ai-agent-dev/01_开发环境搭建.ipynb

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   "source": [
    "# 01 开发环境搭建\n",
    "\n",
    "## 学习目标\n",
    "1. 了解课程整体安排与学习目标\n",
    "2. 明确 LangChain 和 LangGraph 在 AI 智能体开发中的定位\n",
    "3. 熟悉本课程使用的技术栈和工具链（VS Code + Python 3.12 + 虚拟环境）\n",
    "4. 学会使用 venv 创建虚拟环境并使用清华镜像源加速 pip 安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 课程概述与技术栈\n",
    "\n",
    "本课程面向具备 Python 基础语法的大学生，系统讲解 **LangChain** 和 **LangGraph** 两大框架，帮助你从零构建 AI 智能体应用。\n",
    "\n",
    "### 核心技术栈\n",
    "- **开发工具**：VS Code（Visual Studio Code）\n",
    "- **编程语言**：Python 3.12.11\n",
    "- **虚拟环境**：venv（Python 内置）\n",
    "- **主要框架**：LangChain、LangGraph\n",
    "- **镜像源**：清华大学 TUNA 镜像（加速 pip 安装）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. VS Code 安装与配置\n",
    "\n",
    "### 步骤 1：下载并安装 VS Code\n",
    "1. 访问官网：https://code.visualstudio.com/\n",
    "2. 下载 Windows 版本安装包\n",
    "3. 运行安装程序，建议勾选：\n",
    "   - **添加到 PATH**（添加到系统环境变量）\n",
    "   - **右键菜单集成**（在资源管理器中打开）\n",
    "\n",
    "### 步骤 2：安装 Python 扩展\n",
    "打开 VS Code，按 `Ctrl+Shift+X` 进入扩展市场，搜索并安装：\n",
    "- **Python**（Microsoft 官方扩展）\n",
    "- **Jupyter**（用于运行 .ipynb 文件）\n",
    "\n",
    "### 步骤 3：选择 Python 解释器\n",
    "按 `Ctrl+Shift+P` 打开命令面板，输入 `Python: Select Interpreter`，选择后续步骤中创建的虚拟环境。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Python 3.12.11 环境准备\n",
    "\n",
    "本课程统一使用 **Python 3.12.11**，确保版本一致可避免兼容性问题。\n",
    "\n",
    "### 检查当前 Python 版本\n",
    "在 VS Code 终端（`Ctrl+ ``）或 PowerShell 中执行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "print(f\"Python 版本：{sys.version}\")\n",
    "print(f\"Python 路径：{sys.executable}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你的系统中没有 Python 3.12.11，请前往 [Python 官网](https://www.python.org/downloads/release/python-31211/) 下载安装，或使用 `pyenv-win` 管理多版本。\n",
    "\n",
    "> ⚠️ **注意**：安装时请务必勾选 **Add Python to PATH**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 创建虚拟环境（venv）\n",
    "\n",
    "使用虚拟环境可以隔离项目依赖，避免不同项目之间的包冲突。Python 3.12 内置了 `venv` 模块，无需额外安装。\n",
    "\n",
    "### 在项目目录下创建虚拟环境\n",
    "\n",
    "在 VS Code 终端（确保终端为 PowerShell 或 CMD）中执行：\n",
    "\n",
    "```powershell\n",
    "# 创建名为 .venv 的虚拟环境\n",
    "python -m venv .venv\n",
    "```\n",
    "\n",
    "执行后，你会在当前目录下看到 `.venv` 文件夹。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 激活虚拟环境\n",
    "\n",
    "### Windows 系统（PowerShell）\n",
    "```powershell\n",
    ".venv\\Scripts\\Activate.ps1\n",
    "```\n",
    "\n",
    "### Windows 系统（CMD）\n",
    "```cmd\n",
    ".venv\\Scripts\\activate.bat\n",
    "```\n",
    "\n",
    "> ⚠️ **PowerShell 执行策略提示**：如果提示无法执行脚本，请以管理员身份运行 PowerShell 并执行：\n",
    "> ```powershell\n",
    "> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser\n",
    "> ```\n",
    "\n",
    "激活成功后，终端提示符前会显示 `(.venv)`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 使用清华镜像源安装依赖\n",
    "\n",
    "为了加快 pip 下载速度，我们使用清华大学 TUNA 镜像源。\n",
    "\n",
    "### 临时使用清华源（推荐）\n",
    "在 VS Code 终端中执行以下命令（确保虚拟环境已激活）：\n",
    "\n",
    "```powershell\n",
    "pip install langchain langgraph langchain-openai -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "```\n",
    "\n",
    "### 配置全局默认镜像源（可选）\n",
    "如果你想让所有 pip 安装默认使用清华源：\n",
    "\n",
    "```powershell\n",
    "pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 验证环境安装\n",
    "\n",
    "运行以下代码，确认核心库已正确安装："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from importlib.metadata import version\n",
    "\n",
    "print(f\"Python 版本：{sys.version}\")\n",
    "print(f\"LangChain 版本：{version('langchain')}\")\n",
    "print(f\"LangGraph 版本：{version('langgraph')}\")\n",
    "print(\"✅ 环境安装成功！\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. VS Code 关联虚拟环境\n",
    "\n",
    "1. 按 `Ctrl+Shift+P` 打开命令面板\n",
    "2. 输入 `Python: Select Interpreter`\n",
    "3. 选择 `./.venv/Scripts/python.exe`\n",
    "4. 重新启动 Jupyter 内核（点击右上角内核选择器，选择 `.venv` 环境）\n",
    "\n",
    "现在你可以在 VS Code 中直接运行本课程的所有 `.ipynb` 文件了！"
   ]
  }
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