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Fix/memory mcp2 1 (#170)

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* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* feat(celery): add comprehensive logging to worker and write task

- Initialize logging system in Celery worker entry point with LoggingConfig
- Add logger instance and startup message to celery_worker.py
- Reorganize imports in tasks.py for better readability and consistency
- Add detailed logging to write_message_task for debugging and monitoring
- Log task start with group_id, config_id, and storage_type parameters
- Log service execution and completion status with results
- Add exception handling with error logging and stack trace capture
- Log task completion time and Celery task ID for performance tracking
- Improves observability and troubleshooting of async task execution

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 快速检索，需要在接口部分添加LLM整合

* 快速检索，需要在接口部分添加LLM整合

---------

Co-authored-by: Ke Sun <kesun5@illinois.edu>
This commit is contained in:
lixinyue11
2026-01-21 18:21:51 +08:00
committed by GitHub
parent b6e6dbf27f
commit fb25495f1b
5 changed files with 99 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
api/app/controllers/memory_agent_controller.py
View File

@@ -9,6 +9,8 @@ from app.db import get_db
from app.dependencies import cur_workspace_access_guard, get_current_user
from app.models import ModelApiKey
from app.models.user_model import User
from app.core.memory.agent.utils.session_tools import SessionService
from app.core.memory.agent.utils.redis_tool import store
from app.repositories import knowledge_repository, WorkspaceRepository
from app.schemas.memory_agent_schema import UserInput, Write_UserInput
from app.schemas.response_schema import ApiResponse
@@ -291,6 +293,19 @@ async def read_server(
storage_type,
user_rag_memory_id
)
if str(user_input.search_switch) == "2":
retrieve_info = result['answer']
history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id)
query = user_input.message
# 调用 memory_agent_service 的方法生成最终答案
result['answer'] = await memory_agent_service.generate_summary_from_retrieve(
retrieve_info=retrieve_info,
history=history,
query=query,
config_id=config_id,
db=db
)
return success(data=result, msg="回复对话消息成功")
except BaseException as e:
# Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup

41
api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
View File

@@ -18,16 +18,19 @@ template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
db_session = next(get_db())
logger = get_agent_logger(__name__)
class ProblemNodeService(LLMServiceMixin):
"""问题处理节点服务类"""
def __init__(self):
super().__init__()
self.template_service = TemplateService(template_root)
# 创建全局服务实例
problem_service = ProblemNodeService()
async def Split_The_Problem(state: ReadState) -> ReadState:
"""问题分解节点"""
# 从状态中获取数据
@@ -36,10 +39,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
memory_config = state.get('memory_config', None)
history = await SessionService(store).get_history(group_id, group_id, group_id)
# 生成 JSON schema 以指导 LLM 输出正确格式
json_schema = ProblemExtensionResponse.model_json_schema()
system_prompt = await problem_service.template_service.render_template(
template_name='problem_breakdown_prompt.jinja2',
operation_name='split_the_problem',
@@ -47,7 +50,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
sentence=content,
json_schema=json_schema
)
try:
# 使用优化的LLM服务
structured = await problem_service.call_llm_structured(
@@ -57,10 +60,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
response_model=ProblemExtensionResponse,
fallback_value=[]
)
# 添加更详细的日志记录
logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
# 验证结构化响应
if not structured or not hasattr(structured, 'root'):
logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
@@ -73,17 +76,17 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
[item.model_dump() for item in structured.root],
ensure_ascii=False
)
split_result_dict = []
for index, item in enumerate(json.loads(split_result)):
split_data = {
"id": f"Q{index+1}",
"id": f"Q{index + 1}",
"question": item['extended_question'],
"type": item['type'],
"reason": item['reason']
}
split_result_dict.append(split_data)
logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
result = {
@@ -96,13 +99,13 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
"original_query": content
}
}
except Exception as e:
logger.error(
f"Split_The_Problem failed: {e}",
exc_info=True
)
# 提供更详细的错误信息
error_details = {
"error_type": type(e).__name__,
@@ -110,9 +113,9 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
"content_length": len(content),
"llm_model_id": memory_config.llm_model_id if memory_config else None
}
logger.error(f"Split_The_Problem error details: {error_details}")
# 创建默认的空结果
result = {
"context": json.dumps([], ensure_ascii=False),
@@ -126,10 +129,11 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
"error": error_details
}
}
# 返回更新后的状态，包含spit_context字段
return {"spit_data": result}
async def Problem_Extension(state: ReadState) -> ReadState:
"""问题扩展节点"""
# 获取原始数据和分解结果
@@ -153,10 +157,10 @@ async def Problem_Extension(state: ReadState) -> ReadState:
data = []
history = await SessionService(store).get_history(group_id, group_id, group_id)
# 生成 JSON schema 以指导 LLM 输出正确格式
json_schema = ProblemExtensionResponse.model_json_schema()
system_prompt = await problem_service.template_service.render_template(
template_name='Problem_Extension_prompt.jinja2',
operation_name='problem_extension',
@@ -242,7 +246,4 @@ async def Problem_Extension(state: ReadState) -> ReadState:
}
}
return {"problem_extension": result}
return {"problem_extension": result}

1
api/app/core/memory/agent/langgraph_graph/read_graph.py
View File

@@ -59,7 +59,6 @@ async def make_read_graph():
workflow.add_conditional_edges("Retrieve", Retrieve_continue)
workflow.add_edge("Retrieve_Summary", END)
workflow.add_conditional_edges("Verify", Verify_continue)
workflow.add_edge("Summary_fails", END)
workflow.add_edge("Summary", END)

4
api/app/core/memory/agent/services/optimized_llm_service.py
View File

@@ -162,7 +162,7 @@ class OptimizedLLMService:
return fallback_value
elif isinstance(fallback_value, dict):
return response_model(**fallback_value)
# 尝试创建空的响应模型
if hasattr(response_model, 'root'):
# RootModel类型
@@ -170,7 +170,7 @@ class OptimizedLLMService:
else:
# 普通BaseModel类型
return response_model()
except Exception as e:
logger.error(f"创建降级响应失败: {e}")
# 最后的降级策略

62
api/app/services/memory_agent_service.py
View File

@@ -683,7 +683,67 @@ class MemoryAgentService:
logger.debug(f"Message type: {status}")
return status
# ==================== 新增的三个接口方法 ====================
async def generate_summary_from_retrieve(
self,
retrieve_info: str,
history: List[Dict],
query: str,
config_id: str,
db: Session
) -> str:
"""
基于检索信息、历史对话和查询生成最终答案
使用 Retrieve_Summary_prompt.jinja2 模板调用大模型生成答案
Args:
retrieve_info: 检索到的信息
history: 历史对话记录
query: 用户查询
config_id: 配置ID
db: 数据库会话
Returns:
生成的答案文本
"""
logger.info(f"Generating summary from retrieve info for query: {query[:50]}...")
try:
# 加载配置
config_service = MemoryConfigService(db)
memory_config = config_service.load_memory_config(
config_id=config_id,
service_name="MemoryAgentService"
)
# 导入必要的模块
from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import summary_llm
from app.core.memory.agent.models.summary_models import RetrieveSummaryResponse
# 构建状态对象
state = {
"data": query,
"memory_config": memory_config
}
# 直接调用 summary_llm 函数
answer = await summary_llm(
state=state,
history=history,
retrieve_info=retrieve_info,
template_name='Retrieve_Summary_prompt.jinja2',
operation_name='retrieve_summary',
response_model=RetrieveSummaryResponse,
search_mode="1"
)
logger.info(f"Successfully generated summary: {answer[:100] if answer else 'None'}...")
return answer if answer else "信息不足，无法回答。"
except Exception as e:
logger.error(f"生成摘要失败: {str(e)}", exc_info=True)
return "信息不足，无法回答。"
async def get_knowledge_type_stats(
self,