mirror of
https://github.com/EvolutionAPI/adk-python.git
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306 lines
8.9 KiB
Python
306 lines
8.9 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 sys
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from unittest import mock
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from google.adk import version as adk_version
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from google.adk.models.gemini_llm_connection import GeminiLlmConnection
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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.genai import types
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from google.genai import version as genai_version
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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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@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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def test_supported_models():
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models = Gemini.supported_models()
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assert len(models) == 3
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assert models[0] == r"gemini-.*"
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assert models[1] == r"projects\/.+\/locations\/.+\/endpoints\/.+"
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assert (
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models[2]
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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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adk_header = (
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f"google-adk/{adk_version.__version__} gl-python/{sys.version.split()[0]}"
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)
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genai_header = (
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f"google-genai-sdk/{genai_version.__version__} gl-python/{sys.version.split()[0]} "
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)
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expected_header = genai_header + adk_header
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assert (
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expected_header
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in client._api_client._http_options.headers["x-goog-api-client"]
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)
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assert (
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expected_header in client._api_client._http_options.headers["user-agent"]
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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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# Create mock stream responses
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class MockAsyncIterator:
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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:
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raise StopAsyncIteration
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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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class MockAsyncIterator:
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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:
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raise StopAsyncIteration
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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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