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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
  1. 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 pilottai import Serve
from pilottai.core import AgentConfig, LLMConfig

# Initialize and run
async def main():
    pilott = Serve(name="DocumentProcessor")

    # Add document processing agent
    doc_processor = await pilott.add_agent(
        title="document_processor",
        goal="Process documents efficiently",
        tools=["text_extractor", "content_analyzer", "summarizer"]
    )

    # Process a document
    job = {
        "type": "document_analysis",
        "document": {
            "path": "document.pdf",
            "type": "pdf"
        }
    }

    result = await pilott.execute([job])

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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 
    job = {
    "type": "document_analysis",
    "description": "Analyze quarterly report"
}
  2. Text Extraction 
    job = {
    "type": "text_extraction",
    "document": {"path": "file.pdf"}
}
  3. Content Summarization 
    job = {
    "type": "summarization",
    "document": {"path": "article.txt"}
}

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Configuration Options

Customize agent behavior: 
config = AgentConfig(
    title="document_processor",
    goal="Process documents efficiently",
    max_concurrent_jobs=5,
    job_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 job_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