Refine knowledge brain workflow

Align the brain prompts, graph view, and startup defaults with the
latest phase 1 flow so local runs and navigation stay consistent.

backend/app/agents/graph.py

@@ -97,13 +97,49 @@ def _filter_user_messages(messages: list) -> list[BaseMessage]:
     return [m for m in messages if _msg_type(m) in ("human", "user")]
 
 
+def _normalize_user_text(text: str) -> str:
+    return (text or "").strip().lower()
+
+
+def _is_simple_greeting(text: str) -> bool:
+    normalized = _normalize_user_text(text)
+    return normalized in {"你好", "您好", "早", "早上好", "在吗", "嗨", "hi", "hello"}
+
+
+def _is_identity_question(text: str) -> bool:
+    normalized = _normalize_user_text(text)
+    return normalized in {"你是谁", "你是誰"}
+
+
+def _is_capability_question(text: str) -> bool:
+    normalized = _normalize_user_text(text)
+    return normalized in {"你能做什么", "你可以做什么", "你会做什么"}
+
+
 # ===================== 节点定义 (async) =====================
 
 async def master_node(state: AgentState) -> AgentState:
     """主Agent节点: 理解用户意图，决定调用哪个子Agent"""
-    llm = _get_llm_for_state(state)
     messages: list[BaseMessage] = state["messages"]
+    user_msgs = _filter_user_messages(messages)
+    user_query = user_msgs[-1].content.strip() if user_msgs else ""
 
+    if _is_simple_greeting(user_query):
+        state["final_response"] = "您好。我在。\n\n您把问题给我，我先帮您收束重点，再往下推。"
+        state["should_respond"] = True
+        return state
+
+    if _is_identity_question(user_query):
+        state["final_response"] = "我是 Jarvis。\n\n比起做一个泛泛的助手，我更像您的判断型协作伙伴：帮您看清问题、压缩路径、把事情往前推进。"
+        state["should_respond"] = True
+        return state
+
+    if _is_capability_question(user_query):
+        state["final_response"] = "主要做三件事。\n- 帮您判断：看问题本质、梳理取舍、给出方向\n- 帮您收束：把复杂内容理顺，把重点拎出来\n- 帮您推进：拆任务、定步骤、把下一步变清楚\n\n如果您现在有具体目标，我可以直接进入处理。"
+        state["should_respond"] = True
+        return state
+
+    llm = _get_llm_for_state(state)
     system_msgs: list[BaseMessage] = [SystemMessage(content=MASTER_SYSTEM_PROMPT)]
 
     # 注入记忆上下文