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DocumentCleaner

Use DocumentCleaner to make text documents more readable. It removes extra whitespaces, empty lines, specified substrings, regexes, page headers, and footers in this particular order. This is useful for preparing the documents for further processing by LLMs.

Most common position in a pipelineIn indexing pipelines after Converters , before DocumentSplitter
Mandatory run variables"documents": A list of documents
Output variables"documents": A list of documents
API referencePreProcessors
GitHub linkhttps://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/document_cleaner.py

Overview

DocumentCleaner expects a list of documents as input and returns a list of documents with cleaned texts. Selectable cleaning steps for each input document are to remove_empty_lines, remove_extra_whitespaces and to remove_repeated_substrings. These three parameters are booleans that can be set when the component is initialized.

  • unicode_normalization normalizes Unicode characters to a standard form. The parameter can be set to NFC, NFKC, NFD, or NFKD.
  • ascii_only removes accents from characters and replaces them with their closest ASCII equivalents.
  • remove_empty_lines removes empty lines from the document.
  • remove_extra_whitespaces removes extra whitespaces from the document.
  • remove_repeated_substrings removes repeated substrings (headers/footers) from pages in the document. Pages in the text need to be separated by form feed character "\f", which is supported by TextFileToDocument and AzureOCRDocumentConverter.

In addition, you can specify a list of strings that should be removed from all documents as part of the cleaning with the parameter remove_substring. You can also specify a regular expression with the parameter remove_regex and any matches will be removed.

The cleaning steps are executed in the following order:

  1. unicode_normalization
  2. ascii_only
  3. remove_extra_whitespaces
  4. remove_empty_lines
  5. remove_substrings
  6. remove_regex
  7. remove_repeated_substrings

Usage

On its own

You can use it outside of a pipeline to clean up your documents:

from haystack.components.preprocessors import DocumentCleaner

cleaner = DocumentCleaner(
  ascii_only=True,
	remove_empty_lines=True,
	remove_extra_whitespaces=True,
	remove_repeated_substrings=False)

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import TextFileToDocument
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter

document_store = InMemoryDocumentStore()
p = Pipeline()
p.add_component(instance=TextFileToDocument(), name="text_file_converter")
p.add_component(instance=DocumentCleaner(), name="cleaner")
p.add_component(instance=DocumentSplitter(split_by="sentence", split_length=1), name="splitter")
p.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
p.connect("text_file_converter.documents", "cleaner.documents")
p.connect("cleaner.documents", "splitter.documents")
p.connect("splitter.documents", "writer.documents")

p.run({"cleaner": {"documents": your_docs}})

Related Links

See the parameters details in our API reference: