| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102 | import osimport pytestfrom core.model_runtime.entities.rerank_entities import RerankDocument, RerankResultfrom core.model_runtime.errors.validate import CredentialsValidateFailedErrorfrom core.model_runtime.model_providers.localai.rerank.rerank import LocalaiRerankModeldef test_validate_credentials_for_chat_model():    model = LocalaiRerankModel()    with pytest.raises(CredentialsValidateFailedError):        model.validate_credentials(            model='bge-reranker-v2-m3',            credentials={                'server_url': 'hahahaha',                'completion_type': 'completion',            }        )    model.validate_credentials(        model='bge-reranker-base',        credentials={            'server_url': os.environ.get('LOCALAI_SERVER_URL'),            'completion_type': 'completion',        }    )def test_invoke_rerank_model():    model = LocalaiRerankModel()    response = model.invoke(        model='bge-reranker-base',        credentials={            'server_url': os.environ.get('LOCALAI_SERVER_URL')        },        query='Organic skincare products for sensitive skin',        docs=[            "Eco-friendly kitchenware for modern homes",            "Biodegradable cleaning supplies for eco-conscious consumers",            "Organic cotton baby clothes for sensitive skin",            "Natural organic skincare range for sensitive skin",            "Tech gadgets for smart homes: 2024 edition",            "Sustainable gardening tools and compost solutions",            "Sensitive skin-friendly facial cleansers and toners",            "Organic food wraps and storage solutions",            "Yoga mats made from recycled materials"        ],        top_n=3,        score_threshold=0.75,        user="abc-123"    )    assert isinstance(response, RerankResult)    assert len(response.docs) == 3def test__invoke():    model = LocalaiRerankModel()    # Test case 1: Empty docs    result = model._invoke(        model='bge-reranker-base',        credentials={            'server_url': 'https://example.com',            'api_key': '1234567890'        },        query='Organic skincare products for sensitive skin',        docs=[],        top_n=3,        score_threshold=0.75,        user="abc-123"    )    assert isinstance(result, RerankResult)    assert len(result.docs) == 0    # Test case 2: Valid invocation    result = model._invoke(        model='bge-reranker-base',        credentials={            'server_url': 'https://example.com',            'api_key': '1234567890'        },        query='Organic skincare products for sensitive skin',        docs=[            "Eco-friendly kitchenware for modern homes",            "Biodegradable cleaning supplies for eco-conscious consumers",            "Organic cotton baby clothes for sensitive skin",            "Natural organic skincare range for sensitive skin",            "Tech gadgets for smart homes: 2024 edition",            "Sustainable gardening tools and compost solutions",            "Sensitive skin-friendly facial cleansers and toners",            "Organic food wraps and storage solutions",            "Yoga mats made from recycled materials"        ],        top_n=3,        score_threshold=0.75,        user="abc-123"    )    assert isinstance(result, RerankResult)    assert len(result.docs) == 3    assert all(isinstance(doc, RerankDocument) for doc in result.docs)
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