feat：更新记忆系统

src/chat/memory_system/memory_activator.py

@@ -1,15 +1,15 @@
-import difflib
 import json
 
 from json_repair import repair_json
 from typing import List, Dict
-from datetime import datetime
+
 
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
 from src.common.logger import get_logger
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.prompt_builder import Prompt
 from src.chat.memory_system.Hippocampus import hippocampus_manager
+from src.chat.utils.utils import parse_keywords_string
 
 
 logger = get_logger("memory_activator")
@@ -68,8 +68,6 @@ class MemoryActivator:
             request_type="memory.activator",
         )
 
-        self.running_memory = []
-        self.cached_keywords = set()  # 用于缓存历史关键词
 
     async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Dict]:
         """
@@ -78,67 +76,31 @@ class MemoryActivator:
         # 如果记忆系统被禁用，直接返回空列表
         if not global_config.memory.enable_memory:
             return []
-
-        # 将缓存的关键词转换为字符串，用于prompt
-        cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词"
-
-        prompt = await global_prompt_manager.format_prompt(
-            "memory_activator_prompt",
-            obs_info_text=chat_history_prompt,
-            target_message=target_message,
-            cached_keywords=cached_keywords_str,
-        )
-
-        # logger.debug(f"prompt: {prompt}")
-
-        response, (reasoning_content, model_name, _) = await self.key_words_model.generate_response_async(
-            prompt, temperature=0.5
-        )
-
-        keywords = list(get_keywords_from_json(response))
-
-        # 更新关键词缓存
-        if keywords:
-            # 限制缓存大小，最多保留10个关键词
-            if len(self.cached_keywords) > 10:
-                # 转换为列表，移除最早的关键词
-                cached_list = list(self.cached_keywords)
-                self.cached_keywords = set(cached_list[-8:])
-
-            # 添加新的关键词到缓存
-            self.cached_keywords.update(keywords)
-
-        # 调用记忆系统获取相关记忆
+        
+        keywords_list = set()
+        
+        for msg in chat_history_prompt:
+            keywords = parse_keywords_string(msg.get("key_words", ""))
+            if keywords:
+                if len(keywords_list) < 30:
+                # 最多容纳30个关键词
+                    keywords_list.update(keywords)
+                    print(keywords_list)
+                else:
+                    break
+        
+        if not keywords_list:
+            return []
+        
         related_memory = await hippocampus_manager.get_memory_from_topic(
-            valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
+            valid_keywords=list(keywords_list), max_memory_num=10, max_memory_length=3, max_depth=3
         )
+        
 
-        logger.debug(f"当前记忆关键词: {self.cached_keywords} ")
-        logger.debug(f"获取到的记忆: {related_memory}")
+        logger.info(f"当前记忆关键词: {keywords_list} ")
+        logger.info(f"获取到的记忆: {related_memory}")
 
-        # 激活时，所有已有记忆的duration+1，达到3则移除
-        for m in self.running_memory[:]:
-            m["duration"] = m.get("duration", 1) + 1
-        self.running_memory = [m for m in self.running_memory if m["duration"] < 3]
-
-        if related_memory:
-            for topic, memory in related_memory:
-                # 检查是否已存在相同topic或相似内容（相似度>=0.7）的记忆
-                exists = any(
-                    m["topic"] == topic or difflib.SequenceMatcher(None, m["content"], memory).ratio() >= 0.7
-                    for m in self.running_memory
-                )
-                if not exists:
-                    self.running_memory.append(
-                        {"topic": topic, "content": memory, "timestamp": datetime.now().isoformat(), "duration": 1}
-                    )
-                    logger.debug(f"添加新记忆: {topic} - {memory}")
-
-        # 限制同时加载的记忆条数，最多保留最后3条
-        if len(self.running_memory) > 3:
-            self.running_memory = self.running_memory[-3:]
-
-        return self.running_memory
+        return related_memory
 
 
 init_prompt()