feat(expression_selector): 添加温度采样功能以优化表达选择

feat(official_configs): 新增模型温度配置项以支持表达模型采样
chore(bot_config_template): 更新版本号并添加模型温度说明

src/chat/express/expression_selector.py

@@ -1,5 +1,6 @@
 import asyncio
 import hashlib
+import math
 import random
 import time
 from typing import Any
@@ -76,6 +77,45 @@ def weighted_sample(population: list[dict], weights: list[float], k: int) -> lis
 
 
 class ExpressionSelector:
+    @staticmethod
+    def _sample_with_temperature(
+        candidates: list[tuple[Any, float, float, str]],
+        max_num: int,
+        temperature: float,
+    ) -> list[tuple[Any, float, float, str]]:
+        """
+        对候选表达按温度采样，温度越高越均匀。
+
+        Args:
+            candidates: (expr, similarity, count, best_predicted) 列表
+            max_num: 需要返回的数量
+            temperature: 温度参数，0 表示贪婪选择
+        """
+        if max_num <= 0 or not candidates:
+            return []
+
+        if temperature <= 0:
+            return candidates[:max_num]
+
+        adjusted_temp = max(temperature, 1e-6)
+        # 使用与排序相同的打分，但通过 softmax/temperature 放大尾部概率
+        scores = [max(c[1] * (c[2] ** 0.5), 1e-8) for c in candidates]
+        max_score = max(scores)
+        weights = [math.exp((s - max_score) / adjusted_temp) for s in scores]
+
+        # 始终保留最高分一个，剩余的按温度采样，避免过度集中
+        best_idx = scores.index(max_score)
+        selected = [candidates[best_idx]]
+        remaining_indices = [i for i in range(len(candidates)) if i != best_idx]
+
+        while remaining_indices and len(selected) < max_num:
+            current_weights = [weights[i] for i in remaining_indices]
+            picked_pos = random.choices(range(len(remaining_indices)), weights=current_weights, k=1)[0]
+            picked_idx = remaining_indices.pop(picked_pos)
+            selected.append(candidates[picked_idx])
+
+        return selected
+
     def __init__(self, chat_id: str = ""):
         self.chat_id = chat_id
         if model_config is None:
@@ -517,12 +557,21 @@ class ExpressionSelector:
                 )
                 return []
 
-            # 按照相似度*count排序，选择最佳匹配
+            # 按照相似度*count排序，并根据温度采样，避免过度集中
             matched_expressions.sort(key=lambda x: x[1] * (x[2] ** 0.5), reverse=True)
-            expressions_objs = [e[0] for e in matched_expressions[:max_num]]
+            temperature = getattr(global_config.expression, "model_temperature", 0.0)
+            sampled_matches = self._sample_with_temperature(
+                candidates=matched_expressions,
+                max_num=max_num,
+                temperature=temperature,
+            )
+            expressions_objs = [e[0] for e in sampled_matches]
 
             # 显示最佳匹配的详细信息
-            logger.debug(f"模糊匹配成功: 找到 {len(expressions_objs)} 个表达方式")
+            logger.debug(
+                f"模糊匹配成功: 找到 {len(expressions_objs)} 个表达方式 "
+                f"(候选 {len(matched_expressions)}，temperature={temperature})"
+            )
 
             # 转换为字典格式
             expressions = [