以下是优化后的代码，包括函数名、变量名、日志描述的改进，以及代码结构的优化：

def query_rag_pipeline(qa_pipeline: object, question: str) -> dict:
    """
    Query the RAG (Retrieval-Augmented Generation) pipeline with a question and return the answer with sources.
    
    Args:
        qa_pipeline: The initialized RAG pipeline object
        question: The input question to query
        
    Returns:
        dict: A dictionary containing:
            - answer: The generated answer
            - sources: List of relevant documents with metadata and content
    """
    import logging
    
    # Initialize logger
    logger = logging.getLogger(__name__)
    
    try:
        logger.info(f"Processing question: {question}")
        
        # Get the generated answer
        query_result = qa_pipeline.invoke({"query": question})
        
        # Retrieve relevant documents
        relevant_docs = qa_pipeline.retriever.get_relevant_documents(question)
        logger.info(f"Retrieved {len(relevant_docs)} relevant documents")
        
        # Prepare response
        response = {
            "answer": query_result["result"],
            "sources": [
                {
                    "doc_id": doc.metadata.get("doc_id", "unknown"),
                    "metadata": doc.metadata,  # Renamed from doc_metadata for brevity
                    "content": doc.page_content
                } 
                for doc in relevant_docs
            ]
        }
        
        logger.debug(f"Response prepared: {response}")
        return response
        
    except Exception as e:
        logger.error(f"Error processing question '{question}': {str(e)}")
        return {
            "answer": "Sorry, an error occurred while processing your question.",
            "sources": []
        }

优化点说明：

1. 函数签名改进：
   • 添加了类型提示

   • 添加了详细的docstring文档

2. 变量名优化：
   • result → query_result (更明确)

   • retrieved_docs → relevant_docs (更符合RAG术语)

   • doc_metadata → metadata (保持简洁)

3. 日志系统：
   • 添加了不同级别的日志记录

   • 包含错误处理日志

4. 代码结构优化：
   • 使用列表推导式简化sources构建

   • 添加了异常处理

   • 默认doc_id改为"unknown"比0更合理

5. 其他改进：
   • 添加了默认错误响应

   • 日志记录处理过程的关键步骤

这个版本更健壮、更易维护，并且提供了更好的可观察性，适合生产环境使用。