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Add/develop memory (#247)

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* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射
This commit is contained in:
lixinyue11
2026-01-29 19:27:02 +08:00
committed by GitHub
parent a67be85858
commit ee50b25d06
2 changed files with 44 additions and 44 deletions
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8
api/app/controllers/memory_forget_controller.py
View File

@@ -84,10 +84,8 @@ async def trigger_forgetting_cycle(
connected_config = get_end_user_connected_config(end_user_id, db)
config_id = connected_config.get("memory_config_id")
config_id = resolve_config_id(int(config_id), db)
config_id = resolve_config_id((config_id), db)
if config_id is None:
api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
@@ -199,7 +197,7 @@ async def update_forgetting_config(
ApiResponse: 包含更新结果的响应
"""
workspace_id = current_user.current_workspace_id
payload.config_id=resolve_config_id(int(payload.config_id), db)
payload.config_id=resolve_config_id((payload.config_id), db)
# 检查用户是否已选择工作空间
@@ -330,7 +328,7 @@ async def get_forgetting_curve(
ApiResponse: 包含遗忘曲线数据的响应
"""
workspace_id = current_user.current_workspace_id
request.config_id = resolve_config_id(int(request.config_id), db)
request.config_id = resolve_config_id((request.config_id), db)
# 检查用户是否已选择工作空间
if workspace_id is None:
api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间")

80
api/app/core/agent/langchain_agent.py
View File

@@ -177,7 +177,6 @@ class LangChainAgent:
# messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
# retrieved_content.append({query: aimessages})
# return messagss_list,retrieved_content
async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
"""
写入记忆（支持结构化消息）
@@ -200,49 +199,52 @@ class LangChainAgent:
"""
db = next(get_db())
actual_config_id=resolve_config_id(actual_config_id, db)
if storage_type == "rag":
# RAG 模式：组合消息为字符串格式（保持原有逻辑）
combined_message = f"user: {user_message}\nassistant: {ai_message}"
await write_rag(end_user_id, combined_message, user_rag_memory_id)
logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
else:
# Neo4j 模式：使用结构化消息列表
structured_messages = []
try:
actual_config_id=resolve_config_id(actual_config_id, db)
# 始终添加用户消息（如果不为空）
if user_message:
structured_messages.append({"role": "user", "content": user_message})
if storage_type == "rag":
# RAG 模式：组合消息为字符串格式（保持原有逻辑）
combined_message = f"user: {user_message}\nassistant: {ai_message}"
await write_rag(end_user_id, combined_message, user_rag_memory_id)
logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
else:
# Neo4j 模式：使用结构化消息列表
structured_messages = []
# 只有当 AI 回复不为空时才添加 assistant 消息
if ai_message:
structured_messages.append({"role": "assistant", "content": ai_message})
# 始终添加用户消息（如果不为空）
if user_message:
structured_messages.append({"role": "user", "content": user_message})
# 如果没有消息，直接返回
if not structured_messages:
logger.warning(f"No messages to write for user {actual_end_user_id}")
return
# 只有当 AI 回复不为空时才添加 assistant 消息
if ai_message:
structured_messages.append({"role": "assistant", "content": ai_message})
# 调用 Celery 任务，传递结构化消息列表
# 数据流：
# 1. structured_messages 传递给 write_message_task
# 2. write_message_task 调用 memory_agent_service.write_memory
# 3. write_memory 调用 write_tools.write，传递 messages 参数
# 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
# 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
# 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
write_id = write_message_task.delay(
actual_end_user_id, # end_user_id: 用户ID
structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
actual_config_id, # config_id: 配置ID
storage_type, # storage_type: "neo4j"
user_rag_memory_id # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
)
logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
write_status = get_task_memory_write_result(str(write_id))
logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
# 如果没有消息，直接返回
if not structured_messages:
logger.warning(f"No messages to write for user {actual_end_user_id}")
return
# 调用 Celery 任务，传递结构化消息列表
# 数据流：
# 1. structured_messages 传递给 write_message_task
# 2. write_message_task 调用 memory_agent_service.write_memory
# 3. write_memory 调用 write_tools.write，传递 messages 参数
# 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
# 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
# 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
write_id = write_message_task.delay(
actual_end_user_id, # end_user_id: 用户ID
structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
actual_config_id, # config_id: 配置ID
storage_type, # storage_type: "neo4j"
user_rag_memory_id # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
)
logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
write_status = get_task_memory_write_result(str(write_id))
logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
finally:
db.close()
async def chat(
self,
message: str,