Fix/memory mcp2 1 (#170)
* 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>
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lixinyue11
@@ -18,16 +18,19 @@ template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
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db_session = next(get_db())
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logger = get_agent_logger(__name__)
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class ProblemNodeService(LLMServiceMixin):
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"""问题处理节点服务类"""
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def __init__(self):
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super().__init__()
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self.template_service = TemplateService(template_root)
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# 创建全局服务实例
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problem_service = ProblemNodeService()
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async def Split_The_Problem(state: ReadState) -> ReadState:
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"""问题分解节点"""
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# 从状态中获取数据
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@@ -36,10 +39,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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memory_config = state.get('memory_config', None)
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history = await SessionService(store).get_history(group_id, group_id, group_id)
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# 生成 JSON schema 以指导 LLM 输出正确格式
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json_schema = ProblemExtensionResponse.model_json_schema()
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system_prompt = await problem_service.template_service.render_template(
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template_name='problem_breakdown_prompt.jinja2',
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operation_name='split_the_problem',
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@@ -47,7 +50,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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sentence=content,
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json_schema=json_schema
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)
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try:
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# 使用优化的LLM服务
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structured = await problem_service.call_llm_structured(
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@@ -57,10 +60,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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response_model=ProblemExtensionResponse,
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fallback_value=[]
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)
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# 添加更详细的日志记录
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logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
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# 验证结构化响应
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if not structured or not hasattr(structured, 'root'):
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logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
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@@ -73,17 +76,17 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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[item.model_dump() for item in structured.root],
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ensure_ascii=False
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)
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split_result_dict = []
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for index, item in enumerate(json.loads(split_result)):
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split_data = {
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"id": f"Q{index+1}",
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"id": f"Q{index + 1}",
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"question": item['extended_question'],
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"type": item['type'],
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"reason": item['reason']
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}
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split_result_dict.append(split_data)
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logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
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result = {
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@@ -96,13 +99,13 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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"original_query": content
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}
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}
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except Exception as e:
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logger.error(
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f"Split_The_Problem failed: {e}",
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exc_info=True
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)
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# 提供更详细的错误信息
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error_details = {
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"error_type": type(e).__name__,
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@@ -110,9 +113,9 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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"content_length": len(content),
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"llm_model_id": memory_config.llm_model_id if memory_config else None
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}
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logger.error(f"Split_The_Problem error details: {error_details}")
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# 创建默认的空结果
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result = {
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"context": json.dumps([], ensure_ascii=False),
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@@ -126,10 +129,11 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
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"error": error_details
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}
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}
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# 返回更新后的状态，包含spit_context字段
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return {"spit_data": result}
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async def Problem_Extension(state: ReadState) -> ReadState:
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"""问题扩展节点"""
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# 获取原始数据和分解结果
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@@ -153,10 +157,10 @@ async def Problem_Extension(state: ReadState) -> ReadState:
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data = []
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history = await SessionService(store).get_history(group_id, group_id, group_id)
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# 生成 JSON schema 以指导 LLM 输出正确格式
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json_schema = ProblemExtensionResponse.model_json_schema()
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system_prompt = await problem_service.template_service.render_template(
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template_name='Problem_Extension_prompt.jinja2',
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operation_name='problem_extension',
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@@ -242,7 +246,4 @@ async def Problem_Extension(state: ReadState) -> ReadState:
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}
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}
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return {"problem_extension": result}
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return {"problem_extension": result}
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@@ -59,7 +59,6 @@ async def make_read_graph():
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workflow.add_conditional_edges("Retrieve", Retrieve_continue)
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workflow.add_edge("Retrieve_Summary", END)
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workflow.add_conditional_edges("Verify", Verify_continue)
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workflow.add_edge("Summary_fails", END)
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workflow.add_edge("Summary", END)
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@@ -162,7 +162,7 @@ class OptimizedLLMService:
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return fallback_value
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elif isinstance(fallback_value, dict):
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return response_model(**fallback_value)
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# 尝试创建空的响应模型
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if hasattr(response_model, 'root'):
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# RootModel类型
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@@ -170,7 +170,7 @@ class OptimizedLLMService:
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else:
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# 普通BaseModel类型
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return response_model()
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except Exception as e:
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logger.error(f"创建降级响应失败: {e}")
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# 最后的降级策略
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