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https://github.com/deepset-ai/haystack.git
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85258f0654
* Fix types in test_run.py * Get test_run.py to pass fmt-check * Add test_run to mypy checks * Update test folder to pass ruff linting * Fix merge * Fix HF tests * Fix hf test * Try to fix tests * Another attempt * minor fix * fix SentenceTransformersDiversityRanker * skip integrations tests due to model unavailable on HF inference --------- Co-authored-by: anakin87 <stefanofiorucci@gmail.com>
51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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import logging
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import pytest
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from haystack import Document
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from haystack.components.classifiers import DocumentLanguageClassifier
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class TestDocumentLanguageClassifier:
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def test_init(self):
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component = DocumentLanguageClassifier()
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assert component.languages == ["en"]
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def test_non_document_input(self):
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with pytest.raises(TypeError, match="DocumentLanguageClassifier expects a list of Document as input."):
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classifier = DocumentLanguageClassifier()
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classifier.run(documents="This is an english sentence.")
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def test_single_document(self):
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with pytest.raises(TypeError, match="DocumentLanguageClassifier expects a list of Document as input."):
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classifier = DocumentLanguageClassifier()
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classifier.run(documents=Document(content="This is an english sentence."))
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def test_empty_list(self):
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classifier = DocumentLanguageClassifier()
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result = classifier.run(documents=[])
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assert result == {"documents": []}
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def test_detect_language(self):
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classifier = DocumentLanguageClassifier()
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detected_language = classifier._detect_language(Document(content="This is an english sentence."))
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assert detected_language == "en"
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def test_classify_as_en_and_unmatched(self):
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classifier = DocumentLanguageClassifier()
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english_document = Document(content="This is an english sentence.")
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german_document = Document(content="Ein deutscher Satz ohne Verb.")
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result = classifier.run(documents=[english_document, german_document])
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assert result["documents"][0].meta["language"] == "en"
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assert result["documents"][1].meta["language"] == "unmatched"
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def test_warning_if_no_language_detected(self, caplog):
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with caplog.at_level(logging.WARNING):
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classifier = DocumentLanguageClassifier()
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classifier.run(documents=[Document(content=".")])
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assert "Langdetect cannot detect the language of Document with id" in caplog.text
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