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import pytest | |
from embedchain.config.evaluation.base import ContextRelevanceConfig | |
from embedchain.evaluation.metrics import ContextRelevance | |
from embedchain.utils.evaluation import EvalData, EvalMetric | |
def mock_data(): | |
return [ | |
EvalData( | |
contexts=[ | |
"This is a test context 1.", | |
], | |
question="This is a test question 1.", | |
answer="This is a test answer 1.", | |
), | |
EvalData( | |
contexts=[ | |
"This is a test context 2-1.", | |
"This is a test context 2-2.", | |
], | |
question="This is a test question 2.", | |
answer="This is a test answer 2.", | |
), | |
] | |
def mock_context_relevance_metric(monkeypatch): | |
monkeypatch.setenv("OPENAI_API_KEY", "test_api_key") | |
metric = ContextRelevance() | |
return metric | |
def test_context_relevance_init(monkeypatch): | |
monkeypatch.setenv("OPENAI_API_KEY", "test_api_key") | |
metric = ContextRelevance() | |
assert metric.name == EvalMetric.CONTEXT_RELEVANCY.value | |
assert metric.config.model == "gpt-4" | |
assert metric.config.api_key is None | |
assert metric.config.language == "en" | |
monkeypatch.delenv("OPENAI_API_KEY") | |
def test_context_relevance_init_with_config(): | |
metric = ContextRelevance(config=ContextRelevanceConfig(api_key="test_api_key")) | |
assert metric.name == EvalMetric.CONTEXT_RELEVANCY.value | |
assert metric.config.model == "gpt-4" | |
assert metric.config.api_key == "test_api_key" | |
assert metric.config.language == "en" | |
def test_context_relevance_init_without_api_key(monkeypatch): | |
monkeypatch.delenv("OPENAI_API_KEY", raising=False) | |
with pytest.raises(ValueError): | |
ContextRelevance() | |
def test_sentence_segmenter(mock_context_relevance_metric): | |
text = "This is a test sentence. This is another sentence." | |
assert mock_context_relevance_metric._sentence_segmenter(text) == [ | |
"This is a test sentence. ", | |
"This is another sentence.", | |
] | |
def test_compute_score(mock_context_relevance_metric, mock_data, monkeypatch): | |
monkeypatch.setattr( | |
mock_context_relevance_metric.client.chat.completions, | |
"create", | |
lambda model, messages: type( | |
"obj", | |
(object,), | |
{ | |
"choices": [ | |
type("obj", (object,), {"message": type("obj", (object,), {"content": "This is a test reponse."})}) | |
] | |
}, | |
)(), | |
) | |
assert mock_context_relevance_metric._compute_score(mock_data[0]) == 1.0 | |
assert mock_context_relevance_metric._compute_score(mock_data[1]) == 0.5 | |
def test_evaluate(mock_context_relevance_metric, mock_data, monkeypatch): | |
monkeypatch.setattr( | |
mock_context_relevance_metric.client.chat.completions, | |
"create", | |
lambda model, messages: type( | |
"obj", | |
(object,), | |
{ | |
"choices": [ | |
type("obj", (object,), {"message": type("obj", (object,), {"content": "This is a test reponse."})}) | |
] | |
}, | |
)(), | |
) | |
assert mock_context_relevance_metric.evaluate(mock_data) == 0.75 | |