Update agent.py
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@@ -1,59 +1,69 @@
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from langgraph.graph import StateGraph
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from typing import TypedDict, List, Any
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from langgraph import create_agent
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from langgraph.checkpoint.memory import MemorySaver
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from langchain_openai import ChatOpenAI
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from rich.console import Console
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# Define state schema
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class AgentState(TypedDict):
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conversation_history: List[dict]
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last_tool_call_confirmed: bool | None
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from langgraph.tools import tool
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import asyncio
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console = Console()
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# Memory node: just returns the existing history
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async def memory_node(state: AgentState) -> dict:
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return {"memory": state.get("conversation_history", [])}
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@tool
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def get_price(params: dict) -> str:
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"""Return a mock price for a city and date."""
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city = params.get("city", "unknown")
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date = params.get("date", "unknown")
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return f"Цена в {city} на {date}: 1000₽"
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# Confirm tool call node
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async def confirm_tool_node(state: AgentState, tool_call: Any) -> dict:
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llm = ChatOpenAI(
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base_url="http://localhost:11434/v1",
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api_key="ollama",
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model="llama3",
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)
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memory = MemorySaver()
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agent = create_agent(
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model=llm,
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tools=[get_price],
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system_prompt="You are a helpful assistant.",
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checkpointer=memory,
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interrupt_before=["tools"],
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)
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config = {"configurable": {"thread_id": "conversation-1"}}
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async def ask_and_run(user_input, config):
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try:
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console.print(f"[bold cyan]Tool call:[/bold cyan] {tool_call}")
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confirmation = input("Confirm execution? (y/n): ")
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confirmed = confirmation.lower().startswith("y")
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state["last_tool_call_confirmed"] = confirmed
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async for chunk in agent.stream(user_input, config=config, stream_mode=["messages", "updates"]):
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chunk_type, chunk_data = chunk
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if chunk_type == "messages":
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console.print(chunk_data, end="")
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elif chunk_type == "updates":
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if "__interrupt__" in chunk_data and agent.get_state(config).next == ("tools",):
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state = agent.get_state(config)
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tool_calls = state.values.get("messages", [])[-1].tool_calls
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if tool_calls:
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last_call = tool_calls[0]
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console.print(f"\n\nTool call detected: {last_call['name']}{last_call['args']}")
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answer = input("Разрешить? (Y/n): ")
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if answer.lower().strip() == "y":
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await ask_and_run(None, config)
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else:
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console.print("Отменено")
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break
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except Exception as e:
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console.print(f"[red]Error during confirmation:[/red] {e}")
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state["last_tool_call_confirmed"] = False
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return {"confirmed_tool_call": state["last_tool_call_confirmed"]}
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console.print(f"[red]Ошибка во время выполнения:[/red] {e}")
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raise
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# Agent node: placeholder for actual LangChain agent logic
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async def agent_node(state: AgentState, memory: Any, confirmed_tool_call: Any) -> dict:
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# In a real implementation we would invoke the agent here.
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# For demonstration, echo back the conversation history and confirmation flag.
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response = {
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"memory": memory,
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"confirmed": confirmed_tool_call,
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"message": "Agent executed with memory and confirmation."
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}
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return {"response": response}
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# Build graph following execution flow: MemoryNode -> AgentNode -> ConfirmToolNode -> AgentNode
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builder = StateGraph(AgentState)
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builder.add_node("MemoryNode", memory_node)
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builder.add_node("AgentNode", agent_node)
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builder.add_node("ConfirmToolNode", confirm_tool_node)
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# Define transitions according to plan execution flow
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builder.set_entry_point("MemoryNode")
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builder.add_edge("MemoryNode", "AgentNode")
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builder.add_edge("AgentNode", "ConfirmToolNode")
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builder.add_edge("ConfirmToolNode", "AgentNode")
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builder.set_finish_node("AgentNode")
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# Compile graph
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graph = builder.compile()
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def main():
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console.print("Добро пожаловать! Введите 'exit' для выхода.")
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while True:
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user_input = input("\nВы: ")
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if user_input.lower() == "exit":
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break
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msg = {"messages": [{"role": "human", "content": user_input}]}
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asyncio.run(ask_and_run(msg, config))
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console.print("\n---")
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if __name__ == "__main__":
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# Example initial state
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init_state: AgentState = {"conversation_history": [], "last_tool_call_confirmed": None}
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result = graph.invoke(init_state)
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console.print(result)
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main()
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