2025.10.24
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OPENAI_API_KEY=your_openai_api_key
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import os # 导入环境变量
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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# 让ai说一句话
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from langchain_openai import ChatOpenAI
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# 配置deepseek
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=api_key,
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# 注:在.env里把OPENAI_API_KEY改成你自己的api-key即可
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base_url="https://api.deepseek.com"
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)
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# 调用模型
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response = llm.invoke("你好呀")
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print(response.content)
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import os # 导入环境变量
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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from openai import OpenAI
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client = OpenAI(
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api_key=api_key,
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base_url="https://api.deepseek.com")
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response = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": "You are a helpful assistant"}, # 提示词角色
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{"role": "user", "content": "Hello"}, # 用户输入的对话
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],
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stream=False # 非流式输出,只会等语句全部生成才返回
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)
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print(response.choices[0].message.content)
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import os # 导入环境变量
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from dotenv import load_dotenv
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from openai import OpenAI
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load_dotenv()
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def create_client():
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api_key = os.getenv("OPENAI_API_KEY")
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return OpenAI(api_key=api_key,base_url="https://api.deepseek.com")
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def chat_loop(agent_client):
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messages = [
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{"role":"system","content":"你是一个历史老师,你会耐心的教导我有关的事情。同时你的回答会相对精简,在五十字内。"},
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{"role":"user","content":"汉朝存在了多久,其中哪个皇帝你认为最厉害?"}
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]
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while 1:
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response = agent_client.chat.completions.create(
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model="deepseek-chat",
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messages=messages
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)
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answer = response.choices[0].message.content
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print(f'回答:{answer}')
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# 询问是否还有其他问题
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user_input =input('您还有其他想继续问的吗 | (exit退出)\n')
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if user_input == "exit":
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break
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# 继续记录对话(涵盖用户追问 + ai上句回答)
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messages.append({"role":"user","content":user_input})
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messages.append({"role":"assistant","content":answer})
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if __name__ == '__main__':
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client = create_client()
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chat_loop(client)
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# ai-agent-knowledege
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# 💻 Agent Craft
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AI知识仓库,包含博客配套展示代码
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> 智能体开发教学库 | A beginner-friendly guide to building AI agents
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🔥 从零开始上手agent智能体,掌握langchain、rag、prompt、langgraph等核心技术栈
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本仓库专注于 AI Agent 开发的学习与实践,通过系统化的案例和详细的代码注释,帮助开发者快速掌握从基础LLM调用到复杂Agent构建的完整技能栈。
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💡 项目将持续更新,目标是构建一个系统、实用的 AI Agent 学习案例库。
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📘 本仓库开箱即用,仅需配置 API_KEY。
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✍️ 代码可与博客配套使用,博客会重点讲解Agent知识构建与流程解析。
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博客同步发布:
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👉 [我的CSDN主页](https://blog.csdn.net/2401_87328929)
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👉 [CSDN对应专栏](https://blog.csdn.net/2401_87328929/category_12970267.html)
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🙋♂️ 技术交流 / 更新 / 答疑:微信 **a19731567148**(备注 GitHub)
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---
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## 🚅 目录跳转
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| 仓库 | 博客 | 技术栈 | 知识点 | 难度 |
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|---------------------------------------------------|------------------------------------------------|------------------|----------------------|-----|
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| [01 Agent 入门 & 快速运行](01%20Agent%20入门%20&%20环境搭建/) | [🏠](01%20Agent%20入门%20&%20环境搭建/Agent-demo.py) | 基础LLM | Agent概念 | ⭐ |
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| [02 LLM基础与调用](02%20LLM基础与调用/1.%20llm基础调用.py) | [🏠](02%20LLM基础与调用/1.%20llm基础调用.py) | LLM/API调用/Prompt | 调用模型接口、初识prompt、多轮对话 | ⭐ |
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---
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## 🚀 快速开始
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### 1️⃣ 环境准备
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```bash
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# 克隆项目
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git clone https://github.com/Annyfee/agent-craft.git
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cd agent-craft
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# 安装依赖
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pip install -r requirements.txt
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```
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### 2️⃣ API Key 配置
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```bash
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# 复制环境变量模板
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cp .env.example .env
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```
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在 `.env` 文件中配置:
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```env
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OPENAI_API_KEY=your_deepseek_api_key_here
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```
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> 💡 **获取API Key**: 访问 [DeepSeek官网](https://platform.deepseek.com/) 注册并获取API Key
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### 3️⃣ 运行示例
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```bash
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python "01 Agent 入门 & 环境搭建/Agent-demo.py"
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```
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---
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## 📚 学习路径
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### 🎯 第一章:Agent 入门 & 快速运行
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- **核心技能**: 运行Agent-demo
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- **学习目标**: 了解Agent是什么,怎么用
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- **技术要点**: 环境配置
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### 🎯 第二章:LLM基础与调用
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- **核心技能**: 多轮对话Agent构建
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- **学习目标**: 实现智能对话系统
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- **技术要点**: 对话历史、上下文管理、角色设定
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## 🤝 贡献指南
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欢迎贡献代码 / 提交 Issue / Pull Request
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- Fork 仓库
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- 创建分支进行修改
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- 提交 PR 并描述修改内容
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# This is a sample Python script.
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# Press Shift+F10 to execute it or replace it with your code.
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# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
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def print_hi(name):
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# Use a breakpoint in the code line below to debug your script.
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print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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print_hi('PyCharm')
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# See PyCharm help at https://www.jetbrains.com/help/pycharm/
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dotenv~=0.9.9
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python-dotenv~=1.1.1
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openai~=2.6.0
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