Basic Direct LLM Call Example

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

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

GET STARTED

CONCEPTS

STARTING AN AGENT PROJECT

READY TO USE SNIPPETS

DEPLOYMENT

FURTHER READINGS

Basic Direct LLM Call Example

About Example Scenario

Direct LLM Call Configuration

Full Code

​About Example Scenario

​Direct LLM Call Configuration

​Full Code

About Example Scenario

Direct LLM Call Configuration

Full Code