|
|
assert (
|
|
config_arg.speech_config.voice_config.prebuilt_voice_config.voice_name
|
|
== "Puck"
|
|
)
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_speech_config_remains_none_when_both_are_none(
|
|
gemini_llm, llm_request
|
|
):
|
|
"""Tests that speech_config is None when neither Gemini nor the request has it."""
|
|
# Arrange: Ensure both Gemini instance and request have no speech_config
|
|
gemini_llm.speech_config = None
|
|
llm_request.live_connect_config = (
|
|
types.LiveConnectConfig()
|
|
) # speech_config is None
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
# Act
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Assert
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify the final speech_config is still None
|
|
assert config_arg.speech_config is None
|
|
assert isinstance(connection, GeminiLlmConnection)
|