Rebuild Langgraph to new flow with always retrieving information from vector store

app.py

@@ -5,10 +5,12 @@ from langchain.chat_models import init_chat_model
 from langchain_core.tools import tool
 from langchain_aws import BedrockEmbeddings
 from langchain_qdrant import QdrantVectorStore
+from langchain.schema import AIMessage, HumanMessage
+from langchain_core.messages.tool import ToolCall
+
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import MessagesState, StateGraph, END
 from langgraph.prebuilt import ToolNode, tools_condition
-from langgraph.prebuilt import ToolNode
 
 import structlog
 
@@ -107,14 +109,17 @@ def trigger_ai_message_with_tool_call(state: MessagesState) -> AIMessage:
 
 
 graph_builder = StateGraph(MessagesState)
-tools = ToolNode([retrieve])
 memory = MemorySaver()
 
-graph_builder.add_node(tools)
-graph_builder.add_node(generate)
+tool_node = ToolNode([retrieve])
 
+graph_builder.add_node(trigger_ai_message_with_tool_call)
+graph_builder.add_node("tool_node", tool_node)
+graph_builder.add_node(generate)
 
-graph_builder.add_edge("tools", "generate")
+graph_builder.set_entry_point("trigger_ai_message_with_tool_call")
+graph_builder.add_edge("trigger_ai_message_with_tool_call", "tool_node")
+graph_builder.add_edge("tool_node", "generate")
 graph_builder.add_edge("generate", END)
 
 graph = graph_builder.compile(checkpointer=memory)