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* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
import pytest
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from haystack.pipelines import TranslationWrapperPipeline, SearchSummarizationPipeline
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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) combination to reduce test time
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@pytest.mark.slow
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@pytest.mark.elasticsearch
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@pytest.mark.summarizer
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@pytest.mark.parametrize(
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"retriever,document_store",
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[("embedding", "memory"), ("elasticsearch", "elasticsearch")],
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indirect=True,
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)
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def test_summarization_pipeline_with_translator(
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document_store, retriever, summarizer, 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 = SearchSummarizationPipeline(retriever=retriever, summarizer=summarizer)
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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(
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query=query, params={"Retriever": {"top_k": 2}, "Summarizer": {"generate_single_summary": True}}
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)
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# SearchSummarizationPipeline return answers but Summarizer return documents
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documents = output["documents"]
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assert len(documents) == 1
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assert documents[0].content in [
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"Der Eiffelturm ist ein Wahrzeichen in Paris, Frankreich.",
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"Der Eiffelturm, der 1889 in Paris, Frankreich, erbaut wurde, ist das höchste freistehende Bauwerk der Welt.",
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]
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