什麼是 LangGraph？

LangGraph是專(zhuān)為L(zhǎng)LM應(yīng)用程式設(shè)計(jì)的工作流程編排框架。其核心原則為：

• 將複雜任務(wù)分解為狀態(tài)和轉(zhuǎn)換
• 管理狀態(tài)轉(zhuǎn)換邏輯
• 任務(wù)執(zhí)行過(guò)程中各種異常的處理

想想購(gòu)物：瀏覽→加入購(gòu)物車(chē)→結(jié)帳→??付款。 LangGraph 幫助我們有效地管理此類(lèi)工作流程。

核心概念

1. 國(guó)家

狀態(tài)就像任務(wù)執(zhí)行中的檢查點(diǎn)：

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. 狀態(tài)轉(zhuǎn)換

狀態(tài)轉(zhuǎn)換定義任務(wù)流程的「路線圖」：

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. 狀態(tài)持久化

為了確保系統(tǒng)的可靠性，我們需要持久化狀態(tài)資訊：

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. 錯(cuò)誤恢復(fù)機(jī)制

任何步驟都可能失敗，我們需要優(yōu)雅地處理這些情況：

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)

現(xiàn)實(shí)範(fàn)例：智慧客戶服務(wù)系統(tǒng)

讓我們來(lái)看一個(gè)實(shí)際的例子－智慧客服系統(tǒng)：

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
        )

最佳實(shí)踐

1. 陳述設(shè)計(jì)原則

   • 保持狀態(tài)簡(jiǎn)單明了
   • 僅儲(chǔ)存必要的資訊
   • 考慮序列化要求

2. 轉(zhuǎn)換邏輯最佳化

   • 使用條件轉(zhuǎn)換
   • 避免無(wú)限循環(huán)
   • 設(shè)定最大步數(shù)限制

3. 錯(cuò)誤處理策略

   • 實(shí)作優(yōu)雅降級(jí)
   • 記錄詳細(xì)資料
   • 提供回滾機(jī)制

4. 效能最佳化

   • 使用非同步操作
   • 實(shí)作狀態(tài)快取
   • 控制狀態(tài)大小

常見(jiàn)陷阱和解決方案

1. 態(tài)爆炸

   • 問(wèn)題：狀態(tài)太多導(dǎo)致維護(hù)困難
   • 解決方案：合併相似的狀態(tài)，使用狀態(tài)組合而不是創(chuàng)建新的

2. 死鎖情況

   • 問(wèn)題：循環(huán)狀態(tài)轉(zhuǎn)換導(dǎo)致任務(wù)掛起
   • 解決方案：新增超時(shí)機(jī)制和強(qiáng)制退出條件

3. 狀態(tài)一致性

   • 問(wèn)題：分散式環(huán)境狀態(tài)不一致
   • 解決方案：使用分散式鎖定和事務(wù)機(jī)制

概括

LangGraph 狀態(tài)機(jī)為管理複雜的 AI Agent 任務(wù)流程提供了強(qiáng)大的解決方案：

• 清晰的任務(wù)流程管理
• 可靠的狀態(tài)持久性
• 全面的錯(cuò)誤處理
• 靈活的擴(kuò)充性

以上是LangGraph 狀態(tài)機(jī)：管理生產(chǎn)中的複雜代理任務(wù)流的詳細(xì)內(nèi)容。更多資訊請(qǐng)關(guān)注PHP中文網(wǎng)其他相關(guān)文章！