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https://github.com/deepset-ai/haystack.git
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dde9d59271
* fix pip backtracking issue * restrict azure-core version * Remove the trailing comma * Add skip_magic_trailing_comma in pyproject.toml for pydoc compatibility * Pin pydoc-markdown _again_ Co-authored-by: Sara Zan <sarazanzo94@gmail.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
81 lines
3.7 KiB
Python
81 lines
3.7 KiB
Python
import pytest
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from haystack.pipelines import TranslationWrapperPipeline, ExtractiveQAPipeline
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from haystack.schema import Answer
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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def test_extractive_qa_answers(reader, retriever_with_docs, document_store_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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prediction = pipeline.run(query="Who lives in Berlin?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 3}})
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assert prediction is not None
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assert type(prediction["answers"][0]) == Answer
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assert prediction["query"] == "Who lives in Berlin?"
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assert prediction["answers"][0].answer == "Carla"
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assert prediction["answers"][0].score <= 1
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assert prediction["answers"][0].score >= 0
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assert prediction["answers"][0].meta["meta_field"] == "test1"
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assert prediction["answers"][0].context == "My name is Carla and I live in Berlin"
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assert len(prediction["answers"]) == 3
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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def test_extractive_qa_answers_without_normalized_scores(reader_without_normalized_scores, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader_without_normalized_scores, retriever=retriever_with_docs)
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prediction = pipeline.run(query="Who lives in Berlin?", params={"Reader": {"top_k": 3}})
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assert prediction is not None
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assert prediction["query"] == "Who lives in Berlin?"
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assert prediction["answers"][0].answer == "Carla"
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assert prediction["answers"][0].score <= 11
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assert prediction["answers"][0].score >= 10
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assert prediction["answers"][0].meta["meta_field"] == "test1"
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assert prediction["answers"][0].context == "My name is Carla and I live in Berlin"
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assert len(prediction["answers"]) == 3
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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def test_extractive_qa_offsets(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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prediction = pipeline.run(query="Who lives in Berlin?", params={"Retriever": {"top_k": 5}})
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start = prediction["answers"][0].offsets_in_context[0].start
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end = prediction["answers"][0].offsets_in_context[0].end
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assert start == 11
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assert end == 16
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assert prediction["answers"][0].context[start:end] == prediction["answers"][0].answer
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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def test_extractive_qa_answers_single_result(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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query = "testing finder"
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prediction = pipeline.run(query=query, params={"Retriever": {"top_k": 1}, "Reader": {"top_k": 1}})
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assert prediction is not None
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assert len(prediction["answers"]) == 1
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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def test_extractive_qa_answers_with_translator(reader, retriever_with_docs, en_to_de_translator, de_to_en_translator):
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base_pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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pipeline = TranslationWrapperPipeline(
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input_translator=de_to_en_translator, output_translator=en_to_de_translator, pipeline=base_pipeline
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)
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prediction = pipeline.run(query="Wer lebt in Berlin?", params={"Reader": {"top_k": 3}})
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assert prediction is not None
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assert prediction["query"] == "Wer lebt in Berlin?"
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assert "Carla" in prediction["answers"][0].answer
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assert prediction["answers"][0].score <= 1
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assert prediction["answers"][0].score >= 0
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assert prediction["answers"][0].meta["meta_field"] == "test1"
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assert prediction["answers"][0].context == "My name is Carla and I live in Berlin"
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