Textractor

The Textractor pipeline extracts and splits text from documents. This pipeline uses Apache Tika (if Java is available) and BeautifulSoup4. See this link for a list of supported document formats.

Each document goes through the following process.

• Content is retrieved if it's not local
• If the document mime-type isn't plain text or HTML, it's run through Tika and converted to XHTML
• XHTML is converted to Markdown and returned

Without Apache Tika, this pipeline only supports plain text and HTML. Other document types require Tika and Java to be installed. Another option is to start Apache Tika via this Docker Image.

Example

The following shows a simple example using this pipeline.

from txtai.pipeline import Textractor

# Create and run pipeline
textract = Textractor()
textract("https://github.com/neuml/txtai")

See the link below for a more detailed example.

Notebook	Description
Extract text from documents	Extract text from PDF, Office, HTML and more

Configuration-driven example

Pipelines are run with Python or configuration. Pipelines can be instantiated in configuration using the lower case name of the pipeline. Configuration-driven pipelines are run with workflows or the API.

config.yml

# Create pipeline using lower case class name
textractor:

# Run pipeline with workflow
workflow:
  textract:
    tasks:
      - action: textractor

Run with Workflows

from txtai import Application

# Create and run pipeline with workflow
app = Application("config.yml")
list(app.workflow("textract", ["https://github.com/neuml/txtai"]))

Run with API

CONFIG=config.yml uvicorn "txtai.api:app" &

curl \
  -X POST "http://localhost:8000/workflow" \
  -H "Content-Type: application/json" \
  -d '{"name":"textract", "elements":["https://github.com/neuml/txtai"]}'

Methods

Python documentation for the pipeline.

__init__(sentences=False, lines=False, paragraphs=False, minlength=None, join=False, tika=True, sections=False, headers=None)

Source code in txtai/pipeline/data/textractor.py

def __init__(self, sentences=False, lines=False, paragraphs=False, minlength=None, join=False, tika=True, sections=False, headers=None):
    if not TIKA:
        raise ImportError('Textractor pipeline is not available - install "pipeline" extra to enable')

    super().__init__(sentences, lines, paragraphs, minlength, join, sections)

    # Determine if Apache Tika (default if Java is available) or Beautiful Soup should be used
    # Beautiful Soup only supports HTML, Tika supports a wide variety of file formats.
    self.tika = self.checkjava() if tika else False

    # HTML to Text extractor
    self.extract = Extract(self.paragraphs, self.sections)

    # HTTP headers
    self.headers = headers if headers else {}

__call__(text)

Segments text into semantic units.

This method supports text as a string or a list. If the input is a string, the return type is text|list. If text is a list, a list of returned, this could be a list of text or a list of lists depending on the tokenization strategy.

Parameters:

Name	Type	Description	Default
text		text|list	required

Returns:

Type	Description
	segmented text

Source code in txtai/pipeline/data/segmentation.py

def __call__(self, text):
    """
    Segments text into semantic units.

    This method supports text as a string or a list. If the input is a string, the return
    type is text|list. If text is a list, a list of returned, this could be a
    list of text or a list of lists depending on the tokenization strategy.

    Args:
        text: text|list

    Returns:
        segmented text
    """

    # Get inputs
    texts = [text] if not isinstance(text, list) else text

    # Extract text for each input file
    results = []
    for value in texts:
        # Get text
        value = self.text(value)

        # Parse and add extracted results
        results.append(self.parse(value))

    return results[0] if isinstance(text, str) else results