
Most common position in a pipeline	Before PreProcessors or right at the beginning of an indexing pipeline
Mandatory run variables	"sources": DOCX file paths or `ByteStream` objects
Output variables	"documents": A list of documents
API reference	Converters
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/docx.py

Overview

The DOCXToDocument component converts DOCX files into documents. It takes a list of file paths or ByteStream objects as input and outputs the converted result as a list of documents. By defining the table format (CSV or Markdown), you can use this component to extract tables in your DOCX files. Optionally, you can attach metadata to the documents through the meta input parameter.

Usage

First, install thepython-docx package to start using this converter:

pip install python-docx

On its own

from haystack.components.converters.docx import DOCXToDocument, DOCXTableFormat

converter = DOCXToDocument()
# or define the table format
converter = DOCXToDocument(table_format=DOCXTableFormat.CSV)

results = converter.run(sources=["sample.docx"], meta={"date_added": datetime.now().isoformat()})
documents = results["documents"]

print(documents[0].content)

# 'This is the text from the DOCX file.'

In a pipeline

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

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", DOCXToDocument())
pipeline.add_component("cleaner", DocumentCleaner())
pipeline.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=5))
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
pipeline.connect("converter", "cleaner")
pipeline.connect("cleaner", "splitter")
pipeline.connect("splitter", "writer")

pipeline.run({"converter": {"sources": file_names}})