
Most common position in a pipeline	Before PreProcessors or right at the beginning of an indexing pipeline
Mandatory run variables	"sources": 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/xlsx.py

Overview

The XLSXToDocument component converts XLSX files into Haystack Documents with a CSV (default) or Markdown format. It takes a list of file paths or ByteStream objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the meta input parameter.

To see the additional parameters that you can specify with the component initialization, check out the API Reference.

Usage

First, install the openpyxl and tabulate packages to start using this converter:

pip install openpyxl
pip install tabulate

On its own

from haystack.components.converters import XLSXToDocument

converter = XLSXToDocument()
results = converter.run(sources=["sample.xlsx"], meta={"date_added": datetime.now().isoformat()})
documents = results["documents"]
print(documents[0].content)
# ",A,B\n1,col_a,col_b\n2,1.5,test\n"

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import XLSXToDocument
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", XLSXToDocument())
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}})