快速检索，需要在接口部分添加LLM整合

api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py

@@ -1,3 +1,4 @@
+import os
 import json
 import time
 from app.core.logging_config import get_agent_logger
@@ -13,20 +14,23 @@ from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
 from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
 
-template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
+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
@@ -35,13 +39,18 @@ 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',
         history=history,
-        sentence=content
+        sentence=content,
+        json_schema=json_schema
     )
-    
+
     try:
         # 使用优化的LLM服务
         structured = await problem_service.call_llm_structured(
@@ -51,10 +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")
@@ -67,17 +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 = {
@@ -90,13 +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__,
@@ -104,9 +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),
@@ -120,10 +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
@@ -147,11 +157,16 @@ 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',
         history=history,
-        questions=databasets
+        questions=databasets,
+        json_schema=json_schema
     )
 
     try:
@@ -231,7 +246,4 @@ async def Problem_Extension(state: ReadState) -> ReadState:
         }
     }
 
-    return {"problem_extension": result}
-
-
-
+    return {"problem_extension": result}