fix：优化记忆提取，修复破损的tool信息

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -61,10 +61,10 @@ class ExpressionLearner:
     def __init__(self) -> None:
         # TODO: API-Adapter修改标记
         self.express_learn_model: LLMRequest = LLMRequest(
-            model=global_config.model.normal,
+            model=global_config.model.focus_expressor,
             temperature=0.1,
             max_tokens=256,
-            request_type="response_heartflow",
+            request_type="learn_expression",
         )
 
     async def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:

src/chat/focus_chat/heartFC_chat.py

@@ -19,6 +19,7 @@ from src.chat.focus_chat.info_processors.working_memory_processor import Working
 from src.chat.focus_chat.info_processors.action_processor import ActionProcessor
 from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
 from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
+from src.chat.heart_flow.observation.structure_observation import StructureObservation
 from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
 from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
 from src.chat.focus_chat.memory_activator import MemoryActivator
@@ -97,6 +98,7 @@ class HeartFChatting:
         self.log_prefix: str = str(chat_id)  # Initial default, will be updated
         self.hfcloop_observation = HFCloopObservation(observe_id=self.stream_id)
         self.chatting_observation = observations[0]
+        self.structure_observation = StructureObservation(observe_id=self.stream_id)
 
         self.memory_activator = MemoryActivator()
         self.working_memory = WorkingMemory(chat_id=self.stream_id)
@@ -415,11 +417,13 @@ class HeartFChatting:
                 await self.chatting_observation.observe()
                 await self.working_observation.observe()
                 await self.hfcloop_observation.observe()
+                await self.structure_observation.observe()
                 observations: List[Observation] = []
                 observations.append(self.chatting_observation)
                 observations.append(self.working_observation)
                 observations.append(self.hfcloop_observation)
-
+                observations.append(self.structure_observation)
+                
                 loop_observation_info = {
                     "observations": observations,
                 }

src/chat/focus_chat/info/structured_info.py

@@ -76,7 +76,11 @@ class StructuredInfo:
         """
 
         info_str = ""
+        # print(f"self.data: {self.data}")
+        
         for key, value in self.data.items():
+            
+            # print(f"key: {key}, value: {value}")
             info_str += f"信息类型：{key}，信息内容：{value}\n"
 
         return info_str

src/chat/focus_chat/info_processors/tool_processor.py

@@ -75,10 +75,12 @@ class ToolProcessor(BaseProcessor):
                     result, used_tools, prompt = await self.execute_tools(observation, running_memorys)
 
             # 更新WorkingObservation中的结构化信息
+            logger.debug(f"工具调用结果: {result}")
+            
             for observation in observations:
                 if isinstance(observation, StructureObservation):
                     for structured_info in result:
-                        logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
+                        # logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
                         observation.add_structured_info(structured_info)
 
                     working_infos = observation.get_observe_info()
@@ -87,7 +89,12 @@ class ToolProcessor(BaseProcessor):
         structured_info = StructuredInfo()
         if working_infos:
             for working_info in working_infos:
-                structured_info.set_info(working_info.get("type"), working_info.get("content"))
+                # print(f"working_info: {working_info}")
+                # print(f"working_info.get('type'): {working_info.get('type')}")
+                # print(f"working_info.get('content'): {working_info.get('content')}")
+                structured_info.set_info(key=working_info.get('type'), value=working_info.get('content'))
+                # info = structured_info.get_processed_info()
+                # print(f"info: {info}")
 
         return [structured_info]
 
@@ -155,7 +162,7 @@ class ToolProcessor(BaseProcessor):
         )
 
         # 调用LLM，专注于工具使用
-        # logger.debug(f"开始执行工具调用{prompt}")
+        logger.debug(f"开始执行工具调用{prompt}")
         response, _, tool_calls = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
 
         logger.debug(f"获取到工具原始输出:\n{tool_calls}")

src/chat/focus_chat/memory_activator.py

@@ -4,24 +4,58 @@ from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservati
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.common.logger_manager import get_logger
-from src.chat.utils.prompt_builder import Prompt
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from datetime import datetime
 from src.chat.memory_system.Hippocampus import HippocampusManager
 from typing import List, Dict
 import difflib
+import json
+from json_repair import repair_json
 
 
 logger = get_logger("memory_activator")
 
 
+def get_keywords_from_json(json_str):
+    """
+    从JSON字符串中提取关键词列表
+    
+    Args:
+        json_str: JSON格式的字符串
+        
+    Returns:
+        List[str]: 关键词列表
+    """
+    try:
+        # 使用repair_json修复JSON格式
+        fixed_json = repair_json(json_str)
+        
+        # 如果repair_json返回的是字符串，需要解析为Python对象
+        if isinstance(fixed_json, str):
+            result = json.loads(fixed_json)
+        else:
+            # 如果repair_json直接返回了字典对象，直接使用
+            result = fixed_json
+            
+        # 提取关键词
+        keywords = result.get("keywords", [])
+        return keywords
+    except Exception as e:
+        logger.error(f"解析关键词JSON失败: {e}")
+        return []
+
+
 def init_prompt():
     # --- Group Chat Prompt ---
     memory_activator_prompt = """
-    你是一个记忆分析器，你需要根据以下信息来进行会议
+    你是一个记忆分析器，你需要根据以下信息来进行回忆
     以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
     
     {obs_info_text}
     
+    历史关键词（请避免重复提取这些关键词）：
+    {cached_keywords}
+    
     请输出一个json格式，包含以下字段：
     {{
         "keywords": ["关键词1", "关键词2", "关键词3",......]
@@ -39,6 +73,7 @@ class MemoryActivator:
             model=global_config.model.memory_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
         )
         self.running_memory = []
+        self.cached_keywords = set()  # 用于缓存历史关键词
 
     async def activate_memory(self, observations) -> List[Dict]:
         """
@@ -61,31 +96,47 @@ class MemoryActivator:
             elif isinstance(observation, HFCloopObservation):
                 obs_info_text += observation.get_observe_info()
 
-        logger.debug(f"回忆待检索内容：obs_info_text: {obs_info_text}")
+        # logger.debug(f"回忆待检索内容：obs_info_text: {obs_info_text}")
 
-        # prompt = await global_prompt_manager.format_prompt(
+        # 将缓存的关键词转换为字符串，用于prompt
-        #     "memory_activator_prompt",
+        cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词"
-        #     obs_info_text=obs_info_text,
+        
-        # )
+        prompt = await global_prompt_manager.format_prompt(
-
+            "memory_activator_prompt",
-        # logger.debug(f"prompt: {prompt}")
+            obs_info_text=obs_info_text,
-
+            cached_keywords=cached_keywords_str,
-        # response = await self.summary_model.generate_response(prompt)
-
-        # logger.debug(f"response: {response}")
-
-        # # 只取response的第一个元素（字符串）
-        # response_str = response[0]
-        # keywords = list(get_keywords_from_json(response_str))
-
-        # #调用记忆系统获取相关记忆
-        # related_memory = await HippocampusManager.get_instance().get_memory_from_topic(
-        #     valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
-        # )
-        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
-            text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=True
         )
 
+        logger.debug(f"prompt: {prompt}")
+
+        response = await self.summary_model.generate_response(prompt)
+
+        logger.debug(f"response: {response}")
+
+        # 只取response的第一个元素（字符串）
+        response_str = response[0]
+        keywords = list(get_keywords_from_json(response_str))
+        
+        # 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)
+            logger.debug(f"更新关键词缓存: {self.cached_keywords}")
+
+        #调用记忆系统获取相关记忆
+        related_memory = await HippocampusManager.get_instance().get_memory_from_topic(
+            valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
+        )
+        # related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+        #     text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=False
+        # )
+
         # logger.debug(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

src/chat/focus_chat/planners/planner.py

@@ -36,9 +36,8 @@ def init_prompt():
 {mind_info_block}
 {cycle_info_block}
 
-{action_available_block}    
-
 请综合分析聊天内容和你看到的新消息，参考聊天规划，选择合适的action:
+注意，除了下面动作选项之外，你在群聊里不能做其他任何事情，这是你能力的边界，现在请你选择合适的action:
 
 {action_options_text}
 
@@ -126,13 +125,6 @@ class ActionPlanner:
                         action = "no_reply"
                         reasoning = f"之前选择的动作{action}已被移除，原因: {reason}"
 
-            using_actions = self.action_manager.get_using_actions()
-            action_available_block = ""
-            for action_name, action_info in using_actions.items():
-                action_description = action_info["description"]
-                action_available_block += f"\n你在聊天中可以使用{action_name}，这个动作的描述是{action_description}\n"
-            action_available_block += "注意，除了上述动作选项之外，你在群聊里不能做其他任何事情，这是你能力的边界\n"
-
             # 继续处理其他信息
             for info in all_plan_info:
                 if isinstance(info, ObsInfo):
@@ -147,7 +139,8 @@ class ActionPlanner:
                 elif isinstance(info, SelfInfo):
                     self_info = info.get_processed_info()
                 elif isinstance(info, StructuredInfo):
-                    _structured_info = info.get_data()
+                    structured_info = info.get_processed_info()
+                    # print(f"structured_info: {structured_info}")
                 elif not isinstance(info, ActionInfo):  # 跳过已处理的ActionInfo
                     extra_info.append(info.get_processed_info())
 
@@ -178,11 +171,10 @@ class ActionPlanner:
                 chat_target_info=None,
                 observed_messages_str=observed_messages_str,  # <-- Pass local variable
                 current_mind=current_mind,  # <-- Pass argument
-                # structured_info=structured_info,  # <-- Pass SubMind info
+                structured_info=structured_info,  # <-- Pass SubMind info
                 current_available_actions=current_available_actions,  # <-- Pass determined actions
                 cycle_info=cycle_info,  # <-- Pass cycle info
                 extra_info=extra_info,
-                action_available_block=action_available_block,
             )
 
             # --- 调用 LLM (普通文本生成) ---
@@ -268,7 +260,7 @@ class ActionPlanner:
         chat_target_info: Optional[dict],  # Now passed as argument
         observed_messages_str: str,
         current_mind: Optional[str],
-        action_available_block: str,
+        structured_info: Optional[str],
         current_available_actions: Dict[str, ActionInfo],
         cycle_info: Optional[str],
         extra_info: list[str],
@@ -326,7 +318,8 @@ class ActionPlanner:
                 action_options_block += using_action_prompt
 
             extra_info_block = "\n".join(extra_info)
-            if extra_info:
+            extra_info_block += f"\n{structured_info}"
+            if extra_info or structured_info:
                 extra_info_block = f"以下是一些额外的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是一些额外的信息，现在请你阅读以下内容，进行决策"
             else:
                 extra_info_block = ""
@@ -343,7 +336,7 @@ class ActionPlanner:
                 mind_info_block=mind_info_block,
                 cycle_info_block=cycle_info,
                 action_options_text=action_options_block,
-                action_available_block=action_available_block,
+                # action_available_block=action_available_block,
                 extra_info_block=extra_info_block,
                 moderation_prompt=moderation_prompt_block,
             )

src/chat/memory_system/Hippocampus.py

@@ -526,12 +526,12 @@ class Hippocampus:
         if not keywords:
             return []
 
-        # logger.info(f"提取的关键词: {', '.join(keywords)}")
+        logger.info(f"提取的关键词: {', '.join(keywords)}")
 
         # 过滤掉不存在于记忆图中的关键词
         valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
         if not valid_keywords:
-            # logger.info("没有找到有效的关键词节点")
+            logger.info("没有找到有效的关键词节点")
             return []
 
         logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")

src/individuality/expression_style.py

@@ -33,10 +33,10 @@ def init_prompt() -> None:
 class PersonalityExpression:
     def __init__(self):
         self.express_learn_model: LLMRequest = LLMRequest(
-            model=global_config.model.normal,
+            model=global_config.model.focus_expressor,
             temperature=0.1,
             max_tokens=256,
-            request_type="response_heartflow",
+            request_type="learn_expression",
         )
         self.meta_file_path = os.path.join("data", "expression", "personality", "expression_style_meta.json")
         self.expressions_file_path = os.path.join("data", "expression", "personality", "expressions.json")

template/bot_config_template.toml

@@ -255,7 +255,8 @@ provider = "SILICONFLOW"
 pri_in = 2
 pri_out = 8
 
-#表达器模型，用于生成表达方式
+#表达器模型，用于表达麦麦的想法，生成最终回复，对语言风格影响极大
+#也用于表达方式学习
 [model.focus_expressor]
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"