mirror of
https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
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GitHub
750
libs/community/langchain_community/document_loaders/pdf.py
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750
libs/community/langchain_community/document_loaders/pdf.py
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@@ -0,0 +1,750 @@
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import json
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import logging
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import os
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import tempfile
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import time
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from abc import ABC
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from io import StringIO
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from pathlib import Path
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from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Union
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from urllib.parse import urlparse
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import requests
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from langchain_core.documents import Document
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.document_loaders.base import BaseLoader
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from langchain_community.document_loaders.blob_loaders import Blob
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from langchain_community.document_loaders.parsers.pdf import (
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AmazonTextractPDFParser,
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DocumentIntelligenceParser,
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PDFMinerParser,
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PDFPlumberParser,
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PyMuPDFParser,
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PyPDFium2Parser,
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PyPDFParser,
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)
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from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
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logger = logging.getLogger(__file__)
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class UnstructuredPDFLoader(UnstructuredFileLoader):
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"""Load `PDF` files using `Unstructured`.
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You can run the loader in one of two modes: "single" and "elements".
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If you use "single" mode, the document will be returned as a single
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langchain Document object. If you use "elements" mode, the unstructured
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library will split the document into elements such as Title and NarrativeText.
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You can pass in additional unstructured kwargs after mode to apply
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different unstructured settings.
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Examples
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--------
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from langchain_community.document_loaders import UnstructuredPDFLoader
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loader = UnstructuredPDFLoader(
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"example.pdf", mode="elements", strategy="fast",
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)
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docs = loader.load()
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References
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----------
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https://unstructured-io.github.io/unstructured/bricks.html#partition-pdf
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"""
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def _get_elements(self) -> List:
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from unstructured.partition.pdf import partition_pdf
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return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
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class BasePDFLoader(BaseLoader, ABC):
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"""Base Loader class for `PDF` files.
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If the file is a web path, it will download it to a temporary file, use it, then
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clean up the temporary file after completion.
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"""
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def __init__(self, file_path: str, *, headers: Optional[Dict] = None):
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"""Initialize with a file path.
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Args:
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file_path: Either a local, S3 or web path to a PDF file.
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headers: Headers to use for GET request to download a file from a web path.
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"""
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self.file_path = file_path
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self.web_path = None
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self.headers = headers
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if "~" in self.file_path:
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self.file_path = os.path.expanduser(self.file_path)
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# If the file is a web path or S3, download it to a temporary file, and use that
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if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
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self.temp_dir = tempfile.TemporaryDirectory()
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_, suffix = os.path.splitext(self.file_path)
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temp_pdf = os.path.join(self.temp_dir.name, f"tmp{suffix}")
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self.web_path = self.file_path
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if not self._is_s3_url(self.file_path):
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r = requests.get(self.file_path, headers=self.headers)
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if r.status_code != 200:
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raise ValueError(
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"Check the url of your file; returned status code %s"
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% r.status_code
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)
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with open(temp_pdf, mode="wb") as f:
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f.write(r.content)
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self.file_path = str(temp_pdf)
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elif not os.path.isfile(self.file_path):
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raise ValueError("File path %s is not a valid file or url" % self.file_path)
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def __del__(self) -> None:
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if hasattr(self, "temp_dir"):
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self.temp_dir.cleanup()
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@staticmethod
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def _is_valid_url(url: str) -> bool:
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"""Check if the url is valid."""
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parsed = urlparse(url)
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return bool(parsed.netloc) and bool(parsed.scheme)
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@staticmethod
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def _is_s3_url(url: str) -> bool:
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"""check if the url is S3"""
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try:
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result = urlparse(url)
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if result.scheme == "s3" and result.netloc:
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return True
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return False
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except ValueError:
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return False
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@property
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def source(self) -> str:
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return self.web_path if self.web_path is not None else self.file_path
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class OnlinePDFLoader(BasePDFLoader):
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"""Load online `PDF`."""
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def load(self) -> List[Document]:
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"""Load documents."""
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loader = UnstructuredPDFLoader(str(self.file_path))
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return loader.load()
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class PyPDFLoader(BasePDFLoader):
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"""Load PDF using pypdf into list of documents.
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Loader chunks by page and stores page numbers in metadata.
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"""
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def __init__(
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self,
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file_path: str,
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password: Optional[Union[str, bytes]] = None,
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headers: Optional[Dict] = None,
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extract_images: bool = False,
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) -> None:
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"""Initialize with a file path."""
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try:
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import pypdf # noqa:F401
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except ImportError:
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raise ImportError(
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"pypdf package not found, please install it with " "`pip install pypdf`"
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)
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super().__init__(file_path, headers=headers)
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self.parser = PyPDFParser(password=password, extract_images=extract_images)
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def load(self) -> List[Document]:
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"""Load given path as pages."""
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return list(self.lazy_load())
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def lazy_load(
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self,
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) -> Iterator[Document]:
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"""Lazy load given path as pages."""
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if self.web_path:
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blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
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else:
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blob = Blob.from_path(self.file_path)
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yield from self.parser.parse(blob)
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class PyPDFium2Loader(BasePDFLoader):
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"""Load `PDF` using `pypdfium2` and chunks at character level."""
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def __init__(
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self,
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file_path: str,
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*,
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headers: Optional[Dict] = None,
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extract_images: bool = False,
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):
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"""Initialize with a file path."""
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super().__init__(file_path, headers=headers)
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self.parser = PyPDFium2Parser(extract_images=extract_images)
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def load(self) -> List[Document]:
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"""Load given path as pages."""
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return list(self.lazy_load())
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def lazy_load(
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self,
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) -> Iterator[Document]:
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"""Lazy load given path as pages."""
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if self.web_path:
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blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
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else:
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blob = Blob.from_path(self.file_path)
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yield from self.parser.parse(blob)
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class PyPDFDirectoryLoader(BaseLoader):
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"""Load a directory with `PDF` files using `pypdf` and chunks at character level.
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Loader also stores page numbers in metadata.
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"""
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def __init__(
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self,
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path: str,
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glob: str = "**/[!.]*.pdf",
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silent_errors: bool = False,
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load_hidden: bool = False,
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recursive: bool = False,
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extract_images: bool = False,
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):
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self.path = path
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self.glob = glob
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self.load_hidden = load_hidden
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self.recursive = recursive
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self.silent_errors = silent_errors
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self.extract_images = extract_images
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@staticmethod
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def _is_visible(path: Path) -> bool:
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return not any(part.startswith(".") for part in path.parts)
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def load(self) -> List[Document]:
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p = Path(self.path)
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docs = []
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items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
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for i in items:
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if i.is_file():
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if self._is_visible(i.relative_to(p)) or self.load_hidden:
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try:
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loader = PyPDFLoader(str(i), extract_images=self.extract_images)
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sub_docs = loader.load()
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for doc in sub_docs:
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doc.metadata["source"] = str(i)
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docs.extend(sub_docs)
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except Exception as e:
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if self.silent_errors:
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logger.warning(e)
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else:
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raise e
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return docs
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class PDFMinerLoader(BasePDFLoader):
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"""Load `PDF` files using `PDFMiner`."""
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def __init__(
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self,
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file_path: str,
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*,
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headers: Optional[Dict] = None,
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extract_images: bool = False,
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concatenate_pages: bool = True,
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) -> None:
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"""Initialize with file path.
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Args:
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extract_images: Whether to extract images from PDF.
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concatenate_pages: If True, concatenate all PDF pages into one a single
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document. Otherwise, return one document per page.
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"""
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try:
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from pdfminer.high_level import extract_text # noqa:F401
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except ImportError:
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raise ImportError(
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"`pdfminer` package not found, please install it with "
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"`pip install pdfminer.six`"
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)
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super().__init__(file_path, headers=headers)
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self.parser = PDFMinerParser(
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extract_images=extract_images, concatenate_pages=concatenate_pages
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)
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def load(self) -> List[Document]:
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"""Eagerly load the content."""
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return list(self.lazy_load())
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def lazy_load(
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self,
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) -> Iterator[Document]:
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"""Lazily load documents."""
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if self.web_path:
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blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
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else:
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blob = Blob.from_path(self.file_path)
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yield from self.parser.parse(blob)
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class PDFMinerPDFasHTMLLoader(BasePDFLoader):
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"""Load `PDF` files as HTML content using `PDFMiner`."""
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def __init__(self, file_path: str, *, headers: Optional[Dict] = None):
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"""Initialize with a file path."""
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try:
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from pdfminer.high_level import extract_text_to_fp # noqa:F401
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except ImportError:
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raise ImportError(
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"`pdfminer` package not found, please install it with "
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"`pip install pdfminer.six`"
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)
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super().__init__(file_path, headers=headers)
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def load(self) -> List[Document]:
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"""Load file."""
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from pdfminer.high_level import extract_text_to_fp
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from pdfminer.layout import LAParams
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from pdfminer.utils import open_filename
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output_string = StringIO()
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with open_filename(self.file_path, "rb") as fp:
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extract_text_to_fp(
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fp, # type: ignore[arg-type]
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output_string,
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codec="",
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laparams=LAParams(),
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output_type="html",
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)
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metadata = {
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"source": self.file_path if self.web_path is None else self.web_path
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}
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return [Document(page_content=output_string.getvalue(), metadata=metadata)]
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class PyMuPDFLoader(BasePDFLoader):
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"""Load `PDF` files using `PyMuPDF`."""
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def __init__(
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self,
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file_path: str,
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*,
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headers: Optional[Dict] = None,
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extract_images: bool = False,
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||||
**kwargs: Any,
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||||
) -> None:
|
||||
"""Initialize with a file path."""
|
||||
try:
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||||
import fitz # noqa:F401
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"`PyMuPDF` package not found, please install it with "
|
||||
"`pip install pymupdf`"
|
||||
)
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super().__init__(file_path, headers=headers)
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self.extract_images = extract_images
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self.text_kwargs = kwargs
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|
||||
def load(self, **kwargs: Any) -> List[Document]:
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"""Load file."""
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||||
if kwargs:
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||||
logger.warning(
|
||||
f"Received runtime arguments {kwargs}. Passing runtime args to `load`"
|
||||
f" is deprecated. Please pass arguments during initialization instead."
|
||||
)
|
||||
|
||||
text_kwargs = {**self.text_kwargs, **kwargs}
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||||
parser = PyMuPDFParser(
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text_kwargs=text_kwargs, extract_images=self.extract_images
|
||||
)
|
||||
if self.web_path:
|
||||
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
|
||||
else:
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||||
blob = Blob.from_path(self.file_path)
|
||||
return parser.parse(blob)
|
||||
|
||||
|
||||
# MathpixPDFLoader implementation taken largely from Daniel Gross's:
|
||||
# https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
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||||
class MathpixPDFLoader(BasePDFLoader):
|
||||
"""Load `PDF` files using `Mathpix` service."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path: str,
|
||||
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)}
|
||||
|
||||
def send_pdf(self) -> str:
|
||||
with open(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.")
|
||||
|
||||
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)
|
||||
|
||||
if error is not None:
|
||||
raise ValueError(f"Unable to retrieve PDF from Mathpix: {error}")
|
||||
|
||||
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:
|
||||
print(f"Status: {status}, waiting for processing to complete")
|
||||
time.sleep(5)
|
||||
raise TimeoutError
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
|
||||
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}
|
||||
return [Document(page_content=contents, metadata=metadata)]
|
||||
|
||||
|
||||
class PDFPlumberLoader(BasePDFLoader):
|
||||
"""Load `PDF` files using `pdfplumber`."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path: str,
|
||||
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
|
||||
|
||||
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)
|
||||
else:
|
||||
blob = Blob.from_path(self.file_path)
|
||||
return parser.parse(blob)
|
||||
|
||||
|
||||
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()
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path: str,
|
||||
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,
|
||||
) -> 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)
|
||||
|
||||
"""
|
||||
super().__init__(file_path, headers=headers)
|
||||
|
||||
try:
|
||||
import textractcaller as tc # noqa: F401
|
||||
except ImportError:
|
||||
raise ModuleNotFoundError(
|
||||
"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 ModuleNotFoundError(
|
||||
"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 "
|
||||
"profile name are valid."
|
||||
) from e
|
||||
self.parser = AmazonTextractPDFParser(textract_features=features, client=client)
|
||||
|
||||
def load(self) -> List[Document]:
|
||||
"""Load given path as pages."""
|
||||
return list(self.lazy_load())
|
||||
|
||||
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 multi-page documents
|
||||
# raises ValueError when multi-page and not on S3"""
|
||||
|
||||
if self.web_path and self._is_s3_url(self.web_path):
|
||||
blob = Blob(path=self.web_path)
|
||||
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:
|
||||
try:
|
||||
import pypdf
|
||||
from PIL import Image, ImageSequence
|
||||
|
||||
except ImportError:
|
||||
raise ModuleNotFoundError(
|
||||
"Could not import pypdf or Pilloe python package. "
|
||||
"Please install it with `pip install pypdf Pillow`."
|
||||
)
|
||||
if blob.mimetype == "application/pdf":
|
||||
with blob.as_bytes_io() as input_pdf_file:
|
||||
pdf_reader = pypdf.PdfReader(input_pdf_file)
|
||||
return len(pdf_reader.pages)
|
||||
elif blob.mimetype == "image/tiff":
|
||||
num_pages = 0
|
||||
img = Image.open(blob.as_bytes())
|
||||
for _, _ in enumerate(ImageSequence.Iterator(img)):
|
||||
num_pages += 1
|
||||
return num_pages
|
||||
elif blob.mimetype in ["image/png", "image/jpeg"]:
|
||||
return 1
|
||||
else:
|
||||
raise ValueError(f"unsupported mime type: {blob.mimetype}")
|
||||
|
||||
|
||||
class DocumentIntelligenceLoader(BasePDFLoader):
|
||||
"""Loads a PDF with Azure Document Intelligence"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path: str,
|
||||
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"
|
||||
... )
|
||||
"""
|
||||
|
||||
self.parser = DocumentIntelligenceParser(client=client, model=model)
|
||||
super().__init__(file_path, headers=headers)
|
||||
|
||||
def load(self) -> List[Document]:
|
||||
"""Load given path as pages."""
|
||||
return list(self.lazy_load())
|
||||
|
||||
def lazy_load(
|
||||
self,
|
||||
) -> Iterator[Document]:
|
||||
"""Lazy load given path as pages."""
|
||||
blob = Blob.from_path(self.file_path)
|
||||
yield from self.parser.parse(blob)
|
||||
Reference in New Issue
Block a user