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add: docs & docstrings for marker text loader
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docs/document_loader/text_loader/marker_text_loader.md
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## Load text from PDF files (using Marker)
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::: medrag_multi_modal.document_loader.text_loader.marker_text_loader
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medrag_multi_modal/document_loader/text_loader/marker_text_loader.py
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class MarkerTextLoader(BaseTextLoader):
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async def _process_page(self, page_idx: int) -> Dict[str, str]:
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model_lst = load_all_models()
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text, _, out_meta = convert_single_pdf(
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return {
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"text": text,
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"meta": out_meta,
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"page_idx": page_idx,
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"document_name": self.document_name,
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"file_path": self.document_file_path,
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"file_url": self.url,
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}
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class MarkerTextLoader(BaseTextLoader):
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"""
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A concrete implementation of the BaseTextLoader for loading text from a PDF file
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using `marker-pdf`, processing it into a structured text format, and optionally publishing
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it to a Weave dataset.
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This class extends the BaseTextLoader and implements the abstract methods to
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load and process pages from a PDF file using marker-pdf, which is a pipeline of deep learning models.
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This class will handle the downloading of a PDF file from a given URL if it does not already exist locally.
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It uses marker-pdf to read the PDF and extract structured text from each page. The processed pages are stored
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in a list of Page objects, which can be optionally published to a Weave dataset.
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!!! example "Example Usage"
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```python
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import asyncio
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import weave
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from medrag_multi_modal.document_loader.text_loader import MarkerTextLoader
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weave.init(project_name="ml-colabs/medrag-multi-modal")
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url = "https://archive.org/download/GraysAnatomy41E2015PDF/Grays%20Anatomy-41%20E%20%282015%29%20%5BPDF%5D.pdf"
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loader = MarkerTextLoader(
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url=url,
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document_name="Gray's Anatomy",
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document_file_path="grays_anatomy.pdf",
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)
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asyncio.run(
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loader.load_data(
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start_page=31,
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end_page=36,
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weave_dataset_name="grays-anatomy-text",
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)
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)
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```
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Args:
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url (str): The URL of the PDF file to download if not present locally.
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document_name (str): The name of the document for metadata purposes.
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document_file_path (str): The local file path where the PDF is stored or will be downloaded.
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"""
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async def _process_page(self, page_idx: int) -> Dict[str, str]:
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"""
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Process a single page of the PDF and extract its structured text using marker-pdf.
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Returns a dictionary with the processed page data.
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The dictionary will have the following keys and values:
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- "text": (str) the extracted structured text from the page.
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- "page_idx": (int) the index of the page.
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- "document_name": (str) the name of the document.
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- "file_path": (str) the local file path where the PDF is stored.
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- "file_url": (str) the URL of the PDF file.
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- "meta": (dict) the metadata extracted from the page by marker-pdf.
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Args:
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page_idx (int): The index of the page to process.
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Returns:
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Dict[str, str]: A dictionary containing the processed page data.
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"""
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model_lst = load_all_models()
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text, _, out_meta = convert_single_pdf(
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return {
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"text": text,
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"page_idx": page_idx,
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"document_name": self.document_name,
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"file_path": self.document_file_path,
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"file_url": self.url,
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"meta": out_meta,
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}
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mkdocs.yml
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- PyMuPDF4LLM: 'document_loader/text_loader/pymupdf4llm_text_loader.md'
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- PyPDF2: 'document_loader/text_loader/pypdf2_text_loader.md'
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- PDFPlumber: 'document_loader/text_loader/pdfplumber_text_loader.md'
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- Text and Image Loader: 'document_loader/load_text_image.md'
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- Image Loader: 'document_loader/load_image.md'
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- Retrieval:
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- PyMuPDF4LLM: 'document_loader/text_loader/pymupdf4llm_text_loader.md'
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- PyPDF2: 'document_loader/text_loader/pypdf2_text_loader.md'
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- PDFPlumber: 'document_loader/text_loader/pdfplumber_text_loader.md'
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- Marker: 'document_loader/text_loader/marker_text_loader.md'
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- Text and Image Loader: 'document_loader/load_text_image.md'
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- Image Loader: 'document_loader/load_image.md'
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- Retrieval:
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