Module contains common parsers for PDFs.
Abstract base class for parsing image blobs into text.
Parser for extracting text from images using the RapidOCR library.
Parse a blob from a PDF using pypdf library.
This class provides methods to parse a blob from a PDF document, supporting various configurations such as handling password-protected PDFs, extracting images. It integrates the 'pypdf' library for PDF processing and offers synchronous blob parsing.
Examples: Setup:
.. code-block:: bash
pip install -U langchain-community pypdf
Load a blob from a PDF file:
.. code-block:: python
from langchain_core.documents.base import Blob
blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
Instantiate the parser:
.. code-block:: python
from langchain_community.document_loaders.parsers import PyPDFParser
parser = PyPDFParser(
# password = None,
mode = "single",
pages_delimiter = "
", # images_parser = TesseractBlobParser(), )
Lazily parse the blob:
.. code-block:: python
docs = []
docs_lazy = parser.lazy_parse(blob)
for doc in docs_lazy:
docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)
Parse a blob from a PDF using pdfminer.six library.
This class provides methods to parse a blob from a PDF document, supporting various configurations such as handling password-protected PDFs, extracting images, and defining extraction mode. It integrates the 'pdfminer.six' library for PDF processing and offers synchronous blob parsing.
Examples: Setup:
.. code-block:: bash
pip install -U langchain-community pdfminer.six pillow
Load a blob from a PDF file:
.. code-block:: python
from langchain_core.documents.base import Blob
blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
Instantiate the parser:
.. code-block:: python
from langchain_community.document_loaders.parsers import PDFMinerParser
parser = PDFMinerParser(
# password = None,
mode = "single",
pages_delimiter = "
", # extract_images = True, # images_to_text = convert_images_to_text_with_tesseract(), )
Lazily parse the blob:
.. code-block:: python
docs = []
docs_lazy = parser.lazy_parse(blob)
for doc in docs_lazy:
docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)
Parse a blob from a PDF using PyMuPDF library.
This class provides methods to parse a blob from a PDF document, supporting various configurations such as handling password-protected PDFs, extracting images, and defining extraction mode. It integrates the 'PyMuPDF' library for PDF processing and offers synchronous blob parsing.
Examples: Setup:
.. code-block:: bash
pip install -U langchain-community pymupdf
Load a blob from a PDF file:
.. code-block:: python
from langchain_core.documents.base import Blob
blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
Instantiate the parser:
.. code-block:: python
from langchain_community.document_loaders.parsers import PyMuPDFParser
parser = PyMuPDFParser(
# password = None,
mode = "single",
pages_delimiter = "
", # images_parser = TesseractBlobParser(), # extract_tables="markdown", # extract_tables_settings=None, # text_kwargs=None, )
Lazily parse the blob:
.. code-block:: python
docs = []
docs_lazy = parser.lazy_parse(blob)
for doc in docs_lazy:
docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)
Parse a blob from a PDF using PyPDFium2 library.
This class provides methods to parse a blob from a PDF document, supporting various configurations such as handling password-protected PDFs, extracting images, and defining extraction mode. It integrates the 'PyPDFium2' library for PDF processing and offers synchronous blob parsing.
Examples: Setup:
.. code-block:: bash
pip install -U langchain-community pypdfium2
Load a blob from a PDF file:
.. code-block:: python
from langchain_core.documents.base import Blob
blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
Instantiate the parser:
.. code-block:: python
from langchain_community.document_loaders.parsers import PyPDFium2Parser
parser = PyPDFium2Parser(
# password=None,
mode="page",
pages_delimiter="
", # extract_images = True, # images_to_text = convert_images_to_text_with_tesseract(), )
Lazily parse the blob:
.. code-block:: python
docs = []
docs_lazy = parser.lazy_parse(blob)
for doc in docs_lazy:
docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)
Parse PDF with PDFPlumber.
Send PDF files to Amazon Textract and parse them.
For parsing multi-page PDFs, they have to reside on S3.
The AmazonTextractPDFLoader calls the Amazon Textract Service to convert PDFs into a Document structure. Single and multi-page documents are supported with up to 3000 pages and 512 MB of size.
For the call to be successful an AWS account is required, similar to the AWS CLI requirements.
Besides the AWS configuration, it is very similar to the other PDF loaders, while also supporting JPEG, PNG and TIFF and non-native PDF formats.
from langchain_community.document_loaders import AmazonTextractPDFLoader
loader=AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg")
documents = loader.load()One feature is the linearization of the output. When using the features LAYOUT, FORMS or TABLES together with Textract
from langchain_community.document_loaders import AmazonTextractPDFLoader
# you can mix and match each of the features
loader=AmazonTextractPDFLoader(
"example_data/alejandro_rosalez_sample-small.jpeg",
textract_features=["TABLES", "LAYOUT"])
documents = loader.load()it will generate output that formats the text in reading order and try to output the information in a tabular structure or output the key/value pairs with a colon (key: value). This helps most LLMs to achieve better accuracy when processing these texts.
Document objects are returned with metadata that includes the source and
a 1-based index of the page number in page. Note that page represents
the index of the result returned from Textract, not necessarily the as-written
page number in the document.
Loads a PDF with Azure Document Intelligence (formerly Form Recognizer) and chunks at character level.