66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
from unittest.mock import MagicMock, patch
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import pytest
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from langchain.schema import HumanMessage, SystemMessage
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from embedchain.config import BaseLlmConfig
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from embedchain.llm.vertex_ai import VertexAiLlm
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@pytest.fixture
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def vertexai_llm():
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config = BaseLlmConfig(temperature=0.6, model="vertexai_model", system_prompt="System Prompt")
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return VertexAiLlm(config)
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def test_get_llm_model_answer(vertexai_llm):
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with patch.object(VertexAiLlm, "_get_answer", return_value="Test Response") as mock_method:
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prompt = "Test Prompt"
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response = vertexai_llm.get_llm_model_answer(prompt)
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assert response == "Test Response"
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mock_method.assert_called_once_with(prompt=prompt, config=vertexai_llm.config)
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def test_get_answer_with_warning(vertexai_llm, caplog):
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with patch("langchain.chat_models.ChatVertexAI") as mock_chat:
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mock_chat_instance = mock_chat.return_value
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mock_chat_instance.return_value = MagicMock(content="Test Response")
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prompt = "Test Prompt"
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config = vertexai_llm.config
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config.top_p = 0.5
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response = vertexai_llm._get_answer(prompt, config)
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assert response == "Test Response"
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mock_chat.assert_called_once_with(temperature=config.temperature, model=config.model)
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assert "Config option `top_p` is not supported by this model." in caplog.text
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def test_get_answer_no_warning(vertexai_llm, caplog):
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with patch("langchain.chat_models.ChatVertexAI") as mock_chat:
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mock_chat_instance = mock_chat.return_value
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mock_chat_instance.return_value = MagicMock(content="Test Response")
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prompt = "Test Prompt"
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config = vertexai_llm.config
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config.top_p = 1.0
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response = vertexai_llm._get_answer(prompt, config)
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assert response == "Test Response"
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mock_chat.assert_called_once_with(temperature=config.temperature, model=config.model)
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assert "Config option `top_p` is not supported by this model." not in caplog.text
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def test_get_messages(vertexai_llm):
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prompt = "Test Prompt"
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system_prompt = "Test System Prompt"
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messages = vertexai_llm._get_messages(prompt, system_prompt)
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assert messages == [
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SystemMessage(content="Test System Prompt", additional_kwargs={}),
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HumanMessage(content="Test Prompt", additional_kwargs={}, example=False),
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]
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