fix(prompt): remove hard-coded import of prompt file paths (#279)

* Fix/develop memory bug (#274)

* 遗漏的历史映射

* 遗漏的历史映射

* fix_timeline_memories

* fix(web): update retrieve_type key

* Fix/develop memory bug (#276)

* 遗漏的历史映射

* 遗漏的历史映射

* fix_timeline_memories

* fix_timeline_memories

* write_gragp/bug_fix

* write_gragp/bug_fix

* write_gragp/bug_fix

* chore(celery): disable periodic task scheduling

* fix(prompt): remove hard-coded import of prompt file paths

---------

Co-authored-by: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Ke Sun <kesun5@illinois.edu>

api/app/celery_app.py

@@ -3,9 +3,10 @@ import platform
 from datetime import timedelta
 from urllib.parse import quote
 
-from app.core.config import settings
 from celery import Celery
 
+from app.core.config import settings
+
 # 创建 Celery 应用实例
 # broker: 任务队列（使用 Redis DB 0）
 # backend: 结果存储（使用 Redis DB 10）
@@ -79,40 +80,40 @@ celery_app.conf.update(
 celery_app.autodiscover_tasks(['app'])
 
 # Celery Beat schedule for periodic tasks
-memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
-memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
-workspace_reflection_schedule = timedelta(seconds=30)  # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
-forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘周期
+# memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
+# memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
+# workspace_reflection_schedule = timedelta(seconds=30)  # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
+# forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘周期
 
 # 构建定时任务配置
-beat_schedule_config = {
-    "run-workspace-reflection": {
-        "task": "app.tasks.workspace_reflection_task",
-        "schedule": workspace_reflection_schedule,
-        "args": (),
-    },
-    "regenerate-memory-cache": {
-        "task": "app.tasks.regenerate_memory_cache",
-        "schedule": memory_cache_regeneration_schedule,
-        "args": (),
-    },
-    "run-forgetting-cycle": {
-        "task": "app.tasks.run_forgetting_cycle_task",
-        "schedule": forgetting_cycle_schedule,
-        "kwargs": {
-            "config_id": None,  # 使用默认配置，可以通过环境变量配置
-        },
-    },
-}
+# beat_schedule_config = {
+#     "run-workspace-reflection": {
+#         "task": "app.tasks.workspace_reflection_task",
+#         "schedule": workspace_reflection_schedule,
+#         "args": (),
+#     },
+#     "regenerate-memory-cache": {
+#         "task": "app.tasks.regenerate_memory_cache",
+#         "schedule": memory_cache_regeneration_schedule,
+#         "args": (),
+#     },
+#     "run-forgetting-cycle": {
+#         "task": "app.tasks.run_forgetting_cycle_task",
+#         "schedule": forgetting_cycle_schedule,
+#         "kwargs": {
+#             "config_id": None,  # 使用默认配置，可以通过环境变量配置
+#         },
+#     },
+# }
 
 # 如果配置了默认工作空间ID，则添加记忆总量统计任务
-if settings.DEFAULT_WORKSPACE_ID:
-    beat_schedule_config["write-total-memory"] = {
-        "task": "app.controllers.memory_storage_controller.search_all",
-        "schedule": memory_increment_schedule,
-        "kwargs": {
-            "workspace_id": settings.DEFAULT_WORKSPACE_ID,
-        },
-    }
+# if settings.DEFAULT_WORKSPACE_ID:
+#     beat_schedule_config["write-total-memory"] = {
+#         "task": "app.controllers.memory_storage_controller.search_all",
+#         "schedule": memory_increment_schedule,
+#         "kwargs": {
+#             "workspace_id": settings.DEFAULT_WORKSPACE_ID,
+#         },
+#     }
 
-celery_app.conf.beat_schedule = beat_schedule_config
+# celery_app.conf.beat_schedule = beat_schedule_config

api/app/core/memory/agent/langgraph_graph/write_graph.py

@@ -39,7 +39,6 @@ async def make_write_graph():
     graph = workflow.compile()
 
     yield graph
-
 async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6):
     from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment
     from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format
@@ -49,7 +48,7 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[
     db_session = next(get_db())
     config_service = MemoryConfigService(db_session)
     memory_config = config_service.load_memory_config(
-        config_id="08ed205c-0f05-49c3-8e0c-a580d28f5fd4",  # 改为整数
+        config_id=memory_config,  # 改为整数
         service_name="MemoryAgentService"
     )
     if long_term_type=='chunk':
@@ -63,7 +62,7 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[
         """方案三：聚合判断"""
         await aggregate_judgment(end_user_id, langchain_messages, memory_config)
 
-#
+
 # async def main():
 #     """主函数 - 运行工作流"""
 #     langchain_messages = [
@@ -80,14 +79,7 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[
 #     end_user_id = '837fee1b-04a2-48ee-94d7-211488908940'  # 组ID
 #     memory_config="08ed205c-0f05-49c3-8e0c-a580d28f5fd4"
 #     # await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2)
-#     from app.core.memory.agent.utils.redis_tool import write_store
-#     result=write_store.get_session_by_userid(end_user_id)
-#     data=await format_parsing(result,"dict")
-#     chunk_data=data[:6]
-#
-#     long_time_data = write_store.find_user_recent_sessions(end_user_id, 240)
-#     long_=await messages_parse(long_time_data)
-#     print(long_)
+#     result=await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2)
 #
 #
 # if __name__ == "__main__":

api/app/repositories/neo4j/cypher_queries.py

@@ -877,7 +877,8 @@ RETURN
     CASE
       WHEN ms:ExtractedEntity THEN {
         text: ms.name,
-        created_at: ms.created_at
+        created_at: ms.created_at,
+        type: "情景记忆" 
       }
     END
   ) AS ExtractedEntity,
@@ -887,7 +888,8 @@ RETURN
     CASE
       WHEN n:MemorySummary THEN {
         text: n.content,
-        created_at: n.created_at
+        created_at: n.created_at,
+        type: "长期沉淀" 
       }
     END
   ) AS MemorySummary,
@@ -895,7 +897,8 @@ RETURN
   collect(
     DISTINCT {
       text: e.statement,
-      created_at: e.created_at
+      created_at: e.created_at,
+      type: "情绪记忆" 
     }
   ) AS statement;
 """

api/app/services/conversation_service.py

@@ -1,4 +1,5 @@
 """会话服务"""
+import os
 import uuid
 from datetime import datetime, timedelta
 from typing import Annotated
@@ -529,12 +530,12 @@ class ConversationService:
                 takeaways=[],
                 info_score=0,
             )
-
-        with open('app/services/prompt/conversation_summary_system.jinja2', 'r', encoding='utf-8') as f:
+        prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')
+        with open(os.path.join(prompt_path, 'conversation_summary_system.jinja2'), 'r', encoding='utf-8') as f:
             system_prompt = f.read()
         rendered_system_message = Template(system_prompt).render()
 
-        with open('app/services/prompt/conversation_summary_user.jinja2', 'r', encoding='utf-8') as f:
+        with open(os.path.join(prompt_path, 'conversation_summary_user.jinja2'), 'r', encoding='utf-8') as f:
             user_prompt = f.read()
         rendered_user_message = Template(user_prompt).render(
             language=language,

api/app/services/memory_reflection_service.py

@@ -286,7 +286,7 @@ class MemoryReflectionService:
                 # 检查是否需要执行反思
                 should_execute = False
                 hours_diff = 0
-                
+
                 if current_reflection_time is None:
                     # 首次执行反思
                     should_execute = True
@@ -298,11 +298,11 @@ class MemoryReflectionService:
                             reflection_time = datetime.fromisoformat(current_reflection_time)
                         else:
                             reflection_time = current_reflection_time
-                        
+
                         current_time = datetime.now()
                         time_diff = current_time - reflection_time
                         hours_diff = int(time_diff.total_seconds() / 3600)
-                        
+
                         # 检查是否达到反思周期
                         if hours_diff >= iteration_period:
                             should_execute = True
@@ -312,7 +312,7 @@ class MemoryReflectionService:
                     except (ValueError, TypeError) as e:
                         api_logger.warning(f"解析反思时间失败: {e}，将执行反思")
                         should_execute = True
-                
+
                 if should_execute:
                     api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时")
                     # 3. 执行反思引擎
@@ -345,7 +345,7 @@ class MemoryReflectionService:
                         "next_reflection_in_hours": iteration_period - hours_diff
                     }
 
-            
+
         except Exception as e:
             config_id = config_data.get("config_id", "unknown")
             api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}")
@@ -356,7 +356,7 @@ class MemoryReflectionService:
                 "end_user_id": end_user_id,
                 "config_data": config_data
             }
-    
+
     def _create_reflection_config_from_data(self, config_data: Dict[str, Any]) -> ReflectionConfig:
         """Create reflective configuration objects from configuration data"""
 
@@ -364,12 +364,12 @@ class MemoryReflectionService:
         if reflexion_range_value is None or reflexion_range_value == "":
             reflexion_range_value = "partial"
         reflexion_range = ReflectionRange(reflexion_range_value)
-        
+
         baseline_value = config_data.get("baseline")
         if baseline_value is None or baseline_value == "":
             baseline_value = "TIME"
         baseline = ReflectionBaseline(baseline_value)
-        
+
         # iteration_period =
         iteration_period = config_data.get("iteration_period", 24)
         if isinstance(iteration_period, str):
@@ -377,7 +377,6 @@ class MemoryReflectionService:
                 iteration_period = int(iteration_period)
             except (ValueError, TypeError):
                 iteration_period = 24  # 默认24小时
-        
         return ReflectionConfig(
             enabled=config_data.get("enable_self_reflexion", False),
             iteration_period=str(iteration_period),  # ReflectionConfig期望字符串

api/app/services/prompt_optimizer_service.py

@@ -1,3 +1,4 @@
+import os
 import re
 import uuid
 from typing import Any, AsyncGenerator
@@ -182,11 +183,12 @@ class PromptOptimizerService:
             base_url=api_config.api_base
         ), type=ModelType(model_config.type))
         try:
-            with open('app/services/prompt/prompt_optimizer_system.jinja2', 'r', encoding='utf-8') as f:
+            prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')
+            with open(os.path.join(prompt_path, 'prompt_optimizer_system.jinja2'), 'r', encoding='utf-8') as f:
                 opt_system_prompt = f.read()
             rendered_system_message = Template(opt_system_prompt).render()
 
-            with open('app/services/prompt/prompt_optimizer_user.jinja2', 'r', encoding='utf-8') as f:
+            with open(os.path.join(prompt_path, 'prompt_optimizer_user.jinja2'), 'r', encoding='utf-8') as f:
                 opt_user_prompt = f.read()
         except FileNotFoundError:
             raise BusinessException(message="System prompt template not found", code=BizCode.NOT_FOUND)