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Entry point function that instantiates a FixedProjectVictoriaGenerator and executes its complete pipeline to generate fixed disclosure documents.

Purpose

This function serves as the main entry point for running a fixed disclosure generation pipeline. It creates an instance of FixedProjectVictoriaGenerator, executes the complete pipeline workflow, and returns the output file path. This is typically used as the primary execution function for a disclosure document generation system that likely involves RAG (Retrieval-Augmented Generation) operations with vector databases and LLM processing.

Source Code

def main():
    """Main function to run the fixed disclosure generator."""
    generator = FixedProjectVictoriaGenerator()
    output_path = generator.run_complete_pipeline()
    return output_path

Return Value

Returns a string representing the file path where the generated disclosure document has been saved. The exact path format depends on the FixedProjectVictoriaGenerator.run_complete_pipeline() implementation, but typically includes a timestamp and output directory location.

Dependencies

• os
• re
• json
• tiktoken
• typing
• datetime
• chromadb
• langchain_openai
• sentence_transformers
• fitz
• OneCo_hybrid_RAG

Required Imports

from OneCo_hybrid_RAG import FixedProjectVictoriaGenerator

Usage Example

from OneCo_hybrid_RAG import FixedProjectVictoriaGenerator

def main():
    """Main function to run the fixed disclosure generator."""
    generator = FixedProjectVictoriaGenerator()
    output_path = generator.run_complete_pipeline()
    return output_path

if __name__ == '__main__':
    result_path = main()
    print(f'Disclosure document generated at: {result_path}')

Best Practices

• Ensure all required environment variables (especially OPENAI_API_KEY) are set before calling this function
• Verify that the OneCo_hybrid_RAG module and FixedProjectVictoriaGenerator class are properly installed and accessible
• Check that ChromaDB is properly initialized and accessible before running
• Consider wrapping the function call in try-except blocks to handle potential errors from the pipeline execution
• Monitor token usage and API costs when running the pipeline as it likely makes multiple LLM calls
• Ensure sufficient disk space for output files and temporary data
• The function has no parameters, so all configuration must be done within the FixedProjectVictoriaGenerator class or through environment variables
• Consider logging the output_path for tracking and debugging purposes

Tags

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