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469 lines
15 KiB
Python
469 lines
15 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from unittest import mock
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from google.adk.labs.openai._openai_llm import _function_declaration_to_openai_tool
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from google.adk.labs.openai._openai_llm import _part_to_openai_content
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from google.adk.labs.openai._openai_llm import _update_type_string
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from google.adk.labs.openai._openai_llm import OpenAILlm
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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.types import Content
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from google.genai.types import Part
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import pytest
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def test_supported_models():
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models = OpenAILlm.supported_models()
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assert len(models) == 3
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assert models[0] == r"gpt-.*"
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assert models[1] == r"o1-.*"
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assert models[2] == r"o3-.*"
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def test_update_type_string():
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schema = {
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"type": "OBJECT",
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"properties": {
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"name": {"type": "STRING"},
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"age": {"type": "INTEGER"},
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"tags": {"type": "ARRAY", "items": {"type": "STRING"}},
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},
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}
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_update_type_string(schema)
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assert schema["type"] == "object"
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assert schema["properties"]["name"]["type"] == "string"
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assert schema["properties"]["age"]["type"] == "integer"
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assert schema["properties"]["tags"]["type"] == "array"
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assert schema["properties"]["tags"]["items"]["type"] == "string"
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def test_function_declaration_to_openai_tool():
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fd = types.FunctionDeclaration(
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name="get_weather",
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description="Get weather",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={"location": types.Schema(type=types.Type.STRING)},
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required=["location"],
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),
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)
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tool = _function_declaration_to_openai_tool(fd)
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assert tool["type"] == "function"
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assert tool["function"]["name"] == "get_weather"
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assert tool["function"]["parameters"]["type"] == "object"
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assert (
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tool["function"]["parameters"]["properties"]["location"]["type"]
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== "string"
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)
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assert tool["function"]["parameters"]["required"] == ["location"]
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def test_part_to_openai_content():
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# Test text part
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part = types.Part.from_text(text="Hello")
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content = _part_to_openai_content(part)
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assert content == "Hello"
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# Test thought part
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part = types.Part.from_text(text="I am thinking")
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part.thought = True
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content = _part_to_openai_content(part)
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assert content == "Thought: I am thinking"
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# Test image part (inline data)
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part = types.Part(
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inline_data=types.Blob(data=b"fake_data", mime_type="image/png")
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)
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content = _part_to_openai_content(part)
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assert isinstance(content, dict)
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assert content["type"] == "image_url"
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assert content["image_url"]["url"].startswith("data:image/png;base64,")
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def test_content_to_openai_messages_with_empty_response():
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from google.adk.labs.openai._openai_llm import _content_to_openai_messages
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# Test with empty dict response
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content = types.Content(
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role="tool",
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parts=[
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types.Part(
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function_response=types.FunctionResponse(
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id="call_123",
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name="get_weather",
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response={},
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)
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)
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],
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)
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messages = _content_to_openai_messages(content)
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assert len(messages) == 1
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assert messages[0]["role"] == "tool"
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assert messages[0]["tool_call_id"] == "call_123"
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assert messages[0]["content"] == "{}"
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# Test with None response
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content = types.Content(
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role="tool",
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parts=[
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types.Part(
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function_response=types.FunctionResponse(
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id="call_123",
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name="get_weather",
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response=None,
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)
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)
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],
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)
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messages = _content_to_openai_messages(content)
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assert len(messages) == 1
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assert messages[0]["content"] == ""
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@pytest.mark.asyncio
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async def test_generate_content_async():
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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)
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mock_response = mock.MagicMock()
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mock_choice = mock.MagicMock()
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mock_message = mock.MagicMock()
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mock_message.content = "Hello there!"
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mock_message.tool_calls = None
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mock_choice.message = mock_message
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mock_response.choices = [mock_choice]
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mock_response.usage.prompt_tokens = 10
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mock_response.usage.completion_tokens = 5
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mock_response.usage.total_tokens = 15
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async def mock_create(*args, **kwargs):
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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 there!"
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assert responses[0].usage_metadata.total_token_count == 15
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@pytest.mark.asyncio
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async def test_generate_content_async_with_config():
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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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=0.7,
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top_p=0.9,
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stop_sequences=["STOP"],
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max_output_tokens=100,
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),
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)
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mock_response = mock.MagicMock()
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mock_choice = mock.MagicMock()
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mock_message = mock.MagicMock()
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mock_message.content = "Hello there!"
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mock_message.tool_calls = None
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mock_choice.message = mock_message
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mock_response.choices = [mock_choice]
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mock_call = mock.MagicMock(return_value=mock_response)
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mock_response.usage.prompt_tokens = 10
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mock_response.usage.completion_tokens = 5
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mock_response.usage.total_tokens = 15
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create_kwargs = {}
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async def mock_create(*args, **kwargs):
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nonlocal create_kwargs
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create_kwargs = kwargs
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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 create_kwargs["temperature"] == 0.7
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assert create_kwargs["top_p"] == 0.9
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assert create_kwargs["stop"] == ["STOP"]
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assert create_kwargs["max_tokens"] == 100
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@pytest.mark.asyncio
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async def test_generate_content_async_with_system_instruction():
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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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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system_instruction="You are a helpful assistant.",
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),
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)
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mock_response = mock.MagicMock()
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mock_choice = mock.MagicMock()
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mock_message = mock.MagicMock()
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mock_message.content = "Hello there!"
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mock_message.tool_calls = None
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mock_choice.message = mock_message
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mock_response.choices = [mock_choice]
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mock_response.usage.prompt_tokens = 10
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mock_response.usage.completion_tokens = 5
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mock_response.usage.total_tokens = 15
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create_kwargs = {}
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async def mock_create(*args, **kwargs):
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nonlocal create_kwargs
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create_kwargs = kwargs
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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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messages = create_kwargs["messages"]
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assert len(messages) == 2
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assert messages[0]["role"] == "system"
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assert messages[0]["content"] == "You are a helpful assistant."
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assert messages[1]["role"] == "user"
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assert messages[1]["content"] == "Hello"
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@pytest.mark.asyncio
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async def test_generate_content_async_with_image():
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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image_part = Part(
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inline_data=types.Blob(data=b"fake_image_data", mime_type="image/png")
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)
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llm_request = LlmRequest(
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model="gpt-4o",
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contents=[
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Content(
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role="user",
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parts=[Part.from_text(text="Analyze this"), image_part],
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)
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],
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)
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mock_response = mock.MagicMock()
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mock_choice = mock.MagicMock()
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mock_message = mock.MagicMock()
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mock_message.content = "It's an image."
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mock_message.tool_calls = None
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mock_choice.message = mock_message
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mock_response.choices = [mock_choice]
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mock_response.usage.prompt_tokens = 10
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mock_response.usage.completion_tokens = 5
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mock_response.usage.total_tokens = 15
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create_kwargs = {}
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async def mock_create(*args, **kwargs):
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nonlocal create_kwargs
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create_kwargs = kwargs
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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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messages = create_kwargs["messages"]
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assert len(messages) == 1
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assert messages[0]["role"] == "user"
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content = messages[0]["content"]
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assert isinstance(content, list)
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assert len(content) == 2
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assert content[0]["type"] == "text"
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assert content[0]["text"] == "Analyze this"
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assert content[1]["type"] == "image_url"
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assert content[1]["image_url"]["url"].startswith("data:image/png;base64,")
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def _completion_with_cached_tokens(cached_tokens):
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"""Builds a mock ChatCompletion whose usage carries prompt_tokens_details."""
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mock_response = mock.MagicMock()
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mock_choice = mock.MagicMock()
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mock_message = mock.MagicMock()
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mock_message.content = "Hello there!"
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mock_message.tool_calls = None
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mock_choice.message = mock_message
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mock_response.choices = [mock_choice]
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mock_response.usage.prompt_tokens = 100
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mock_response.usage.completion_tokens = 5
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mock_response.usage.total_tokens = 105
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if cached_tokens is None:
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mock_response.usage.prompt_tokens_details = None
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else:
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mock_response.usage.prompt_tokens_details.cached_tokens = cached_tokens
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return mock_response
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@pytest.mark.asyncio
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async def test_generate_content_async_reports_cached_tokens():
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"""prompt_tokens_details.cached_tokens populates cached_content_token_count."""
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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)
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mock_response = _completion_with_cached_tokens(64)
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async def mock_create(*args, **kwargs):
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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 responses[0].usage_metadata.cached_content_token_count == 64
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assert responses[0].usage_metadata.prompt_token_count == 100
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@pytest.mark.asyncio
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async def test_generate_content_async_zero_cached_tokens():
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"""No cache hit (cached_tokens=0) reports 0, not a regression."""
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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)
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mock_response = _completion_with_cached_tokens(0)
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async def mock_create(*args, **kwargs):
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return mock_response
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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 responses[0].usage_metadata.cached_content_token_count == 0
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@pytest.mark.asyncio
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async def test_generate_content_async_absent_prompt_tokens_details():
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"""Missing prompt_tokens_details maps to None (no cached count reported)."""
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with mock.patch.dict(os.environ, {"OPENAI_API_KEY": "test_key"}):
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openai_llm = OpenAILlm(model="gpt-4o")
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llm_request = LlmRequest(
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model="gpt-4o",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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)
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mock_response = _completion_with_cached_tokens(None)
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async def mock_create(*args, **kwargs):
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return mock_response
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|
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with mock.patch(
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"google.adk.labs.openai._openai_llm.AsyncOpenAI"
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) as mock_client_class:
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mock_client = mock.MagicMock()
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mock_client_class.return_value = mock_client
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mock_client.chat.completions.create = mock_create
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responses = [
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resp
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async for resp in openai_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 responses[0].usage_metadata.cached_content_token_count is None
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