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

# Basic Direct LLM Call Example

> Extract structured information from a business document

## About Example Scenario

This example demonstrates a practical use case for Direct LLM Call: extracting structured information from a business document. We have a PDF invoice and need to extract key financial information in a type-safe, validated format. This scenario showcases:

* Document processing with attachments
* Structured output using Pydantic models
* Simple, direct execution without agent complexity
* Type-safe data extraction

## Direct LLM Call Configuration

**Model Selection**: We use `"openai/gpt-4o"` for its strong vision and reasoning capabilities, essential for document understanding.

**Task Configuration**:

* **Description**: Clear instruction for the LLM
* **Attachments**: PDF document passed as attachment for processing
* **Response Format**: Pydantic model (`InvoiceData`) ensures validated, structured output with required fields

**Pydantic Model**: Defines the expected structure with field types, ensuring runtime validation and type safety. The model includes:

* Invoice number (string)
* Total amount (float)
* Issue date (string)
* List of line items with name and amount

## Full Code

```python theme={null}
from upsonic import Direct, Task
from pydantic import BaseModel


# Define the structured response format
class LineItem(BaseModel):
    name: str
    amount: float


class InvoiceData(BaseModel):
    invoice_number: str
    total_amount: float
    issue_date: str
    line_items: list[LineItem]


# Initialize Direct with GPT-4o
direct = Direct(model="anthropic/claude-sonnet-4-5")

# Create task with document attachment and structured response
task = Task(
    description="Extract invoice data from this document including invoice number, total amount, issue date, and all line items with their amounts.",
    attachments=["invoice.pdf"],
    response_format=InvoiceData
)

# Execute and get structured result
result = direct.do(task)

# Access structured data with type safety
print(f"Invoice: {result.invoice_number}")
print(f"Total: ${result.total_amount}")
print(f"Date: {result.issue_date}")
print(f"\nLine Items:")
for item in result.line_items:
    print(f"  - {item.name}: ${item.amount}")
```

**Expected Output Structure**:

```
Invoice: INV-2024-001
Total: $1250.50
Date: 2024-10-29

Line Items:
  - Consulting Services: $1000.00
  - Software License: $250.50
```

**Key Features Demonstrated**:

* Automatic PDF processing through attachments
* Type-safe structured output with Pydantic validation
* Clean, synchronous API for straightforward use cases
* Zero configuration for memory or tools
* Automatic MIME type detection for attachments