Parsed Document Reference

result = doc_ai.get_parsed_result(parse_id)

class ParseResult(BaseModel):
    # Parsed document specific fields
    chunks: Optional[List[Chunk]] = Field(
        default=None,
        description="Chunks of layout text extracted from the document. This is a vector of `Chunk` objects, each containing a piece of text extracted from the document. The chunks are typically used for further processing, such as indexing or searching. The value will vary depending on the chunking strategy used during parsing.",
    )
    pages: Optional[List[Page]] = Field(
        default=None,
        description="The layout of the document. This is a JSON object that contains the layout information of the document. It can be used to understand the structure of the document, such as the position of text, tables, figures, etc.",
    )
    page_classes: Optional[List[PageClass]] = Field(
        default=None,
        description="Page classes extracted from the document. This is a list of `PageClass` objects containing the class name and page numbers where each page class appears.",
    )
    structured_data: Optional[List[StructuredData]] = Field(
        default=None,
        description="Structured data extracted from the document. The structured data is a list of `StructuredData` objects containing the structured data extracted from the document; formatted according to the schema. This is used to extract structured information from the document, such as tables, forms, or other structured content.",
    )

    # Parse details
    parse_id: str = Field(description="The unique identifier for the parse job")
    parsed_pages_count: int = Field(
        description="The number of pages that were parsed successfully.", ge=0
    )
    status: ParseStatus = Field(description="The status of the parse job.")
    created_at: str = Field(
        description="The date and time when the parse job was created in RFC 3339 format."
    )
    error: Optional[str] = Field(
        default=None,
        description="Error occurred during any part of the parse execution.",
    )
    finished_at: Optional[str] = Field(
        default=None,
        description="The date and time when the parse job was finished in RFC 3339 format.",
    )
    labels: Optional[dict] = Field(
        default=None,
        description="Labels associated with the parse job.",
    )

​

Output Response Fields

• parse_id: The unique identifier for the parse job.
• parsed_pages_count: An integer representing the number of pages that were parsed successfully.
• total_pages: An integer representing the total number of pages in the document.
• status: The status of the parse job.
• created_at: The date and time when the parse job was created in RFC 3339 format.
• finished_at: The date and time when the parse job was finished in RFC 3339 format.
• error: Any errors encountered while parsing the document.
• labels: Labels associated with the parse job.
• chunks: An array of objects that contain the markdown content for each chunk. The number of chunks depends on the chunking strategy you chose. See more below.
• pages: A comprehensive JSON representation of the document’s visual structure, including page dimensions, bounding boxes for each element, and reading order. See more below.
• page_classes: A map where the keys are page class names provided in the parse request, and the values are PageClass objects containing class names and page numbers where each page class appears. See more below.
• structured_data: A map where the keys are the names of the JSON schema provided in the parse request, and the values are StructuredData objects containing the extracted structured data. See more below.
• usage: An object containing usage statistics for the parse job, including token counts and parsed page counts for various extraction tasks.

​

Markdown Chunks

• None - The whole document is returned as a single chunk. This allows you to use your own chunking logic.
• Page - Each page is returned as a separate chunk. You should receive as many chunks as the number of pages in the document.
• Section - The document is split into chunks based on the section headers detected in the document.
• Fragment - Every page fragment (e.g. table, figure, paragraph) is returned as a separate chunk. You will most likely have to merge these chunks based on your use-case.

​

DOCX Tracked Changes and Comments

• Insertions: <ins>inserted text</ins> - Text that was added to the document
• Deletions: <del>deleted text</del> - Text that was removed or struck through

• Comment ranges: <span class="comment" data-note="comment text">highlighted text</span> - Comments anchored to selected text
• Comment references: <!-- Comment: comment text --> - Comments at cursor positions without highlighted text

​

Example Output

<p>Initial damage estimates suggest total losses between $2.8M and 
<span class="comment" data-note="Michael Torres: Need to verify this upper bound">$3.4M</span>,
<ins>based on preliminary contractor assessments,</ins> which falls within policy limits
<del>though a complete forensic analysis is pending</del>.</p>

​

Extracting Change Data Programmatically

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')

# Extract all comments
comments = []
for span in soup.find_all('span', class_='comment'):
    comments.append({
        'text': span.get_text(strip=True),
        'comment': span.get('data-note', '')
    })

# Extract all deletions
deletions = [del_tag.get_text() for del_tag in soup.find_all('del')]
for deletion in deletions:
    print(f"Deleted: {deletion}")

# Extract all insertions
insertions = [ins_tag.get_text() for ins_tag in soup.find_all('ins')]
for insertion in insertions:
    print(f"Inserted: {insertion}")

# Print all comments
for comment in comments:
    print(f"Comment: {comment['text']} - {comment['comment']}")

​

Document Layout and Bounding Boxes

• page_number - The page number of the page.
• dimensions - The width and height of the page in pixels.
• page_fragments - The list of objects on the page. Each page fragment has the following attributes:
  • fragment_type - The type of the object: section_header, title, text, table, figure, formula, form, key_value_region, document_index, list_item, table_caption, figure_caption, formula_caption, page_footer, page_header, page_number, signature, strikethrough
  • reading_order - The reading order of the page fragments. This is the order in which the fragment would be read by a human.
  • bbox - The bounding box of the page fragment, in the format [x1, y1, x2, y2].
  • content - The actual content that is found on that fragment of the page.

​

Page Classifications

• page_class: The classification name you provided. This will match the name field in your PageClassConfig.
• page_numbers: An array of page numbers (1-indexed) that match this classification.

​

Structured Extraction

• data: The JSON object representing the data extracted that matches the input schema.
• page_numbers: A list of page numbers where the structured data was searched for.
• schema_name: The name of the schema provided by the user.

​

Usage

• pages_parsed: The number of pages that were parsed.
• signature_detected_pages: The number of pages where signatures were detected. This is only applicable if signature detection was enabled.
• strikethrough_detected_pages: The number of pages where strikethroughs were detected. This is only applicable if strikethrough lines detection was enabled.
• ocr_input_tokens_used: The number of input tokens used for OCR processing.
• ocr_output_tokens_used: The number of output tokens generated from OCR processing.
• extraction_input_tokens_used: The number of input tokens used for text extraction. This is only applicable if structured extraction options were enabled.
• extraction_output_tokens_used: The number of output tokens generated from text extraction. This is only applicable if structured extraction options were enabled.
• summarization_input_tokens_used: The number of input tokens used for summarization.
• summarization_output_tokens_used: The number of output tokens generated from summarization.