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

Overview

The HTMLToDocument component converts HTML files into documents. It can be used in an indexing pipeline to index the contents of an HTML file into a Document Store or even in a querying pipeline after the LinkContentFetcher. The HTMLToDocument component takes a list of HTML file paths or ByteStream objects as input and converts the files to a list of documents. Optionally, you can attach metadata to the documents through the meta input parameter.

When you initialize the component, you can optionally set the extractor_type, which is the type of boilerpy3 extractor to use. It defaults to DefaultExtractor. For more information on extractors, refer to the boilerpy3 documentation.

Usage

On its own

from pathlib import Path
from haystack.components.converters import HTMLToDocument

converter = HTMLToDocument()

docs = converter.run(sources=[Path("saved_page.html")])

In a pipeline

Here's an example of an indexing pipeline that writes the contents of an HTML file into an InMemoryDocumentStore:

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