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mratanusarkar
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Parent(s):
9761deb
add: docs & docstrings for base + pymupdf4llm
Browse files
docs/document_loader/text_loader/base_text_loader.md
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## Load text from PDF files
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::: medrag_multi_modal.document_loader.text_loader.base_text_loader
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docs/document_loader/text_loader/pymupdf4llm_text_loader.md
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## Load text from PDF files
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::: medrag_multi_modal.document_loader.text_loader.pymupdf4llm_text_loader
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medrag_multi_modal/document_loader/text_loader/base_text_loader.py
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@@ -10,6 +10,20 @@ from firerequests import FireRequests
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class BaseTextLoader(ABC):
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def __init__(self, url: str, document_name: str, document_file_path: str):
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self.url = url
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self.document_name = document_name
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def get_page_indices(
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self, start_page: Optional[int] = None, end_page: Optional[int] = None
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) -> tuple[int, int]:
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if start_page:
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if start_page > self.page_count:
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raise ValueError(
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@abstractmethod
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async def _process_page(self, page_idx: int) -> Dict[str, str]:
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pass
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async def load_data(
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end_page: Optional[int] = None,
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weave_dataset_name: Optional[str] = None,
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) -> List[Dict[str, str]]:
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start_page, end_page = self.get_page_indices(start_page, end_page)
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pages = []
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processed_pages_counter: int = 1
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class BaseTextLoader(ABC):
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"""
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An abstract base class for loading text from a PDF file, processing it into markdown, and optionally publishing it to a Weave dataset.
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This class handles the downloading of a PDF file from a given URL if it does not already exist locally.
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Subclasses should implement the specific PDF reading, text extraction, and markdown conversion methods.
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The processed pages are finally stored in a list of Page objects, which can be optionally published to a Weave dataset.
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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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def __init__(self, url: str, document_name: str, document_file_path: str):
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self.url = url
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self.document_name = document_name
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def get_page_indices(
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self, start_page: Optional[int] = None, end_page: Optional[int] = None
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) -> tuple[int, int]:
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"""
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Get the start and end page indices for processing.
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Args:
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start_page (Optional[int]): The starting page index (0-based) to process. Defaults to the first page.
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end_page (Optional[int]): The ending page index (0-based) to process. Defaults to the last page.
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Returns:
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tuple[int, int]: A tuple containing the start and end page indices.
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"""
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if start_page:
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if start_page > self.page_count:
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raise ValueError(
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@abstractmethod
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async def _process_page(self, page_idx: int) -> Dict[str, str]:
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"""
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Abstract method to process a single page of the PDF.
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Overwrite this method in the subclass to provide the actual implementation and
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processing logic for each page of the PDF using various PDF processing libraries.
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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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pass
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async def load_data(
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end_page: Optional[int] = None,
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weave_dataset_name: Optional[str] = None,
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) -> List[Dict[str, str]]:
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"""
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Asynchronously loads text from a PDF file specified by a URL or local file path.
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The overrided processing abstract method then processes the text into markdown format,
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and optionally publishes it to a Weave dataset.
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This function downloads a PDF from a given URL if it does not already exist locally,
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reads the specified range of pages, converts each page's content to markdown, and
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returns a list of Page objects containing the text and metadata.
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It uses `PyPDF2` to calculate the number of pages in the PDF and the
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overriden `_process_page` method provides the actual implementation to process
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each page, extract the text from the PDF, and convert it to markdown.
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It processes pages concurrently using `asyncio` for efficiency.
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If a weave_dataset_name is provided, the processed pages are published to a Weave dataset.
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Args:
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start_page (Optional[int]): The starting page index (0-based) to process. Defaults to the first page.
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end_page (Optional[int]): The ending page index (0-based) to process. Defaults to the last page.
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weave_dataset_name (Optional[str]): The name of the Weave dataset to publish the pages to, if provided.
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Returns:
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List[Dict[str, str]]: A list of dictionaries, each containing the text and metadata for a processed page.
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Each dictionary will have the following keys and values:
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- "text": (str) the processed page data in markdown format.
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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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Raises:
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ValueError: If the specified start_page or end_page is out of bounds of the document's page count.
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"""
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start_page, end_page = self.get_page_indices(start_page, end_page)
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pages = []
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processed_pages_counter: int = 1
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medrag_multi_modal/document_loader/text_loader/pymupdf4llm_text_loader.py
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class PyMuPDF4LLMTextLoader(BaseTextLoader):
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async def _process_page(self, page_idx: int) -> Dict[str, str]:
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text = pymupdf4llm.to_markdown(
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doc=self.document_file_path, pages=[page_idx], show_progress=False
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)
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class PyMuPDF4LLMTextLoader(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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processing it into markdown using `pymupdf4llm`, and optionally publishing it to a Weave dataset.
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This class extends the BaseTextLoader and implements the abstract methods to load and process pages from a PDF file.
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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 PyPDF2 to read the PDF and pymupdf4llm to convert pages to markdown. The processed pages are stored in a list
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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 (
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PyMuPDF4LLMTextLoader
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)
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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 = PyMuPDF4LLMTextLoader(
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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 convert it to markdown using `pymupdf4llm`.
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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 processed page data in markdown format.
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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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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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text = pymupdf4llm.to_markdown(
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doc=self.document_file_path, pages=[page_idx], show_progress=False
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)
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mkdocs.yml
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- Installation: 'installation/install.md'
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- Development: 'installation/development.md'
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- Document Loader:
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-
- Text Loader:
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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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- Installation: 'installation/install.md'
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- Development: 'installation/development.md'
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- Document Loader:
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- Text Loader:
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- Base: 'document_loader/text_loader/base_text_loader.md'
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- PyMuPDF4LLM: 'document_loader/text_loader/pymupdf4llm_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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