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Document Processing Agent Example

This example demonstrates how to set up and use a document processing agent with the PilottAI framework.

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Features

• Text extraction from various document formats
• Content analysis capabilities
• Document summarization
• Configurable processing tools

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Setup

1. Install required dependencies:

pip install pilott

2. Configure your environment:

export OPENAI_API_KEY="your-api-key"

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Tools Included

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Text Extractor

Extracts text content from documents:

text_extractor = Tool(
    name="text_extractor",
    parameters={
        "file_path": "str",
        "format": "str"
    }
)

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Content Analyzer

Analyzes document content:

content_analyzer = Tool(
    name="content_analyzer",
    parameters={
        "text": "str",
        "analysis_type": "str"
    }
)

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Summarizer

Generates document summaries:

summarizer = Tool(
    name="summarizer",
    parameters={
        "text": "str",
        "max_length": "int"
    }
)

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Quick Start

from pilott import Serve
from pilott.core import AgentConfig, LLMConfig

# Initialize and run
async def main():
    pilott = Serve(name="DocumentProcessor")
    
    # Add document processing agent
    doc_processor = await pilott.add_agent(
        role="document_processor",
        goal="Process documents efficiently",
        tools=["text_extractor", "content_analyzer", "summarizer"]
    )

    # Process a document
    task = {
        "type": "document_analysis",
        "document": {
            "path": "document.pdf",
            "type": "pdf"
        }
    }
    
    result = await pilott.execute([task])

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Supported Document Types

• PDF files
• Text documents
• Word documents (docx)
• HTML files

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Common Use Cases

1. Document Analysis

   task = {
       "type": "document_analysis",
       "description": "Analyze quarterly report"
   }
   

2. Text Extraction

   task = {
       "type": "text_extraction",
       "document": {"path": "file.pdf"}
   }
   

3. Content Summarization

   task = {
       "type": "summarization",
       "document": {"path": "article.txt"}
   }
   

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Configuration Options

Customize agent behavior:

config = AgentConfig(
    role="document_processor",
    goal="Process documents efficiently",
    max_concurrent_tasks=5,
    task_timeout=300
)

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Best Practices

1. Document Handling

   • Validate document formats before processing
   • Handle large documents in chunks
   • Implement proper error handling

2. Performance

   • Configure appropriate timeouts
   • Use concurrent processing when possible
   • Monitor memory usage for large documents

3. Error Handling

   • Validate input documents
   • Handle unsupported formats gracefully
   • Implement retry logic for failed operations

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Troubleshooting

Common issues and solutions:

1. File Access Errors

   • Ensure proper file permissions
   • Verify file paths are correct
   • Check file format compatibility

2. Processing Timeouts

   • Adjust task_timeout in configuration
   • Process large documents in smaller chunks
   • Monitor system resources

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Example Output

# Example result
{
    'success': True,
    'output': {
        'summary': 'Document summary...',
        'analysis': 'Content analysis...',
        'metadata': {
            'pages': 5,
            'format': 'pdf',
            'processing_time': '2.3s'
        }
    }
}

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Code

Ready to use code document_processor.py