Deprecated Parsing Functions

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Deprecated Parsing Functions

• parse_documents: Use if you want to parse one or more documents and return the output as objects.
• parse_and_save_documents: Use if you want to parse one or more documents and save the output as JSON files
• parse_and_save_document: Use if you want to parse only one file. You can have the output returned as objects or saved as a JSON file.

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Parse Documents and Return Results as Objects

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When to Use: Immediate Processing

• You’re running the script in a notebook or web service that will immediately process, transform, or display the data.
• You need to pass the data to another function or microservice as part of a larger pipeline.
• You’re working in an interactive environment (like a Jupyter Notebook or a web-based UI).
• You want to avoid writing to disk due to permission issues or cloud function constraints.

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Sample Script

from agentic_doc.parse import parse_documents

# Parse documents from URLs
results = parse_documents(["https://satsuite.collegeboard.org/media/pdf/sample-sat-score-report.pdf", "https://www.rbcroyalbank.com/banking-services/_assets-custom/pdf/eStatement.pdf"])
parsed_doc = results[0]

# Get the extracted data as markdown
print(parsed_doc.markdown)  

# Get the extracted data as structured chunks of content in a JSON schema
print(parsed_doc.chunks)  

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Function Signature

def parse_documents(
    documents: list[Union[str, Path, Url]],
    *,
    include_marginalia: bool = True,
    include_metadata_in_markdown: bool = True,
    grounding_save_dir: Union[str, Path, None] = None,
    extraction_model: Optional[type[T]] = None,
    extraction_schema: Optional[dict[str, Any]] = None,
    config: Optional[ParseConfig] = None,
) -> list[ParsedDocument[T]]:

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Parameters

• documents: List of paths to documents or URLs pointing to documents.
• include_marginalia: If True, includes marginalia chunks (text in the header, footer, and margins) in the output. For more information, go to Chunk Types. Defaults to True. (Optional)
• include_metadata_in_markdown: If True, includes metadata in the Markdown output. Defaults to True. (Optional)
• grounding_save_dir: The directory where grounding images will be saved. For more information, go to Save Groundings as Images. (Optional)
• extraction_model: Pydantic model schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• extraction_schema: JSON schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• config: Pass configuration settings with the ParseConfig object. For more information about using this parameter, go to Pass Settings with ParseConfig. (Optional)

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Returns

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Raises

• FileNotFoundError: This error is raised if the provided file path does not exist.
• ValueError: This error is raised if the file type is not supported or a URL is invalid.

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Parse Documents and Save Results as JSON Files

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When to Use: Persistence and Auditing

• You want to store the extracted output for future reference, manual review, or archiving.
• You have a pipeline that uses batch processing or file watchers.
• You need to debug the output separately or share it with others.
• Your process includes a manual review step, such as human-in-the-loop verification.

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Sample Script

from agentic_doc.parse import parse_and_save_documents

# URLs to the document
documents = ["https://satsuite.collegeboard.org/media/pdf/sample-sat-score-report.pdf", "https://www.rbcroyalbank.com/banking-services/_assets-custom/pdf/eStatement.pdf"]

# Directory where the parsed results will be saved
result_save_dir = "./parsed_results"

# Parse the documents and save the results
result_paths = parse_and_save_documents(documents=documents, result_save_dir=result_save_dir)

print(f"Result saved to: {result_paths}")

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Function Signature

def parse_and_save_documents(
    documents: list[Union[str, Path, Url]],
    *,
    result_save_dir: Union[str, Path],
    grounding_save_dir: Union[str, Path, None] = None,
    include_marginalia: bool = True,
    include_metadata_in_markdown: bool = True,
    extraction_model: Optional[type[T]] = None,
    extraction_schema: Optional[dict[str, Any]] = None,
    config: Optional[ParseConfig] = None,
) -> list[Path]:

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Parameters

• documents: List of paths to documents or URLs pointing to documents.
• result_save_dir: The directory where the JSON files will be saved.
• include_marginalia: If True, includes marginalia chunks (text in the header, footer, and margins) in the output. For more information, go to Chunk Types. Defaults to True. (Optional)
• include_metadata_in_markdown: If True, includes metadata in the Markdown output. Defaults to True. (Optional)
• grounding_save_dir: The directory where grounding images will be saved. For more information, go to Save Groundings as Images. (Optional)
• extraction_model: Pydantic model schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• extraction_schema: JSON schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• config: Pass configuration settings with the ParseConfig object. For more information about using this parameter, go to Pass Settings with ParseConfig. (Optional)

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Returns

Result saved to: [PosixPath('parsed_results/sample-sat-score-report_20250508_094455.json'), PosixPath('parsed_results/eStatement_20250508_094347.json')]

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Raises

• FileNotFoundError: This error is raised if the provided file path does not exist.
• ValueError: This error is raised if the file type is not supported or a URL is invalid.

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Parse One Document

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Sample Script

from agentic_doc.parse import parse_and_save_document

# URL to the document
document = "https://satsuite.collegeboard.org/media/pdf/sample-sat-score-report.pdf"

# Directory where the parsed result will be saved
result_save_dir = "./parsed_results"

# Parse the document and save the result
result_paths = parse_and_save_document(document=document, result_save_dir=result_save_dir)

print(f"Result saved to: {result_paths}")

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Function Signature

def parse_and_save_document(
    document: Union[str, Path, Url],
    *,
    include_marginalia: bool = True,
    include_metadata_in_markdown: bool = True,
    result_save_dir: Union[str, Path, None] = None,
    grounding_save_dir: Union[str, Path, None] = None,
    extraction_model: Optional[type[T]] = None,
    extraction_schema: Optional[dict[str, Any]] = None,
    config: Optional[ParseConfig] = None,
) -> Union[Path, ParsedDocument[T]]:

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Parameters

• document: The path to a document or URL pointing to a document.
• result_save_dir: The directory where the JSON files will be saved.
• include_marginalia: If True, includes marginalia chunks (text in the header, footer, and margins) in the output. For more information, go to Chunk Types. Defaults to True. (Optional)
• include_metadata_in_markdown: If True, includes metadata in the Markdown output. Defaults to True. (Optional)
• grounding_save_dir: The directory where grounding images will be saved. For more information, go to Save Groundings as Images. (Optional)
• connector_path: Path for connector to search (when using connectors).
• connector_pattern: Pattern to filter files (when using connectors)
• extraction_model: Pydantic model schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• extraction_schema: JSON schema for field extraction. For more information about extraction, go to Extract Data with the Library. (Optional)
• config: Pass configuration settings with the ParseConfig object. For more information about using this parameter, go to Pass Settings with ParseConfig. (Optional)

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Returns

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Raises

• FileNotFoundError: This error is raised if the provided file path does not exist.
• ValueError: This error is raised if the file type is not supported or a URL is invalid.