64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import importlib
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import os
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import pytest
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from embedchain.config import BaseLlmConfig
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from embedchain.llm.huggingface import HuggingFaceLlm
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@pytest.fixture
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def huggingface_llm_config():
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os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "test_access_token"
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config = BaseLlmConfig(model="google/flan-t5-xxl", max_tokens=50, temperature=0.7, top_p=0.8)
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yield config
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os.environ.pop("HUGGINGFACE_ACCESS_TOKEN")
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def test_init_raises_value_error_without_api_key(mocker):
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mocker.patch.dict(os.environ, clear=True)
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with pytest.raises(ValueError):
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HuggingFaceLlm()
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def test_get_llm_model_answer_raises_value_error_for_system_prompt(huggingface_llm_config):
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llm = HuggingFaceLlm(huggingface_llm_config)
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llm.config.system_prompt = "system_prompt"
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with pytest.raises(ValueError):
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llm.get_llm_model_answer("prompt")
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def test_top_p_value_within_range():
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config = BaseLlmConfig(top_p=1.0)
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with pytest.raises(ValueError):
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HuggingFaceLlm._get_answer("test_prompt", config)
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def test_dependency_is_imported():
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importlib_installed = True
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try:
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importlib.import_module("huggingface_hub")
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except ImportError:
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importlib_installed = False
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assert importlib_installed
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def test_get_llm_model_answer(huggingface_llm_config, mocker):
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mocker.patch("embedchain.llm.huggingface.HuggingFaceLlm._get_answer", return_value="Test answer")
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llm = HuggingFaceLlm(huggingface_llm_config)
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answer = llm.get_llm_model_answer("Test query")
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assert answer == "Test answer"
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def test_hugging_face_mock(huggingface_llm_config, mocker):
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mock_llm_instance = mocker.Mock(return_value="Test answer")
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mocker.patch("embedchain.llm.huggingface.HuggingFaceHub", return_value=mock_llm_instance)
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llm = HuggingFaceLlm(huggingface_llm_config)
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answer = llm.get_llm_model_answer("Test query")
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assert answer == "Test answer"
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mock_llm_instance.assert_called_once_with("Test query")
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