fix:ruff

src/chat/focus_chat/info_processors/chattinginfo_processor.py

@@ -55,7 +55,7 @@ class ChattingInfoProcessor(BaseProcessor):
                 # print(f"obs: {obs}")
                 if isinstance(obs, ChattingObservation):
                     # print("1111111111111111111111读取111111111111111")
-                    
+
                     obs_info = ObsInfo()
 
                     await self.chat_compress(obs)

src/chat/focus_chat/info_processors/mind_processor.py

@@ -6,11 +6,9 @@ import time
 import traceback
 from src.common.logger_manager import get_logger
 from src.individuality.individuality import Individuality
-import random
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.json_utils import safe_json_dumps
 from src.chat.message_receive.chat_stream import chat_manager
-import difflib
 from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
 from src.chat.focus_chat.info.mind_info import MindInfo
@@ -202,7 +200,6 @@ class MindProcessor(BaseProcessor):
         for person in person_list:
             relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
 
-
         template_name = "sub_heartflow_prompt_before" if is_group_chat else "sub_heartflow_prompt_private_before"
         logger.debug(f"{self.log_prefix} 使用{'群聊' if is_group_chat else '私聊'}思考模板")
 
@@ -218,7 +215,6 @@ class MindProcessor(BaseProcessor):
             chat_target_name=chat_target_name,
         )
 
-
         content = "(不知道该想些什么...)"
         try:
             content, _ = await self.llm_model.generate_response_async(prompt=prompt)

src/chat/focus_chat/info_processors/self_processor.py

@@ -6,14 +6,10 @@ import time
 import traceback
 from src.common.logger_manager import get_logger
 from src.individuality.individuality import Individuality
-import random
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.chat.utils.json_utils import safe_json_dumps
 from src.chat.message_receive.chat_stream import chat_manager
-import difflib
 from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
-from src.chat.focus_chat.info.mind_info import MindInfo
 from typing import List, Optional
 from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
 from typing import Dict
@@ -44,7 +40,6 @@ def init_prompt():
     Prompt(indentify_prompt, "indentify_prompt")
 
 
-
 class SelfProcessor(BaseProcessor):
     log_prefix = "自我认同"
 
@@ -63,7 +58,6 @@ class SelfProcessor(BaseProcessor):
         name = chat_manager.get_stream_name(self.subheartflow_id)
         self.log_prefix = f"[{name}] "
 
-
     async def process_info(
         self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
     ) -> List[InfoBase]:
@@ -76,7 +70,7 @@ class SelfProcessor(BaseProcessor):
             List[InfoBase]: 处理后的结构化信息列表
         """
         self_info_str = await self.self_indentify(observations, running_memorys)
-        
+
         if self_info_str:
             self_info = SelfInfo()
             self_info.set_self_info(self_info_str)
@@ -102,14 +96,12 @@ class SelfProcessor(BaseProcessor):
                 tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt
         """
 
-
         memory_str = ""
         if running_memorys:
             memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
             for running_memory in running_memorys:
                 memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
 
-
         if observations is None:
             observations = []
         for observation in observations:
@@ -127,8 +119,8 @@ class SelfProcessor(BaseProcessor):
                 chat_observe_info = observation.get_observe_info()
                 person_list = observation.person_list
             if isinstance(observation, HFCloopObservation):
-                hfcloop_observe_info = observation.get_observe_info()
-
+                # hfcloop_observe_info = observation.get_observe_info()
+                pass
 
         individuality = Individuality.get_instance()
         personality_block = individuality.get_prompt(x_person=2, level=2)
@@ -137,7 +129,6 @@ class SelfProcessor(BaseProcessor):
         for person in person_list:
             relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
 
-
         prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format(
             bot_name=individuality.name,
             prompt_personality=personality_block,
@@ -147,7 +138,6 @@ class SelfProcessor(BaseProcessor):
             chat_observe_info=chat_observe_info,
         )
 
-
         content = ""
         try:
             content, _ = await self.llm_model.generate_response_async(prompt=prompt)
@@ -159,7 +149,7 @@ class SelfProcessor(BaseProcessor):
             logger.error(traceback.format_exc())
             content = "自我识别过程中出现错误"
 
-        if content == 'None':
+        if content == "None":
             content = ""
         # 记录初步思考结果
         logger.debug(f"{self.log_prefix} 自我识别prompt: \n{prompt}\n")
@@ -168,5 +158,4 @@ class SelfProcessor(BaseProcessor):
         return content
 
 
-
 init_prompt()

src/chat/focus_chat/info_processors/tool_processor.py

@@ -4,7 +4,7 @@ from src.config.config import global_config
 import time
 from src.common.logger_manager import get_logger
 from src.individuality.individuality import Individuality
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager 
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.tools.tool_use import ToolUser
 from src.chat.utils.json_utils import process_llm_tool_calls
 from src.chat.person_info.relationship_manager import relationship_manager
@@ -68,7 +68,7 @@ class ToolProcessor(BaseProcessor):
         """
 
         working_infos = []
-        
+
         if observations:
             for observation in observations:
                 if isinstance(observation, ChattingObservation):
@@ -134,7 +134,7 @@ class ToolProcessor(BaseProcessor):
 
         # 获取个性信息
         individuality = Individuality.get_instance()
-        prompt_personality = individuality.get_prompt(x_person=2, level=2)
+        # prompt_personality = individuality.get_prompt(x_person=2, level=2)
 
         # 获取时间信息
         time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())

src/chat/focus_chat/info_processors/working_memory_processor.py

@@ -5,17 +5,11 @@ from src.config.config import global_config
 import time
 import traceback
 from src.common.logger_manager import get_logger
-from src.individuality.individuality import Individuality
-import random
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.chat.utils.json_utils import safe_json_dumps
 from src.chat.message_receive.chat_stream import chat_manager
-import difflib
-from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
 from src.chat.focus_chat.info.mind_info import MindInfo
 from typing import List, Optional
-from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
 from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
 from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
 from typing import Dict
@@ -76,8 +70,6 @@ class WorkingMemoryProcessor(BaseProcessor):
         name = chat_manager.get_stream_name(self.subheartflow_id)
         self.log_prefix = f"[{name}] "
 
-
-
     async def process_info(
         self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
     ) -> List[InfoBase]:
@@ -95,11 +87,11 @@ class WorkingMemoryProcessor(BaseProcessor):
             for observation in observations:
                 if isinstance(observation, WorkingMemoryObservation):
                     working_memory = observation.get_observe_info()
-                    working_memory_obs = observation
+                    # working_memory_obs = observation
                 if isinstance(observation, ChattingObservation):
                     chat_info = observation.get_observe_info()
                     # chat_info_truncate = observation.talking_message_str_truncate
-                    
+
             if not working_memory:
                 logger.warning(f"{self.log_prefix} 没有找到工作记忆对象")
                 mind_info = MindInfo()
@@ -108,44 +100,42 @@ class WorkingMemoryProcessor(BaseProcessor):
             logger.error(f"{self.log_prefix} 处理观察时出错: {e}")
             logger.error(traceback.format_exc())
             return []
-                
+
         all_memory = working_memory.get_all_memories()
         memory_prompts = []
         for memory in all_memory:
-            memory_content = memory.data
+            # memory_content = memory.data
             memory_summary = memory.summary
             memory_id = memory.id
             memory_brief = memory_summary.get("brief")
-            memory_detailed = memory_summary.get("detailed")
+            # memory_detailed = memory_summary.get("detailed")
             memory_keypoints = memory_summary.get("keypoints")
             memory_events = memory_summary.get("events")
             memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
             memory_prompts.append(memory_single_prompt)
-                
+
         memory_choose_str = "".join(memory_prompts)
-            
+
         # 使用提示模板进行处理
         prompt = (await global_prompt_manager.get_prompt_async("prompt_memory_proces")).format(
             bot_name=global_config.BOT_NICKNAME,
             time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
             chat_observe_info=chat_info,
-            memory_str=memory_choose_str
+            memory_str=memory_choose_str,
         )
-            
+
         # 调用LLM处理记忆
         content = ""
         try:
-            
             logger.debug(f"{self.log_prefix} 处理工作记忆的prompt: {prompt}")
-            
-            
+
             content, _ = await self.llm_model.generate_response_async(prompt=prompt)
             if not content:
                 logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
         except Exception as e:
             logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
             logger.error(traceback.format_exc())
-            
+
         # 解析LLM返回的JSON
         try:
             result = repair_json(content)
@@ -154,7 +144,7 @@ class WorkingMemoryProcessor(BaseProcessor):
             if not isinstance(result, dict):
                 logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败，结果不是字典类型: {type(result)}")
                 return []
-                
+
             selected_memory_ids = result.get("selected_memory_ids", [])
             new_memory = result.get("new_memory", "")
             merge_memory = result.get("merge_memory", [])
@@ -162,20 +152,20 @@ class WorkingMemoryProcessor(BaseProcessor):
             logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败: {e}")
             logger.error(traceback.format_exc())
             return []
-        
+
         logger.debug(f"{self.log_prefix} 解析LLM返回的JSON成功: {result}")
-        
+
         # 根据selected_memory_ids，调取记忆
         memory_str = ""
         if selected_memory_ids:
             for memory_id in selected_memory_ids:
                 memory = await working_memory.retrieve_memory(memory_id)
                 if memory:
-                    memory_content = memory.data
+                    # memory_content = memory.data
                     memory_summary = memory.summary
                     memory_id = memory.id
                     memory_brief = memory_summary.get("brief")
-                    memory_detailed = memory_summary.get("detailed")
+                    # memory_detailed = memory_summary.get("detailed")
                     memory_keypoints = memory_summary.get("keypoints")
                     memory_events = memory_summary.get("events")
                     for keypoint in memory_keypoints:
@@ -184,21 +174,20 @@ class WorkingMemoryProcessor(BaseProcessor):
                         memory_str += f"记忆事件:{event}\n"
                     # memory_str += f"记忆摘要:{memory_detailed}\n"
                     # memory_str += f"记忆主题:{memory_brief}\n"
-                
-                
+
         working_memory_info = WorkingMemoryInfo()
         if memory_str:
             working_memory_info.add_working_memory(memory_str)
             logger.debug(f"{self.log_prefix} 取得工作记忆: {memory_str}")
         else:
             logger.warning(f"{self.log_prefix} 没有找到工作记忆")
-        
+
         # 根据聊天内容添加新记忆
         if new_memory:
             # 使用异步方式添加新记忆，不阻塞主流程
             logger.debug(f"{self.log_prefix} {new_memory}新记忆: ")
             asyncio.create_task(self.add_memory_async(working_memory, chat_info))
-            
+
         if merge_memory:
             for merge_pairs in merge_memory:
                 memory1 = await working_memory.retrieve_memory(merge_pairs[0])
@@ -207,12 +196,12 @@ class WorkingMemoryProcessor(BaseProcessor):
                     memory_str = f"记忆id:{memory1.id},记忆摘要:{memory1.summary.get('brief')}\n"
                     memory_str += f"记忆id:{memory2.id},记忆摘要:{memory2.summary.get('brief')}\n"
                     asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
-                
+
         return [working_memory_info]
 
     async def add_memory_async(self, working_memory: WorkingMemory, content: str):
         """异步添加记忆，不阻塞主流程
-        
+
         Args:
             working_memory: 工作记忆对象
             content: 记忆内容
@@ -223,10 +212,10 @@ class WorkingMemoryProcessor(BaseProcessor):
         except Exception as e:
             logger.error(f"{self.log_prefix} 异步添加新记忆失败: {e}")
             logger.error(traceback.format_exc())
-            
+
     async def merge_memory_async(self, working_memory: WorkingMemory, memory_id1: str, memory_id2: str):
         """异步合并记忆，不阻塞主流程
-        
+
         Args:
             working_memory: 工作记忆对象
             memory_str: 记忆内容
@@ -238,7 +227,7 @@ class WorkingMemoryProcessor(BaseProcessor):
             logger.debug(f"{self.log_prefix} 合并后的记忆详情: {merged_memory.summary.get('detailed')}")
             logger.debug(f"{self.log_prefix} 合并后的记忆要点: {merged_memory.summary.get('keypoints')}")
             logger.debug(f"{self.log_prefix} 合并后的记忆事件: {merged_memory.summary.get('events')}")
-            
+
         except Exception as e:
             logger.error(f"{self.log_prefix} 异步合并记忆失败: {e}")
             logger.error(traceback.format_exc())