Vision

Provides machine learning and computer vision capabilities for document processing. Enables AI-powered document description and content extraction.

from nutrient_sdk import Vision

Construction

Class Methods

set

@classmethod
def set(cls, document: Document) -> Vision

Creates a Vision instance for the specified document.

Parameters:

Returns: Vision - A Vision instance ready to perform analysis on the document. Raises:

• NullOrEmptyParameterException: Thrown when document is null.

Methods

describe

def describe(self) -> str

Generates an AI-powered description of the document content.

Returns: str - A string containing the document description.

detect_forms

def detect_forms(self) -> str

Detects form fields on the document and exports the result. Output format is determined by (JSON or IR Lite). Each detected field carries its type and bounding box. To also assign AI semantic labels (e.g. “First name”), set FormLabelingSettings.EnableAiLabeling on the document’s settings before calling — no separate method is needed.

Returns: str - The exported content as a string (JSON or IR Lite JSON depending on settings).

detect_forms_to_file

def detect_forms_to_file(self, output_path: str) -> None

Detects form fields on the document and writes the exported result to a file. Output format is determined by (JSON or IR Lite).

Parameters:

extract_content

def extract_content(self) -> str

def extract_content(self, settings: DocumentLayoutJsonExportSettings) -> str

Extracts structured content from the document using machine vision processing. The pipeline used is determined by the setting and the output format by .

Parameters:

Returns: str - The exported content as a string (JSON, Markdown, or IR Lite JSON depending on settings).

extract_content_to_file

def extract_content_to_file(self, output_path: str) -> None

def extract_content_to_file(self, output_path: str, settings: DocumentLayoutJsonExportSettings) -> None

Extracts structured content from the document and writes it to a file. The pipeline used is determined by the setting and the output format by .

Parameters:

extract_structured

def extract_structured(self, request: StructuredExtractionRequest) -> str

Extracts structured data from the document, shaped to the JSON Schema carried by the ‘s {“schema”: …} envelope. The document is first read by the extraction pipeline selected by , then an AI model fills the schema from the recognized content. Provider, model, endpoint, and confidence reporting are driven by AiProcessingSettings on the document’s settings.

Parameters:

Returns: str - A JSON string with two top-level nodes: extraction (the schema-shaped extracted fields) and metadata (per-field source locations and grounding labels).

extract_structured_to_file

def extract_structured_to_file(self, request: StructuredExtractionRequest, output_path: str) -> None

Extracts structured data from the document, shaped to the JSON Schema carried by the ‘s {“schema”: …} envelope, and writes the JSON result to a file. See for the result shape.

Parameters:

warmup

def warmup(self) -> None

Preloads (warms up) all resources needed for vision processing. This downloads all model files based on the document’s VisionSettings before execution. Call this to avoid download delays during ExtractContent().