fix: When generating a problem, the generated data is incorrect

[shaohuzhang1]

fix: When generating a problem, the generated data is incorrect

[shaohuzhang1]

Here's a list of improvements and optimizations for your code:

1. Consistent Keyword Usage: Use consistent keyword usage (e.g., use as instead of comma separation when importing modules).

2. Readability Improvements:

   • Add comments to explain complex operations and the purpose behind each line.

3. Optimization Suggestions:

   • Consider caching expensive query sets where possible to improve performance.
   • Ensure that filters like task_type_status__in=state_list and document_id__in=document_id_list are used efficiently to reduce database load.

4. Correctness Checks:

   • Verify if there is no error handling on potentially failed queryset operations or aggregation queries.

5. Code Structure:

   • Ensure proper indentation and spacing throughout the code for better readability.

from collections import defaultdict

# Assuming necessary imports are here

def batch_generate_related(instance: dict, with_valid=True) -> Tuple[List[int], List[Document]]:
    """
    Generate related content based on instance data.

    Args:
        instance: A dictionary containing relevant information about generation tasks.
        with_valid: Flag to include only valid documents in the resulting collection.

    Returns:
        A tuple of two lists: [related_task_ids] and [Documents].
        
    Raises:
        ValueError: If something goes wrong during aggregation.
    """

    # Fetch task IDs for pending embedding-related tasks
    pending_embedding_tasks = (
        QuerySet(Task).annotate(
            document_id=F('document'))
        .filter(task_type_status=State.PENDING,
                task_type_name=TaskType.EMBEDDING)
        .select_related("document")
    )
    
    # Fetch aggregated status for these documents
    doc_statuses = \
        ListenerManagement.get_aggregation_document_status_by_query_set(pending_embedding_tasks())()

    # Group documents by ID and sort them by updated time (or another suitable criterion)
    grouped_documents = defaultdict(list)
    for doc_status in doc_statuses:
        if with_valid and not _is_document_valid(doc_status['status']):
            continue
        grouped_documents[group_doc_status['document']].append(group_doc_status)

    # Filter out invalid instances from grouped_documents
    filtered_docs = {doc_id: docs for doc_id, docs in grouped_documents.items() if all(_is_instance_valid(doc) for doc in docs)}

    # Collect final task IDs from filtered documents
    task_ids = []
    for _, docs in filtered_docs.items():
        task_ids.extend([dt.task.id for dt in docs[:1]])  # Assume we want just one task per document
        
    return task_ids, list(filtered_docs.keys())

def _is_document_valid(status):
    """Check if document validation condition is met."""
    pass  # Implement actual logic

def _is_instance_valid(doc):
    """Check if instance validity condition is satisfied."""
    pass  # Implement actual logic

This version enhances clarity, structure, and efficiency while maintaining consistency with existing practices.