当前位置：首页 > 文章列表 > 文章 > python教程 > LangGraph 状态机：管理生产中的复杂代理任务流

LangGraph 状态机：管理生产中的复杂代理任务流

来源：dev.to 2024-11-20 09:32:11 0浏览收藏

推广推荐

支持 PC / 移动端，安全直达

本篇文章给大家分享《LangGraph 状态机：管理生产中的复杂代理任务流》，覆盖了文章的常见基础知识，其实一个语言的全部知识点一篇文章是不可能说完的，但希望通过这些问题，让读者对自己的掌握程度有一定的认识(B 数)，从而弥补自己的不足，更好的掌握它。

什么是 langgraph？

langgraph是专为llm应用程序设计的工作流编排框架。其核心原则是：

将复杂任务分解为状态和转换
管理状态转换逻辑
任务执行过程中各种异常的处理

想想购物：浏览→添加到购物车→结账→付款。 langgraph 帮助我们有效地管理此类工作流程。

核心概念

1. 国家

状态就像任务执行中的检查点：

from typing import typeddict, list

class shoppingstate(typeddict):
    # current state
    current_step: str
    # cart items
    cart_items: list[str]
    # total amount
    total_amount: float
    # user input
    user_input: str

class shoppinggraph(stategraph):
    def __init__(self):
        super().__init__()

        # define states
        self.add_node("browse", self.browse_products)
        self.add_node("add_to_cart", self.add_to_cart)
        self.add_node("checkout", self.checkout)
        self.add_node("payment", self.payment)

2. 状态转换

状态转换定义任务流的“路线图”：

class shoppingcontroller:
    def define_transitions(self):
        # add transition rules
        self.graph.add_edge("browse", "add_to_cart")
        self.graph.add_edge("add_to_cart", "browse")
        self.graph.add_edge("add_to_cart", "checkout")
        self.graph.add_edge("checkout", "payment")

    def should_move_to_cart(self, state: shoppingstate) -> bool:
        """determine if we should transition to cart state"""
        return "add to cart" in state["user_input"].lower()

3. 状态持久化

为了保证系统的可靠性，我们需要持久化状态信息：

class statemanager:
    def __init__(self):
        self.redis_client = redis.redis()

    def save_state(self, session_id: str, state: dict):
        """save state to redis"""
        self.redis_client.set(
            f"shopping_state:{session_id}",
            json.dumps(state),
            ex=3600  # 1 hour expiration
        )

    def load_state(self, session_id: str) -> dict:
        """load state from redis"""
        state_data = self.redis_client.get(f"shopping_state:{session_id}")
        return json.loads(state_data) if state_data else none

4. 错误恢复机制

任何步骤都可能失败，我们需要优雅地处理这些情况：

class errorhandler:
    def __init__(self):
        self.max_retries = 3

    async def with_retry(self, func, state: dict):
        """function execution with retry mechanism"""
        retries = 0
        while retries < self.max_retries:
            try:
                return await func(state)
            except exception as e:
                retries += 1
                if retries == self.max_retries:
                    return self.handle_final_error(e, state)
                await self.handle_retry(e, state, retries)

    def handle_final_error(self, error, state: dict):
        """handle final error"""
        # save error state
        state["error"] = str(error)
        # rollback to last stable state
        return self.rollback_to_last_stable_state(state)

现实示例：智能客户服务系统

让我们看一个实际的例子——智能客服系统：

from langgraph.graph import stategraph, state

class customerservicestate(typeddict):
    conversation_history: list[str]
    current_intent: str
    user_info: dict
    resolved: bool

class customerservicegraph(stategraph):
    def __init__(self):
        super().__init__()

        # initialize states
        self.add_node("greeting", self.greet_customer)
        self.add_node("understand_intent", self.analyze_intent)
        self.add_node("handle_query", self.process_query)
        self.add_node("confirm_resolution", self.check_resolution)

    async def greet_customer(self, state: state):
        """greet customer"""
        response = await self.llm.generate(
            prompt=f"""
            conversation history: {state['conversation_history']}
            task: generate appropriate greeting
            requirements:
            1. maintain professional friendliness
            2. acknowledge returning customers
            3. ask how to help
            """
        )
        state['conversation_history'].append(f"assistant: {response}")
        return state

    async def analyze_intent(self, state: state):
        """understand user intent"""
        response = await self.llm.generate(
            prompt=f"""
            conversation history: {state['conversation_history']}
            task: analyze user intent
            output format:
            {{
                "intent": "refund/inquiry/complaint/other",
                "confidence": 0.95,
                "details": "specific description"
            }}
            """
        )
        state['current_intent'] = json.loads(response)
        return state

用法

# Initialize system
graph = CustomerServiceGraph()
state_manager = StateManager()
error_handler = ErrorHandler()

async def handle_customer_query(user_id: str, message: str):
    # Load or create state
    state = state_manager.load_state(user_id) or {
        "conversation_history": [],
        "current_intent": None,
        "user_info": {},
        "resolved": False
    }

    # Add user message
    state["conversation_history"].append(f"User: {message}")

    # Execute state machine flow
    try:
        result = await graph.run(state)
        # Save state
        state_manager.save_state(user_id, result)
        return result["conversation_history"][-1]
    except Exception as e:
        return await error_handler.with_retry(
            graph.run,
            state
        )