# 示例：意图分类与实体识别
class NLUProcessor:
    def __init__(self):
        self.intent_classifier = load_intent_model()
        self.entity_recognizer = load_ner_model()
    
    def parse_query(self, user_input):
        intent = self.intent_classifier.predict(user_input)
        entities = self.entity_recognizer.extract(user_input)
        return {
            "intent": intent,  # 如："数据汇总"、"趋势分析"、"异常检测"
            "entities": entities,  # 如：时间范围、指标名称、维度
            "original_query": user_input
        }

class QueryGenerator:
    def generate_sql(self, parsed_query, schema_info):
        if parsed_query["intent"] == "销售汇总":
            return self._build_sales_summary_sql(parsed_query["entities"])
        elif parsed_query["intent"] == "趋势分析":
            return self._build_trend_analysis_sql(parsed_query["entities"])
        # ... 其他意图处理
    
    def _build_sales_summary_sql(self, entities):
        # 基于实体构建具体SQL查询
        base_query = """
        SELECT {metrics} 
        FROM sales_data 
        WHERE {conditions}
        GROUP BY {dimensions}
        """
        return format_query(base_query, entities)

class ResultInterpreter:
    def interpret(self, data_result, original_query):
        # 生成自然语言总结
        summary = self._generate_summary(data_result)
        
        # 识别关键洞察
        insights = self._extract_insights(data_result)
        
        # 生成可视化建议
        visualization = self._suggest_visualization(data_result)
        
        return {
            "summary": summary,
            "insights": insights,
            "visualization": visualization,
            "raw_data": data_result
        }