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Entry point function that orchestrates the Project Victoria disclosure analysis by initializing the generator, running the complete analysis, and displaying results with next steps.

Purpose

This function serves as the main entry point for the Project Victoria Disclosure Generator application. It provides a user-friendly command-line interface that initializes the disclosure generator, executes the complete analysis workflow, and provides feedback on success or failure along with actionable next steps for the user. It's designed to be called directly when running the script as a standalone application.

Source Code

def main():
    """Main function to run the Project Victoria disclosure analysis."""
    print("Project Victoria Disclosure Generator")
    print("====================================")
    
    # Initialize generator
    generator = ProjectVictoriaDisclosureGenerator()
    
    # Run complete analysis
    output_file = generator.run_complete_analysis()
    
    if output_file:
        print(f"\\n✅ Disclosure report generated: {output_file}")
        print("\\nNext steps:")
        print("1. Review the generated disclosures")
        print("2. Verify accuracy against source documents")
        print("3. Add any additional information as needed")
        print("4. Format for final legal documentation")
    else:
        print("\\n❌ Analysis failed. Please check error messages above.")

Return Value

This function does not return any value (implicitly returns None). It produces side effects by printing output to the console and generating a disclosure report file through the ProjectVictoriaDisclosureGenerator instance.

Dependencies

• os
• re
• json
• pandas
• numpy
• typing
• datetime
• tiktoken
• chromadb
• langchain_openai
• langchain
• warnings
• fitz
• sentence_transformers
• openai
• traceback

Required Imports

from typing import List, Dict, Any, Tuple, Optional
import os
import re
import json
import pandas as pd
import numpy as np
from datetime import datetime
import tiktoken
import chromadb
from chromadb import Documents, EmbeddingFunction, Embeddings
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
import warnings
import fitz
from sentence_transformers import CrossEncoder
from openai import OpenAI
import traceback

Usage Example

# Assuming this is in a file named disclosure_generator.py
# and ProjectVictoriaDisclosureGenerator is defined in the same file

if __name__ == '__main__':
    main()

# Or call directly from another module:
# from disclosure_generator import main
# main()

Best Practices

• This function should be called as the entry point when running the script directly using if __name__ == '__main__': main()
• Ensure ProjectVictoriaDisclosureGenerator is properly initialized with all required dependencies before calling this function
• Check that all required environment variables (especially OPENAI_API_KEY) are set before execution
• Verify that input data files and source documents are available in the expected locations
• Monitor console output for error messages if the analysis fails
• Follow the printed next steps after successful generation to ensure proper review and validation of the disclosure report
• This function is designed for interactive command-line use and may not be suitable for automated/batch processing without modification

Tags

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