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async def extract_structured_data(
    *,
    content: list[ContentItem] | ContentItem,
    data_schema: type[BaseModel] | dict[str, Any],
    prompt: str | None = None,
    max_retires: int = 3,
    enable_cache: bool = True,
    model: SUPPORTED_MODELS = 'claude-3-5-haiku-latest',
    api_key: str | None = None,
) -> Any
Extracts structured data from content items (text, images) using AI-powered analysis.This overload provides a simplified interface for data extraction from various content types without requiring a page source or extraction strategy. It accepts text content, image buffers, or image URLs and extracts structured data according to the provided schema.

​
Examples

from pydantic import BaseModel, Field
from intuned_browser.ai import extract_structured_data, TextContentItem
# Define schema using Pydantic
class Person(BaseModel):
    name: str = Field(description="Person's full name")
    age: int = Field(description="Person's age")
    occupation: str = Field(description="Person's job title")
    company: str = Field(description="Company name")
async def automation(page, params, **_kwargs):
    text_content: TextContentItem = {
        "type": "text",
        "data": "John Doe, age 30, works as a Software Engineer at Tech Corp"
    }
    person = await extract_structured_data(
        content=text_content,
        model="gpt-4o",
        data_schema=Person,  # Pass Pydantic model directly
        prompt="Extract person information from the text"
    )
    print(f"Found person: {person['name']}, {person['age']} years old")

​
Arguments

​
content
list[ContentItem] | ContentItem
required
Content to extract data from - can be a single content item or array of content items. Check ContentItem for more details.
​
data_schema
BaseModel | dict[str, Any]
required
Schema defining the expected structure of the extracted data. Can be either a Pydantic BaseModel class or a JSON Schema dictionary.
​
prompt
str
default:"None"
Optional prompt to guide the extraction process and provide more context. Defaults to None.
​
max_retires
int
default:"3"
Maximum number of retry attempts on failures. Failures can be validation errors, API errors, output errors, etc. Defaults to 3.
​
enable_cache
bool
default:"True"
Whether to enable caching of the extracted data. Defaults to True.
​
model
SUPPORTED_MODELS
default:"'claude-3-5-haiku-latest'"
AI model to use for extraction. See SUPPORTED_MODELS for all supported models. Defaults to “claude-3-5-haiku-latest”.
​
api_key
str
default:"None"
Optional API key for AI extraction (if provided, will not be billed to your account). Defaults to None.

​
Returns: Any

The extracted structured data conforming to the provided schema.
Key Features & Limitations
  • No DOM Matching: This overload does not support DOM matching since it doesn’t operate on web pages.
  • Smart Caching: Caching is based on content hash to avoid redundant API calls.
  • Automatic Image Fetching: Image URLs are automatically fetched and converted to image buffers for processing.
  • Batch Processing: Multiple content items can be processed together for comprehensive extraction.