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-{
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 9,
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- "id": "03d8e409",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "微积分是研究函数的变化率（微分）与累积量（积分）及其相互关系的数学分支。\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_openai import ChatOpenAI\n",
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- "from dotenv import load_dotenv\n",
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- "import os\n",
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- "\n",
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- "api_key = os.getenv('OPENAI_API_KEY')\n",
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- "\n",
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- "# 创建 DeepSeek 聊天模型实例\n",
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- "# base_url 指向 DeepSeek 的兼容端点，而非 OpenAI 官方地址\n",
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- "load_dotenv()\n",
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- "llm = ChatOpenAI(\n",
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- " model_name=\"deepseek-v4-flash\", # DeepSeek 的对话模型\n",
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- " api_key=api_key, # 在 platform.deepseek.com 获取\n",
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- " base_url=\"https://api.deepseek.com\" # DeepSeek API 地址\n",
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- ")\n",
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- "\n",
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- "# invoke 是 LangChain 统一的调用方法，返回 AIMessage 对象\n",
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- "response = llm.invoke(\"用一句话解释什么是微积分\")\n",
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- "print(response.content) # .content 拿到纯文本"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 3,
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- "id": "366dc781",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "装饰器（Decorator）在你看来可能挺唬人，但说白了它就是“给函数穿衣服”——在不改变函数本身代码的前提下，给函数加一些额外的功能，比如记录日志、计算运行时间、校验权限等。\n",
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- "\n",
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- "想象一下：你有一个非常简单的函数，它只是说“你好”。如果每次调用这个函数之前，你都想先确认一下对方有没有权限（比如是否登录），你不想改动函数内部的代码（因为可能有很多地方都用它），那就可以写一个“包装器”函数，把这个“你好”函数像礼物一样包起来，在这个包装器里先做权限检查，再调用原函数。这个包装器就是**装饰器**。\n",
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- "\n",
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- "Python 里的装饰器用 `@` 符号放在函数定义上面，像这样：\n",
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- "\n",
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- "```python\n",
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- "def decorator(func):\n",
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- " def wrapper():\n",
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- " print(\"检查权限...\") # 额外功能\n",
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- " func() # 调用原函数\n",
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- " print(\"记录日志...\") # 额外功能\n",
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- " return wrapper\n",
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- "\n",
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- "@decorator\n",
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- "def say_hello():\n",
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- " print(\"你好\")\n",
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- "```\n",
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- "\n",
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- "当你调用 `say_hello()` 时，实际上执行的是装饰器内部的 `wrapper` 函数，它会先打印“检查权限…”，然后打印“你好”，最后打印“记录日志…”。而 `say_hello` 本身的代码完全没有被改动过。\n",
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- "\n",
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- "所以，装饰器就是一个**高阶函数**（接收函数作为参数），它内部定义一个新函数（包装函数），在新函数中加入额外操作，然后返回这个新函数。Python 的 `@` 语法糖只是让你写起来更方便——本质上就是 `say_hello = decorator(say_hello)` 这一步的简写。\n",
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- "\n",
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- "**一句话总结**：装饰器就是给函数“套个壳”，在不改原函数代码的前提下，让函数拥有新的技能。\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_core.messages import SystemMessage, HumanMessage\n",
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- "\n",
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- "# SystemMessage：设定模型的\"人设\"，相当于给它一个角色卡\n",
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- "# HumanMessage：用户说的话\n",
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- "# AIMessage：模型之前的回答（用于多轮对话）\n",
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- "\n",
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- "messages = [\n",
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- " SystemMessage(content=\"你是一个资深的 Python 技术博主，擅长用大白话解释概念\"),\n",
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- " HumanMessage(content=\"解释一下什么是装饰器？\"),\n",
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- "]\n",
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- "\n",
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- "response = llm.invoke(messages)\n",
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- "print(response.content)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 10,
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- "id": "3ef122de",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "片名：星际穿越（Interstellar）\n",
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- "年份：2014\n",
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- "导演：克里斯托弗·诺兰\n",
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- "评分：8.6\n"
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- ]
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- }
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- ],
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- "source": [
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- "from pydantic import BaseModel, Field\n",
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- "from langchain.chat_models import init_chat_model\n",
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- "\n",
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- "# 定义你期望的输出结构（Pydantic 模型）\n",
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- "class MovieInfo(BaseModel):\n",
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- " \"\"\"电影信息\"\"\"\n",
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- " title: str = Field(description=\"电影名称\")\n",
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- " year: int = Field(description=\"上映年份\")\n",
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- " director: str = Field(description=\"导演\")\n",
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- " rating: float = Field(description=\"评分（10分制）\")\n",
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- "\n",
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- "llm = init_chat_model(\n",
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- " model=\"qwen-plus\",\n",
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- " model_provider=\"openai\",\n",
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- " api_key=os.getenv('QWEN_API_KEY'),\n",
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- " base_url=\"https://dashscope.aliyuncs.com/compatible-mode/v1\"\n",
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- ")\n",
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- "\n",
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- "# .with_structured_output() 会自动把 Schema 注入 prompt，\n",
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- "# 并在底层做 JSON 解析和类型校验\n",
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- "structured_llm = llm.with_structured_output(MovieInfo)\n",
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- "\n",
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- "result = structured_llm.invoke(\"介绍一下电影《星际穿越》\")\n",
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- "\n",
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- "# 返回的是 MovieInfo 对象，可以直接用属性访问\n",
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- "print(f\"片名：{result.title}\")\n",
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- "print(f\"年份：{result.year}\")\n",
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- "print(f\"导演：{result.director}\")\n",
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- "print(f\"评分：{result.rating}\")"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 12,
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- "id": "5ddf6b79",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "流浪地球\n"
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- ]
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- }
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- ],
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- "source": [
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- "from typing_extensions import TypedDict\n",
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- "\n",
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- "# 方式二：TypedDict（更轻量，无运行时校验）\n",
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- "class MovieTypedDict(TypedDict):\n",
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- " title: str\n",
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- " year: int\n",
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- " director: str\n",
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- " rating: float\n",
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- "\n",
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- "structured_llm = llm.with_structured_output(MovieTypedDict)\n",
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- "result = structured_llm.invoke(\"介绍一下电影《流浪地球》\")\n",
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- "# 返回的是普通 dict\n",
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- "print(result[\"title\"])"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 13,
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- "id": "2b6e9160",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "{'director': '饺子', 'rating': 8.4, 'title': '哪吒之魔童降世', 'year': 2019}\n"
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- ]
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- }
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- ],
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- "source": [
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- "# 方式三：JSON Schema（最灵活，跨语言通用）\n",
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- "json_schema = {\n",
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- " \"title\": \"MovieInfo\",\n",
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- " \"description\": \"电影信息对象\",\n",
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- " \"type\": \"object\",\n",
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- " \"properties\": {\n",
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- " \"title\": {\"type\": \"string\", \"description\": \"电影名称\"},\n",
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- " \"year\": {\"type\": \"integer\", \"description\": \"上映年份\"},\n",
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- " \"director\": {\"type\": \"string\", \"description\": \"导演\"},\n",
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- " \"rating\": {\"type\": \"number\", \"description\": \"评分（10分制）\"}\n",
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- " },\n",
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- " \"required\": [\"title\", \"year\", \"director\", \"rating\"]\n",
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- "}\n",
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- "\n",
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- "structured_llm = llm.with_structured_output(json_schema)\n",
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- "result = structured_llm.invoke(\"介绍一下电影《哪吒之魔童降世》\")\n",
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- "print(result) # 返回的是 dict"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 14,
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- "id": "cf4a8346",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "[SystemMessage(content='你是一个叫 小助手 的 AI 助手，说话风格简洁专业', additional_kwargs={}, response_metadata={}), HumanMessage(content='什么是机器学习？', additional_kwargs={}, response_metadata={})]\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_core.prompts import ChatPromptTemplate\n",
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- "\n",
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- "# 定义一个带变量的对话模板\n",
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- "# {name} 和 {question} 是占位符，运行时会被替换\n",
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- "chat_template = ChatPromptTemplate.from_messages([\n",
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- " (\"system\", \"你是一个叫 {name} 的 AI 助手，说话风格简洁专业\"),\n",
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- " (\"human\", \"{question}\")\n",
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- "])\n",
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- "\n",
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- "# 填充变量，生成最终的消息列表\n",
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- "messages = chat_template.format_messages(name=\"小助手\", question=\"什么是机器学习？\")\n",
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- "print(messages)\n",
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- "# 输出：\n",
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- "# [SystemMessage(content='你是一个叫 小助手 的 AI 助手，说话风格简洁专业'),\n",
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- "# HumanMessage(content='什么是机器学习？')]"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 15,
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- "id": "dfd55f3a",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "给我讲一个关于程序员的笑话\n",
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- "请用幽默的风格，写一篇关于加班的短文，字数不超过100字\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_core.prompts import PromptTemplate\n",
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- "\n",
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- "# 单变量模板\n",
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- "prompt = PromptTemplate.from_template(\"给我讲一个关于{topic}的笑话\")\n",
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- "print(prompt.format(topic=\"程序员\"))\n",
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- "# → \"给我讲一个关于程序员的笑话\"\n",
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- "\n",
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- "# 多变量模板\n",
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- "prompt = PromptTemplate(\n",
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- " template=\"请用{style}的风格，写一篇关于{topic}的短文，字数不超过{limit}字\",\n",
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- " input_variables=[\"style\", \"topic\", \"limit\"]\n",
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- ")\n",
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- "print(prompt.format(style=\"幽默\", topic=\"加班\", limit=100))"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 16,
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- "id": "86f7eb00",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "请分析成都在2026年06/26/2026, 16:36:19的天气趋势\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_core.prompts import PromptTemplate\n",
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- "from datetime import datetime\n",
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- "\n",
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- "# 一个需要三个变量的模板\n",
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- "prompt = PromptTemplate(\n",
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- " template=\"请分析{city}在{year}年{date}的天气趋势\",\n",
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- " input_variables=[\"city\", \"year\", \"date\"]\n",
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- ")\n",
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- "\n",
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- "def get_datetime():\n",
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- " now = datetime.now()\n",
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- " return now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n",
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- "\n",
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- "# partial() 可以预填部分变量\n",
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- "# 注意：date 传的是函数引用而非调用结果，每次 format 时会重新执行\n",
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- "partial_prompt = prompt.partial(\n",
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- " city=\"成都\",\n",
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- " year=\"2026\",\n",
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- " date=get_datetime) # 动态获取当前日期\n",
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- "\n",
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- "\n",
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- "# 只需填剩余的变量（这里已经全部填完了）\n",
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- "print(partial_prompt.format())"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 17,
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- "id": "74918bf5",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\n",
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- "\n",
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- "\n",
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- "黑话: YYDS\n",
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- "翻译: 永远的神（极度赞美）\n",
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- "\n",
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- "黑话: 绝绝子\n",
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- "翻译: 太棒了（强烈赞叹）\n",
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- "\n",
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- "黑话: 躺平\n",
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- "翻译: 心态平和、不再内卷（不争不抢的生活态度）\n",
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- "\n",
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- "黑话: 下头\n",
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- "翻译:\n"
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- ]
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- }
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- ],
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- "source": [
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- "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
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|
- "\n",
|
|
|
- "# 第一步：准备示例数据\n",
|
|
|
- "examples = [\n",
|
|
|
- " {\"input\": \"YYDS\", \"output\": \"永远的神（极度赞美）\"},\n",
|
|
|
- " {\"input\": \"绝绝子\", \"output\": \"太棒了（强烈赞叹）\"},\n",
|
|
|
- " {\"input\": \"躺平\", \"output\": \"心态平和、不再内卷（不争不抢的生活态度）\"},\n",
|
|
|
- "]\n",
|
|
|
- "\n",
|
|
|
- "# 第二步：定义每个示例的展示格式\n",
|
|
|
- "example_prompt = PromptTemplate(\n",
|
|
|
- " input_variables=[\"input\", \"output\"],\n",
|
|
|
- " template=\"黑话: {input}\\n翻译: {output}\"\n",
|
|
|
- ")\n",
|
|
|
- "\n",
|
|
|
- "# 第三步：组装完整的 Few-Shot Prompt\n",
|
|
|
- "prompt = FewShotPromptTemplate(\n",
|
|
|
- " examples=examples, # 示例列表\n",
|
|
|
- " example_prompt=example_prompt, # 每个示例的格式\n",
|
|
|
- "\n",
|
|
|
- " prefix=\"你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\\n\", # 前缀（指令）\n",
|
|
|
- " suffix=\"黑话: {input}\\n翻译:\", # 后缀（用户问题）\n",
|
|
|
- " input_variables=[\"input\"]\n",
|
|
|
- ")\n",
|
|
|
- "\n",
|
|
|
- "# 第四步：生成最终 prompt\n",
|
|
|
- "final_prompt = prompt.format(input=\"下头\")\n",
|
|
|
- "print(final_prompt)\n",
|
|
|
- "# 输出效果：\n",
|
|
|
- "# 你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\n",
|
|
|
- "#\n",
|
|
|
- "# 黑话: YYDS\n",
|
|
|
- "# 翻译: 永远的神（极度赞美）\n",
|
|
|
- "#\n",
|
|
|
- "# 黑话: 绝绝子\n",
|
|
|
- "# 翻译: 太棒了（强烈赞叹）\n",
|
|
|
- "#\n",
|
|
|
- "# 黑话: 躺平\n",
|
|
|
- "# 翻译: 心态平和、不再内卷（不争不抢的生活态度）\n",
|
|
|
- "#\n",
|
|
|
- "# 黑话: 下头\n",
|
|
|
- "# 翻译:"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 18,
|
|
|
- "id": "9c999e85",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "量子计算是一种利用量子力学中的叠加和纠缠等特性，通过量子比特并行处理信息，从而在特定问题上实现远超经典计算机性能的新型计算方式。\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "from langchain_core.prompts import PromptTemplate\n",
|
|
|
- "from langchain_openai import ChatOpenAI\n",
|
|
|
- "\n",
|
|
|
- "llm = ChatOpenAI(\n",
|
|
|
- " model_name=\"deepseek-v4-flash\",\n",
|
|
|
- " api_key=api_key,\n",
|
|
|
- " base_url=\"https://api.deepseek.com\"\n",
|
|
|
- ")\n",
|
|
|
- "\n",
|
|
|
- "prompt = PromptTemplate.from_template(\"请用一句话解释什么是{concept}\")\n",
|
|
|
- "\n",
|
|
|
- "# 用管道符 | 把 prompt 和 llm 连起来，就构成了一条链\n",
|
|
|
- "chain = prompt | llm\n",
|
|
|
- "\n",
|
|
|
- "# invoke 时只需传入变量，prompt 填充和模型调用自动完成\n",
|
|
|
- "result = chain.invoke({\"concept\": \"量子计算\"})\n",
|
|
|
- "print(result.content)"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 19,
|
|
|
- "id": "da3321b8",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "秦始皇统一六国的顺序为：韩、赵、魏、楚、燕、齐。\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "from langchain_core.prompts import ChatPromptTemplate\n",
|
|
|
- "from langchain_core.output_parsers import StrOutputParser\n",
|
|
|
- "from langchain_openai import ChatOpenAI\n",
|
|
|
- "\n",
|
|
|
- "llm = ChatOpenAI(\n",
|
|
|
- " model_name=\"deepseek-v4-flash\",\n",
|
|
|
- " api_key=api_key,\n",
|
|
|
- " base_url=\"https://api.deepseek.com\"\n",
|
|
|
- ")\n",
|
|
|
- "\n",
|
|
|
- "# 定义对话模板\n",
|
|
|
- "prompt = ChatPromptTemplate.from_messages([\n",
|
|
|
- " (\"system\", \"你是一个中国历史专家，回答简洁明了\"),\n",
|
|
|
- " (\"user\", \"{input}\")\n",
|
|
|
- "])\n",
|
|
|
- "\n",
|
|
|
- "# StrOutputParser 把 AIMessage 转成纯字符串\n",
|
|
|
- "# 这样链的输出就是 str，而非 AIMessage 对象\n",
|
|
|
- "parser = StrOutputParser()\n",
|
|
|
- "\n",
|
|
|
- "# 三段式链：Prompt → LLM → Parser\n",
|
|
|
- "chain = prompt | llm | parser\n",
|
|
|
- "\n",
|
|
|
- "result = chain.invoke({\"input\": \"秦始皇统一六国的顺序是什么？\"})\n",
|
|
|
- "print(result) # 直接是字符串，不需要 .content"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 20,
|
|
|
- "id": "3d1fab11",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "['大熊猫繁育研究基地', '宽窄巷子', '武侯祠']\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "from langchain_core.output_parsers import CommaSeparatedListOutputParser\n",
|
|
|
- "from langchain_core.prompts import ChatPromptTemplate\n",
|
|
|
- "\n",
|
|
|
- "parser = CommaSeparatedListOutputParser()\n",
|
|
|
- "\n",
|
|
|
- "# get_format_instructions() 会生成一段\"输出格式要求\"的文字\n",
|
|
|
- "# 比如 \"Your response should be a list of comma separated values...\"\n",
|
|
|
- "format_instructions = parser.get_format_instructions()\n",
|
|
|
- "\n",
|
|
|
- "prompt = ChatPromptTemplate.from_messages([\n",
|
|
|
- " (\"system\", f\"你是一个旅游顾问。{{format_instructions}}\"),\n",
|
|
|
- " (\"human\", \"推荐{city}的{count}个必去景点\")\n",
|
|
|
- "])\n",
|
|
|
- "\n",
|
|
|
- "chain = prompt | llm | parser\n",
|
|
|
- "\n",
|
|
|
- "result = chain.invoke({\n",
|
|
|
- " \"city\": \"成都\",\n",
|
|
|
- " \"count\": 3,\n",
|
|
|
- " \"format_instructions\": format_instructions\n",
|
|
|
- "})\n",
|
|
|
- "print(result) # ['武侯祠', '锦里', '大熊猫繁育研究基地'] ← 直接是 list"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 22,
|
|
|
- "id": "cd62de23",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "书名：朝花夕拾\n",
|
|
|
- "作者：鲁迅\n",
|
|
|
- "体裁：['散文', '回忆性散文']\n",
|
|
|
- "<class '__main__.BookInfo'>\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "from typing import List\n",
|
|
|
- "from pydantic import BaseModel, Field\n",
|
|
|
- "from langchain_core.output_parsers import PydanticOutputParser\n",
|
|
|
- "from langchain_core.prompts import ChatPromptTemplate\n",
|
|
|
- "\n",
|
|
|
- "# 定义数据结构\n",
|
|
|
- "class BookInfo(BaseModel):\n",
|
|
|
- " \"\"\"书籍信息\"\"\"\n",
|
|
|
- " book_name: str = Field(description=\"书名\")\n",
|
|
|
- " author: str = Field(description=\"作者\")\n",
|
|
|
- " genres: List[str] = Field(description=\"体裁列表\")\n",
|
|
|
- "\n",
|
|
|
- "# 创建解析器，它会自动生成 JSON Schema 格式要求\n",
|
|
|
- "parser = PydanticOutputParser(pydantic_object=BookInfo)\n",
|
|
|
- "\n",
|
|
|
- "prompt = ChatPromptTemplate.from_messages([\n",
|
|
|
- " (\"system\", \"你是一个图书管理员。请按格式要求输出，使用中文。\\n{format_instructions}\"),\n",
|
|
|
- " (\"human\", \"从以下简介中提取书籍信息：\\n{introduction}\")\n",
|
|
|
- "])\n",
|
|
|
- "\n",
|
|
|
- "chain = prompt | llm | parser\n",
|
|
|
- "\n",
|
|
|
- "introduction = \"\"\"\n",
|
|
|
- "《朝花夕拾》原名《旧事重提》，是鲁迅的散文集，收录1926年创作的10篇回忆性散文。\n",
|
|
|
- "文集以记事为主，饱含抒情气息，反映了作者青少年时期的生活。\n",
|
|
|
- "\"\"\"\n",
|
|
|
- "\n",
|
|
|
- "result = chain.invoke({\n",
|
|
|
- " \"introduction\": introduction,\n",
|
|
|
- " \"format_instructions\": parser.get_format_instructions()\n",
|
|
|
- "})\n",
|
|
|
- "\n",
|
|
|
- "print(f\"书名：{result.book_name}\") # 朝花夕拾\n",
|
|
|
- "print(f\"作者：{result.author}\") # 鲁迅\n",
|
|
|
- "print(f\"体裁：{result.genres}\") # ['散文集', '回忆性散文']\n",
|
|
|
- "print(type(result)) # <class '__main__.BookInfo'>"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 23,
|
|
|
- "id": "9f0e16c5",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "嗨，小明！很高兴认识你，作为一名程序员同行，咱们可是“自己人”啦！👨💻 不知道你平时主要写什么方向的代码？是前端、后端、全栈，还是对某个领域（比如AI、游戏、嵌入式）特别感兴趣？或者最近在忙什么有趣的项目吗？\n",
|
|
|
- "\n",
|
|
|
- "如果有任何技术问题、职业发展困惑，或者想聊聊编程的趣事，随时都可以问我～ 当然，如果你只是想打个招呼，我也很乐意陪你闲聊！😄\n",
|
|
|
- "你的名字我目前还不知道呢！😊 我们的对话刚刚开始，你还没有告诉我你的名字。如果你愿意的话，可以告诉我，这样我就能用你的名字来称呼你啦！或者你希望我继续用“你”来称呼也行。\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "\n",
|
|
|
- "# 第一轮\n",
|
|
|
- "r1 = llm.invoke(\"我叫小明，我是一名程序员\")\n",
|
|
|
- "print(r1.content) # \"你好，小明！程序员是个很棒的职业...\"\n",
|
|
|
- "\n",
|
|
|
- "# 第二轮：问它记不记得\n",
|
|
|
- "r2 = llm.invoke(\"我叫什么名字？\")\n",
|
|
|
- "print(r2.content) # \"抱歉，我无法知道你的名字...\" ← 完全忘了"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 1,
|
|
|
- "id": "5758f6ef",
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "get_weather\n",
|
|
|
- "查询指定城市的实时天气信息\n",
|
|
|
- "\n",
|
|
|
- " Args:\n",
|
|
|
- " location: 城市名称，如\"北京\"、\"成都\"\n",
|
|
|
- "{'location': {'title': 'Location', 'type': 'string'}}\n",
|
|
|
- "成都的天气：Partly cloudy，温度：27°C\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "from langchain.tools import tool\n",
|
|
|
- "import requests\n",
|
|
|
- "\n",
|
|
|
- "@tool\n",
|
|
|
- "def get_weather(location: str) -> str:\n",
|
|
|
- " \"\"\"查询指定城市的实时天气信息\n",
|
|
|
- " \n",
|
|
|
- " Args:\n",
|
|
|
- " location: 城市名称，如\"北京\"、\"成都\"\n",
|
|
|
- " \"\"\"\n",
|
|
|
- " # 调用免费天气 API\n",
|
|
|
- " url = f\"https://wttr.in/{location}?format=j1&lang=zh\"\n",
|
|
|
- " response = requests.get(url)\n",
|
|
|
- " data = response.json()\n",
|
|
|
- "\n",
|
|
|
- " current = data['current_condition'][0]\n",
|
|
|
- " weather_desc = current['weatherDesc'][0]['value']\n",
|
|
|
- " temp = current['temp_C']\n",
|
|
|
- "\n",
|
|
|
- " return f\"{location}的天气：{weather_desc}，温度：{temp}°C\"\n",
|
|
|
- "\n",
|
|
|
- "# @tool 装饰器自动提取的信息\n",
|
|
|
- "print(get_weather.name) # \"get_weather\"\n",
|
|
|
- "print(get_weather.description) # \"查询指定城市的实时天气信息\"\n",
|
|
|
- "print(get_weather.args) # {'location': {'title': 'Location', 'type': 'string'}}\n",
|
|
|
- "\n",
|
|
|
- "# 工具可以直接调用\n",
|
|
|
- "result = get_weather.invoke({\"location\": \"成都\"})\n",
|
|
|
- "print(result) # \"成都的天气：Sunny，温度：28°C\""
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
- "metadata": {
|
|
|
- "kernelspec": {
|
|
|
- "display_name": "02_langchain (3.11.x)",
|
|
|
- "language": "python",
|
|
|
- "name": "python3"
|
|
|
- },
|
|
|
- "language_info": {
|
|
|
- "codemirror_mode": {
|
|
|
- "name": "ipython",
|
|
|
- "version": 3
|
|
|
- },
|
|
|
- "file_extension": ".py",
|
|
|
- "mimetype": "text/x-python",
|
|
|
- "name": "python",
|
|
|
- "nbconvert_exporter": "python",
|
|
|
- "pygments_lexer": "ipython3",
|
|
|
- "version": "3.11.15"
|
|
|
- }
|
|
|
- },
|
|
|
- "nbformat": 4,
|
|
|
- "nbformat_minor": 5
|
|
|
-}
|
leon