File size: 4,233 Bytes
362a075 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
import json
from haystack import Pipeline
from haystack.components.classifiers import DocumentLanguageClassifier
from haystack.components.converters import TextFileToDocument
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.routers import FileTypeRouter, MetadataRouter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
def test_preprocessing_pipeline(tmp_path):
# Create the pipeline and its components
document_store = InMemoryDocumentStore()
preprocessing_pipeline = Pipeline()
preprocessing_pipeline.add_component(instance=FileTypeRouter(mime_types=["text/plain"]), name="file_type_router")
preprocessing_pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
preprocessing_pipeline.add_component(instance=DocumentLanguageClassifier(), name="language_classifier")
preprocessing_pipeline.add_component(
instance=MetadataRouter(rules={"en": {"field": "language", "operator": "==", "value": "en"}}), name="router"
)
preprocessing_pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
preprocessing_pipeline.add_component(
instance=DocumentSplitter(split_by="sentence", split_length=1), name="splitter"
)
preprocessing_pipeline.add_component(
instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
)
preprocessing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
preprocessing_pipeline.connect("file_type_router.text/plain", "text_file_converter.sources")
preprocessing_pipeline.connect("text_file_converter.documents", "language_classifier.documents")
preprocessing_pipeline.connect("language_classifier.documents", "router.documents")
preprocessing_pipeline.connect("router.en", "cleaner.documents")
preprocessing_pipeline.connect("cleaner.documents", "splitter.documents")
preprocessing_pipeline.connect("splitter.documents", "embedder.documents")
preprocessing_pipeline.connect("embedder.documents", "writer.documents")
# Draw the pipeline
preprocessing_pipeline.draw(tmp_path / "test_preprocessing_pipeline.png")
# Serialize the pipeline to YAML
with open(tmp_path / "test_preprocessing_pipeline.yaml", "w") as f:
preprocessing_pipeline.dump(f)
# Load the pipeline back
with open(tmp_path / "test_preprocessing_pipeline.yaml", "r") as f:
preprocessing_pipeline = Pipeline.load(f)
# Write a txt file
with open(tmp_path / "test_file_english.txt", "w") as f:
f.write(
"This is an english sentence. There is more to it. It's a long text."
"Spans multiple lines."
""
"Even contains empty lines. And extra whitespaces."
)
# Write a txt file
with open(tmp_path / "test_file_german.txt", "w") as f:
f.write("Ein deutscher Satz ohne Verb.")
# Add two txt files and one non-txt file
paths = [
tmp_path / "test_file_english.txt",
tmp_path / "test_file_german.txt",
tmp_path / "test_preprocessing_pipeline.json",
]
result = preprocessing_pipeline.run({"file_type_router": {"sources": paths}})
assert result["writer"]["documents_written"] == 6
filled_document_store = preprocessing_pipeline.get_component("writer").document_store
assert filled_document_store.count_documents() == 6
# Check preprocessed texts
stored_documents = filled_document_store.filter_documents()
expected_texts = [
"This is an english sentence.",
" There is more to it.",
" It's a long text.",
"Spans multiple lines.",
"Even contains empty lines.",
" And extra whitespaces.",
]
assert expected_texts == [document.content for document in stored_documents]
assert all(document.meta["language"] == "en" for document in stored_documents)
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