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

Overview

The PyPDFToDocument component converts PDF files into documents. You can use it in an indexing pipeline to index the contents of a PDF file into a Document Store. 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.

Usage

You need to install pypdf package to use the PyPDFToDocument converter:

pip install pypdf

On its own

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

converter = PyPDFToDocument()

docs = converter.run(sources=[Path("my_file.pdf")])

In a pipeline

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

Additional References

🧑‍🍳 Cookbook: PDF-Based Question Answering with Amazon Bedrock and Haystack

📓 Tutorial: Preprocessing Different File Types