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Haystack

PreProcessor

Use the PreProcessor to normalize white spaces, get rid of headers and footers, clean empty lines in your Documents, or split them into smaller pieces. PreProcessor is useful in an indexing pipeline to prepare your files for search.

Splitting is generally recommended for long Documents as it makes the Retriever's job easier and speeds up Question Answering. For suggestions on how best to split your documents, see Optimization.

Position in a PipelineAs early in an indexing Pipeline as possible but after File Converters and Crawlers
InputDocuments
OutputDocuments
ClassesPreProcessor

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Tutorial

To start working with code examples, have a look at the How to Preprocess Documents tutorial. For ideas on what you can do at indexing time, see the DocumentClassifier at Index Time tutorial.

Usage

To initialize PreProcessor, run:

from haystack.nodes import PreProcessor

processor = PreProcessor(
    clean_empty_lines=True,
    clean_whitespace=True,
    clean_header_footer=True,
    split_by="word",
    split_length=200,
    split_respect_sentence_boundary=True,
    split_overlap=0
)
ArgumentTypeDescription
clean_empty_linesboolNormalizes 3 or more consecutive empty lines to be just a two empty lines.
clean_whitespaceboolRemoves any whitespace at the beginning or end of each line in the text.
clean_header_footerboolRemoves any long header or footer texts that are repeated on each page.
split_bystringDetermines what unit the document is split by. Choose from 'word', 'sentence' or 'passage'.
split_lengthintSets a maximum number of 'word', 'sentence' or 'passage' units per output document
split_respect_sentence_boundaryboolEnsures that document boundaries do not fall in the middle of sentences
split_overlapintSets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.

To run the PreProcessor by itself, run:

doc = converter.convert(file_path=file, meta=None)
docs = processor.process(doc)

To use PreProcessor in a pipeline, run:

from haystack.pipelines import Pipeline
from haystack.nodes import PreProcessor, TextConverter, Retriever
from haystack.nodes import DeepsetCloudDocumentStore

pipeline = Pipeline()
pipeline.add_node(component=text_converter, name="TextConverter", inputs=["File"])
pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["TextConverter"])
pipeline.add_node(component=retriever, name="EmbeddingRetriever", inputs=["PreProcessor"])
pipeline.add_node(component=document_store, name="DeepsetCloudDocumentStore", inputs="EmbeddingRetriever")

Document Format

When you are not using an indexing Pipeline, the PreProcessor can take either Document objects (recommended) as input or plain dictionaries. To learn more about the Document class, see Documents, Answers, and Labels.

# Option 1: Native Haystack Documents
docs = [
    Document(
        content='DOCUMENT_TEXT_HERE',
        meta={'name': DOCUMENT_NAME, ...}
        ...
    ), ...
]

# Option 2: Plain dictionary
docs = [
    {
        'content': 'DOCUMENT_TEXT_HERE',
        'meta': {'name': DOCUMENT_NAME, ...}
    }, ...
]

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