# 确认 Python 版本（3.10 及以上）
python3 --version

# 创建虚拟环境
python3 -m venv .venv

# 激活虚拟环境（Linux / macOS）
source .venv/bin/activate

# Windows PowerShell 激活方式
# .venv\Scripts\Activate.ps1

# 安装 LangGraph 与 LangChain 基础依赖
pip install -U langgraph langchain-core langchain-openai

# 如需接入 Ollama 本地模型，再安装适配器
pip install -U langchain-ollama

# 验证安装成功
python -c "import langgraph; print(langgraph.__version__)"

# OpenAI 兼容接口（按所用服务商替换 base_url 与 key）
export OPENAI_API_KEY="sk-你的密钥"

# 若使用其他 OpenAI 兼容服务商，可同时指定接口地址
export OPENAI_BASE_URL="https://你的兼容接口地址/v1"

# 启动 Ollama 服务并拉取一个中文能力较好的模型
ollama serve
ollama pull qwen2.5:7b

git clone https://github.com/langchain-ai/langgraph.git
cd langgraph
pip install -e .

# 依赖：pip install -U langgraph langchain-core langchain-openai
from typing import Annotated
from typing_extensions import TypedDict
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

# 定义共享状态：messages 字段由 add_messages 自动归并新消息
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]

# 准备模型与示例工具（一个简单计算器）
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

def calculator(expression: str) -> str:
    """计算传入的数学表达式，例如 128 * 36"""
    try:
        return str(eval(expression, {"__builtins__": {}}, {}))
    except Exception as exc:
        return f"计算失败: {exc}"

tools = [calculator]
llm_with_tools = llm.bind_tools(tools)

# 定义节点：调用模型生成下一步动作
def call_model(state: AgentState):
    response = llm_with_tools.invoke(state["messages"])
    return {"messages": [response]}

# 构图：注册节点、连接起点、配置条件分支与回边
builder = StateGraph(AgentState)
builder.add_node("agent", call_model)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "agent")
builder.add_conditional_edges(
    "agent",
    tools_condition,
    {"tools": "tools", END: END},
)
builder.add_edge("tools", "agent")

# 编译图并发起一次调用
graph = builder.compile()
result = graph.invoke(
    {"messages": [HumanMessage(content="帮我算一下 128 × 36 等于多少")]}
)
for message in result["messages"]:
    message.pretty_print()

# 依赖：pip install -U langchain-ollama，且本机已运行 ollama serve
from langchain_ollama import ChatOllama

llm = ChatOllama(model="qwen2.5:7b", temperature=0)
llm_with_tools = llm.bind_tools(tools)

from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

# 第一次对话，绑定线程标识
graph.invoke(
    {"messages": [HumanMessage(content="我叫周若琳")]},
    config={"configurable": {"thread_id": "user-001"}},
)

# 同线程再次调用，模型可以读到之前的自我介绍
graph.invoke(
    {"messages": [HumanMessage(content="我刚才说我叫什么？")]},
    config={"configurable": {"thread_id": "user-001"}},
)

for chunk in graph.stream(
    {"messages": [HumanMessage(content="用三句话介绍什么是向量数据库")]},
    config={"configurable": {"thread_id": "user-002"}},
    stream_mode="messages",
):
    print(chunk[0].content, end="", flush=True)