HTMLToDocument
A component that converts HTML files to documents.
Name | HTMLToDocument |
Folder path | /converters/ |
Most common position in a pipeline | Before PreProcessors, or right at the beginning of an indexing pipeline |
Mandatory input variables | "sources": A list of HTML file paths or ByteStream objects |
Output variables | "documents": A list of documents |
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}})
Updated 7 months ago