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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

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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

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Full Code

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="openai/gpt-4o")

# 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