Tables are common UI elements for displaying structured data. Learn how to extract table data into structured Python objects using Pydantic models.

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Basic Table Extraction

from askui import VisionAgent, ResponseSchemaBase
from typing import List

class Entry(ResponseSchemaBase):
    company: str
    contact: str
    country: str

class Table(ResponseSchemaBase):
    rows: List[Entry]

with VisionAgent() as agent:
    agent.tools.webbrowser.open_new("https://www.w3schools.com/html/html_tables.asp")
    agent.wait(3)  # Wait for page to load

    result = agent.get("Can you extract me the table?", response_schema=Table)
    print(result.model_dump_json(indent=2))

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Best Practices

  1. Match Schema to Table Structure: Design your Pydantic model to match the actual table columns 
    # Look at the table first
headers = agent.get("What are the table headers?", response_schema=List[str])
print(f"Table columns: {headers}")

# Then design your model accordingly
  2. Type Conversion: Specify correct types for numeric columns 
    class DataRow(ResponseSchemaBase):
    product: str
    quantity: int  # Will convert "5" to 5
    price: float   # Will convert "$9.99" to 9.99