fix: Every time a vectorized document is generated, the entire vectorized data of the document is deleted

[shaohuzhang1]

fix: Every time a vectorized document is generated, the entire vectorized data of the document is deleted

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[shaohuzhang1]

The provided code snippet does not contain any obvious irregularities or potential issues. However, there are some general optimizations that can be made:

Potential Improvements

1. Use of Generators for Data Handling: If QuerySet supports generator operations, you might use generators to handle data more efficiently, especially if dealing with large datasets.

2. Exception Handling: Consider adding try-except blocks around database operations and document management tasks to ensure robustness against exceptions during execution.

3. Code Clarity: Ensure that variable names and function structures are clear and concise. This makes the code easier to understand and maintain.

4. Performance Optimization: If VectorStore's delete_by_document_id method is slow due to indexing or other optimizations, consider caching results or using batch deletion when applicable.

Here's a slightly optimized version of the relevant part of the code (assuming document_id is valid):

def is_the_task_interrupted():
    try:
        # Update listener status
        ListenerManagement.update_status(
            QuerySet(Document).filter(id=document_id),
            TaskType.EMBEDDING,
            State.STARTED
        )
        
        # Delete document vector data
        VectorStore.get_embedding_vector().delete_by_document_id(document_id)
        
        # According to paragraphs for vectorization processing
        page_desc(QuerySet(Paragraph))
    
    except Exception as e:
        print(f"An error occurred: {e}")

By implementing these suggestions, the code will be more robust, efficient, and readable. Adjustments may vary based on specific requirements and constraints within your project.