> ## Documentation Index
> Fetch the complete documentation index at: https://docs.askui.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Extract Data from UI Overview

> Learn how to extract various types of data from UI elements using AskUI's get() method

This guide shows you how to extract different types of data from your UI using AskUI's [`get()`](/04-reference/01-agent-frameworks/02-python/02-vision-agent-api/agent#get) method. Data extraction is a fundamental capability for building AI agents that can read and understand UI content.

## Quick Start

The [`get()`](/04-reference/01-agent-frameworks/02-python/02-vision-agent-api/agent#get) method allows you to ask questions about your UI and receive structured responses:

```python theme={null}
from askui import VisionAgent

with VisionAgent() as agent:
    # Basic text extraction
    text = agent.get("What is the main heading?", response_schema=str)
    
    # Boolean questions
    is_loaded = agent.get("Is the page fully loaded?", response_schema=bool)
    
    # Numeric values
    count = agent.get("How many items are in the cart?", response_schema=int)
```

## Data Extraction Types

<CardGroup cols={2}>
  <Card title="Yes/No Questions" icon="circle-check" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/yes-no-questions">
    Get boolean answers about UI state and conditions
  </Card>

  <Card title="Text Extraction" icon="font" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/text-extraction">
    Extract text content from UI elements
  </Card>

  <Card title="Numbers" icon="hashtag" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/numbers">
    Get numeric values like counts, prices, and metrics
  </Card>

  <Card title="Tables" icon="table" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/tables">
    Extract structured data from tables
  </Card>

  <Card title="Lists & Navigation" icon="list" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/lists-navigation">
    Extract sidebar items, menu options, and navigation structures
  </Card>

  <Card title="Tabs" icon="folder-open" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/tabs">
    Get information about tabs and their states
  </Card>

  <Card title="Cards & Tiles" icon="square" href="/02-how-to-guides/05-build-ai-agents/extract-data-from-ui/cards">
    Extract data from card layouts and tile grids
  </Card>
</CardGroup>

## Common Patterns

### Form Validation

```python theme={null}
with VisionAgent() as agent:
    # Check form state before submission
    has_errors = agent.get(
        "Are there any validation errors?", 
        response_schema=bool
    )
    
    if not has_errors:
        agent.click("Submit")
    else:
        error_messages = agent.get(
            "What are the error messages?",
            response_schema=List[str]
        )
        print(f"Fix these errors: {error_messages}")
```

### Dashboard Metrics

```python theme={null}
from askui import ResponseSchemaBase

class Metrics(ResponseSchemaBase):
    total_users: int
    active_sessions: int
    revenue: float
    growth_percentage: float

with VisionAgent() as agent:
    dashboard = agent.get(
        "Extract the dashboard metrics",
        response_schema=Metrics
    )
```

### Search Results

```python theme={null}
class SearchResult(ResponseSchemaBase):
    title: str
    url: str
    description: str

with VisionAgent() as agent:
    results = agent.get(
        "Get all search results",
        response_schema=List[SearchResult]
    )
```

## Best Practices

1. **Be Specific**: More specific questions yield better results
   ```python theme={null}
   # Good
   price = agent.get("What is the total price in the checkout?", response_schema=float)

   # Less specific
   price = agent.get("What's the price?", response_schema=float)
   ```

2. **Use Appropriate Types**: Match response schema to expected data
   ```python theme={null}
   # For counts, use int
   count = agent.get("How many notifications?", response_schema=int)

   # For prices, use float
   price = agent.get("What's the price?", response_schema=float)
   ```

3. **Handle Missing Data**: Use Optional fields when data might not exist
   ```python theme={null}
   from typing import Optional

   class UserProfile(ResponseSchemaBase):
       name: str
       email: str
       phone: Optional[str] = None
   ```

## Troubleshooting

<Card title="Data Extraction Issues?" icon="question-circle" href="/02-how-to-guides/04-troubleshooting/04-data-extraction">
  Getting empty results or type errors? Our data extraction troubleshooting guide covers common problems and solutions for the `get()` method.
</Card>

<Card title="Performance Optimization" icon="rocket" href="/03-explanation/02-best-practices/03-interactions#data-extraction-performance">
  Learn how to optimize data extraction performance with batch extraction and caching strategies.
</Card>

## Next Steps

* Learn about [advanced interaction patterns](/03-explanation/02-best-practices/03-interactions)
* Explore [best practices](/03-explanation/02-best-practices/04-agentic-mode)
* See detailed examples for each data type in the sections above