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