69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
import json
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import logging
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import time
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from pathlib import Path
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from typing import Iterable
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from docling.datamodel.base_models import ConversionStatus
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from docling.datamodel.document import ConvertedDocument, DocumentConversionInput
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from docling.document_converter import DocumentConverter
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_log = logging.getLogger(__name__)
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def export_documents(
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converted_docs: Iterable[ConvertedDocument],
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output_dir: Path,
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):
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output_dir.mkdir(parents=True, exist_ok=True)
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success_count = 0
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failure_count = 0
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for doc in converted_docs:
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if doc.status == ConversionStatus.SUCCESS:
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success_count += 1
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doc_filename = doc.input.file.stem
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# Export Deep Search document JSON format:
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with (output_dir / f"{doc_filename}.json").open("w") as fp:
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fp.write(json.dumps(doc.render_as_dict()))
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# Export Markdown format:
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with (output_dir / f"{doc_filename}.md").open("w") as fp:
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fp.write(doc.render_as_markdown())
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else:
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_log.info(f"Document {doc.input.file} failed to convert.")
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failure_count += 1
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_log.info(
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f"Processed {success_count + failure_count} docs, of which {failure_count} failed"
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)
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def main():
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logging.basicConfig(level=logging.INFO)
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input_doc_paths = [
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Path("./test/data/2206.01062.pdf"),
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Path("./test/data/2203.01017v2.pdf"),
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Path("./test/data/2305.03393v1.pdf"),
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]
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doc_converter = DocumentConverter()
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input = DocumentConversionInput.from_paths(input_doc_paths)
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start_time = time.time()
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converted_docs = doc_converter.convert(input)
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export_documents(converted_docs, output_dir=Path("./scratch"))
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end_time = time.time() - start_time
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_log.info(f"All documents were converted in {end_time:.2f} seconds.")
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if __name__ == "__main__":
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main()
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