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title DocumentCleaner
id documentcleaner
slug /documentcleaner
description 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.

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 pipeline In indexing pipelines after Converters , after DocumentSplitter
Mandatory run variables documents: A list of documents
Output variables documents: A list of documents
API reference PreProcessors
GitHub link https://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, AzureOCRDocumentConverter, MistralOCRDocumentConverter, and PaddleOCRVLDocumentConverter.

:::note remove_extra_whitespaces and remove_empty_lines work best on plain-text content. If your converter returns Markdown, such as AzureDocumentIntelligenceConverter, MarkItDownConverter, MistralOCRDocumentConverter, or PaddleOCRVLDocumentConverter, disable those options to preserve headings, tables, lists, and image tags. :::

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 import Document
from haystack.components.preprocessors import DocumentCleaner

doc = Document(content="This   is  a  document  to  clean\n\n\nsubstring to remove")

cleaner = DocumentCleaner(remove_substrings=["substring to remove"])
result = cleaner.run(documents=[doc])

assert result["documents"][0].content == "This is a document to clean "

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({"text_file_converter": {"sources": your_files}})

In YAML

components:
  cleaner:
    init_parameters:
      ascii_only: false
      keep_id: false
      remove_empty_lines: true
      remove_extra_whitespaces: true
      remove_regex: null
      remove_repeated_substrings: false
      remove_substrings: null
      replace_regexes: null
      strip_whitespaces: false
      unicode_normalization: null
    type: haystack.components.preprocessors.document_cleaner.DocumentCleaner
  splitter:
    init_parameters:
      extend_abbreviations: true
      language: en
      respect_sentence_boundary: false
      skip_empty_documents: true
      split_by: sentence
      split_length: 1
      split_overlap: 0
      split_threshold: 0
      use_split_rules: true
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
  text_file_converter:
    init_parameters:
      encoding: utf-8
      store_full_path: false
    type: haystack.components.converters.txt.TextFileToDocument
  writer:
    init_parameters:
      document_store:
        init_parameters:
          bm25_algorithm: BM25L
          bm25_parameters: {}
          bm25_tokenization_regex: (?u)\\b\\w+\\b
          embedding_similarity_function: dot_product
          index: 64e4f9ab-87fb-47fd-b390-dabcfda61447
          return_embedding: true
        type: haystack.document_stores.in_memory.document_store.InMemoryDocumentStore
      policy: NONE
    type: haystack.components.writers.document_writer.DocumentWriter
connection_type_validation: true
connections:
- receiver: cleaner.documents
  sender: text_file_converter.documents
- receiver: splitter.documents
  sender: cleaner.documents
- receiver: writer.documents
  sender: splitter.documents
max_runs_per_component: 100
metadata: {}