# `LayoutLMDocumentQA` Documentation

## Introduction

Welcome to the documentation for LayoutLMDocumentQA, a multimodal model designed for visual question answering (QA) on real-world documents, such as invoices, PDFs, and more. This comprehensive documentation will provide you with a deep understanding of the LayoutLMDocumentQA class, its architecture, usage, and examples.

## Overview

LayoutLMDocumentQA is a versatile model that combines layout-based understanding of documents with natural language processing to answer questions about the content of documents. It is particularly useful for automating tasks like invoice processing, extracting information from PDFs, and handling various document-based QA scenarios.

## Class Definition

```python
class LayoutLMDocumentQA(AbstractModel):
    def __init__(
        self, 
        model_name: str = "impira/layoutlm-document-qa",
        task: str = "document-question-answering",
    ):
```

## Purpose

The LayoutLMDocumentQA class serves the following primary purposes:

1. **Document QA**: LayoutLMDocumentQA is specifically designed for document-based question answering. It can process both the textual content and the layout of a document to answer questions.

2. **Multimodal Understanding**: It combines natural language understanding with document layout analysis, making it suitable for documents with complex structures.

## Parameters

- `model_name` (str): The name or path of the pretrained LayoutLMDocumentQA model. Default: "impira/layoutlm-document-qa".
- `task` (str): The specific task for which the model will be used. Default: "document-question-answering".

## Usage

To use LayoutLMDocumentQA, follow these steps:

1. Initialize the LayoutLMDocumentQA instance:

```python
from swarms.models import LayoutLMDocumentQA

layout_lm_doc_qa = LayoutLMDocumentQA()
```

### Example 1 - Initialization

```python
layout_lm_doc_qa = LayoutLMDocumentQA()
```

2. Ask a question about a document and provide the document's image path:

```python
question = "What is the total amount?"
image_path = "path/to/document_image.png"
answer = layout_lm_doc_qa(question, image_path)
```

### Example 2 - Document QA

```python
layout_lm_doc_qa = LayoutLMDocumentQA()
question = "What is the total amount?"
image_path = "path/to/document_image.png"
answer = layout_lm_doc_qa(question, image_path)
```

## How LayoutLMDocumentQA Works

LayoutLMDocumentQA employs a multimodal approach to document QA. Here's how it works:

1. **Initialization**: When you create a LayoutLMDocumentQA instance, you can specify the model to use and the task, which is "document-question-answering" by default.

2. **Question and Document**: You provide a question about the document and the image path of the document to the LayoutLMDocumentQA instance.

3. **Multimodal Processing**: LayoutLMDocumentQA processes both the question and the document image. It combines layout-based analysis with natural language understanding.

4. **Answer Generation**: The model generates an answer to the question based on its analysis of the document layout and content.

## Additional Information

- LayoutLMDocumentQA uses the "impira/layoutlm-document-qa" pretrained model, which is specifically designed for document-based question answering.
- You can adapt this model to various document QA scenarios by changing the task and providing relevant questions and documents.
- This model is particularly useful for automating document-based tasks and extracting valuable information from structured documents.

That concludes the documentation for LayoutLMDocumentQA. We hope you find this tool valuable for your document-based question answering needs. If you have any questions or encounter any issues, please refer to the LayoutLMDocumentQA documentation for further assistance. Enjoy using LayoutLMDocumentQA!