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={"extract_table_structure": true}' | tee document.json

All of the available options are listed below, and are optional unless specified otherwise.

  • 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.

  • text_extraction_options: A map with string keys specifying options for text extraction.

    • ocr_text_mode: A string that specifies the mode to use for OCR text extraction. The default is 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 non-table elements (standard will be used for table elements) and for PAYG users only.

  • extract_table_structure: 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 is False.

  • 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.

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

    • 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 is False

    • model_selection: String. An expression to instruct DocParse how to select the table model to use for extraction. Default is "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.

  • extract_images: A boolean that determines whether to extract images from the document. Default: False.

  • 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 is False.

  • ocr_language: A string that specifies the language to use for OCR. The default is english (English). The full list of supported languages can be found here.

  • 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. 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.

    • 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:
      • 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. 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.

  • threshold: This represents the threshold for accepting the model’s predicted 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 (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.

  • 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.

    • promote_title: A boolean that specifies whether to promote an element to title if there’s no title in the output.
    • title_candidate_elements: A list of strings that are candidate elements to be promoted to title.
    • orientation_correction: A boolean value that specifies whether to correct the orientation of rotated pages during the preprocessing step.

  • markdown_options: A dictionary of options to specify what to include in the markdown output.

    • include_pagenum: A boolean that specifies whether to include page numbers in the markdown output. Default is False.
    • include_headers: A boolean that specifies whether to include headers in the markdown output. Default is False.
    • include_footers: A boolean that specifies whether to include footers in the markdown output. Default is False.

Here is an example of how you can use some of these options in a curl command or in Python code with the Aryn SDK.

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={"use_ocr": true, "extract_table_structure": true, "threshold": 0.2}' | tee document.json

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