Merge branch 'release/v0.2.3' into develop

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -43,6 +43,7 @@ async def write_messages(end_user_id,langchain_messages,memory_config):
                 for node_name, node_data in update_event.items():
                     if 'save_neo4j' == node_name:
                         massages = node_data
+            # TODO：删除
             massagesstatus = massages.get('write_result')['status']
             contents = massages.get('write_result')
             print(contents)
@@ -60,6 +61,7 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):
         scope：窗口大小
     '''
     scope=scope
+    redis_messages = []
     is_end_user_id = count_store.get_sessions_count(end_user_id)
     if is_end_user_id is not False:
         is_end_user_id = count_store.get_sessions_count(end_user_id)[0]
@@ -91,6 +93,9 @@ async def memory_long_term_storage(end_user_id,memory_config,time):
         memory_config: 内存配置对象
     '''
     long_time_data = write_store.find_user_recent_sessions(end_user_id, time)
+    # Handle case where no session exists in Redis (returns False or empty)
+    if not long_time_data or long_time_data is False:
+        return
     format_messages = await chat_data_format(long_time_data)
     if format_messages!=[]:
         await write_messages(end_user_id, format_messages, memory_config)
@@ -108,8 +113,9 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config
     try:
         # 1. 获取历史会话数据（使用新方法）
         result = write_store.get_all_sessions_by_end_user_id(end_user_id)
-        history = await format_parsing(result)
-        if not result:
+        
+        # Handle case where no session exists in Redis (returns False or empty)
+        if not result or result is False:
             history = []
         else:
             history = await format_parsing(result)

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

@@ -1,18 +1,14 @@
 
 import asyncio
-import json
 import sys
 import warnings
 from contextlib import asynccontextmanager
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
 
-from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, chat_data_format, messages_parse
-from app.db import get_db
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import WriteState
 from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
-from app.services.memory_config_service import MemoryConfigService
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
@@ -40,27 +36,55 @@ async def make_write_graph():
 
     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
-    from app.core.memory.agent.utils.redis_tool import write_store
-    write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages))
-    # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=memory_config,  # 改为整数
-        service_name="MemoryAgentService"
+    """Dispatch long-term memory storage to Celery background tasks.
+    
+    Args:
+        long_term_type: Storage strategy - 'chunk' (window), 'time', or 'aggregate'
+        langchain_messages: List of messages to store
+        memory_config: Memory configuration ID (string)
+        end_user_id: End user identifier
+        scope: Window size for 'chunk' strategy (default: 6)
+    """
+    from app.tasks import (
+        long_term_storage_window_task,
+        # TODO: Uncomment when implemented
+        # long_term_storage_time_task,
+        # long_term_storage_aggregate_task,
     )
-    if long_term_type=='chunk':
-        '''方案一:对话窗口6轮对话'''
-        await window_dialogue(end_user_id,langchain_messages,memory_config,scope)
-    if long_term_type=='time':
-        """时间"""
-        await memory_long_term_storage(end_user_id, memory_config,5)
-    if  long_term_type=='aggregate':
-
-        """方案三：聚合判断"""
-        await aggregate_judgment(end_user_id, langchain_messages, memory_config)
+    from app.core.logging_config import get_logger
+    
+    logger = get_logger(__name__)
+    
+    # Convert config to string if needed
+    config_id = str(memory_config) if memory_config else ''
+    
+    if long_term_type == 'chunk':
+        # Strategy 1: Window-based batching (6 rounds of dialogue)
+        logger.info(f"[LONG_TERM] Dispatching window task - end_user_id={end_user_id}, scope={scope}")
+        long_term_storage_window_task.delay(
+            end_user_id=end_user_id,
+            langchain_messages=langchain_messages,
+            config_id=config_id,
+            scope=scope
+        )
+    # TODO: Uncomment when time-based strategy is fully implemented
+    # elif long_term_type == 'time':
+    #     # Strategy 2: Time-based retrieval
+    #     logger.info(f"[LONG_TERM] Dispatching time task - end_user_id={end_user_id}")
+    #     long_term_storage_time_task.delay(
+    #         end_user_id=end_user_id,
+    #         config_id=config_id,
+    #         time_window=5
+    #     )
+    # TODO: Uncomment when aggregate strategy is fully implemented
+    # elif long_term_type == 'aggregate':
+    #     # Strategy 3: Aggregate judgment (deduplication)
+    #     logger.info(f"[LONG_TERM] Dispatching aggregate task - end_user_id={end_user_id}")
+    #     long_term_storage_aggregate_task.delay(
+    #         end_user_id=end_user_id,
+    #         langchain_messages=langchain_messages,
+    #         config_id=config_id
+    #     )
 
 
 # async def main():