feat：LLM 分析文件并生成结构化建议

app/processor/nodes/qa_node.py

@@ -10,11 +10,10 @@ from langchain_core.messages import AIMessageChunk, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
 from langgraph.config import get_stream_writer
 
-from app.agent.data_tools import DataContext
 from app.agent.qa_agent import QAContext, get_qa_agent
 from app.processor.base import BaseNode
 from app.processor.history import history_messages
-from app.processor.state import QueryState
+from app.processor.state import QueryState, run_data
 from app.processor.summary import data_file_summary
 
 # 模型在一次回答里分几次输出文字（例如查询前后各说一段）时，段落之间空一行
@@ -56,7 +55,7 @@ class QANode(BaseNode):
                         HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
                     ]
                 },
-                context=QAContext(data=DataContext(tuple(context.data_files))),
+                context=QAContext(data=run_data()),
                 stream_mode="messages",
             ):
                 # 工具结果、工具调用和中间件插入的消息都不推送

app/processor/nodes/screen_agent_node.py

@@ -7,12 +7,11 @@ import json
 
 from langchain_core.messages import HumanMessage
 
-from app.agent.data_tools import DataContext
 from app.agent.screen_agent import AgentReply, get_screen_agent
 from app.agent.tools import ScreenAgentContext
 from app.processor.base import BaseNode
 from app.processor.history import history_messages
-from app.processor.state import QueryState
+from app.processor.state import QueryState, run_data
 from app.processor.summary import request_summary
 from app.resources.component import get_catalog_cache
 
@@ -68,7 +67,7 @@ class ScreenAgentNode(BaseNode):
             context=ScreenAgentContext(
                 screen=context.screen,
                 catalog=catalog,
-                data=DataContext(tuple(context.data_files)),
+                data=run_data(),
             ),
         )
         if result.get("structured_response") is None: