Partition - Aryn Documentation

Please find the documentation for the Aryn SDK Partition module below. All parameters are optional unless specified otherwise.

partition_file

Sends a file to Aryn DocParse and returns a Python dictionary with elements containing its document structure and text.

file: Required. A file opened in binary mode to parse or a path expressed as a str or a PathLike object specifying where the file to parse is.
threshold: Either the specific string auto or a float between 0.0 and 1.0, inclusive. This value specifies the cutoff for detecting bounding boxes. It defaults to auto, where the service uses a processing method to find the best prediction for each possible bounding box. This is the recommended setting. However, this can be overridden by specifying a numerical threshold between 0 and 1. If you specify a numerical threshold, only bounding boxes with confidence scores higher than the threshold will be returned. A lower value will include more objects but may have overlaps, while a higher value will reduce the number of overlaps but may miss legitimate objects. If you do set the threshold manually, we recommend starting with a value of 0.32.
use_ocr: A boolean value that, when set to True, causes DocParse to extract text using an OCR model. This is useful when the text is not directly extractable from the PDF, such as when the text is part of an image or when the text is rotated. When set to False, DocParse extracts embedded text from the input document. Default is False.
extract_table_structure: A boolean that, when True, enables DocParse to extract tables and their structural content partitioner using a purpose built table extraction model. If set to False, tables are still identified but not analyzed for their structure; as a result, table cells and their bounding boxes are not included in the response. Default is False.
table_extraction_options: A map with string keys specifying options for table extraction, which currently only supports the boolean include_additional_text, which will add in text from OCR boxes. When include_additional_text is set to true and table extraction is enabled, DocParse will attempt to enhance the table structure by merging in tokens from text extraction. This can be useful for working with tables that have missing or misaligned text. include_additional_text is False by default. The default table_extraction_options is {}.
extract_images: A boolean that determines whether to extract images from the document. Default: False.
ocr_images: A boolean that, when True, causes DocParse to use OCR to attempt to generate a text representation of detected images. When False, images do not contain a text_representation. Default is False.
selected_pages: A list specifying individual pages (1-indexed) and page ranges from the document to partition. Single pages are specified as integers and ranges are specified as lists with two integer entries in ascending order. A valid example value for selected_pages is [1, 10, [15, 20]] which would include pages 1, 10, 15, 16, 17 …, 20. selected_pages is None by default, which results in all pages of the document being parsed.
chunking_options: A dictionary of options for specifying chunking behavior. Chunking is only performed when this option is present, and default options are chosen when chunking_options is specified as {}.
- strategy: A string specifying the strategy to use to combine and split chunks. Valid values are context_rich and maximize_within_limit. The default and recommended chunker is context_rich as {'strategy': 'context_rich'}.
  - Behavior of context_rich chunker: The goal of this strategy is to add context to evenly-sized chunks. This is most useful for retrieval based GenAI applications. Context_rich chunking combines adjacent section-header and title elements into a new section-header element. Merges elements into a chunk with its most recent section-header. If the chunk would contain too many tokens, then it starts a new chunk copying the section-header to the start of this new chunk and continues. Merges elements on different pages, unless merge_across_pages is set to False.
  - Behavior of maximize_within_limit chunker: The goal of the maximize_within_limit chunker is to make the chunks as large as possible. Merges elements into the last most recently merged set of elements unless doing so would make its token count exceed max_tokens. In that case, it would keep the new element separate and start merging subsequent elements into that one, following the same rule. Merges elements on different pages, unless merge_across_pages is set to False.
- max_tokens: An integer specifying the cutoff for splitting chunks that are too large. Default value is 512.
- tokenizer: A string specifying the tokenizer to use when determining how characters in a chunk are grouped. Valid values are openai_tokenizer, character_tokenizer, and huggingface_tokenizer. Defaults to openai_tokenizer.
- tokenizer_options: A tree with string keys specifying the options for the chosen tokenizer. Defaults to {'model_name': 'text-embedding-3-small'}, which works with the OpenAI tokenizer.
  - Available options for openai_tokenizer:
    - model_name: Accepts all models supported by OpenAI’s tiktoken tokenizer. Default is “text-embedding-3-small”
  - Available options for HuggingFaceTokenizer:
    - model_name: Accepts all huggingface tokenizers from the huggingface/tokenizers repo.
  - character_tokenizer does not take any options.
- merge_across_pages: A boolean that when True the selected chunker will attempt to merge chunks across page boundaries. Does not apply to the mixed_multi_column merger, which never merges across pages. Defaults to True.
output_format: A string controlling the output representation. Defaults to json which yields an array called elements which contains the partitioned elements, represented in JSON. If set to markdown the service response will instead include a field called markdown that contains a string representing the entire document in Markdown format.
aps_url: A string specifying the URL of the Aryn DocParse endpoint. Default URL: “https://api.aryn.cloud/v1/document/partition”.
ssl_verify: A boolean that controls whether the client verifies the SSL certificate of the chosen DocParse server. In Databricks, set this to False to fix SSL incompatibilities. ssl_verify is True by default, enforcing SSL verification.
aryn_api_key: An Aryn API key, provided as a string. You can get one for free at aryn.ai/get-started. Default is None (aryn-sdk will look for it in the aryn_config parameter (which by default looks in ~/.aryn/config.yaml and then in your environment variables (ARYN_API_KEY by default))).
aryn_config: An ArynConfig object (defined in aryn_sdk/config.py), used for finding an api key. If aryn_api_key is set it will override this. The default ArynConfig looks in the env var ARYN_API_KEY and then in the file ~/.aryn/config.yaml. Default is None (aryn-sdk will look in the aryn_api_key parameter, in your environment variables, and then in ~/.aryn/config.yaml).

from aryn_sdk.partition import partition_file

with open("my-favorite-pdf.pdf", "rb") as f:
    data = partition_file(
        f,
        aryn_api_key="MY-API-KEY",
        use_ocr=True,
        extract_table_structure=True,
        extract_images=True
    )
elements = data['elements']

convert_image_element

Convert an image element to a more usable format. If no format is specified, create a PIL Image object. If a format is specified, output the bytes of the image in that format. If b64encode is set to True, base64-encode the bytes and return them as a string.

from aryn_sdk.partition import partition_file, convert_image_element

with open("my-favorite-pdf.pdf", "rb") as f:
    data = partition_file(
        f,
        extract_images=True
    )
image_elts = [e for e in data['elements'] if e['type'] == 'Image']

pil_img = convert_image_element(image_elts[0])
jpg_bytes = convert_image_element(image_elts[1], format='JPEG')
png_str = convert_image_element(image_elts[2], format="PNG", b64encode=True)

draw_with_boxes

Create a list of images from the provided PDF, one for each page, with bounding boxes detected by the partitioner drawn on.

from aryn_sdk.partition import partition_file, draw_with_boxes

with open("my-favorite-pdf.pdf", "rb") as f:
    data = partition_file(
        f,
        aryn_api_key="MY-ARYN-TOKEN",
        use_ocr=True,
        extract_table_structure=True,
        extract_images=True
    )
pages = draw_with_boxes("my-favorite-pdf.pdf", data, draw_table_cells=True)

table_elem_to_dataframe

Create a pandas DataFrame representing the tabular data inside the provided table element. If the element is not of type table or doesn’t contain any table data, return None instead.

from aryn_sdk.partition import partition_file, table_elem_to_dataframe

with open("partition-me.pdf", "rb") as f:
    data = partition_file(
        f,
        use_ocr=True,
        extract_table_structure=True,
        extract_images=True
    )

# Find the first table and convert it to a dataframe
df = None
for element in data['elements']:
    if element['type'] == 'table':
        df = table_elem_to_dataframe(element)
        break

tables_to_pandas

For every table element in the provided partitioning response, create a pandas DataFrame representing the tabular data. Return a list containing all the elements, with tables paired with their corresponding DataFrames.

from aryn_sdk.partition import partition_file, tables_to_pandas

with open("my-favorite-pdf.pdf", "rb") as f:
    data = partition_file(
        f,
        aryn_api_key="MY-ARYN-TOKEN",
        use_ocr=True,
        extract_table_structure=True,
        extract_images=True
    )
elts_and_dataframes = tables_to_pandas(data)

Aryn SDK Reference

​partition_file

​convert_image_element

​draw_with_boxes

​table_elem_to_dataframe

​tables_to_pandas

partition_file

convert_image_element

draw_with_boxes

table_elem_to_dataframe

tables_to_pandas