Add multiple LangGraph examples including basic agents, chatbots, conditional edges, checkpointers, tool calls, and human-in-the-loop approval processes. Update .gitignore to include local environment files.
This commit is contained in:
Eric Wang
3
.gitignore
vendored
3
.gitignore
vendored
@@ -16,6 +16,9 @@ __pycache__/
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venv/
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env/
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# Local uv environment/version files
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.python-version
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# IDE and OS files
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.vscode/
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.idea/
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38
01_basic_agent_no_llm.py
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38
01_basic_agent_no_llm.py
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@@ -0,0 +1,38 @@
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from typing import TypedDict
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from langgraph.graph import StateGraph, START, END
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class State(TypedDict):
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text: str
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length: int
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def normalize_text(state: State):
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"""清理文本。"""
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normalized = state["text"].strip().lower()
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return {"text": normalized}
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def calculate_length(state: State):
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"""计算文本长度。"""
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return {"length": len(state["text"])}
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builder = StateGraph(State)
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builder.add_node("normalize_text", normalize_text)
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builder.add_node("calculate_length", calculate_length)
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builder.add_edge(START, "normalize_text")
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builder.add_edge("normalize_text", "calculate_length")
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builder.add_edge("calculate_length", END)
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graph = builder.compile()
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result = graph.invoke({
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"text": " Hello LangGraph! ",
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"length": 0,
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})
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print(result)
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41
02_chat_graph.py
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41
02_chat_graph.py
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@@ -0,0 +1,41 @@
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from typing import Annotated, TypedDict
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from langchain_openai import ChatOpenAI
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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model = ChatOpenAI(
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model="deepseek-v4-flash",
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temperature=0,
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base_url="https://api.deepseek.com/v1",
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)
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def chatbot(state: State):
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response = model.invoke(state["messages"])
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return {"messages": [response]}
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builder = StateGraph(State)
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builder.add_node("chatbot", chatbot)
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builder.add_edge(START, "chatbot")
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builder.add_edge("chatbot", END)
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graph = builder.compile()
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result = graph.invoke({
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"messages": [
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{
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"role": "user",
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"content": "请用一句话解释 LangGraph。",
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}
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]
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})
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print(result["messages"][-1].content)
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54
03_conditional_edge.py
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54
03_conditional_edge.py
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@@ -0,0 +1,54 @@
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from typing import Literal, TypedDict
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from langgraph.graph import StateGraph, START, END
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class State(TypedDict):
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score: int
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result: str
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def evaluate(state: State):
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return {}
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def success(state: State):
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return {"result": "通过"}
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def retry(state: State):
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return {"result": "需要重试"}
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def route_after_evaluate(
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state: State,
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) -> Literal["success", "retry"]:
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if state["score"] >= 60:
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return "success"
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return "retry"
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builder = StateGraph(State)
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builder.add_node("evaluate", evaluate)
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builder.add_node("success", success)
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builder.add_node("retry", retry)
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builder.add_edge(START, "evaluate")
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builder.add_conditional_edges(
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"evaluate",
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route_after_evaluate,
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{
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"success": "success",
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"retry": "retry",
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},
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)
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builder.add_edge("success", END)
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builder.add_edge("retry", END)
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graph = builder.compile()
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print(graph.invoke({"score": 80, "result": ""}))
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print(graph.invoke({"score": 40, "result": ""}))
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64
04_checkpointer.py
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64
04_checkpointer.py
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from typing import Annotated, TypedDict
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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model = ChatOpenAI(
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model="deepseek-v4-flash",
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temperature=0,
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base_url="https://api.deepseek.com/v1"
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)
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def chatbot(state: State):
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return {
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"messages": [model.invoke(state["messages"])]
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}
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builder = StateGraph(State)
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builder.add_node("chatbot", chatbot)
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builder.add_edge(START, "chatbot")
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builder.add_edge("chatbot", END)
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checkpointer = MemorySaver()
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graph = builder.compile(checkpointer=checkpointer)
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config = {
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"configurable": {
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"thread_id": "user-001",
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}
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}
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first = graph.invoke(
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{
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"messages": [
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{
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"role": "user",
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"content": "我叫小明，请记住。",
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}
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]
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},
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config=config,
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)
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second = graph.invoke(
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{
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"messages": [
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{
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"role": "user",
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"content": "我叫什么名字？",
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}
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]
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},
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config=config,
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)
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print(second["messages"][-1].content)
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143
05_tool_call.py
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143
05_tool_call.py
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@@ -0,0 +1,143 @@
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"""LangGraph 工具调用示例：天气查询 + 计算器。
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图结构：
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START -> model
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| 有 tool_calls
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v
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tools
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+---------> model
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model -- 无 tool_calls --> END
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天气数据是本地模拟数据，重点是学习 LangGraph 的工具调用循环。
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"""
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import os
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from typing import Literal
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from dotenv import load_dotenv
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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load_dotenv()
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# 1. 定义工具。类型注解和 docstring 会变成提供给模型的工具 schema。
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@tool
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def get_weather(city: str) -> str:
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"""查询指定城市当前的天气。城市名称使用中文，例如北京、上海。"""
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weather_data = {
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"北京": "晴，25°C，北风 2 级",
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"上海": "多云，28°C，东南风 3 级",
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"广州": "雷阵雨，30°C，南风 2 级",
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"深圳": "阴，29°C，东风 2 级",
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}
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return weather_data.get(city, f"暂时没有 {city} 的天气数据")
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@tool
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def calculator(a: float, b: float, operation: Literal["add", "subtract", "multiply", "divide"]) -> float:
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"""对两个数字进行加、减、乘、除运算。
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Args:
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a: 第一个数字。
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b: 第二个数字。
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operation: 运算类型：add、subtract、multiply 或 divide。
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"""
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if operation == "add":
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return a + b
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if operation == "subtract":
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return a - b
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if operation == "multiply":
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return a * b
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if b == 0:
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raise ValueError("除数不能为 0")
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return a / b
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# 2. 将工具绑定给模型。绑定后，模型可以在 AIMessage.tool_calls 中请求工具。
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tools = [get_weather, calculator]
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model = ChatOpenAI(
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model=os.getenv("OPENAI_MODEL", "openai/gpt-5.4"),
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
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temperature=0,
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max_retries=2,
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)
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model_with_tools = model.bind_tools(tools)
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# 3. 模型节点：读取完整消息历史，返回一条新的 AIMessage。
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def call_model(state: MessagesState) -> dict:
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response = model_with_tools.invoke(state["messages"])
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return {"messages": [response]}
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# 4. 路由函数：检查模型是否发起了工具调用。
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def route_after_model(state: MessagesState) -> Literal["tools", "__end__"]:
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last_message = state["messages"][-1]
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if last_message.tool_calls:
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return "tools"
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return END
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# 5. ToolNode 自动完成：找到工具、传入参数、执行工具、生成 ToolMessage。
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tool_node = ToolNode(tools)
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builder = StateGraph(MessagesState)
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builder.add_node("model", call_model)
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builder.add_node("tools", tool_node)
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builder.add_edge(START, "model")
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builder.add_conditional_edges(
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"model",
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route_after_model,
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{
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"tools": "tools",
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END: END,
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},
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)
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builder.add_edge("tools", "model")
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graph = builder.compile()
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def ask(question: str) -> str:
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"""调用助手，并打印每个节点产生的更新。"""
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final_answer = ""
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for event in graph.stream(
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{"messages": [{"role": "user", "content": question}]},
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stream_mode="updates",
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):
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for node_name, update in event.items():
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print(f"\n[{node_name}]")
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for message in update.get("messages", []):
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message.pretty_print()
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if node_name == "model" and not message.tool_calls:
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final_answer = str(message.content)
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return final_answer
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def main() -> None:
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print("=== 天气 + 计算器工具助手 ===")
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print("输入 quit 退出。")
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while True:
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question = input("\n用户：").strip()
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if question.lower() in {"quit", "exit", "q"}:
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break
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if not question:
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continue
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answer = ask(question)
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print(f"\n最终回答：{answer}")
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if __name__ == "__main__":
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main()
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315
06_human_in_the_loop.py
Normal file
315
06_human_in_the_loop.py
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@@ -0,0 +1,315 @@
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"""LangGraph 人工审批示例：普通工具直接执行，敏感工具先暂停审批。
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图结构：
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START -> model
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| 无 tool_calls ------------------------> END
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| 普通工具调用 -> tools ----------------> model
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| 敏感工具调用 -> approval
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| 批准 -> tools ----> model
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| 拒绝 -> model
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运行：
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uv run --with langgraph --with langchain-openai --with python-dotenv \
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python 06_human_in_the_loop.py
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说明：send_email 是模拟工具，不会真正发送邮件。
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"""
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import os
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import uuid
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from typing import Literal
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from dotenv import load_dotenv
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from langchain_core.messages import ToolMessage
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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from langgraph.types import Command, interrupt
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load_dotenv()
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# -----------------------------------------------------------------------------
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# 1. 定义工具
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# -----------------------------------------------------------------------------
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@tool
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def get_weather(city: str) -> str:
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"""查询指定城市当前的天气。城市名称使用中文，例如北京、上海。"""
|
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weather_data = {
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"北京": "晴，25°C，北风 2 级",
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"上海": "多云，28°C，东南风 3 级",
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"广州": "雷阵雨，30°C，南风 2 级",
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"深圳": "阴，29°C，东风 2 级",
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}
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return weather_data.get(city, f"暂时没有 {city} 的天气数据")
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@tool
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def calculator(
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a: float,
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b: float,
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operation: Literal["add", "subtract", "multiply", "divide"],
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) -> float:
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"""对两个数字进行加、减、乘、除运算。
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|
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Args:
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a: 第一个数字。
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b: 第二个数字。
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operation: 运算类型：add、subtract、multiply 或 divide。
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"""
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if operation == "add":
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return a + b
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if operation == "subtract":
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return a - b
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if operation == "multiply":
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return a * b
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if b == 0:
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raise ValueError("除数不能为 0")
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return a / b
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@tool
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def send_email(recipient: str, subject: str, body: str) -> str:
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"""向指定邮箱发送邮件。这是有外部副作用的敏感操作，执行前必须审批。
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Args:
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recipient: 收件人的邮箱地址。
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subject: 邮件主题。
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body: 邮件正文。
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"""
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# 教学示例：这里只模拟发送，不连接真实邮件服务。
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print("\n[模拟发送邮件]")
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print(f"收件人：{recipient}")
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print(f"主题：{subject}")
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print(f"正文：{body}")
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return f"邮件已成功发送给 {recipient}"
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TOOLS = [get_weather, calculator, send_email]
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SENSITIVE_TOOL_NAMES = {"send_email"}
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# -----------------------------------------------------------------------------
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# 2. 创建模型并绑定工具
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# -----------------------------------------------------------------------------
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model = ChatOpenAI(
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model=os.getenv("OPENAI_MODEL", "openai/gpt-5.4"),
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
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temperature=0,
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max_retries=2,
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)
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model_with_tools = model.bind_tools(TOOLS)
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# -----------------------------------------------------------------------------
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# 3. 定义节点与路由
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# -----------------------------------------------------------------------------
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||||
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def call_model(state: MessagesState) -> dict:
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"""调用模型；模型可以直接回答，也可以生成一个或多个 tool_calls。"""
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response = model_with_tools.invoke(state["messages"])
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return {"messages": [response]}
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||||
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def route_after_model(
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state: MessagesState,
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) -> Literal["tools", "approval", "__end__"]:
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"""无工具调用则结束；敏感工具去审批；其余工具直接执行。"""
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tool_calls = state["messages"][-1].tool_calls
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if not tool_calls:
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return END
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if any(call["name"] in SENSITIVE_TOOL_NAMES for call in tool_calls):
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return "approval"
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||||
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||||
return "tools"
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||||
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||||
|
||||
def approval_node(
|
||||
state: MessagesState,
|
||||
) -> Command[Literal["tools", "model"]]:
|
||||
"""暂停图执行，等待用户批准或拒绝当前批次的工具调用。"""
|
||||
tool_calls = state["messages"][-1].tool_calls
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||||
|
||||
# interrupt() 应放在副作用之前。恢复时节点可能从函数开头重新执行。
|
||||
decision = interrupt(
|
||||
{
|
||||
"type": "tool_approval",
|
||||
"message": "模型请求执行敏感工具，是否批准？",
|
||||
"tool_calls": tool_calls,
|
||||
}
|
||||
)
|
||||
|
||||
approved = (
|
||||
bool(decision.get("approved"))
|
||||
if isinstance(decision, dict)
|
||||
else bool(decision)
|
||||
)
|
||||
|
||||
if approved:
|
||||
# ToolNode 将执行这一批工具调用，并生成对应的 ToolMessage。
|
||||
return Command(goto="tools")
|
||||
|
||||
# 每个 tool_call 都必须得到对应的 ToolMessage，否则消息协议不完整，
|
||||
# 部分模型服务会拒绝下一次请求。整个批次在审批拒绝后均不执行。
|
||||
rejection_messages = [
|
||||
ToolMessage(
|
||||
content="用户拒绝了本批次工具调用，工具未执行。请告知用户并询问下一步。",
|
||||
tool_call_id=call["id"],
|
||||
name=call["name"],
|
||||
)
|
||||
for call in tool_calls
|
||||
]
|
||||
return Command(
|
||||
update={"messages": rejection_messages},
|
||||
goto="model",
|
||||
)
|
||||
|
||||
|
||||
# ToolNode 自动查找并执行工具，再把结果包装为 ToolMessage。
|
||||
tool_node = ToolNode(TOOLS)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# 4. 构建图并启用 Checkpointer
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
builder = StateGraph(MessagesState)
|
||||
builder.add_node("model", call_model)
|
||||
builder.add_node("approval", approval_node)
|
||||
builder.add_node("tools", tool_node)
|
||||
|
||||
builder.add_edge(START, "model")
|
||||
builder.add_conditional_edges(
|
||||
"model",
|
||||
route_after_model,
|
||||
{
|
||||
"tools": "tools",
|
||||
"approval": "approval",
|
||||
END: END,
|
||||
},
|
||||
)
|
||||
builder.add_edge("tools", "model")
|
||||
|
||||
# interrupt() 必须配合 checkpointer 使用。InMemorySaver 只适合本地学习，
|
||||
# 进程退出后 checkpoint 会消失。
|
||||
checkpointer = InMemorySaver()
|
||||
graph = builder.compile(checkpointer=checkpointer)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# 5. 命令行交互
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def print_update(event: dict) -> None:
|
||||
"""打印 model/tools 节点产生的消息更新。"""
|
||||
for node_name, update in event.items():
|
||||
if node_name == "__interrupt__" or not isinstance(update, dict):
|
||||
continue
|
||||
|
||||
messages = update.get("messages", [])
|
||||
if not messages:
|
||||
continue
|
||||
|
||||
print(f"\n[{node_name}]")
|
||||
for message in messages:
|
||||
message.pretty_print()
|
||||
|
||||
|
||||
def run_until_pause_or_end(graph_input: dict | Command, config: dict):
|
||||
"""运行图，返回本轮产生的 interrupt；正常结束则返回 None。"""
|
||||
pending_interrupt = None
|
||||
|
||||
for event in graph.stream(
|
||||
graph_input,
|
||||
config=config,
|
||||
stream_mode="updates",
|
||||
):
|
||||
print_update(event)
|
||||
|
||||
interrupts = event.get("__interrupt__", ())
|
||||
if interrupts:
|
||||
pending_interrupt = interrupts[0]
|
||||
|
||||
return pending_interrupt
|
||||
|
||||
|
||||
def ask_for_approval(interrupt_value) -> bool:
|
||||
"""在命令行展示待执行工具，并读取审批结果。"""
|
||||
payload = interrupt_value.value
|
||||
|
||||
print("\n=== 需要人工审批 ===")
|
||||
print(payload.get("message", "是否批准工具调用？"))
|
||||
|
||||
for index, call in enumerate(payload.get("tool_calls", []), start=1):
|
||||
print(f"{index}. 工具：{call['name']}")
|
||||
print(f" 参数：{call['args']}")
|
||||
|
||||
while True:
|
||||
answer = input("批准执行吗？[y/n]：").strip().lower()
|
||||
if answer in {"y", "yes"}:
|
||||
return True
|
||||
if answer in {"n", "no"}:
|
||||
return False
|
||||
print("请输入 y 或 n。")
|
||||
|
||||
|
||||
def run_turn(question: str, config: dict) -> None:
|
||||
"""执行一轮对话；如果暂停，则审批后使用 Command(resume=...) 恢复。"""
|
||||
pending = run_until_pause_or_end(
|
||||
{"messages": [{"role": "user", "content": question}]},
|
||||
config,
|
||||
)
|
||||
|
||||
# 理论上一次模型响应可能导致多次暂停，因此使用循环处理。
|
||||
while pending is not None:
|
||||
approved = ask_for_approval(pending)
|
||||
pending = run_until_pause_or_end(
|
||||
Command(resume={"approved": approved}),
|
||||
config,
|
||||
)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
print("=== LangGraph 工具助手（带人工审批）===")
|
||||
print("天气和计算器直接执行；发送邮件需要人工批准。")
|
||||
print("输入 /new 创建新会话，输入 quit 退出。")
|
||||
|
||||
# 同一 thread_id 的多轮输入共享 checkpoint 和消息历史。
|
||||
thread_id = str(uuid.uuid4())
|
||||
|
||||
while True:
|
||||
question = input(f"\n用户[{thread_id[:8]}]：").strip()
|
||||
|
||||
if question.lower() in {"quit", "exit", "q"}:
|
||||
break
|
||||
if question == "/new":
|
||||
thread_id = str(uuid.uuid4())
|
||||
print(f"已创建新会话：{thread_id}")
|
||||
continue
|
||||
if not question:
|
||||
continue
|
||||
|
||||
config = {
|
||||
"configurable": {"thread_id": thread_id},
|
||||
"recursion_limit": 20,
|
||||
}
|
||||
|
||||
try:
|
||||
run_turn(question, config)
|
||||
except Exception as exc:
|
||||
print(f"\n执行失败：{type(exc).__name__}: {exc}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
51
07_command.py
Normal file
51
07_command.py
Normal file
@@ -0,0 +1,51 @@
|
||||
from typing import Literal, TypedDict
|
||||
|
||||
from langgraph.graph import END, START, StateGraph
|
||||
from langgraph.types import Command
|
||||
|
||||
|
||||
class State(TypedDict):
|
||||
number: int
|
||||
result: str
|
||||
|
||||
|
||||
def check_number(
|
||||
state: State,
|
||||
) -> Command[Literal["positive", "negative"]]:
|
||||
if state["number"] >= 0:
|
||||
return Command(
|
||||
update={"result": "这是非负数"},
|
||||
goto="positive",
|
||||
)
|
||||
|
||||
return Command(
|
||||
update={"result": "这是负数"},
|
||||
goto="negative",
|
||||
)
|
||||
|
||||
|
||||
def positive(state: State):
|
||||
print(state["result"])
|
||||
return {}
|
||||
|
||||
|
||||
def negative(state: State):
|
||||
print(state["result"])
|
||||
return {}
|
||||
|
||||
|
||||
builder = StateGraph(State)
|
||||
builder.add_node("check", check_number)
|
||||
builder.add_node("positive", positive)
|
||||
builder.add_node("negative", negative)
|
||||
|
||||
builder.add_edge(START, "check")
|
||||
builder.add_edge("positive", END)
|
||||
builder.add_edge("negative", END)
|
||||
|
||||
graph = builder.compile()
|
||||
|
||||
graph.invoke({
|
||||
"number": -5,
|
||||
"result": "",
|
||||
})
|
||||
Reference in New Issue
Block a user