修改了模型命名

src/plugins/chat/config.py

@@ -43,8 +43,12 @@ class BotConfig:
     llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
     llm_normal: Dict[str, str] = field(default_factory=lambda: {})
     llm_normal_minor: Dict[str, str] = field(default_factory=lambda: {})
+    llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
+    llm_summary_by_topic: Dict[str, str] = field(default_factory=lambda: {})
+    llm_emotion_judge: Dict[str, str] = field(default_factory=lambda: {})
     embedding: Dict[str, str] = field(default_factory=lambda: {})
     vlm: Dict[str, str] = field(default_factory=lambda: {})
+    moderation: Dict[str, str] = field(default_factory=lambda: {})
 
     MODEL_R1_PROBABILITY: float = 0.8  # R1模型概率
     MODEL_V3_PROBABILITY: float = 0.1  # V3模型概率
@@ -112,8 +116,6 @@ class BotConfig:
                 config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
                 config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
                 config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY)
-                config.API_USING = response_config.get("api_using", config.API_USING)
-                config.API_PAID = response_config.get("api_paid", config.API_PAID)
                 config.max_response_length = response_config.get("max_response_length", config.max_response_length)
                 
             # 加载模型配置
@@ -132,14 +134,23 @@ class BotConfig:
                 if "llm_normal_minor" in model_config:
                     config.llm_normal_minor = model_config["llm_normal_minor"]
                     
+                if "llm_topic_judge" in model_config:
+                    config.llm_topic_judge = model_config["llm_topic_judge"]
+                
+                if "llm_summary_by_topic" in model_config:
+                    config.llm_summary_by_topic = model_config["llm_summary_by_topic"]
+                
+                if "llm_emotion_judge" in model_config:
+                    config.llm_emotion_judge = model_config["llm_emotion_judge"]
+                
                 if "vlm" in model_config:
                     config.vlm = model_config["vlm"]
                     
                 if "embedding" in model_config:
                     config.embedding = model_config["embedding"]
                 
-                if "rerank" in model_config:
+                if "moderation" in model_config:
-                    config.rerank = model_config["rerank"]
+                    config.moderation = model_config["moderation"]
                 
             # 消息配置
             if "message" in toml_dict:

src/plugins/chat/emoji_manager.py

@@ -41,8 +41,8 @@ class EmojiManager:
     def __init__(self):
         self.db = Database.get_instance()
         self._scan_task = None
-        self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
+        self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
-        self.lm = LLM_request(model=global_config.llm_normal_minor, max_tokens=1000)
+        self.llm_emotion_judge = LLM_request(model=global_config.llm_normal_minor, max_tokens=60,temperature=0.8) #更高的温度，更少的token（后续可以根据情绪来调整温度）
         
     def _ensure_emoji_dir(self):
         """确保表情存储目录存在"""
@@ -69,7 +69,17 @@ class EmojiManager:
             raise RuntimeError("EmojiManager not initialized")
         
     def _ensure_emoji_collection(self):
-        """确保emoji集合存在并创建索引"""
+        """确保emoji集合存在并创建索引
+        
+        这个函数用于确保MongoDB数据库中存在emoji集合,并创建必要的索引。
+        
+        索引的作用是加快数据库查询速度:
+        - embedding字段的2dsphere索引: 用于加速向量相似度搜索,帮助快速找到相似的表情包
+        - tags字段的普通索引: 加快按标签搜索表情包的速度
+        - filename字段的唯一索引: 确保文件名不重复,同时加快按文件名查找的速度
+        
+        没有索引的话,数据库每次查询都需要扫描全部数据,建立索引后可以大大提高查询效率。
+        """
         if 'emoji' not in self.db.db.list_collection_names():
             self.db.db.create_collection('emoji')
             self.db.db.emoji.create_index([('embedding', '2dsphere')])
@@ -93,6 +103,11 @@ class EmojiManager:
             text: 输入文本
         Returns:
             Optional[str]: 表情包文件路径，如果没有找到则返回None
+            
+        
+        可不可以通过 配置文件中的指令 来自定义使用表情包的逻辑？
+        我觉得可行    
+
         """
         try:
             self._ensure_db()
@@ -152,7 +167,8 @@ class EmojiManager:
                         {'$inc': {'usage_count': 1}}
                     )
                     logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
-                    return selected_emoji['path'],"[表情包: %s]" % selected_emoji.get('discription', '无描述')
+                    # 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了
+                    return selected_emoji['path'],"[ %s ]" % selected_emoji.get('discription', '无描述')
                     
             except Exception as search_error:
                 logger.error(f"搜索表情包失败: {str(search_error)}")
@@ -169,7 +185,7 @@ class EmojiManager:
         try:
             prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感'
             
-            content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
+            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
             logger.debug(f"输出描述: {content}")
             return content
             
@@ -181,7 +197,7 @@ class EmojiManager:
         try:
             prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
             
-            content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
+            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
             logger.debug(f"输出描述: {content}")
             return content
             
@@ -193,7 +209,7 @@ class EmojiManager:
         try:
             prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
             
-            content, _ = await self.lm.generate_response_async(prompt)
+            content, _ = await self.llm_emotion_judge.generate_response_async(prompt)
             logger.info(f"输出描述: {content}")
             return content
             

src/plugins/chat/topic_identifier.py

@@ -11,7 +11,7 @@ config = driver.config
 
 class TopicIdentifier:
     def __init__(self):
-        self.llm_client = LLM_request(model=global_config.llm_topic_extract)
+        self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge)
 
     async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
         """识别消息主题，返回主题列表"""
@@ -23,7 +23,7 @@ class TopicIdentifier:
 消息内容：{text}"""
 
         # 使用 LLM_request 类进行请求
-        topic, _ = await self.llm_client.generate_response(prompt)
+        topic, _ = await self.llm_topic_judge.generate_response(prompt)
         
         if not topic:
             print(f"\033[1;31m[错误]\033[0m LLM API 返回为空")

src/plugins/memory_system/memory.py

@@ -132,8 +132,8 @@ class Memory_graph:
 class Hippocampus:
     def __init__(self,memory_graph:Memory_graph):
         self.memory_graph = memory_graph
-        self.llm_model_get_topic = LLM_request(model = global_config.llm_normal_minor,temperature=0.5)
+        self.llm_topic_judge = LLM_request(model = global_config.llm_topic_judge,temperature=0.5)
-        self.llm_model_summary = LLM_request(model = global_config.llm_normal,temperature=0.5)
+        self.llm_summary_by_topic = LLM_request(model = global_config.llm_summary_by_topic,temperature=0.5)
         
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表
@@ -179,7 +179,7 @@ class Hippocampus:
         
         #获取topics
         topic_num = self.calculate_topic_num(input_text, compress_rate)
-        topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num))
+        topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
         # 修改话题处理逻辑
         # 定义需要过滤的关键词
         filter_keywords = ['表情包', '图片', '回复', '聊天记录']
@@ -196,7 +196,7 @@ class Hippocampus:
         for topic in filtered_topics:
             topic_what_prompt = self.topic_what(input_text, topic)
             # 创建异步任务
-            task = self.llm_model_summary.generate_response_async(topic_what_prompt)
+            task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt)
             tasks.append((topic.strip(), task))
             
         # 等待所有任务完成
@@ -506,7 +506,7 @@ class Hippocampus:
         Returns:
             list: 识别出的主题列表
         """
-        topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5))
+        topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
         # print(f"话题: {topics_response[0]}")
         topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
         # print(f"话题: {topics}")

src/plugins/models/utils_model.py

@@ -197,13 +197,12 @@ class LLM_request:
         )
         return content, reasoning_content
 
-    async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
+    async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple[str, str]]:
         """异步方式根据输入的提示生成模型的响应"""
         # 构建请求体
         data = {
             "model": self.model_name,
             "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.5,
             "max_tokens": global_config.max_response_length,
             **self.params
         }