Merge pull request #28 from Gardelll/dev

修复一些LLM响应解析问题和添加memory.use_judge配置项

src/chat/emoji_system/emoji_manager.py

@@ -4,6 +4,7 @@ import binascii
 import hashlib
 import io
 import json
+import json_repair
 import os
 import random
 import re
@@ -1022,6 +1023,15 @@ class EmojiManager:
     -   必须是表情包，非普通截图。
     -   图中文字不超过5个。
 请确保你的最终输出是严格的JSON对象，不要添加任何额外解释或文本。
+输出格式:
+```json
+{{
+  "detailed_description": "",
+  "keywords": [],
+  "refined_sentence": "",
+  "is_compliant": true
+}}
+```
 """
 
                 image_data_for_vlm, image_format_for_vlm = image_base64, image_format
@@ -1041,9 +1051,7 @@ class EmojiManager:
                         if not vlm_response_str:
                             continue
 
-                        match = re.search(r"\{.*\}", vlm_response_str, re.DOTALL)
-                        if match:
-                            vlm_response_json = json.loads(match.group(0))
+                        vlm_response_json = self._parse_json_response(vlm_response_str)
                         description = vlm_response_json.get("detailed_description", "")
                         emotions = vlm_response_json.get("keywords", [])
                         refined_description = vlm_response_json.get("refined_sentence", "")
@@ -1195,6 +1203,29 @@ class EmojiManager:
                     logger.error(f"[错误] 删除异常处理文件时出错: {remove_error}")
             return False
 
+    @classmethod
+    def _parse_json_response(cls, response: str) -> dict[str, Any] | None:
+        """解析 LLM 的 JSON 响应"""
+        try:
+            # 尝试提取 JSON 代码块
+            json_match = re.search(r"```json\s*(.*?)\s*```", response, re.DOTALL)
+            if json_match:
+                json_str = json_match.group(1)
+            else:
+                # 尝试直接解析
+                json_str = response.strip()
+
+            # 移除可能的注释
+            json_str = re.sub(r"//.*", "", json_str)
+            json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
+
+            data = json_repair.loads(json_str)
+            return data
+
+        except json.JSONDecodeError as e:
+            logger.warning(f"JSON 解析失败: {e}, 响应: {response[:200]}")
+            return None
+
 
 emoji_manager = None
 

src/chat/replyer/default_generator.py

@@ -614,7 +614,7 @@ class DefaultReplyer:
             # 使用统一管理器的智能检索（Judge模型决策）
             search_result = await unified_manager.search_memories(
                 query_text=query_text,
-                use_judge=True,
+                use_judge=global_config.memory.use_judge,
                 recent_chat_history=chat_history,  # 传递最近聊天历史
             )
 
@@ -1799,6 +1799,7 @@ class DefaultReplyer:
             )
 
             if content:
+                if not global_config.response_splitter.enable or global_config.response_splitter.split_mode != 'llm':
                     # 移除 [SPLIT] 标记，防止消息被分割
                     content = content.replace("[SPLIT]", "")
 

src/config/official_configs.py

@@ -514,6 +514,7 @@ class MemoryConfig(ValidatedConfigBase):
     short_term_decay_factor: float = Field(default=0.98, description="衰减因子")
 
     # 长期记忆层配置
+    use_judge: bool = Field(default=True, description="使用评判模型决定是否检索长期记忆")
     long_term_batch_size: int = Field(default=10, description="批量转移大小")
     long_term_decay_factor: float = Field(default=0.95, description="衰减因子")
     long_term_auto_transfer_interval: int = Field(default=60, description="自动转移间隔（秒）")

src/plugins/built_in/kokoro_flow_chatter/context_builder.py

@@ -235,7 +235,7 @@ class KFCContextBuilder:
 
             search_result = await unified_manager.search_memories(
                 query_text=query_text,
-                use_judge=True,
+                use_judge=config.memory.use_judge,
                 recent_chat_history=chat_history,
             )
 

template/bot_config_template.toml

@@ -313,6 +313,7 @@ short_term_search_top_k = 5 # 搜索时返回的最大数量
 short_term_decay_factor = 0.98 # 衰减因子
 
 # 长期记忆层配置
+use_judge = true # 使用评判模型决定是否检索长期记忆
 long_term_batch_size = 10 # 批量转移大小
 long_term_decay_factor = 0.95 # 衰减因子
 long_term_auto_transfer_interval = 180 # 自动转移间隔（秒）