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https://github.com/deepset-ai/haystack.git
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93817f63b4
* Changed summarizer model to a smaller one (2GB to 500MB) to save on space and speed up the tests. * Removed google pegasus from cache
37 lines
1.6 KiB
Python
37 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.integration
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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", [("embedding", "memory"), ("elasticsearch", "elasticsearch")], 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 in Paris ist die höchste von Menschen geschaffene Struktur der Welt geworden.",
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"Der Eiffelturm in Paris hat offiziell seine Türen für die Öffentlichkeit geöffnet.",
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]
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