PyPDFLoader#

class langchain_community.document_loaders.pdf.PyPDFLoader(file_path: str | PurePath, password: str | bytes | None = None, headers: dict | None = None, extract_images: bool = False, *, mode: Literal['single', 'page'] = 'page', images_parser: BaseImageBlobParser | None = None, images_inner_format: Literal['text', 'markdown-img', 'html-img'] = 'text', pages_delimiter: str = '\n\x0c', extraction_mode: Literal['plain', 'layout'] = 'plain', extraction_kwargs: dict | None = None)[source]#

Load and parse a PDF file using ‘pypdf’ library.

This class provides methods to load and parse PDF documents, supporting various configurations such as handling password-protected files, extracting images, and defining extraction mode. It integrates the pypdf library for PDF processing and offers both synchronous and asynchronous document loading.
Examples:
Setup:
pip install -U langchain-community pypdf
Instantiate the loader:
from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader(
    file_path = "./example_data/layout-parser-paper.pdf",
    # headers = None
    # password = None,
    mode = "single",
    pages_delimiter = "

“,

# extract_images = True, # images_parser = RapidOCRBlobParser(),

)

Lazy load documents:

docs = []
docs_lazy = loader.lazy_load()

for doc in docs_lazy:
    docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)

Load documents asynchronously:

docs = await loader.aload()
print(docs[0].page_content[:100])
print(docs[0].metadata)

Initialize with a file path.

Parameters:

file_path (str | PurePath) – The path to the PDF file to be loaded.
headers (dict | None) – Optional headers to use for GET request to download a file from a web path.
password (str | bytes | None) – Optional password for opening encrypted PDFs.
mode (Literal['single', 'page']) – The extraction mode, either “single” for the entire document or “page” for page-wise extraction.
pages_delimiter (str) – A string delimiter to separate pages in single-mode extraction.
extract_images (bool) – Whether to extract images from the PDF.
images_parser (BaseImageBlobParser | None) – Optional image blob parser.
images_inner_format (Literal['text', 'markdown-img', 'html-img']) – The format for the parsed output. - “text” = return the content as is - “markdown-img” = wrap the content into an image markdown link, w/ link pointing to (![body)(#)] - “html-img” = wrap the content as the alt text of an tag and link to (<img alt=”{body}” src=”#”/>)
extraction_mode (Literal['plain', 'layout']) – “plain” for legacy functionality, “layout” extract text in a fixed width format that closely adheres to the rendered layout in the source pdf
extraction_kwargs (dict | None) – Optional additional parameters for the extraction process.

Returns:

This method does not directly return data. Use the load, lazy_load or aload methods to retrieve parsed documents with content and metadata.

Attributes

source

Methods

`__init__`(file_path[, password, headers, ...])	Initialize with a file path.
`alazy_load`()	A lazy loader for Documents.
`aload`()	Load data into Document objects.
`lazy_load`()	Lazy load given path as pages.
`load`()	Load data into Document objects.
`load_and_split`([text_splitter])	Load Documents and split into chunks.

__init__(file_path: str | PurePath, password: str | bytes | None = None, headers: dict | None = None, extract_images: bool = False, *, mode: Literal['single', 'page'] = 'page', images_parser: BaseImageBlobParser | None = None, images_inner_format: Literal['text', 'markdown-img', 'html-img'] = 'text', pages_delimiter: str = '\n\x0c', extraction_mode: Literal['plain', 'layout'] = 'plain', extraction_kwargs: dict | None = None) → None[source]#

Initialize with a file path.

Parameters:

file_path (str | PurePath) – The path to the PDF file to be loaded.
headers (dict | None) – Optional headers to use for GET request to download a file from a web path.
password (str | bytes | None) – Optional password for opening encrypted PDFs.
mode (Literal['single', 'page']) – The extraction mode, either “single” for the entire document or “page” for page-wise extraction.
pages_delimiter (str) – A string delimiter to separate pages in single-mode extraction.
extract_images (bool) – Whether to extract images from the PDF.
images_parser (BaseImageBlobParser | None) – Optional image blob parser.
images_inner_format (Literal['text', 'markdown-img', 'html-img']) – The format for the parsed output. - “text” = return the content as is - “markdown-img” = wrap the content into an image markdown link, w/ link pointing to (![body)(#)] - “html-img” = wrap the content as the alt text of an tag and link to (<img alt=”{body}” src=”#”/>)
extraction_mode (Literal['plain', 'layout']) – “plain” for legacy functionality, “layout” extract text in a fixed width format that closely adheres to the rendered layout in the source pdf
extraction_kwargs (dict | None) – Optional additional parameters for the extraction process.

Returns:

This method does not directly return data. Use the load, lazy_load or aload methods to retrieve parsed documents with content and metadata.

Return type:

None

async alazy_load() → AsyncIterator[Document]#

A lazy loader for Documents.

Return type:: AsyncIterator[Document]

async aload() → list[Document]#

Load data into Document objects.

Return type:: list[Document]

lazy_load() → Iterator[Document][source]#

Lazy load given path as pages. Insert image, if possible, between two paragraphs. In this way, a paragraph can be continued on the next page.

Return type:: Iterator[Document]

load() → list[Document]#

Load data into Document objects.

Return type:: list[Document]

load_and_split(text_splitter: TextSplitter | None = None) → list[Document]#

Load Documents and split into chunks. Chunks are returned as Documents.

Do not override this method. It should be considered to be deprecated!

Parameters:: text_splitter (Optional[TextSplitter]) – TextSplitter instance to use for splitting documents. Defaults to RecursiveCharacterTextSplitter.
Returns:: List of Documents.
Return type:: list[Document]

Examples using PyPDFLoader