There are several options you can specify when calling DocParse. For example, we can extract the table structure from our document with the following curl command. 
export ARYN_API_KEY="PUT API KEY HERE"
curl -s -N -D headers "https://api.aryn.cloud/v1/document/partition" \
    -H "Authorization: Bearer $ARYN_API_KEY" -F "file=@document.pdf" \
    -F 'options={"table_mode": "standard"}' | tee document.json
All of the available options are listed below, and are optional unless specified otherwise.

​
General

​
text_mode

A string that specifies the mode to use for text extraction. Default: "inline_fallback_to_ocr". Valid options are inline, inline_fallback_to_ocr, ocr_standard, and ocr_vision.
  • inline extracts the embedded text
  • inline_fallback_to_ocr extract the embedded text elementwise when present and falls back to performing OCR otherwise
  • ocr_standard uses the classical OCR pipeline
  • ocr_vision uses a vision model for OCR. Note that ocr_vision is only available for PAYG users.

​
table_mode

A string specifying table structure extraction mode. Default: "standard". Valid options are ‘standard’, ‘vision’, ‘none’, and ‘custom’.
  • none will not extract table structure (equivalent to extract_table_structure = False)
  • standard will use the standard hybrid table structure extraction pipeline
  • vision will use a vision model to extract table structure
  • custom will use the custom expression described by the model_selection parameter in the table_extraction_options

​
chunking_options.strategy

chunking_options is 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 {}. Specify the chunking strategy using the strategy key. 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.
  • 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. The context_rich chunking combines adjacent Section-header and Title elements into a new Section-header element. It merges elements into a chunk with its most recent Section-header. If the chunk would contain too many tokens, it starts a new chunk by copying the Section-header to the start of this new chunk and continues. The chunker 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 the chunking option merge_across_pages is set to False.

​
output_format

A string controlling the output representation. Default: "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.

​
summarize_images (PAYG Only)

A boolean that, when True, generates a summary of the images in the document and returns it as the text_representation. When False, images are not summarized. Default: False. summarize_images is only available for Pay-As-You-Go (PAYG) users.

​
use_ocr (Deprecated)

Use text_mode instead.
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: False.

​
extract_table_structure (Deprecated)

Use table_mode instead.
A boolean that, when True, enables DocParse to extract tables and their structural content 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: True.

​
Tables

​
table_extraction_options

A map with string keys specifying options for table extraction. Only applied when extract_table_structure is True. Default: {}.

​
table_extraction_options.include_additional_text

Boolean. When True, 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. Default: True.

​
table_extraction_options.model_selection

String. An expression to instruct DocParse how to select the table model to use for extraction. Default: "pixels > 500 -> deformable_detr; table_transformer", which means “if the largest dimension of the table is more than 500 pixels, use deformable_detr; otherwise use table_transformer.” To use only deformable_detr or table_transformer, set model_selection="deformable_detr" or model_selection="table_transformer". Selection expressions are of the form 
metric cmp threshold -> model; metric cmp threshold -> model; model
And should be read as a series of if metric compares to threshold, then use model statements. Statements are processed from left to right.
  • Supported models are table_transformer, which tends to do well with smaller tables, and deformable_detr, which tends to do better with larger tables.
  • Supported metrics are pixels, which corresponds to the maximum dimension of the bounding box containing the table (we find this to be easier to reason about than the total number of pixels which depends on two numbers), and chars, which corresponds to the total number of characters within the table as determined by the OCR/text extraction step.
  • Thresholds must be numeric.
  • Supported comparison operators are <, >, <=, >=, ==, !=.
A statement with no metric, comparison, and threshold can be thought of as a default, where statements after the default will not be processed. If no such ‘unconditional’ statement is included and no conditions match, DocParse will default to table_transformer. Anything after the unconditional statement will not be processed. Examples:
  • table_transformer => always use table transformer
  • pixels > 500 -> deformable_detr; table_transformer => if the biggest dimension of the table is greater than 500 pixels use deformable detr. Otherwise use table_transformer.
  • pixels>50->table_transformer; chars<30->deformable_detr;chars>35->table_transformer;pixels>2->deformable_detr;table_transformer;comment => if the biggest dimension is more than 50 pixels use table transformer. Else if the total number of chars in the table is less than 30 use deformable_detr. Else if there are mode than 35 chars use table transformer. Else if there are more than 2 pixels in the biggest dimension use deformable detr. Otherwise use table transformer. comment is not processed.

​
Text

​
ocr_language

A string that specifies the language model to use for OCR. Default: "english" (English). The full list of supported languages can be found here.

​
text_extraction_options

A map with string keys specifying options for text extraction.

​
text_extraction_options.remove_line_breaks

A boolean that specifies whether to remove line breaks from the text. Default: True.

​
text_extraction_options.ocr_text_mode (Deprecated)

Use text_mode instead.
A string that specifies the mode to use for OCR text extraction. Default: "standard", which uses the conventional classical OCR pipeline to process documents. The other option is vision, which uses a vision model for OCR. Note that vision is only available for PAYG users only.

​
Chunking

​
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 {}.

​
chunking_options.strategy

A string specifying the strategy to use to combine and split chunks. Valid values are context_rich and maximize_within_limit. The recommended chunker Default: "context_rich" is denoted 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. The context_rich chunking combines adjacent Section-header and Title elements into a new Section-header element. It merges elements into a chunk with its most recent Section-header. If the chunk would contain too many tokens, it starts a new chunk by copying the Section-header to the start of this new chunk and continues. The chunker 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.

​
chunking_options.max_tokens

An integer specifying the cutoff for splitting chunks that are too large. Default: 512.

​
chunking_options.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.

​
chunking_options.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:
  • character_tokenizer does not take any options.

​
chunking_options.merge_across_pages

A boolean that when True the selected chunker will attempt to merge chunks across page boundaries. Default: True.

​
Images

​
extract_images

A boolean that determines whether to extract images from the document. The format is determined by the value of extract_image_format. Default: False.

​
extract_image_format

A string indicating what in what format extracted images should be returned. Must be one of ppm, png, or jpeg. In all cases, the result will be base64 encoded before being returned. Default: "ppm".

​
Advanced

​
add_to_docset_id

A string that specifies the DocSet ID to store your parsed document in. By default, DocParse will use the DocSet named docparse_storage unless you have disabled data retention.

​
threshold

This represents the threshold for accepting the model’s predicted bounding boxes. Default: "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 (instead of using the processing method described above). 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. Either the specific string auto or a float between 0.0 and 1.0, inclusive. This value specifies the cutoff for detecting bounding boxes. 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. Default is auto (DocParse will choose optimal bounding boxes).

​
pages_per_call

This is only available when using the Partition function in Sycamore. This option divides the processing of your document into batches of pages, and you specify the size of each batch (number of pages). This is useful when running OCR on large documents. Default: -1. Use -1 to process all pages at once.

​
output_label_options

A dictionary of options to specify which heuristic to apply to enforce certain label outputs. If this option is not specified, no heuristic is applied. The options the dictionary supports are listed below.

​
output_label_options.promote_title

A boolean that specifies whether to promote an element to title if there’s no title in the output. Default: False.

​
output_label_options.title_candidate_elements

A list of strings that are candidate elements to be promoted to title. Default: ["Section-header", "Caption"].

​
output_label_options.orientation_correction

A boolean value that specifies whether to correct the orientation of rotated pages during the preprocessing step. Default: False.

​
markdown_options

A dictionary of options to specify what to include in the markdown output. Default: {}.

​
markdown_options.include_pagenum

A boolean that specifies whether to include page numbers in the markdown output. Default: False.

​
markdown_options.include_headers

A boolean that specifies whether to include headers in the markdown output. Default: False.

​
markdown_options.include_footers

A boolean that specifies whether to include footers in the markdown output. Default: False.

​
Examples

Here are examples of how you can use multiple of these options in a curl command or in Python code with the Aryn SDK.
curl
export ARYN_API_KEY="PUT API KEY HERE"
curl -s -N -D headers "https://api.aryn.cloud/v1/document/partition" -H "Authorization: Bearer $ARYN_API_KEY" -F "file=@document.pdf" -F 'options={"text_mode": "standard_ocr", "table_mode": "standard", "threshold": 0.2}' | tee document.json
aryn_sdk.py
import os
import json
from aryn_sdk.partition import partition_file
os.environ["ARYN_API_KEY"] = "PUT API KEY HERE"
with open("document.pdf", "rb") as f:
    ans = partition_file(
        file=f,
        text_mode="standard_ocr",
        table_mode="standard",
        threshold=0.2
    )
with open("document.json", "w") as f:
    json.dump(ans, f)