Bỏ router, để orchestrator quyết định khi nào trả lời directly

graph.py

@@ -1,42 +1,33 @@
 from langgraph.graph import END, START, StateGraph
 
 from src.nodes import (
+    direct_response_node,
     final_response_node,
-    llm_response_node,
     orchestrator_node,
-    route_decision,
-    router_node,
     should_call_tool,
     tool_node,
 )
-from src.state import MAX_ITERS, AgentState
+from src.state import AgentState
 
 
 def build_graph():
     builder = StateGraph(AgentState)
 
-    builder.add_node("router",         router_node)
-    builder.add_node("llm_response",   llm_response_node)
-    builder.add_node("orchestrator",   orchestrator_node)
-    builder.add_node("tool_node",      tool_node)
-    builder.add_node("final_response", final_response_node)
+    builder.add_node("orchestrator",    orchestrator_node)
+    builder.add_node("tool_node",       tool_node)
+    builder.add_node("final_response",  final_response_node)
+    builder.add_node("direct_response", direct_response_node)
 
-    builder.add_edge(START, "router")
-
-    builder.add_conditional_edges(
-        "router", route_decision,
-        {"respond": "llm_response", "orchestrator": "orchestrator"},
-    )
-
-    builder.add_edge("llm_response", END)
+    builder.add_edge(START, "orchestrator")
 
     builder.add_conditional_edges(
         "orchestrator", should_call_tool,
-        {"tool_node": "tool_node", "final_response": "final_response"},
+        {"tool_node": "tool_node", "direct_response": "direct_response"},
     )
 
-    builder.add_edge("tool_node",      "final_response")
-    builder.add_edge("final_response", END)
+    builder.add_edge("tool_node",       "final_response")
+    builder.add_edge("final_response",  END)
+    builder.add_edge("direct_response", END)
 
     return builder.compile()
 
@@ -61,7 +52,6 @@ def print_result(result: AgentState, label: str = "") -> None:
     SEP = "═" * 60
     if label:
         print(f"\n{SEP}\n  {label}\n{SEP}")
-    print(f"  📌 query_type  : {result.get('query_type')}")
     print(f"  📌 iters       : {result.get('iters')} / {result.get('max_iters')}")
     print(f"  📌 # messages  : {len(result.get('messages', []))}")
     print(f"{'─' * 60}")

nodes.py

@@ -7,12 +7,15 @@ from typing import Any, Literal
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
 from src.llm import llm, multimodal_llm
+from src.pdf_processing import format_chat_history
 from src.prompts import (
+    direct_response_prompt,
     final_response_prompt,
     orchestrator_prompt,
     respond_prompt,
     router_prompt,
 )
+from src.redis_client import redis_client
 from src.state import MAX_ITERS, AgentState, QueryType
 from src.tools.chat_tools import TOOL_MAP, TOOLS
 
@@ -114,16 +117,41 @@ def orchestrator_node(state: AgentState) -> AgentState:
     return {**state, "messages": [response], "iters": iters + 1}
 
 
-def should_call_tool(state: AgentState) -> Literal["tool_node", "final_response"]:
-    """Conditional edge: orchestrator → tool_node | final_response."""
+def should_call_tool(state: AgentState) -> Literal["tool_node", "direct_response"]:
+    """Conditional edge: orchestrator → tool_node | direct_response."""
     last_msg = state["messages"][-1] if state["messages"] else None
     if isinstance(last_msg, AIMessage) and last_msg.tool_calls:
         return "tool_node"
-    return "final_response"
+    return "direct_response"
 
 
 # ════════════════════════════════════════════════════════════════════
-# NODE 4 — ToolNode (custom)
+# NODE 4 — DirectResponseNode
+# ════════════════════════════════════════════════════════════════════
+def direct_response_node(state: AgentState) -> AgentState:
+    """Trả lời trực tiếp bằng LLM, kèm lịch sử chat từ Redis làm context."""
+    logger.info("[DirectResponseNode] Trả lời trực tiếp cho %s", state["sender_id"])
+
+    raw_history = redis_client.get_chat_history(state["conversation_id"])
+    chat_history = format_chat_history(raw_history) if raw_history else "(Chưa có lịch sử trò chuyện)"
+
+    msgs = direct_response_prompt.format_messages(
+        sender_id=state["sender_id"],
+        raw_query=state["raw_query"],
+        chat_history=chat_history,
+    )
+    if cp := state.get("custom_prompt"):
+        msgs[0] = SystemMessage(content=msgs[0].content + f"\n\n═══ YÊU CẦU BỔ SUNG ═══\n{cp}")
+
+    response = llm.invoke(msgs)
+    answer   = response.content
+
+    logger.info("[DirectResponseNode] Hoàn thành (%d ký tự)", len(answer))
+    return {**state, "messages": [AIMessage(content=answer)], "final_answer": answer}
+
+
+# ════════════════════════════════════════════════════════════════════
+# NODE 5 — ToolNode (custom)
 # ════════════════════════════════════════════════════════════════════
 def _run_tool(tool_name: str, tool_args: dict[str, Any]) -> str:
     fn = TOOL_MAP.get(tool_name)