feat:客户agent以及记忆模块优化

tool/confidence.py

@@ -6,7 +6,7 @@ from typing import Any
 
 
 class BaseConfidenceCalcTool:
-    """根据来源、证据、冲突和时间计算单条记忆的长期置信度。"""
+    """根据来源、证据和时间计算单条客服记忆的长期置信度。"""
 
     SOURCE_INITIAL = {
         "dialogue_confirmed": 0.75,
@@ -20,23 +20,21 @@ class BaseConfidenceCalcTool:
         "SERVICE_FACT": 0.75,
     }
     DEFAULT_THRESHOLD = 0.80
-    VERSION = "confidence-v1"
+    VERSION = "confidence-v2"
 
     def calc(
         self,
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
     ) -> float:
         """计算基础置信度分数，返回范围为 0 到 1 的浮点数。"""
-        self._validate(tag, source, evidence_count, conflict_count, age_days)
+        self._validate(tag, source, evidence_count, age_days)
         base = self.SOURCE_INITIAL[source]
         gain = min(evidence_count * 0.05, 0.30)
-        penalty = min(conflict_count * 0.10, 0.50)
         decay = max(0.80, 1 - age_days / 365 * 0.20)
-        return max(0.0, min(1.0, (base + gain - penalty) * decay))
+        return max(0.0, min(1.0, (base + gain) * decay))
 
     def evaluate(
         self,
@@ -44,13 +42,12 @@ class BaseConfidenceCalcTool:
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
         memory_type: str | None = None,
         threshold: float | None = None,
     ) -> dict[str, Any]:
         """返回可供记忆模块保存的完整置信度评估结果。"""
-        score = self.calc(tag, source, evidence_count, conflict_count, age_days)
+        score = self.calc(tag, source, evidence_count, age_days)
         if threshold is None:
             threshold = self.MEMORY_THRESHOLDS.get(
                 memory_type or "", self.DEFAULT_THRESHOLD
@@ -64,11 +61,10 @@ class BaseConfidenceCalcTool:
             "confidence": score,
             "status": status,
             "evidence_count": evidence_count,
-            "conflict_count": conflict_count,
             "age_days": age_days,
             "threshold": threshold,
             "confidence_reason": self._reason(
-                source, evidence_count, conflict_count, age_days
+                source, evidence_count, age_days
             ),
             "confidence_version": self.VERSION,
         }
@@ -87,7 +83,6 @@ class BaseConfidenceCalcTool:
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
     ) -> None:
         """校验工具输入，避免非法计数污染记忆分数。"""
@@ -97,18 +92,17 @@ class BaseConfidenceCalcTool:
             raise ValueError(f"不支持的客服对话来源: {source}")
         for name, value in (
             ("evidence_count", evidence_count),
-            ("conflict_count", conflict_count),
             ("age_days", age_days),
         ):
             if not isinstance(value, int) or isinstance(value, bool) or value < 0:
                 raise ValueError(f"{name} 必须是非负整数")
 
     @staticmethod
-    def _reason(source: str, evidence_count: int, conflict_count: int, age_days: int) -> str:
+    def _reason(source: str, evidence_count: int, age_days: int) -> str:
         """生成便于审计和排查的评分原因。"""
         return (
-            f"来源={source}; 支持证据={evidence_count}; 冲突证据={conflict_count}; "
-            f"存在天数={age_days}; 采用证据增益、冲突惩罚和时间衰减"
+            f"来源={source}; 支持证据={evidence_count}; "
+            f"存在天数={age_days}; 采用证据增益和时间衰减"
         )
 
 

tool/confidence_rank.py

@@ -14,8 +14,7 @@ class FinalConfidenceRankTool:
         "semantic": 0.30,
         "timeliness": 0.20,
         "accuracy": 0.20,
-        "base": 0.25,
-        "conflict": 0.05,
+        "base": 0.30,
     }
     INVALID_STATUSES = frozenset({"expired", "rejected", "archived"})
 
@@ -39,13 +38,11 @@ class FinalConfidenceRankTool:
             timeliness = self._calc_timeliness(unit.get("age_days", 0))
             accuracy = self._bounded(unit.get("historical_accuracy", 0.5), 0.5)
             base = self._bounded(unit.get("confidence", 0.5), 0.5)
-            conflict_penalty = min(self._non_negative_int(unit.get("conflict_count", 0)), 5) * 0.1
             final_score = (
                 self.WEIGHTS["semantic"] * semantic
                 + self.WEIGHTS["timeliness"] * timeliness
                 + self.WEIGHTS["accuracy"] * accuracy
                 + self.WEIGHTS["base"] * base
-                - self.WEIGHTS["conflict"] * conflict_penalty
             )
             unit["final_score"] = max(0.0, min(1.0, final_score))
             ranked.append(unit)
@@ -78,14 +75,6 @@ class FinalConfidenceRankTool:
             return default
         return max(0.0, min(1.0, value))
 
-    @staticmethod
-    def _non_negative_int(value: Any) -> int:
-        """将冲突次数转换为非负整数。"""
-        try:
-            return max(0, int(value))
-        except (TypeError, ValueError):
-            return 0
-
     @staticmethod
     def _calc_timeliness(age_days: Any) -> float:
         """按每年 20% 计算平滑时效分，最低保留 0.8。"""