mirror of
https://github.com/deepset-ai/haystack.git
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fd16ec63cb
* Rework filter logic for InMemoryDocumentStore to support new filters declaration * Fix legacy filters tests * Simplify logic and handle dates comparison * Rework MetadataRouter to support new filters * Update docstrings * Add release notes * Fix linting * Avoid duplicating filters specifications * Handle corner case * Simplify docstring * Fix filters logic and tests * Fix Document Store testing legacy filters tests
90 lines
4.2 KiB
Python
90 lines
4.2 KiB
Python
import json
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from haystack.preview import Pipeline
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from haystack.preview.components.embedders import SentenceTransformersDocumentEmbedder
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from haystack.preview.components.converters import TextFileToDocument
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from haystack.preview.components.preprocessors import DocumentSplitter, DocumentCleaner
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from haystack.preview.components.classifiers import DocumentLanguageClassifier
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from haystack.preview.components.routers import FileTypeRouter, MetadataRouter
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from haystack.preview.components.writers import DocumentWriter
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from haystack.preview.document_stores import InMemoryDocumentStore
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def test_preprocessing_pipeline(tmp_path):
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# Create the pipeline and its components
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document_store = InMemoryDocumentStore()
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preprocessing_pipeline = Pipeline()
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preprocessing_pipeline.add_component(instance=FileTypeRouter(mime_types=["text/plain"]), name="file_type_router")
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preprocessing_pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
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preprocessing_pipeline.add_component(instance=DocumentLanguageClassifier(), name="language_classifier")
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preprocessing_pipeline.add_component(
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instance=MetadataRouter(rules={"en": {"field": "language", "operator": "==", "value": "en"}}), name="router"
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)
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preprocessing_pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
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preprocessing_pipeline.add_component(
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instance=DocumentSplitter(split_by="sentence", split_length=1), name="splitter"
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)
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preprocessing_pipeline.add_component(
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instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
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name="embedder",
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)
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preprocessing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
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preprocessing_pipeline.connect("file_type_router.text/plain", "text_file_converter.sources")
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preprocessing_pipeline.connect("text_file_converter.documents", "language_classifier.documents")
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preprocessing_pipeline.connect("language_classifier.documents", "router.documents")
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preprocessing_pipeline.connect("router.en", "cleaner.documents")
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preprocessing_pipeline.connect("cleaner.documents", "splitter.documents")
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preprocessing_pipeline.connect("splitter.documents", "embedder.documents")
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preprocessing_pipeline.connect("embedder.documents", "writer.documents")
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# Draw the pipeline
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preprocessing_pipeline.draw(tmp_path / "test_preprocessing_pipeline.png")
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# Serialize the pipeline to JSON
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with open(tmp_path / "test_preprocessing_pipeline.json", "w") as f:
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print(json.dumps(preprocessing_pipeline.to_dict(), indent=4))
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json.dump(preprocessing_pipeline.to_dict(), f)
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# Load the pipeline back
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with open(tmp_path / "test_preprocessing_pipeline.json", "r") as f:
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preprocessing_pipeline = Pipeline.from_dict(json.load(f))
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# Write a txt file
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with open(tmp_path / "test_file_english.txt", "w") as f:
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f.write(
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"This is an english sentence. There is more to it. It's a long text."
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"Spans multiple lines."
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""
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"Even contains empty lines. And extra whitespaces."
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)
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# Write a txt file
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with open(tmp_path / "test_file_german.txt", "w") as f:
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f.write("Ein deutscher Satz ohne Verb.")
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# Add two txt files and one non-txt file
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paths = [
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tmp_path / "test_file_english.txt",
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tmp_path / "test_file_german.txt",
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tmp_path / "test_preprocessing_pipeline.json",
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]
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result = preprocessing_pipeline.run({"file_type_router": {"sources": paths}})
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assert result["writer"]["documents_written"] == 6
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filled_document_store = preprocessing_pipeline.get_component("writer").document_store
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assert filled_document_store.count_documents() == 6
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# Check preprocessed texts
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stored_documents = filled_document_store.filter_documents()
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expected_texts = [
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"This is an english sentence.",
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" There is more to it.",
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" It's a long text.",
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"Spans multiple lines.",
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"Even contains empty lines.",
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" And extra whitespaces.",
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]
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assert expected_texts == [document.content for document in stored_documents]
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assert all(document.meta["language"] == "en" for document in stored_documents)
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