> ## 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.

# Document Analyzer

> Use the Upsonic framework to extract company names from Turkish Tax Certificates using computer vision and LLM reasoning.

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.

## 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.

## 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

## Code Structure

### Response Model

```python theme={null}
class CompanyResponse(BaseModel):
    company_name: str
```

### Agent Setup

```python theme={null}
doc_agent = Agent(name="document_reader")
```

### Task Definition

```python theme={null}
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=["examples/document_analyzer/assets/vergi_levhasi.png"],
    response_format=CompanyResponse
)
```

## Complete Implementation

```python theme={null}
# 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=["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)
```

## 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

## Usage

### Setup

```bash theme={null}
uv sync
```

### Run the example

```bash theme={null}
python examples/document_analyzer/extract_company_name.py
```

### Expected Output

```yaml theme={null}
Extracted Company Name: UPSONIC TEKNOLOJİ ANONİM ŞİRKETİ
```

**Note**: Output may vary slightly depending on Upsonic version and OCR results.

## File Structure

```bash theme={null}
examples/document_analyzer/
├── extract_company_name.py      # Main document analysis script
├── assets/
│   └── vergi_levhasi.png       # Sample tax certificate
└── README.md                    # Documentation
```

## 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

## Advanced Features

### Multiple Document Types

You can extend this example to handle various document types:

```python theme={null}
# Process multiple document types
documents = [
    "invoice.pdf",
    "contract.docx", 
    "certificate.png"
]

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

### Custom Field Extraction

```python theme={null}
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",
    context=["vergi_levhasi.png"],
    response_format=DocumentFields
)
```

## 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

## Repository

View the complete example: [Document Analyzer Example](https://github.com/Upsonic/Examples/tree/master/examples/document_analyzer)