Why does this method not need to be protected by a lock?

[fenixc9]

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Content

class RetrievalService:

   @classmethod
   def retrieve(cls, retrival_method: str, dataset_id: str, query: str,
                top_k: int, score_threshold: Optional[float] = .0, reranking_model: Optional[dict] = None):
       dataset = db.session.query(Dataset).filter(
           Dataset.id == dataset_id
       ).first()
       if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0:
           return []
       all_documents = []
       threads = []
       # retrieval_model source with keyword
       if retrival_method == 'keyword_search':
           keyword_thread = threading.Thread(target=RetrievalService.keyword_search, kwargs={
               'flask_app': current_app._get_current_object(),
               'dataset_id': dataset_id,
               'query': query,
               'top_k': top_k,
               'all_documents': all_documents
           })
           threads.append(keyword_thread)
           keyword_thread.start()
       # retrieval_model source with semantic
       if retrival_method == 'semantic_search' or retrival_method == 'hybrid_search':
           embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
               'flask_app': current_app._get_current_object(),
               'dataset_id': dataset_id,
               'query': query,
               'top_k': top_k,
               'score_threshold': score_threshold,
               'reranking_model': reranking_model,
               'all_documents': all_documents,
               'retrival_method': retrival_method
           })
           threads.append(embedding_thread)
           embedding_thread.start()

       # retrieval source with full text
       if retrival_method == 'full_text_search' or retrival_method == 'hybrid_search':
           full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search, kwargs={
               'flask_app': current_app._get_current_object(),
               'dataset_id': dataset_id,
               'query': query,
               'retrival_method': retrival_method,
               'score_threshold': score_threshold,
               'top_k': top_k,
               'reranking_model': reranking_model,
               'all_documents': all_documents
           })
           threads.append(full_text_index_thread)
           full_text_index_thread.start()

       for thread in threads:
           thread.join()

       if retrival_method == 'hybrid_search':
           data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
           all_documents = data_post_processor.invoke(
               query=query,
               documents=all_documents,
               score_threshold=score_threshold,
               top_n=top_k
           )
       return all_documents