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This example demonstrates how to use the Upsonic framework to extract the company name from a Turkish Tax Certificate (Vergi Levhası) using computer vision and LLM reasoning.

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Overview

The Document Analyzer showcases Upsonic’s ability to process visual documents and extract structured information. In this example, the agent:
  1. Processes a Turkish Tax Certificate image
  2. Extracts the company name from the “TICARET UNVANI” field
  3. Returns structured data using Pydantic models
This demonstrates how Upsonic can handle multimodal inputs (text + images) and provide reliable document processing capabilities.

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Key Features

  • Multimodal Processing: Handles both text and image inputs
  • Structured Output: Uses Pydantic models for type-safe responses
  • Document OCR: Automatically extracts text from images
  • Precise Extraction: Focuses on specific document fields
  • Error Handling: Robust processing of document variations

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Code Structure

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Response Model

class CompanyResponse(BaseModel):
    company_name: str

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Agent Setup

doc_agent = Agent(name="document_reader")

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Task Definition

task = Task(
    description=(
        "Read the attached Turkish tax certificate (Vergi Levhası) and return the company name "
        "exactly as it appears in the field 'TICARET UNVANI'. "
        "Do not invent, shorten, or replace words. "
        "Return only the full legal company name, nothing else."
    ),
    attachements=["task_examples/document_analyzer/assets/vergi_levhasi.png"],
    response_format=CompanyResponse
)

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Complete Implementation

# task_examples/document_analyzer/extract_company_name.py

from upsonic import Task, Agent
from pydantic import BaseModel

# Define the response format
class CompanyResponse(BaseModel):
    company_name: str

# Create the agent
doc_agent = Agent(name="document_reader")

# Create the task
task = Task(
    description=(
        "Read the attached Turkish tax certificate (Vergi Levhası) and return the company name "
        "exactly as it appears in the field 'TICARET UNVANI'. "
        "Do not invent, shorten, or replace words. "
        "Return only the full legal company name, nothing else."
    ),
    attachements=["task_examples/document_analyzer/assets/vergi_levhasi.png"],
    response_format=CompanyResponse
)

# Run the task
result = doc_agent.do(task)

# Print the result
print("Extracted Company Name:", result.company_name)

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How It Works

  1. Document Input: The agent receives a Turkish Tax Certificate image
  2. OCR Processing: Upsonic automatically extracts text from the image
  3. Field Identification: The LLM identifies the “TICARET UNVANI” field
  4. Name Extraction: Extracts the company name exactly as it appears
  5. Structured Output: Returns the result in a structured Pydantic model

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Usage

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Setup

uv sync

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Run the example

python task_examples/document_analyzer/extract_company_name.py

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Expected Output

Extracted Company Name: UPSONIC TEKNOLOJİ ANONİM ŞİRKETİ
Note: Output may vary slightly depending on Upsonic version and OCR results.

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File Structure

task_examples/document_analyzer/
├── extract_company_name.py      # Main document analysis script
├── assets/
│   └── vergi_levhasi.png       # Sample tax certificate
└── README.md                    # Documentation

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Use Cases

  • Document Processing: Extract information from official documents
  • Form Processing: Automate data extraction from forms and certificates
  • Compliance: Process regulatory documents and certificates
  • Data Entry: Automate manual data extraction tasks
  • Multilingual Documents: Handle documents in various languages

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Advanced Features

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Multiple Document Types

You can extend this example to handle various document types: 
# Process multiple document types
documents = [
    "invoice.pdf",
    "contract.docx", 
    "certificate.png"
]

for doc in documents:
    task = Task(
        description=f"Extract key information from {doc}",
        attachments=[doc],
        response_format=DocumentInfo
    )
    result = agent.do(task)

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Custom Field Extraction

class DocumentFields(BaseModel):
    company_name: str
    tax_number: str
    address: str
    registration_date: str

task = Task(
    description="Extract all key fields from the tax certificate",
    attachments=["vergi_levhasi.png"],
    response_format=DocumentFields
)

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Notes

  • Tested with: upsonic==0.61.1a1758720414
  • Image Formats: Supports PNG, JPG, PDF, and other common formats
  • OCR Quality: Results depend on image quality and text clarity
  • Language Support: Works with documents in various languages
  • Error Handling: Gracefully handles unclear or damaged documents

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Repository

View the complete example: Document Analyzer Example
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