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The Read API converts Documents to Markdown and provides spatial layouts of pages. The response of the Read API contains:
  • Markdown representation of pages. The elements in pages ordered by their natural reading order
  • Tables encoded as Markdown or HTML
  • Summary of tables and figures guided by custom prompts
  • Bounding boxes for each page element(e.g. signature, key-value pair, figure)
Read the Overview for understanding how to integrate Document Parsing to your existing workflows.

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API Usage Guide

Calling the read endpoint will create a new document parsing job, starting in the pending state. It will transition to the processing state and then to the successful state when it’s parsed successfully.
  • Python SDK
  • REST API
If you are using the Python SDK, all the configuration options described above are expressed through the ParsingOptions class.
from tensorlake.documentai import (
  DocumentAI,
  ParsingOptions,
  ChunkingStrategy,
  TableOutputMode,
  TableParsingFormat,
)

doc_ai = DocumentAI(api_key="xxxx")
file_id = "file_xxxx"

parsing_options = ParsingOptions(
    chunking_strategy=ChunkingStrategy.FRAGMENT,
    table_output_mode=TableOutputMode.MARKDOWN
)

parse_id = doc_ai.read(file_id=file_id, page_range="1-2", parsing_options=parsing_options)

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Options for Parsing Documents

Document Parsing can be customized by providing the parsing_options and enrichment_options in your request.
ParameterDescription
parsing_optionsCustomizes the OCR and table parsing process and chunking strategies. See Parsing Options.
enrichment_optionsEnables and configures table and figure summarization. See Summarization.
Get a full list of the configuration setting options on the /parse section of the API reference.

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Parsing Options

ParameterDescriptionDefault Value
chunking_strategyChoose between , , , or .None
table_output_modeChoose between Markdown, .HTML
table_parsing_formatChoose between or .TSR
disable_layout_detectionBoolean flag to skip layout detection and directly extract text. Useful for documents with many tables or images.false
skew_detectionDetect and correct skewed or rotated pages. Please note this can increase the processing time.false
signature_detectionDetect signatures in the document. Please note this can increase the processing time, and incurs additional costs.false
remove_strikethrough_linesRemove strikethrough lines from the document. Please note this can increase the processing time, and incurs additional costs.false
ignore_sectionsA set of document fragments to ignore during parsing. This can be useful for excluding irrelevant sections from the output.[]
cross_page_header_detectionA boolean flag to enable header hierarchy detection across pages. This can improve the accuracy of header extraction in multi-page documents.false

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Retrieve Output

The parsed document output can be retrieved using the /parse/{parse_id} endpoint, or using the get_job SDK function. 
result = doc_ai.get_parsed_result(parse_id)

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Markdown Chunks

Leveraging the markdown chunks is a common next step after parsing documents. 
for chunk in result.chunks:
print(f"## Page Number: {chunk.page_number}\n")
print(f"## Content: {chunk.content}\n")
See Parse Output for more details about the output.

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Bounding Boxes

Each page fragment includes bounding box coordinates that specify the exact location of the content on the page. This is useful for creating citations, highlighting source content in a UI, or debugging extraction quality.
Google Colab Notebook

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Accessing Bounding Boxes

result = doc_ai.parse_and_wait(file_id)

for page in result.pages:
  for fragment in page.page_fragments:
    bbox = fragment.bbox
    print(f"Fragment type: {fragment.fragment_type}")
    print(f"Top-left: ({bbox['x1']}, {bbox['y1']})")
    print(f"Bottom-right: ({bbox['x2']}, {bbox['y2']})")

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Coordinate System

Bounding boxes use the following coordinate system:
  • x1, y1: Top-left corner of the bounding box
  • x2, y2: Bottom-right corner of the bounding box
  • Origin (0,0): Top-left corner of the page
  • Units: Pixels
All fragment types include bounding box coordinates.

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Table and Figure Summarization

Document Ingestion API can be used to summarize tables and figures in documents.
ParameterDescriptionDefault Value
table_summarizationEnable summarization of tables present in the document. This will generate a summary of the table content, including key insights and trends.false
figure_summarizationEnable summarization of figures present in the document. This will generate a summary of the figure content, including key insights and trends.false
table_summarization_promptA custom prompt to use for table summarization. This can be used to provide additional context or instructions to the LLM. If not specified, the default prompt will be used.-
figure_summarization_promptA custom prompt to use for figure summarization. This can be used to provide additional context or instructions to the LLM. If not specified, the default prompt will be used.-
include_full_page_imageInclude the full page image as additional context when summarizing tables and figures, which can improve accuracy by capturing surrounding headers, captions, and related content.false

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Tables

Tales can be summarized by setting table_summarization to true in the enrichment_options JSON object when calling the parse API.
Google Colab Notebook
from tensorlake.documentai import DocumentAI
from tensorlake.documentai.models.options import (
    EnrichmentOptions,
)

enrichment_options = EnrichmentOptions(
    table_summarization=True,
    table_summarization_prompt="Summarize the table in a concise manner.",
)

doc_ai = DocumentAI(api_key=API_KEY)

parse_id = doc_ai.read(
    file_id="file_XXX",  # Replace with your file ID or URL
    enrichment_options=enrichment_options,
)

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Figures

Figures can be summarized by setting figure_summarization to true in the enrichment_options JSON object when calling the parse API.
Google Colab Notebook
from tensorlake.documentai import (
    DocumentAI,
    EnrichmentOptions,
)

doc_ai = DocumentAI(api_key=API_KEY)

enrichment_options = EnrichmentOptions(
    figure_summarization=True,
    figure_summary_prompt="Summarize the figure in a way that is easy to understand and use for answering questions.",
)

parse_id = doc_ai.read(
    file_id="file_XXX",  # Replace with your file ID or URL
    enrichment_options=enrichment_options,
)

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Full Page Image Context

When summarizing tables and figures, you can optionally include the full page image as additional context. This helps the model better understand the surrounding content, headers, footers, and relationships between elements on the page.
Google Colab Notebook
enrichment_options = EnrichmentOptions(
    table_summarization=True,
    figure_summarization=True,
    include_full_page_image=True
)

result = doc_ai.parse_and_wait(
    file_id,
    enrichment_options=enrichment_options
)
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