Source code for langchain_community.document_loaders.pdf

import json
import logging
import os
import re
import tempfile
import time
from abc import ABC
from io import StringIO
from pathlib import Path, PurePath
from typing import (
    TYPE_CHECKING,
    Any,
    BinaryIO,
    Iterator,
    Literal,
    Mapping,
    Optional,
    Sequence,
    Union,
    cast,
)
from urllib.parse import urlparse

import requests
from langchain_core.documents import Document
from langchain_core.utils import get_from_dict_or_env

from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.dedoc import DedocBaseLoader
from langchain_community.document_loaders.parsers.images import BaseImageBlobParser
from langchain_community.document_loaders.parsers.pdf import (
    _DEFAULT_PAGES_DELIMITER,
    AmazonTextractPDFParser,
    DocumentIntelligenceParser,
    PDFMinerParser,
    PDFPlumberParser,
    PyMuPDFParser,
    PyPDFium2Parser,
    PyPDFParser,
)
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader

if TYPE_CHECKING:
    from textractor.data.text_linearization_config import TextLinearizationConfig

logger = logging.getLogger(__file__)


[docs] class UnstructuredPDFLoader(UnstructuredFileLoader): """Load `PDF` files using `Unstructured`. You can run the loader in one of two modes: "single" and "elements". If you use "single" mode, the document will be returned as a single langchain Document object. If you use "elements" mode, the unstructured library will split the document into elements such as Title and NarrativeText. You can pass in additional unstructured kwargs after mode to apply different unstructured settings. Examples -------- from langchain_community.document_loaders import UnstructuredPDFLoader loader = UnstructuredPDFLoader( "example.pdf", mode="elements", strategy="fast", ) docs = loader.load() References ---------- https://unstructured-io.github.io/unstructured/bricks.html#partition-pdf """
[docs] def __init__( self, file_path: Union[str, Path], mode: str = "single", **unstructured_kwargs: Any, ): """ Args: file_path: The path to the PDF file to load. mode: The mode to use when loading the file. Can be one of "single", "multi", or "all". Default is "single". **unstructured_kwargs: Any kwargs to pass to the unstructured. """ file_path = str(file_path) super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> list: from unstructured.partition.pdf import partition_pdf return partition_pdf(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
[docs] class BasePDFLoader(BaseLoader, ABC): """Base Loader class for `PDF` files. If the file is a web path, it will download it to a temporary file, use it, then clean up the temporary file after completion. """
[docs] def __init__( self, file_path: Union[str, PurePath], *, headers: Optional[dict] = None ): """Initialize with a file path. Args: file_path: Either a local, S3 or web path to a PDF file. headers: Headers to use for GET request to download a file from a web path. """ self.file_path = str(file_path) self.web_path = None self.headers = headers if "~" in self.file_path: self.file_path = os.path.expanduser(self.file_path) # If the file is a web path or S3, download it to a temporary file, # and use that. It's better to use a BlobLoader. if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path): self.temp_dir = tempfile.TemporaryDirectory() _, suffix = os.path.splitext(self.file_path) if self._is_s3_presigned_url(self.file_path): suffix = urlparse(self.file_path).path.split("/")[-1] temp_pdf = os.path.join(self.temp_dir.name, f"tmp{suffix}") self.web_path = self.file_path if not self._is_s3_url(self.file_path): r = requests.get(self.file_path, headers=self.headers) if r.status_code != 200: raise ValueError( "Check the url of your file; returned status code %s" % r.status_code ) with open(temp_pdf, mode="wb") as f: f.write(r.content) self.file_path = str(temp_pdf) elif not os.path.isfile(self.file_path): raise ValueError("File path %s is not a valid file or url" % self.file_path)
def __del__(self) -> None: if hasattr(self, "temp_dir"): self.temp_dir.cleanup() @staticmethod def _is_valid_url(url: str) -> bool: """Check if the url is valid.""" parsed = urlparse(url) return bool(parsed.netloc) and bool(parsed.scheme) @staticmethod def _is_s3_url(url: str) -> bool: """check if the url is S3""" try: result = urlparse(url) if result.scheme == "s3" and result.netloc: return True return False except ValueError: return False @staticmethod def _is_s3_presigned_url(url: str) -> bool: """Check if the url is a presigned S3 url.""" try: result = urlparse(url) return bool(re.search(r"\.s3\.amazonaws\.com$", result.netloc)) except ValueError: return False @property def source(self) -> str: return self.web_path if self.web_path is not None else self.file_path
[docs] class OnlinePDFLoader(BasePDFLoader): """Load online `PDF`."""
[docs] def load(self) -> list[Document]: """Load documents.""" loader = UnstructuredPDFLoader(str(self.file_path)) return loader.load()
[docs] class PyPDFLoader(BasePDFLoader): """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: .. code-block:: bash pip install -U langchain-community pypdf Instantiate the loader: .. code-block:: python 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 = "\n\f", # extract_images = True, # images_parser = RapidOCRBlobParser(), ) Lazy load documents: .. code-block:: python 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: .. code-block:: python docs = await loader.aload() print(docs[0].page_content[:100]) print(docs[0].metadata) """
[docs] def __init__( self, file_path: Union[str, PurePath], password: Optional[Union[str, bytes]] = None, headers: Optional[dict] = None, extract_images: bool = False, *, mode: Literal["single", "page"] = "page", images_parser: Optional[BaseImageBlobParser] = None, images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, extraction_mode: Literal["plain", "layout"] = "plain", extraction_kwargs: Optional[dict] = None, ) -> None: """Initialize with a file path. Args: file_path: The path to the PDF file to be loaded. headers: Optional headers to use for GET request to download a file from a web path. password: Optional password for opening encrypted PDFs. mode: The extraction mode, either "single" for the entire document or "page" for page-wise extraction. pages_delimiter: A string delimiter to separate pages in single-mode extraction. extract_images: Whether to extract images from the PDF. images_parser: Optional image blob parser. images_inner_format: 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: β€œplain” for legacy functionality, β€œlayout” extract text in a fixed width format that closely adheres to the rendered layout in the source pdf extraction_kwargs: 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. """ super().__init__(file_path, headers=headers) self.parser = PyPDFParser( password=password, mode=mode, extract_images=extract_images, images_parser=images_parser, images_inner_format=images_inner_format, pages_delimiter=pages_delimiter, extraction_mode=extraction_mode, extraction_kwargs=extraction_kwargs, )
[docs] def lazy_load( self, ) -> Iterator[Document]: """ 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. """ if self.web_path: blob = Blob.from_data( # type: ignore[attr-defined] open(self.file_path, "rb").read(), path=self.web_path ) else: blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] yield from self.parser.lazy_parse(blob)
[docs] class PyPDFium2Loader(BasePDFLoader): """Load and parse a PDF file using the `pypdfium2` 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 `pypdfium2` library for PDF processing and offers both synchronous and asynchronous document loading. Examples: Setup: .. code-block:: bash pip install -U langchain-community pypdfium2 Instantiate the loader: .. code-block:: python from langchain_community.document_loaders import PyPDFium2Loader loader = PyPDFium2Loader( file_path = "./example_data/layout-parser-paper.pdf", # headers = None # password = None, mode = "single", pages_delimiter = "\n\f", # extract_images = True, # images_to_text = convert_images_to_text_with_tesseract(), ) Lazy load documents: .. code-block:: python 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: .. code-block:: python docs = await loader.aload() print(docs[0].page_content[:100]) print(docs[0].metadata) """
[docs] def __init__( self, file_path: Union[str, PurePath], *, mode: Literal["single", "page"] = "page", pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, password: Optional[str] = None, extract_images: bool = False, images_parser: Optional[BaseImageBlobParser] = None, images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", headers: Optional[dict] = None, ): """Initialize with a file path. Args: file_path: The path to the PDF file to be loaded. headers: Optional headers to use for GET request to download a file from a web path. password: Optional password for opening encrypted PDFs. mode: The extraction mode, either "single" for the entire document or "page" for page-wise extraction. pages_delimiter: A string delimiter to separate pages in single-mode extraction. extract_images: Whether to extract images from the PDF. images_parser: Optional image blob parser. images_inner_format: 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="#"/>`) Returns: This class does not directly return data. Use the `load`, `lazy_load` or `aload` methods to retrieve parsed documents with content and metadata. """ super().__init__(file_path, headers=headers) self.parser = PyPDFium2Parser( mode=mode, password=password, extract_images=extract_images, images_parser=images_parser, images_inner_format=images_inner_format, pages_delimiter=pages_delimiter, )
[docs] def lazy_load( self, ) -> Iterator[Document]: """ 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. """ if self.web_path: blob = Blob.from_data( # type: ignore[attr-defined] open(self.file_path, "rb").read(), path=self.web_path ) else: blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] yield from self.parser.parse(blob)
[docs] class PyPDFDirectoryLoader(BaseLoader): """Load and parse a directory of PDF files using 'pypdf' library. This class provides methods to load and parse multiple PDF documents in a directory, supporting options for recursive search, handling password-protected files, extracting images, and defining extraction modes. It integrates the `pypdf` library for PDF processing and offers synchronous document loading. Examples: Setup: .. code-block:: bash pip install -U langchain-community pypdf Instantiate the loader: .. code-block:: python from langchain_community.document_loaders import PyPDFDirectoryLoader loader = PyPDFDirectoryLoader( path = "./example_data/", glob = "**/[!.]*.pdf", silent_errors = False, load_hidden = False, recursive = False, extract_images = False, password = None, mode = "page", images_to_text = None, headers = None, extraction_mode = "plain", # extraction_kwargs = None, ) Load documents: .. code-block:: python docs = loader.load() print(docs[0].page_content[:100]) print(docs[0].metadata) Load documents asynchronously: .. code-block:: python docs = await loader.aload() print(docs[0].page_content[:100]) print(docs[0].metadata) """
[docs] def __init__( self, path: Union[str, PurePath], glob: str = "**/[!.]*.pdf", silent_errors: bool = False, load_hidden: bool = False, recursive: bool = False, extract_images: bool = False, *, password: Optional[str] = None, mode: Literal["single", "page"] = "page", images_parser: Optional[BaseImageBlobParser] = None, headers: Optional[dict] = None, extraction_mode: Literal["plain", "layout"] = "plain", extraction_kwargs: Optional[dict] = None, ): """Initialize with a directory path. Args: path: The path to the directory containing PDF files to be loaded. glob: The glob pattern to match files in the directory. silent_errors: Whether to log errors instead of raising them. load_hidden: Whether to include hidden files in the search. recursive: Whether to search subdirectories recursively. extract_images: Whether to extract images from PDFs. password: Optional password for opening encrypted PDFs. mode: The extraction mode, either "single" for extracting the entire document or "page" for page-wise extraction. images_parser: Optional image blob parser.. headers: Optional headers to use for GET request to download a file from a web path. extraction_mode: β€œplain” for legacy functionality, β€œlayout” for experimental layout mode functionality extraction_kwargs: Optional additional parameters for the extraction process. Returns: This method does not directly return data. Use the `load` method to retrieve parsed documents with content and metadata. """ self.password = password self.mode = mode self.path = path self.glob = glob self.load_hidden = load_hidden self.recursive = recursive self.silent_errors = silent_errors self.extract_images = extract_images self.images_parser = images_parser self.headers = headers self.extraction_mode = extraction_mode self.extraction_kwargs = extraction_kwargs
@staticmethod def _is_visible(path: PurePath) -> bool: return not any(part.startswith(".") for part in path.parts)
[docs] def load(self) -> list[Document]: p = Path(self.path) docs = [] items = p.rglob(self.glob) if self.recursive else p.glob(self.glob) for i in items: if i.is_file(): if self._is_visible(i.relative_to(p)) or self.load_hidden: try: loader = PyPDFLoader( str(i), password=self.password, mode=self.mode, extract_images=self.extract_images, images_parser=self.images_parser, headers=self.headers, extraction_mode=self.extraction_mode, extraction_kwargs=self.extraction_kwargs, ) sub_docs = loader.load() for doc in sub_docs: doc.metadata["source"] = str(i) docs.extend(sub_docs) except Exception as e: if self.silent_errors: logger.warning(e) else: raise e return docs
[docs] class PDFMinerLoader(BasePDFLoader): """Load and parse a PDF file using 'pdfminer.six' 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 `pdfminer.six` library for PDF processing and offers both synchronous and asynchronous document loading. Examples: Setup: .. code-block:: bash pip install -U langchain-community pdfminer.six Instantiate the loader: .. code-block:: python from langchain_community.document_loaders import PDFMinerLoader loader = PDFMinerLoader( file_path = "./example_data/layout-parser-paper.pdf", # headers = None # password = None, mode = "single", pages_delimiter = "\n\f", # extract_images = True, # images_to_text = convert_images_to_text_with_tesseract(), ) Lazy load documents: .. code-block:: python 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: .. code-block:: python docs = await loader.aload() print(docs[0].page_content[:100]) print(docs[0].metadata) """
[docs] def __init__( self, file_path: Union[str, PurePath], *, password: Optional[str] = None, mode: Literal["single", "page"] = "single", pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, extract_images: bool = False, images_parser: Optional[BaseImageBlobParser] = None, images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", headers: Optional[dict] = None, concatenate_pages: Optional[bool] = None, ) -> None: """Initialize with a file path. Args: file_path: The path to the PDF file to be loaded. headers: Optional headers to use for GET request to download a file from a web path. password: Optional password for opening encrypted PDFs. mode: The extraction mode, either "single" for the entire document or "page" for page-wise extraction. pages_delimiter: A string delimiter to separate pages in single-mode extraction. extract_images: Whether to extract images from the PDF. images_parser: Optional image blob parser. images_inner_format: 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="#"/>`) concatenate_pages: Deprecated. If True, concatenate all PDF pages into one a single document. Otherwise, return one document per page. Returns: This method does not directly return data. Use the `load`, `lazy_load` or `aload` methods to retrieve parsed documents with content and metadata. """ super().__init__(file_path, headers=headers) self.parser = PDFMinerParser( password=password, extract_images=extract_images, images_parser=images_parser, concatenate_pages=concatenate_pages, mode=mode, pages_delimiter=pages_delimiter, images_inner_format=images_inner_format, )
[docs] def lazy_load( self, ) -> Iterator[Document]: """ 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. """ if self.web_path: blob = Blob.from_data( # type: ignore[attr-defined] open(self.file_path, "rb").read(), path=self.web_path ) else: blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] yield from self.parser.lazy_parse(blob)
[docs] class PDFMinerPDFasHTMLLoader(BasePDFLoader): """Load `PDF` files as HTML content using `PDFMiner`."""
[docs] def __init__( self, file_path: Union[str, PurePath], *, headers: Optional[dict] = None ): """Initialize with a file path.""" try: from pdfminer.high_level import extract_text_to_fp # noqa:F401 except ImportError: raise ImportError( "`pdfminer` package not found, please install it with " "`pip install pdfminer.six`" ) super().__init__(file_path, headers=headers)
[docs] def lazy_load(self) -> Iterator[Document]: """Load file.""" from pdfminer.high_level import extract_text_to_fp from pdfminer.layout import LAParams from pdfminer.utils import open_filename output_string = StringIO() with open_filename(self.file_path, "rb") as fp: extract_text_to_fp( cast(BinaryIO, fp), output_string, codec="", laparams=LAParams(), output_type="html", ) metadata = { "source": str(self.file_path) if self.web_path is None else self.web_path } yield Document(page_content=output_string.getvalue(), metadata=metadata)
[docs] class PyMuPDFLoader(BasePDFLoader): """Load and parse a PDF file using 'PyMuPDF' library. This class provides methods to load and parse PDF documents, supporting various configurations such as handling password-protected files, extracting tables, extracting images, and defining extraction mode. It integrates the `PyMuPDF` library for PDF processing and offers both synchronous and asynchronous document loading. Examples: Setup: .. code-block:: bash pip install -U langchain-community pymupdf Instantiate the loader: .. code-block:: python from langchain_community.document_loaders import PyMuPDFLoader loader = PyMuPDFLoader( file_path = "./example_data/layout-parser-paper.pdf", # headers = None # password = None, mode = "single", pages_delimiter = "\n\f", # extract_images = True, # images_parser = TesseractBlobParser(), # extract_tables = "markdown", # extract_tables_settings = None, ) Lazy load documents: .. code-block:: python 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: .. code-block:: python docs = await loader.aload() print(docs[0].page_content[:100]) print(docs[0].metadata) """
[docs] def __init__( self, file_path: Union[str, PurePath], *, password: Optional[str] = None, mode: Literal["single", "page"] = "page", pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, extract_images: bool = False, images_parser: Optional[BaseImageBlobParser] = None, images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", extract_tables: Union[Literal["csv", "markdown", "html"], None] = None, headers: Optional[dict] = None, extract_tables_settings: Optional[dict[str, Any]] = None, **kwargs: Any, ) -> None: """Initialize with a file path. Args: file_path: The path to the PDF file to be loaded. headers: Optional headers to use for GET request to download a file from a web path. password: Optional password for opening encrypted PDFs. mode: The extraction mode, either "single" for the entire document or "page" for page-wise extraction. pages_delimiter: A string delimiter to separate pages in single-mode extraction. extract_images: Whether to extract images from the PDF. images_parser: Optional image blob parser. images_inner_format: 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="#"/>`) extract_tables: Whether to extract tables in a specific format, such as "csv", "markdown", or "html". extract_tables_settings: Optional dictionary of settings for customizing table extraction. **kwargs: Additional keyword arguments for customizing text extraction behavior. Returns: This method does not directly return data. Use the `load`, `lazy_load`, or `aload` methods to retrieve parsed documents with content and metadata. Raises: ValueError: If the `mode` argument is not one of "single" or "page". """ if mode not in ["single", "page"]: raise ValueError("mode must be single or page") super().__init__(file_path, headers=headers) self.parser = PyMuPDFParser( password=password, mode=mode, pages_delimiter=pages_delimiter, text_kwargs=kwargs, extract_images=extract_images, images_parser=images_parser, images_inner_format=images_inner_format, extract_tables=extract_tables, extract_tables_settings=extract_tables_settings, )
def _lazy_load(self, **kwargs: Any) -> Iterator[Document]: """Lazy load given path as pages or single document (see `mode`). Insert image, if possible, between two paragraphs. In this way, a paragraph can be continued on the next page. """ if kwargs: logger.warning( f"Received runtime arguments {kwargs}. Passing runtime args to `load`" f" is deprecated. Please pass arguments during initialization instead." ) parser = self.parser if self.web_path: blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path) # type: ignore[attr-defined] else: blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] yield from parser._lazy_parse(blob, text_kwargs=kwargs)
[docs] def load(self, **kwargs: Any) -> list[Document]: return list(self._lazy_load(**kwargs))
[docs] def lazy_load(self) -> Iterator[Document]: yield from self._lazy_load()
# MathpixPDFLoader implementation taken largely from Daniel Gross's: # https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
[docs] class MathpixPDFLoader(BasePDFLoader): """Load `PDF` files using `Mathpix` service."""
[docs] def __init__( self, file_path: Union[str, PurePath], processed_file_format: str = "md", max_wait_time_seconds: int = 500, should_clean_pdf: bool = False, extra_request_data: Optional[dict[str, Any]] = None, **kwargs: Any, ) -> None: """Initialize with a file path. Args: file_path: a file for loading. processed_file_format: a format of the processed file. Default is "md". max_wait_time_seconds: a maximum time to wait for the response from the server. Default is 500. should_clean_pdf: a flag to clean the PDF file. Default is False. extra_request_data: Additional request data. **kwargs: additional keyword arguments. """ self.mathpix_api_key = get_from_dict_or_env( kwargs, "mathpix_api_key", "MATHPIX_API_KEY" ) self.mathpix_api_id = get_from_dict_or_env( kwargs, "mathpix_api_id", "MATHPIX_API_ID" ) # The base class isn't expecting these and doesn't collect **kwargs kwargs.pop("mathpix_api_key", None) kwargs.pop("mathpix_api_id", None) super().__init__(file_path, **kwargs) self.processed_file_format = processed_file_format self.extra_request_data = ( extra_request_data if extra_request_data is not None else {} ) self.max_wait_time_seconds = max_wait_time_seconds self.should_clean_pdf = should_clean_pdf
@property def _mathpix_headers(self) -> dict[str, str]: return {"app_id": self.mathpix_api_id, "app_key": self.mathpix_api_key} @property def url(self) -> str: return "https://api.mathpix.com/v3/pdf" @property def data(self) -> dict: options = { "conversion_formats": {self.processed_file_format: True}, **self.extra_request_data, } return {"options_json": json.dumps(options)}
[docs] def send_pdf(self) -> str: with open(str(self.file_path), "rb") as f: files = {"file": f} response = requests.post( self.url, headers=self._mathpix_headers, files=files, data=self.data ) response_data = response.json() if "error" in response_data: raise ValueError(f"Mathpix request failed: {response_data['error']}") if "pdf_id" in response_data: pdf_id = response_data["pdf_id"] return pdf_id else: raise ValueError("Unable to send PDF to Mathpix.")
[docs] def wait_for_processing(self, pdf_id: str) -> None: """Wait for processing to complete. Args: pdf_id: a PDF id. Returns: None """ url = self.url + "/" + pdf_id for _ in range(0, self.max_wait_time_seconds, 5): response = requests.get(url, headers=self._mathpix_headers) response_data = response.json() # This indicates an error with the request (e.g. auth problems) error = response_data.get("error", None) error_info = response_data.get("error_info", None) if error is not None: error_msg = f"Unable to retrieve PDF from Mathpix: {error}" if error_info is not None: error_msg += f" ({error_info['id']})" raise ValueError(error_msg) status = response_data.get("status", None) if status == "completed": return elif status == "error": # This indicates an error with the PDF processing raise ValueError("Unable to retrieve PDF from Mathpix") else: logger.info("Status: %s, waiting for processing to complete", status) time.sleep(5) raise TimeoutError
[docs] def get_processed_pdf(self, pdf_id: str) -> str: self.wait_for_processing(pdf_id) url = f"{self.url}/{pdf_id}.{self.processed_file_format}" response = requests.get(url, headers=self._mathpix_headers) return response.content.decode("utf-8")
[docs] def clean_pdf(self, contents: str) -> str: """Clean the PDF file. Args: contents: a PDF file contents. Returns: """ contents = "\n".join( [line for line in contents.split("\n") if not line.startswith("![]")] ) # replace \section{Title} with # Title contents = contents.replace("\\section{", "# ").replace("}", "") # replace the "\" slash that Mathpix adds to escape $, %, (, etc. contents = ( contents.replace(r"\$", "$") .replace(r"\%", "%") .replace(r"\(", "(") .replace(r"\)", ")") ) return contents
[docs] def load(self) -> list[Document]: pdf_id = self.send_pdf() contents = self.get_processed_pdf(pdf_id) if self.should_clean_pdf: contents = self.clean_pdf(contents) metadata = {"source": self.source, "file_path": self.source, "pdf_id": pdf_id} return [Document(page_content=contents, metadata=metadata)]
[docs] class PDFPlumberLoader(BasePDFLoader): """Load `PDF` files using `pdfplumber`."""
[docs] def __init__( self, file_path: Union[str, PurePath], text_kwargs: Optional[Mapping[str, Any]] = None, dedupe: bool = False, headers: Optional[dict] = None, extract_images: bool = False, ) -> None: """Initialize with a file path.""" try: import pdfplumber # noqa:F401 except ImportError: raise ImportError( "pdfplumber package not found, please install it with " "`pip install pdfplumber`" ) super().__init__(file_path, headers=headers) self.text_kwargs = text_kwargs or {} self.dedupe = dedupe self.extract_images = extract_images
[docs] def load(self) -> list[Document]: """Load file.""" parser = PDFPlumberParser( text_kwargs=self.text_kwargs, dedupe=self.dedupe, extract_images=self.extract_images, ) if self.web_path: blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path) # type: ignore[attr-defined] else: blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] return parser.parse(blob)
[docs] class AmazonTextractPDFLoader(BasePDFLoader): """Load `PDF` files from a local file system, HTTP or S3. To authenticate, the AWS client uses the following methods to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used. Make sure the credentials / roles used have the required policies to access the Amazon Textract service. Example: .. code-block:: python from langchain_community.document_loaders import AmazonTextractPDFLoader loader = AmazonTextractPDFLoader( file_path="s3://pdfs/myfile.pdf" ) document = loader.load() """
[docs] def __init__( self, file_path: Union[str, PurePath], textract_features: Optional[Sequence[str]] = None, client: Optional[Any] = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, endpoint_url: Optional[str] = None, headers: Optional[dict] = None, *, linearization_config: Optional["TextLinearizationConfig"] = None, ) -> None: """Initialize the loader. Args: file_path: A file, url or s3 path for input file textract_features: Features to be used for extraction, each feature should be passed as a str that conforms to the enum `Textract_Features`, see `amazon-textract-caller` pkg client: boto3 textract client (Optional) credentials_profile_name: AWS profile name, if not default (Optional) region_name: AWS region, eg us-east-1 (Optional) endpoint_url: endpoint url for the textract service (Optional) linearization_config: Config to be used for linearization of the output should be an instance of TextLinearizationConfig from the `textractor` pkg """ super().__init__(file_path, headers=headers) try: import textractcaller as tc except ImportError: raise ImportError( "Could not import amazon-textract-caller python package. " "Please install it with `pip install amazon-textract-caller`." ) if textract_features: features = [tc.Textract_Features[x] for x in textract_features] else: features = [] if credentials_profile_name or region_name or endpoint_url: try: import boto3 if credentials_profile_name is not None: session = boto3.Session(profile_name=credentials_profile_name) else: # use default credentials session = boto3.Session() client_params = {} if region_name: client_params["region_name"] = region_name if endpoint_url: client_params["endpoint_url"] = endpoint_url client = session.client("textract", **client_params) except ImportError: raise ImportError( "Could not import boto3 python package. " "Please install it with `pip install boto3`." ) except Exception as e: raise ValueError( "Could not load credentials to authenticate with AWS client. " "Please check that credentials in the specified " f"profile name are valid. {e}" ) from e self.parser = AmazonTextractPDFParser( textract_features=features, client=client, linearization_config=linearization_config, )
[docs] def load(self) -> list[Document]: """Load given path as pages.""" return list(self.lazy_load())
[docs] def lazy_load( self, ) -> Iterator[Document]: """Lazy load documents""" # the self.file_path is local, but the blob has to include # the S3 location if the file originated from S3 for multipage documents # raises ValueError when multipage and not on S3""" if self.web_path and self._is_s3_url(self.web_path): blob = Blob(path=self.web_path) # type: ignore[call-arg] # type: ignore[misc] else: blob = Blob.from_path(self.file_path) if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1: raise ValueError( f"the file {blob.path} is a multi-page document, \ but not stored on S3. \ Textract requires multi-page documents to be on S3." ) yield from self.parser.parse(blob)
@staticmethod def _get_number_of_pages(blob: Blob) -> int: # type: ignore[valid-type] try: import pypdf from PIL import Image, ImageSequence except ImportError: raise ImportError( "Could not import pypdf or Pilloe python package. " "Please install it with `pip install pypdf Pillow`." ) if blob.mimetype == "application/pdf": # type: ignore[attr-defined] with blob.as_bytes_io() as input_pdf_file: # type: ignore[attr-defined] pdf_reader = pypdf.PdfReader(input_pdf_file) return len(pdf_reader.pages) elif blob.mimetype == "image/tiff": # type: ignore[attr-defined] num_pages = 0 img = Image.open(blob.as_bytes()) # type: ignore[attr-defined] for _, _ in enumerate(ImageSequence.Iterator(img)): num_pages += 1 return num_pages elif blob.mimetype in ["image/png", "image/jpeg"]: # type: ignore[attr-defined] return 1 else: raise ValueError( # type: ignore[attr-defined] f"unsupported mime type: {blob.mimetype}" )
[docs] class DedocPDFLoader(DedocBaseLoader): """DedocPDFLoader document loader integration to load PDF files using `dedoc`. The file loader can automatically detect the correctness of a textual layer in the PDF document. Note that `__init__` method supports parameters that differ from ones of DedocBaseLoader. Setup: Install ``dedoc`` package. .. code-block:: bash pip install -U dedoc Instantiate: .. code-block:: python from langchain_community.document_loaders import DedocPDFLoader loader = DedocPDFLoader( file_path="example.pdf", # split=..., # with_tables=..., # pdf_with_text_layer=..., # pages=..., # ... ) Load: .. code-block:: python docs = loader.load() print(docs[0].page_content[:100]) print(docs[0].metadata) .. code-block:: python Some text { 'file_name': 'example.pdf', 'file_type': 'application/pdf', # ... } Lazy load: .. code-block:: python docs = [] docs_lazy = loader.lazy_load() for doc in docs_lazy: docs.append(doc) print(docs[0].page_content[:100]) print(docs[0].metadata) .. code-block:: python Some text { 'file_name': 'example.pdf', 'file_type': 'application/pdf', # ... } Parameters used for document parsing via `dedoc` (https://dedoc.readthedocs.io/en/latest/parameters/pdf_handling.html): with_attachments: enable attached files extraction recursion_deep_attachments: recursion level for attached files extraction, works only when with_attachments==True pdf_with_text_layer: type of handler for parsing, available options ["true", "false", "tabby", "auto", "auto_tabby" (default)] language: language of the document for PDF without a textual layer, available options ["eng", "rus", "rus+eng" (default)], the list of languages can be extended, please see https://dedoc.readthedocs.io/en/latest/tutorials/add_new_language.html pages: page slice to define the reading range for parsing is_one_column_document: detect number of columns for PDF without a textual layer, available options ["true", "false", "auto" (default)] document_orientation: fix document orientation (90, 180, 270 degrees) for PDF without a textual layer, available options ["auto" (default), "no_change"] need_header_footer_analysis: remove headers and footers from the output result need_binarization: clean pages background (binarize) for PDF without a textual layer need_pdf_table_analysis: parse tables for PDF without a textual layer """ def _make_config(self) -> dict: from dedoc.utils.langchain import make_manager_pdf_config return make_manager_pdf_config( file_path=str(self.file_path), parsing_params=self.parsing_parameters, split=self.split, )
[docs] class DocumentIntelligenceLoader(BasePDFLoader): """Load a PDF with Azure Document Intelligence"""
[docs] def __init__( self, file_path: Union[str, PurePath], client: Any, model: str = "prebuilt-document", headers: Optional[dict] = None, ) -> None: """Initialize the object for file processing with Azure Document Intelligence (formerly Form Recognizer). This constructor initializes a DocumentIntelligenceParser object to be used for parsing files using the Azure Document Intelligence API. The load method generates a Document node including metadata (source blob and page number) for each page. Parameters: ----------- file_path : str The path to the file that needs to be parsed. client: Any A DocumentAnalysisClient to perform the analysis of the blob model : str The model name or ID to be used for form recognition in Azure. Examples: --------- >>> obj = DocumentIntelligenceLoader( ... file_path="path/to/file", ... client=client, ... model="prebuilt-document" ... ) """ super().__init__(file_path, headers=headers) self.parser = DocumentIntelligenceParser(client=client, model=model)
[docs] def load(self) -> list[Document]: """Load given path as pages.""" return list(self.lazy_load())
[docs] def lazy_load( self, ) -> Iterator[Document]: """Lazy load given path as pages.""" blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] yield from self.parser.parse(blob)
[docs] class ZeroxPDFLoader(BasePDFLoader): """Document loader utilizing Zerox library: https://github.com/getomni-ai/zerox Zerox converts PDF document to series of images (page-wise) and uses vision-capable LLM model to generate Markdown representation. Zerox utilizes anyc operations. Therefore when using this loader inside Jupyter Notebook (or any environment running async) you will need to: ```python import nest_asyncio nest_asyncio.apply() ``` """
[docs] def __init__( self, file_path: Union[str, PurePath], model: str = "gpt-4o-mini", **zerox_kwargs: Any, ) -> None: super().__init__(file_path=file_path) """Initialize the parser with arguments to be passed to the zerox function. Make sure to set necessary environment variables such as API key, endpoint, etc. Check zerox documentation for list of necessary environment variables for any given model. Args: file_path: Path or url of the pdf file model: Vision capable model to use. Defaults to "gpt-4o-mini". Hosted models are passed in format "<provider>/<model>" Examples: "azure/gpt-4o-mini", "vertex_ai/gemini-1.5-flash-001" See more details in zerox documentation. **zerox_kwargs: Arguments specific to the zerox function. see datailed list of arguments here in zerox repository: https://github.com/getomni-ai/zerox/blob/main/py_zerox/pyzerox/core/zerox.py#L25 """ # noqa: E501 self.zerox_kwargs = zerox_kwargs self.model = model
[docs] def lazy_load(self) -> Iterator[Document]: """Lazily load pages.""" import asyncio from pyzerox import zerox # Directly call asyncio.run to execute zerox synchronously zerox_output = asyncio.run( zerox(file_path=str(self.file_path), model=self.model, **self.zerox_kwargs) ) # Convert zerox output to Document instances and yield them if len(zerox_output.pages) > 0: num_pages = zerox_output.pages[-1].page for page in zerox_output.pages: yield Document( page_content=page.content, metadata={ "source": self.source, "page": page.page, "num_pages": num_pages, }, )
# Legacy: only for backwards compatibility. Use PyPDFLoader instead PagedPDFSplitter = PyPDFLoader