将agent下config中的阈值配置参数迁移到了yaml文件中.

agent/classifier.py

@@ -20,29 +20,11 @@ class QuestionClassifier:
         # 初始化日志
         self.logger = get_agent_logger("Classifier")
         
-        # 从配置文件加载阈值参数
-        try:
-            from agent.config import get_current_config, get_nested_config
-            config = get_current_config()
-            self.high_confidence_threshold = get_nested_config(config, "classification.high_confidence_threshold", 0.7)
-            self.low_confidence_threshold = get_nested_config(config, "classification.low_confidence_threshold", 0.4)
-            self.max_confidence = get_nested_config(config, "classification.max_confidence", 0.9)
-            self.base_confidence = get_nested_config(config, "classification.base_confidence", 0.4)
-            self.confidence_increment = get_nested_config(config, "classification.confidence_increment", 0.08)
-            self.llm_fallback_confidence = get_nested_config(config, "classification.llm_fallback_confidence", 0.5)
-            self.uncertain_confidence = get_nested_config(config, "classification.uncertain_confidence", 0.2)
-            self.medium_confidence_threshold = get_nested_config(config, "classification.medium_confidence_threshold", 0.6)
-            self.logger.info("从配置文件加载分类器参数完成")
-        except ImportError:
-            self.high_confidence_threshold = 0.7
-            self.low_confidence_threshold = 0.4
-            self.max_confidence = 0.9
-            self.base_confidence = 0.4
-            self.confidence_increment = 0.08
-            self.llm_fallback_confidence = 0.5
-            self.uncertain_confidence = 0.2
-            self.medium_confidence_threshold = 0.6
-            self.logger.warning("配置文件不可用，使用默认分类器参数")
+        # 初始化默认参数（作为后备）
+        self.high_confidence_threshold = 0.7
+        self.max_confidence = 0.9
+        self.llm_fallback_confidence = 0.5
+        self.uncertain_confidence = 0.2
         
         # 加载词典配置（新增逻辑）
         self._load_dict_config()
@@ -63,6 +45,12 @@ class QuestionClassifier:
             # 加载权重配置
             self.weights = dict_config.weights
             
+            # 从YAML权重配置中加载分类器参数（优先使用YAML配置）
+            self.high_confidence_threshold = self.weights.get('high_confidence_threshold', self.high_confidence_threshold)
+            self.max_confidence = self.weights.get('max_confidence', self.max_confidence)
+            self.llm_fallback_confidence = self.weights.get('llm_fallback_confidence', self.llm_fallback_confidence)
+            self.uncertain_confidence = self.weights.get('uncertain_confidence', self.uncertain_confidence)
+            
             # 加载其他配置
             self.metadata = dict_config.metadata
             
@@ -75,6 +63,8 @@ class QuestionClassifier:
             )
             
             self.logger.info(f"从YAML配置文件加载词典完成，共加载 {total_keywords} 个关键词")
+            self.logger.info(f"从YAML配置文件加载分类器参数完成：高置信度阈值={self.high_confidence_threshold}")
+            self.logger.debug(f"所有分类器参数：high_threshold={self.high_confidence_threshold}, max_conf={self.max_confidence}, llm_fallback={self.llm_fallback_confidence}")
             
         except Exception as e:
             self.logger.warning(f"加载YAML词典配置失败: {str(e)}，使用代码中的备用配置")
@@ -406,6 +396,8 @@ class QuestionClassifier:
                 question=classification_prompt,
                 system_prompt=system_prompt
             )
+
+            self.logger.debug(f"LLM原始分类响应信息: {response}")
             
             # 解析响应
             return self._parse_llm_response(response)

agent/classifier_dict.yaml

@@ -62,6 +62,46 @@ weights:
   strong_business_min_intent: 1    # 强业务特征最低意图分要求
   medium_business_min_score: 4     # 中等业务特征最低业务分要求
   chat_min_score: 1               # 聊天特征最低聊天分要求
+  
+  # ===========================================
+  # 从config.py迁移的分类器配置
+  # ===========================================
+  
+  # 高置信度阈值：当规则分类的置信度 >= 此值时，直接使用规则分类结果，不再调用LLM
+  # 建议范围：0.7-0.9，过高可能错过需要LLM辅助的边界情况，过低会增加LLM调用成本
+  high_confidence_threshold: 0.7
+  
+  # 低置信度阈值：当规则分类的置信度 <= 此值时，启用LLM二次分类进行辅助判断
+  # 建议范围：0.2-0.5，过高会频繁调用LLM，过低可能错过需要LLM辅助的情况
+  # low_confidence_threshold: 0.4  # 未使用 - 已注释
+  
+  # 最大置信度上限：规则分类计算出的置信度不会超过此值，防止过度自信
+  # 建议范围：0.8-1.0，通常设为0.9以保留不确定性空间
+  max_confidence: 0.9
+  
+  # 基础置信度：规则分类的起始置信度，会根据匹配的关键词数量递增
+  # 建议范围：0.3-0.6，这是匹配到1个关键词时的基础置信度
+  # base_confidence: 0.4  # 未使用，实际使用chat_base_confidence - 已注释
+  
+  # 置信度增量步长：每匹配一个额外关键词，置信度增加的数值
+  # 建议范围：0.05-0.2，过大会导致置信度增长过快，过小则区分度不够
+  # confidence_increment: 0.08  # 未使用，实际使用chat_confidence_increment - 已注释
+  
+  # LLM分类失败时的默认置信度：当LLM调用异常或解析失败时使用
+  # 建议范围：0.3-0.6，通常设为中等水平，避免过高或过低的错误影响
+  llm_fallback_confidence: 0.5
+  
+  # 不确定分类的默认置信度：当规则分类无法明确判断时使用
+  # 建议范围：0.1-0.3，应设为较低值，表示确实不确定
+  uncertain_confidence: 0.2
+  
+  # LLM业务上下文加载失败时的置信度：用于混合分类模式的置信度比较
+  # 建议范围：0.05-0.2，设为极低值表示上下文加载失败的严重性
+  llm_error_confidence: 0.1
+  
+  # 中等置信度阈值：用于三级置信度判断的中间阈值
+  # 建议范围：0.5-0.7，位于low_confidence_threshold和high_confidence_threshold之间
+  # medium_confidence_threshold: 0.6  # 未使用 - 已注释
 
 # ===========================================
 # 强业务关键词（字典结构，保持原有层次）
@@ -75,8 +115,8 @@ strong_business_keywords:
       - 商铺
       - 收费站
       - 高速公路
-      - 驿美          # 业务系统名称
-      - 驿购          # 业务系统名称
+      - 驿美          
+      - 驿购          
       - 北区          # 物理分区
       - 南区
       - 西区
@@ -209,6 +249,7 @@ query_intent_keywords:
     # 聚合函数
     - 最大
     - 最小
+    - 最多
     - 最高
     - 最低
     - 平均
@@ -217,6 +258,7 @@ query_intent_keywords:
     - 累计
     - 求和
     - 求平均
+    - 数量
     
     # 输出动作
     - 生成
@@ -307,6 +349,10 @@ non_business_keywords:
     - 冠军
     - 夺冠
     - 高考
+    - 菜谱
+    - 食谱
+    - 烹饪
+    - 联赛
     
   旅游出行:
     - 旅游

agent/config.py

@@ -10,36 +10,8 @@ Agent配置文件
 """
 
 AGENT_CONFIG = {
-    # ==================== 问题分类器配置 ====================
-    "classification": {
-        # 高置信度阈值：当规则分类的置信度 >= 此值时，直接使用规则分类结果，不再调用LLM
-        # 建议范围：0.7-0.9，过高可能错过需要LLM辅助的边界情况，过低会增加LLM调用成本
-        "high_confidence_threshold": 0.7,
-        
-        # 低置信度阈值：当规则分类的置信度 <= 此值时，启用LLM二次分类进行辅助判断
-        # 建议范围：0.2-0.5，过高会频繁调用LLM，过低可能错过需要LLM辅助的情况
-        "low_confidence_threshold": 0.4,
-        
-        # 最大置信度上限：规则分类计算出的置信度不会超过此值，防止过度自信
-        # 建议范围：0.8-1.0，通常设为0.9以保留不确定性空间
-        "max_confidence": 0.9,
-        
-        # 基础置信度：规则分类的起始置信度，会根据匹配的关键词数量递增
-        # 建议范围：0.3-0.6，这是匹配到1个关键词时的基础置信度
-        "base_confidence": 0.4,
-        
-        # 置信度增量步长：每匹配一个额外关键词，置信度增加的数值
-        # 建议范围：0.05-0.2，过大会导致置信度增长过快，过小则区分度不够
-        "confidence_increment": 0.08,
-        
-        # LLM分类失败时的默认置信度：当LLM调用异常或解析失败时使用
-        # 建议范围：0.3-0.6，通常设为中等水平，避免过高或过低的错误影响
-        "llm_fallback_confidence": 0.5,
-        
-        # 不确定分类的默认置信度：当规则分类无法明确判断时使用
-        # 建议范围：0.1-0.3，应设为较低值，表示确实不确定
-        "uncertain_confidence": 0.2,
-    },
+    # ==================== 问题分类器配置已迁移到 classifier_dict.yaml ====================
+    # 注意：问题分类器的所有配置参数已迁移到 agent/classifier_dict.yaml 文件的 weights 部分
     
     # ==================== 数据库Agent配置 ====================
     "database_agent": {
@@ -133,11 +105,11 @@ def get_nested_config(config: dict, key_path: str, default=None):
         配置值或默认值
         
     Example:
-        >>> config = {"classification": {"high_confidence_threshold": 0.8}}
-        >>> get_nested_config(config, "classification.high_confidence_threshold", 0.5)
-        0.8
-        >>> get_nested_config(config, "classification.missing_key", 0.5)
-        0.5
+        >>> config = {"database_agent": {"max_iterations": 10}}
+        >>> get_nested_config(config, "database_agent.max_iterations", 5)
+        10
+        >>> get_nested_config(config, "database_agent.missing_key", 5)
+        5
     """
     keys = key_path.split('.')
     current = config

agent/dict_loader.py

@@ -112,7 +112,12 @@ class DictLoader:
             'query_intent',
             'sql_pattern',
             'chat_keyword',
-            'non_business_confidence'
+            'non_business_confidence',
+            'high_confidence_threshold',
+            'max_confidence',
+            'llm_fallback_confidence',
+            'uncertain_confidence',
+            'llm_error_confidence'
         ]
         
         for weight in required_weights:

api_response_full.json

@@ -1,397 +0,0 @@
-{
-  "data": {
-    "message_count": 36,
-    "messages": [
-      {
-        "content": "请问系统中哪个服务区档口最多？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [],
-              "question": "请问系统中哪个服务区档口最多？"
-            },
-            "id": "call_d5af6e8c32584f75b919c5",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 \nFROM bss_branch b \nJOIN bss_service_area sa ON b.service_area_id = sa.id \nWHERE b.delete_ts IS NULL \nGROUP BY sa.service_area_name \nORDER BY 档口数量 DESC \nLIMIT 1;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT sa.service_area_name AS 系统中哪个服务区档口最多？, COUNT(b.id) AS 档口数量 \nFROM bss_branch b \nJOIN bss_service_area sa ON b.service_area_id = sa.id \nWHERE b.delete_ts IS NULL \nGROUP BY sa.service_area_name \nORDER BY 档口数量 DESC \nLIMIT 1;"
-            },
-            "id": "call_f439ef1e9a6f478ba82b7e",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 \nFROM bss_branch b \nJOIN bss_service_area sa ON b.service_area_id = sa.id \nWHERE b.delete_ts IS NULL \nGROUP BY sa.service_area_name \nORDER BY 档口数量 DESC \nLIMIT 1;"
-            },
-            "id": "call_404f72df6c784c468745d2",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u670d\\u52a1\\u533a\\u540d\\u79f0\":\"\\u5357\\u57ce\\u670d\\u52a1\\u533a\",\"\\u6863\\u53e3\\u6570\\u91cf\":39}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个服务区有几个餐饮档口？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个服务区有几个餐饮档口？"
-            },
-            "id": "call_5b56370f16d74020869270",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;"
-            },
-            "id": "call_8b91fa05949746a4b554d2",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;"
-            },
-            "id": "call_0055c7d1a25d4884b940f7",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u9910\\u996e\\u6863\\u53e3\\u6570\\u91cf\":6}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "南城服务区共有6个餐饮档口。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个服务区属于哪个管理公司？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区共有6个餐饮档口。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个服务区属于哪个管理公司？"
-            },
-            "id": "call_6ef648b63159487caf2bbc",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
-            },
-            "id": "call_b44aeb9830c746aa9c8275",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
-            },
-            "id": "call_13d3a8c66c19403ab1fd65",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u7ba1\\u7406\\u516c\\u53f8\":\"\\u629a\\u5dde\\u5206\\u516c\\u53f8\"}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "南城服务区属于“抚州分公司”管理。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个抚州分公司管理几个服务区？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区共有6个餐饮档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区属于哪个管理公司？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区属于“抚州分公司”管理。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个抚州分公司管理几个服务区？"
-            },
-            "id": "call_989edf48f8df4438bfe1fb",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT COUNT(*) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '抚州分公司' AND sa.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
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-        "content": "请问中国高速公路充电桩共有多少个?",
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api_response_no_tools.json

@@ -1,397 +0,0 @@
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-                  "content": "请问系统中哪个服务区档口最多？",
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-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
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-                  "content": "南城服务区共有6个餐饮档口。",
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-        "type": "ai"
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-      {
-        "content": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;",
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-      {
-        "content": "请问这个抚州分公司管理几个服务区？",
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-                  "content": "请问系统中哪个服务区档口最多？",
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-                  "content": "请问这个服务区有几个餐饮档口？",
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-                  "content": "请问这个服务区有几个餐饮档口？",
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-                {
-                  "content": "请问这个抚州分公司管理几个服务区？",
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-                {
-                  "content": "抚州分公司目前管理着12个服务区。",
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-      },
-      {
-        "content": "Database query failed. Reason: 提供的上下文未包含充电桩相关数据表或字段，无法统计中国高速公路充电桩总数。需要补充充电桩信息存储表结构（如充电桩编码、所属服务区ID、状态等字段）后方可进行统计分析。",
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-        "content": "中国高速公路的充电桩数量是一个动态变化的数据，具体总数会随着新充电桩的建设和旧设备的淘汰而变化。根据2023年的数据，中国高速公路沿线已建成超过**2万个充电桩**，覆盖了全国主要的高速公路网络。这一数字由中国国家电网公司和各地交通管理部门共同维护，并持续增长中。\n\n如需获取最新的充电桩数量和分布情况，建议查询中国国家电网公司或交通运输部发布的最新统计数据。",
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api_response_with_tools.json

@@ -1,397 +0,0 @@
-{
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-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 \nFROM bss_branch b \nJOIN bss_service_area sa ON b.service_area_id = sa.id \nWHERE b.delete_ts IS NULL \nGROUP BY sa.service_area_name \nORDER BY 档口数量 DESC \nLIMIT 1;"
-            },
-            "id": "call_404f72df6c784c468745d2",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u670d\\u52a1\\u533a\\u540d\\u79f0\":\"\\u5357\\u57ce\\u670d\\u52a1\\u533a\",\"\\u6863\\u53e3\\u6570\\u91cf\":39}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个服务区有几个餐饮档口？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个服务区有几个餐饮档口？"
-            },
-            "id": "call_5b56370f16d74020869270",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;"
-            },
-            "id": "call_8b91fa05949746a4b554d2",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch b JOIN bss_service_area sa ON b.service_area_id = sa.id WHERE sa.service_area_name = '南城服务区' AND b.classify = '餐饮' AND b.delete_ts IS NULL;"
-            },
-            "id": "call_0055c7d1a25d4884b940f7",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u9910\\u996e\\u6863\\u53e3\\u6570\\u91cf\":6}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "南城服务区共有6个餐饮档口。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个服务区属于哪个管理公司？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区共有6个餐饮档口。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个服务区属于哪个管理公司？"
-            },
-            "id": "call_6ef648b63159487caf2bbc",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
-            },
-            "id": "call_b44aeb9830c746aa9c8275",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT c.company_name AS 管理公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_area_name = '南城服务区' AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
-            },
-            "id": "call_13d3a8c66c19403ab1fd65",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u7ba1\\u7406\\u516c\\u53f8\":\"\\u629a\\u5dde\\u5206\\u516c\\u53f8\"}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "南城服务区属于“抚州分公司”管理。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问这个抚州分公司管理几个服务区？",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区共有6个餐饮档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区属于哪个管理公司？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区属于“抚州分公司”管理。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问这个抚州分公司管理几个服务区？"
-            },
-            "id": "call_989edf48f8df4438bfe1fb",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SELECT COUNT(*) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '抚州分公司' AND sa.delete_ts IS NULL;",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '抚州分公司' AND sa.delete_ts IS NULL;"
-            },
-            "id": "call_2d0bb63fa9734577b634f4",
-            "name": "valid_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "SQL验证通过：语法正确且字段存在",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "sql": "SELECT COUNT(*) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '抚州分公司' AND sa.delete_ts IS NULL;"
-            },
-            "id": "call_fc4f28a4e1694c8f9084af",
-            "name": "run_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "[{\"\\u670d\\u52a1\\u533a\\u6570\\u91cf\":12}]",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "抚州分公司目前管理着12个服务区。",
-        "tool_calls": [],
-        "type": "ai"
-      },
-      {
-        "content": "请问中国高速公路充电桩共有多少个?",
-        "tool_calls": null,
-        "type": "human"
-      },
-      {
-        "content": "",
-        "tool_calls": [
-          {
-            "args": {
-              "history_messages": [
-                {
-                  "content": "请问系统中哪个服务区档口最多？",
-                  "type": "human"
-                },
-                {
-                  "content": "系统中档口最多的服务区是“南城服务区”，共有39个档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区有几个餐饮档口？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区共有6个餐饮档口。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个服务区属于哪个管理公司？",
-                  "type": "human"
-                },
-                {
-                  "content": "南城服务区属于“抚州分公司”管理。",
-                  "type": "ai"
-                },
-                {
-                  "content": "请问这个抚州分公司管理几个服务区？",
-                  "type": "human"
-                },
-                {
-                  "content": "抚州分公司目前管理着12个服务区。",
-                  "type": "ai"
-                }
-              ],
-              "question": "请问中国高速公路充电桩共有多少个?"
-            },
-            "id": "call_e4a0fba988a848ddbb95b5",
-            "name": "generate_sql",
-            "type": "tool_call"
-          }
-        ],
-        "type": "ai"
-      },
-      {
-        "content": "Database query failed. Reason: 提供的上下文未包含充电桩相关数据表或字段，无法统计中国高速公路充电桩总数。需要补充充电桩信息存储表结构（如充电桩编码、所属服务区ID、状态等字段）后方可进行统计分析。",
-        "tool_calls": null,
-        "type": "tool"
-      },
-      {
-        "content": "中国高速公路的充电桩数量是一个动态变化的数据，具体总数会随着新充电桩的建设和旧设备的淘汰而变化。根据2023年的数据，中国高速公路沿线已建成超过**2万个充电桩**，覆盖了全国主要的高速公路网络。这一数字由中国国家电网公司和各地交通管理部门共同维护，并持续增长中。\n\n如需获取最新的充电桩数量和分布情况，建议查询中国国家电网公司或交通运输部发布的最新统计数据。",
-        "tool_calls": [],
-        "type": "ai"
-      }
-    ],
-    "thread_id": "wang10:20250717211620915",
-    "user_id": "wang10"
-  },
-  "success": true,
-  "timestamp": "2025-07-19T11:55:46.039648"
-}

docs/问题分类器规则判断逻辑详解.md

@@ -244,83 +244,64 @@ elif system_indicator_score > 0:
 - 当用户说"当前系统有哪些服务区"时，"当前系统"(+1) + "服务区"(+2) + 组合加分(+3) = 总计6分
 - 仅有"当前系统"时，只加1分，表示轻微的数据查询倾向
 
-## 3. 分类决策规则
-
-### 3.1 优先级决策顺序
-
-1. **非业务实体词检查** (最高优先级)
-   - 条件: 包含任何非业务实体词
-   - 结果: `CHAT`, 置信度=0.85
-   - 理由: 明确的非业务领域问题
-
-2. **强业务特征** (次高优先级)
-   - 条件: `business_score ≥ 2` AND `intent_score ≥ 1`
-   - 结果: `DATABASE`
-   - 置信度计算: `min(max_confidence, 0.8 + (total_business_score * 0.05))`
-   - 理由: 既有业务实体，又有查询意图
-
-3. **中等业务特征**
-   - 条件: `business_score ≥ 4`
-   - 结果: `DATABASE`
-   - 置信度计算: `min(max_confidence, 0.7 + (business_score * 0.03))`
-   - 理由: 包含多个业务实体词
-
-4. **聊天特征**
-   - 条件: `chat_score ≥ 1` AND `business_score = 0`
-   - 结果: `CHAT`
-   - 置信度计算: `min(max_confidence, base_confidence + (chat_score * confidence_increment))`
-   - 理由: 有聊天意图且无业务特征
-
-5. **不确定情况** (最低优先级)
-   - 条件: 不满足以上任何条件
-   - 结果: `UNCERTAIN`
-   - 置信度: `uncertain_confidence` (默认0.2)
-   - 理由: 规则无法明确判断
-
-### 3.2 置信度计算公式
-
-#### 强业务特征置信度
-```
-confidence = min(max_confidence, 0.8 + (total_business_score * 0.05))
-其中: total_business_score = business_score + intent_score
-```
+## 4. 混合决策流程与阈值机制
 
-#### 中等业务特征置信度
-```
-confidence = min(max_confidence, 0.7 + (business_score * 0.03))
-```
-
-#### 聊天特征置信度
-```
-confidence = min(max_confidence, base_confidence + (chat_score * confidence_increment))
-默认: base_confidence=0.4, confidence_increment=0.08
-```
-
-## 4. 置信度阈值决策机制
+分类器的核心决策逻辑并非单一的规则判断，而是一个分为两个阶段的混合流程，旨在平衡效率与准确性。
 
 ### 4.1 决策流程图
 
+此流程图准确地描述了代码中的两层决策逻辑：首先是高效的**规则预分类**，然后是根据置信度决定是否启动**LLM二次研判**的**混合决策**。
+
 ```mermaid
 graph TD
-    A[规则分类完成] --> B{非业务词匹配?}
-    B -->|是| C[直接CHAT<br/>置信度=0.85<br/>🔴王炸优先级]
-    B -->|否| D{置信度 ≥ 0.7?}
-    D -->|是| E[🟢毫不犹豫<br/>直接使用规则结果<br/>不调用LLM]
-    D -->|否| F[🟡调用LLM分类<br/>进行二次判断]
-    F --> G[比较两个置信度]
-    G --> H[选择置信度更高的结果]
+    subgraph "第一层：规则预分类 (_rule_based_classify)"
+        A["用户问题输入"] --> B{"包含非业务词?"};
+        B -- "是<br/>(王炸规则)" --> C["直接分类为CHAT<br/>置信度=0.85<br/>结束规则判断"];
+        B -- "否" --> D["业务/意图/聊天/SQL<br/>关键词评分计算"];
+        D --> E{"根据评分规则<br/>进行初步分类"};
+        E --> F["生成规则分类结果<br/>(类型 + 置信度)"];
+    end
+
+    subgraph "第二层：混合决策 (_hybrid_classify)"
+        F --> G{"规则置信度 ≥ 0.7?"};
+        G -- "是" --> H["🟢 毫不犹豫<br/>直接采纳规则结果"];
+        G -- "否" --> I["🟡 调用LLM<br/>进行二次分类"];
+        I --> J["比较规则与LLM的置信度"];
+        J --> K["择优选择置信度<br/>更高的结果"];
+    end
+    
+    H --> L["最终分类结果"];
+    K --> L;
 ```
 
-### 4.2 关键阈值说明
+### 4.2 流程阶段详解
+
+1.  **第一层：规则预分类 (`_rule_based_classify`方法)**
+    *   **非业务词优先判断**: 这是最高优先级的“王炸规则”。系统首先检查问题是否包含“非业务实体词”（如“苹果”、“天气”）。如果匹配，则**立即将其分类为`CHAT`并赋予0.85的高置信度**，后续所有评分和判断流程都将被跳过。
+    *   **关键词评分**: 如果没有匹配到非业务词，系统会根据业务、意图、聊天、SQL模式等多种关键词进行综合评分。
+    *   **生成初步结果**: 根据评分规则，系统会得出一个初步的分类（`DATABASE`或`CHAT`）以及对应的置信度，形成`rule_result`。
+
+2.  **第二层：混合决策 (`_hybrid_classify`方法)**
+    *   **高置信度捷径**: `_hybrid_classify`方法接收到第一层的`rule_result`后，首先检查其置信度。如果**置信度大于等于`0.7`**，表明规则分类已经非常有把握，系统会直接采纳该结果，**不再调用LLM**，从而实现高效决策。
+    *   **LLM二次研判**: 如果规则置信度低于`0.7`，说明情况比较模糊或规则不足以做出高可信度判断，系统会启动LLM进行更深层次的语义理解和分类，得到`llm_result`。
+    *   **择优录取**: 最后，系统会比较`rule_result`和`llm_result`的置信度，选择**置信度更高的一方**作为最终的分类结果。这种机制确保了即使规则分类的把握不大，也能通过LLM进行补足，同时在LLM判断不准时，仍能依赖保底的规则结果。
+
+### 4.3 设计理念与关键阈值
+
+*   **成本与效率的平衡**: 设置`0.7`作为高置信度阈值，是在API调用成本和决策速度之间取得的平衡。对于绝大多数意图明确的问题，可以通过高效的规则快速解决，避免了不必要的LLM调用。
+*   **准确性的双重保障**: 对于意图模糊的边界案例，通过引入LLM作为“第二意见”，可以显著提升分类的准确率和鲁棒性。
+*   **强制优先级**: “非业务词”规则的设计，确保了系统不会错误地尝试去查询明显与业务无关的问题，提升了用户体验。
+*   **鲁棒性：通过组合门槛拦截无效查询**: 当前的评分机制并非简单的分数累加，而是设立了“组合门槛”。例如，“强业务特征”要求`business_score`和`intent_score`**同时满足**最低要求。这种设计的核心价值在于，它可以精准地阻挡那些**“有查询意图，但查询对象并非业务范畴”**的无效问题。例如，对于问题`“分析汇总我这个月的花费”`，尽管“分析”、“汇总”有很高的意图分，但由于“花费”不是业务关键词，导致`business_score`为0，无法通过任何数据库查询的门槛，从而被正确地识别为不确定问题，避免了错误的数据库查询。这套机制确保了只有意图和业务主体有效组合的问题才能被处理，大大提升了系统的鲁棒性。
+
+#### 关键阈值表
 
 | 置信度范围 | 决策行为 | 代码位置 | 说明 |
 |------------|----------|----------|------|
-| **非业务词匹配** | 🔴 直接CHAT，置信度=0.85 | `classifier.py:354-361` | 最高优先级，立即决策 |
-| **≥ 0.7** | 🟢 毫不犹豫使用规则结果 | `classifier.py:291-292` | 高置信度，不调用LLM |
-| **0.4 - 0.69** | 🟡 规则+LLM双重判断 | `classifier.py:294-301` | 取置信度更高者 |
-| **< 0.4** | 🟡 规则+LLM双重判断 | `classifier.py:294-301` | 取置信度更高者 |
+| **非业务词匹配** | 🔴 直接CHAT，置信度=0.85 | `classifier.py:231-238` | 最高优先级，立即决策 |
+| **≥ 0.7** | 🟢 毫不犹豫使用规则结果 | `classifier.py:171-173` | 高置信度，不调用LLM |
+| **< 0.7** | 🟡 规则+LLM双重判断 | `classifier.py:175-182` | 取置信度更高者 |
 
-### 4.3 决策示例分析
+### 4.4 决策示例分析
 
 #### 示例1：毫不犹豫决策 (≥0.7)
 ```
@@ -344,41 +325,6 @@ LLM分类: DATABASE, 置信度=0.8
 决策: 直接CHAT，置信度=0.85，跳过所有其他判断 ✓
 ```
 
-### 4.4 设计理念
-
-1. **成本控制**: 0.7是经济平衡点，避免过度调用LLM
-2. **准确性保障**: 低置信度时用LLM作为"第二意见"
-3. **强制优先级**: 非业务词具有绝对优先权
-4. **智能选择**: 总是选择置信度更高的分类结果
-
-### 4.5 混合分类模式说明
-
-**代码位置**: `agent/classifier.py:283-301` (`_hybrid_classify`方法)
-
-```python
-def _hybrid_classify(self, question: str) -> ClassificationResult:
-    # 第一步：规则预筛选
-    rule_result = self._rule_based_classify(question)
-    
-    # 如果规则分类有高置信度，直接使用
-    if rule_result.confidence >= self.high_confidence_threshold:  # 0.7
-        return rule_result  # 毫不犹豫使用规则结果
-    
-    # 第二步：使用增强的LLM分类
-    llm_result = self._enhanced_llm_classify(question)
-    
-    # 选择置信度更高的结果
-    if llm_result.confidence > rule_result.confidence:
-        return llm_result
-    else:
-        return rule_result
-```
-
-**关键特点**:
-- 没有"必须交给LLM"的阈值下限
-- 即使规则置信度很低（如0.2），如果LLM置信度更低，仍会选择规则结果
-- 这样避免了强制依赖LLM，保持了系统的鲁棒性
-
 ## 5. 配置参数说明
 
 ### 5.1 核心阈值参数