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https://github.com/deepset-ai/haystack.git
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1a60e21137
* remove generate_single_summary * update schemas * remove unused import * fix mypy * fix mypy * test: summarizer doesnt change content * other test correction * move test_summarizer_translation to test_extractor_translation * fix test * first try for doc merger * reintroduce and deprecate generate_single_summary * progress in document merger * document merger! * mypy, pylint fixes * use generator * added test that will fail in 1.12 * adapt to review * extended deprecation docstring * Update test/nodes/test_extractor_translation.py * Update test/nodes/test_summarizer.py * Update test/nodes/test_summarizer.py * black * documents fixture Co-authored-by: Sara Zan <sarazanzo94@gmail.com>
30 lines
1.3 KiB
Python
30 lines
1.3 KiB
Python
import pytest
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from haystack.pipelines import TranslationWrapperPipeline, ExtractiveQAPipeline
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from haystack.nodes import DensePassageRetriever, EmbeddingRetriever
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from .test_summarizer import SPLIT_DOCS
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# Keeping few (retriever,document_store,reader) combination to reduce test time
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@pytest.mark.integration
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@pytest.mark.elasticsearch
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@pytest.mark.summarizer
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@pytest.mark.parametrize("retriever,document_store,reader", [("embedding", "memory", "farm")], indirect=True)
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def test_extractive_qa_pipeline_with_translator(
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document_store, retriever, reader, en_to_de_translator, de_to_en_translator
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):
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document_store.write_documents(SPLIT_DOCS)
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if isinstance(retriever, EmbeddingRetriever) or isinstance(retriever, DensePassageRetriever):
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document_store.update_embeddings(retriever=retriever)
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query = "Wo steht der Eiffelturm?"
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base_pipeline = ExtractiveQAPipeline(retriever=retriever, reader=reader)
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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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output = pipeline.run(query=query, params={"Retriever": {"top_k": 2}})
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assert len(output["documents"]) == 2
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answers_texts = [el.answer for el in output["answers"]]
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assert "Frankreich" in answers_texts
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