Source code for nv_ingest.extraction_workflows.docx.docx_helper
# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES.
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# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# pylint: disable=too-many-locals
"""
Helper function for docx extraction
"""
import logging
from pathlib import Path
from typing import IO
from typing import Union
from nv_ingest.extraction_workflows.docx.docxreader import DocxReader
from nv_ingest.schemas.metadata_schema import AccessLevelEnum
from nv_ingest.schemas.metadata_schema import SourceTypeEnum
from nv_ingest.schemas.metadata_schema import TextTypeEnum
logger = logging.getLogger(__name__)
[docs]
def python_docx(
docx: Union[str, Path, IO],
extract_text: bool,
extract_images: bool,
extract_tables: bool,
extract_charts: bool,
**kwargs
):
"""
Helper function that use python-docx to extract text from a bytestream document
A document has three levels - document, paragraphs and runs. To align with the
pdf extraction paragraphs are aliased as block. python-docx leaves the page number
and line number to the renderer so we assume that the entire document is a single
page.
Run level parsing has been skipped but can be added as needed.
Parameters
----------
docx:
Bytestream
extract_text : bool
Specifies whether to extract text.
extract_images : bool
Specifies whether to extract images.
extract_tables : bool
Specifies whether to extract tables.
extract_charts : bool
Specifies whether to extract charts.
**kwargs
The keyword arguments are used for additional extraction parameters.
Returns
-------
str
A string of extracted text.
"""
logger.debug("Extracting docx with python-docx backend")
row_data = kwargs.get("row_data")
# get source_id
source_id = row_data["source_id"]
# get text_depth
text_depth = kwargs.get("text_depth", "document")
text_depth = TextTypeEnum(text_depth)
# get base metadata
metadata_col = kwargs.get("metadata_column", "metadata")
docx_extractor_config = kwargs.get("docx_extraction_config", {})
base_unified_metadata = row_data[metadata_col] if metadata_col in row_data.index else {}
# get base source_metadata
base_source_metadata = base_unified_metadata.get("source_metadata", {})
# get source_location
source_location = base_source_metadata.get("source_location", "")
# get collection_id (assuming coming in from source_metadata...)
collection_id = base_source_metadata.get("collection_id", "")
# get partition_id (assuming coming in from source_metadata...)
partition_id = base_source_metadata.get("partition_id", -1)
# get access_level (assuming coming in from source_metadata...)
access_level = base_source_metadata.get("access_level", AccessLevelEnum.LEVEL_1)
# python-docx doesn't maintain filename; re-use source_id
source_metadata = {
"source_name": source_id,
"source_id": source_id,
"source_location": source_location,
"source_type": SourceTypeEnum.DOCX,
"collection_id": collection_id,
"partition_id": partition_id,
"access_level": access_level,
"summary": "",
}
# Extract data from the document using python-docx
doc = DocxReader(docx, source_metadata, extraction_config=docx_extractor_config)
extracted_data = doc.extract_data(
base_unified_metadata, text_depth, extract_text, extract_charts, extract_tables, extract_images
)
return extracted_data
