how to use API Key

⋈┈◎ ollama --version                                                                                            ⌂ 18:12
ollama version is 0.21.2

⋈┈◎ ollama run qwen2.5:7b-instruct-q4_K_M                                                                       ⌂ 18:14
pulling manifest
pulling 2bada8a74506: 100% ▕██████████████████████████████████████████████████████████▏ 4.7 GB
pulling 66b9ea09bd5b: 100% ▕██████████████████████████████████████████████████████████▏   68 B
pulling eb4402837c78: 100% ▕██████████████████████████████████████████████████████████▏ 1.5 KB
pulling 832dd9e00a68: 100% ▕██████████████████████████████████████████████████████████▏  11 KB
pulling 2f15b3218f05: 100% ▕██████████████████████████████████████████████████████████▏  487 B
verifying sha256 digest
writing manifest
success
>>> hello,我是Yolo,你是我最好的AI助手,请多多关照
你好，Yolo！很高兴成为你的AI助手，我会尽力为你提供帮助和支持。你可以告诉我需要解决的问题或想要了解的内容，无论是学习
知识、工作上的问题还是生活中的困惑，我都会尽全力协助你。请尽管吩咐吧！

◎ sudo systemctl status ollama                                                                                  ⌂ 18:42
[sudo] password for yolo:
● ollama.service - Ollama Service
     Loaded: loaded (/etc/systemd/system/ollama.service; enabled; preset: enabled)
     Active: active (running) since Sun 2026-04-26 18:12:50 CST; 30min ago
 Invocation: 5694944697324dd28ad287f296eec71b
   Main PID: 23260 (ollama)
      Tasks: 57 (limit: 9283)
     Memory: 4.6G (peak: 5.4G)
        CPU: 2min 48.083s
     CGroup: /system.slice/ollama.service
             ├─23260 /usr/local/bin/ollama serve
             └─26485 /usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blobs/sha256-2bada8a74506770>

Apr 26 18:37:40 Yolo ollama[23260]: llama_context:  CUDA_Host compute buffer size =    15.01 MiB
Apr 26 18:37:40 Yolo ollama[23260]: llama_context: graph nodes  = 959
Apr 26 18:37:40 Yolo ollama[23260]: llama_context: graph splits = 2
Apr 26 18:37:40 Yolo ollama[23260]: time=2026-04-26T18:37:40.289+08:00 level=INFO source=server.go:1402 msg="llama runn>
Apr 26 18:37:40 Yolo ollama[23260]: time=2026-04-26T18:37:40.289+08:00 level=INFO source=sched.go:561 msg="loaded runne>
Apr 26 18:37:40 Yolo ollama[23260]: time=2026-04-26T18:37:40.289+08:00 level=INFO source=server.go:1364 msg="waiting fo>
Apr 26 18:37:40 Yolo ollama[23260]: time=2026-04-26T18:37:40.290+08:00 level=INFO source=server.go:1402 msg="llama runn>
Apr 26 18:37:40 Yolo ollama[23260]: [GIN] 2026/04/26 - 18:37:40 | 200 | 41.474437331s |       127.0.0.1 | POST     "/ap>
Apr 26 18:39:51 Yolo ollama[23260]: [GIN] 2026/04/26 - 18:39:51 | 200 |  1.707250887s |       127.0.0.1 | POST     "/ap>
Apr 26 18:40:13 Yolo ollama[23260]: [GIN] 2026/04/26 - 18:40:13 | 200 |  8.028045434s |       127.0.0.1 | POST     "/ap>

⋈┈◎ sudo systemctl disable ollama                                                                               ⌂ 18:43
Removed '/etc/systemd/system/default.target.wants/ollama.service'.


⋈┈◎ sudo systemctl stop ollama                  

⋈┈◎ curl http://localhost:11434/api/tags                                                                        ⌂ 23:01
{"models":[{"name":"qwen2.5:7b-instruct-q4_K_M","model":"qwen2.5:7b-instruct-q4_K_M","modified_at":"2026-04-26T18:36:58.576822483+08:00","size":4683087332,"digest":"845dbda0ea48ed749caafd9e6037047aa19acfcfd82e704d7ca97d631a0b697e","details":{"parent_model":"","format":"gguf","family":"qwen2","families":["qwen2"],"parameter_size":"7.6B","quantization_level":"Q4_K_M"}}]}

⋈┈◎ curl http://localhost:11434/api/generate -d '{                                                              ⌂ 23:04
"model": "qwen2.5:7b-instruct-q4_K_M",
"prompt": "为什么天空是蓝色的？",
"stream": false
}'
{"model":"qwen2.5:7b-instruct-q4_K_M","created_at":"2026-04-26T15:05:04.113515261Z","response":"天空之所以呈现蓝色，主要是因为大气中的气体分子和其他微小颗粒会对太阳光产生散射作用。这种现象被称为瑞利散射（Rayleigh scattering），由英国物理学家约翰·威廉·斯特拉特（Lord Rayleigh）在19世纪末提出。\n\n当太阳光进入地球的大气层时，它包含了所有颜色的光波。这些光线中的蓝色和紫色光波较短、能量较高，在遇到大气分子和其他微小颗粒时更容易发生散射。相比之下，红色和橙色等较长波长的光线则较少被散射。因此，这些颜色的光在穿过大气层后直接到达地面。\n\n由于我们的眼睛对蓝色较为敏感且蓝光比紫光更加常见（因为太阳辐射中的蓝色光成分较多），所以我们看到的是天空呈现出蓝色。当我们观察日出和日落时，太阳位于地平线附近，光线需要穿过更厚的大气层才能到达观察者，这时较长波长的红、橙色光也会被散射出来，使得太阳本身以及周围的天际显得更加红润。\n\n总之，大气中的气体分子对蓝紫色光的强烈散射是天空呈现蓝色的主要原因。","done":true,"done_reason":"stop","context":[151644,8948,198,2610,525,1207,16948,11,3465,553,54364,14817,13,1446,525,264,10950,17847,13,151645,198,151644,872,198,100678,101916,20412,105681,9370,11319,151645,198,151644,77091,198,101916,105133,104401,105681,3837,104312,99519,105797,101047,107155,102388,105504,48934,30709,107561,108772,101281,99225,100394,99632,99759,100154,1773,100137,102060,106253,100705,59532,99632,99759,9909,29187,62969,71816,48272,67071,104025,102462,108026,111028,13935,116094,13935,105829,72225,65278,9909,51082,13255,62969,7552,18493,16,24,101186,100072,101080,3407,39165,101281,99225,101040,102493,104197,99180,99371,13343,3837,99652,115191,55338,102284,9370,99225,99804,1773,100001,109587,101047,105681,33108,111413,99225,99804,99260,99534,5373,101426,105540,96050,104011,105797,102388,105504,48934,30709,107561,13343,108478,99726,99632,99759,1773,117646,3837,104165,33108,107678,38035,49567,112228,99804,45861,9370,109587,46448,109186,99250,99632,99759,1773,101886,3837,100001,102284,9370,99225,18493,109239,105797,99371,33447,101041,104658,104722,3407,101887,97639,106975,32664,105681,104735,105521,100136,100400,99225,56006,101168,99225,101896,101536,9909,99519,101281,105357,101047,105681,99225,105024,106204,48272,108728,101038,100146,101916,107433,105681,1773,109046,104144,8903,20221,33108,8903,99297,13343,3837,101281,103987,29490,49111,43268,102205,3837,109587,85106,109239,33126,99696,104197,99180,99371,101901,104658,104144,28946,3837,104616,112228,99804,45861,9370,99425,5373,107678,38035,99225,103977,99250,99632,99759,99898,3837,104193,101281,100775,101034,109292,35727,99326,104392,101896,99425,99842,3407,106279,3837,105797,101047,107155,102388,32664,100400,111413,99225,9370,102637,99632,99759,20412,101916,104401,105681,104396,99917,1773],"total_duration":21699424926,"load_duration":16026154457,"prompt_eval_count":35,"prompt_eval_duration":294415779,"eval_count":247,"eval_duration":5125061517}

⋈┈◎ cat test.py                                                                                /tmp py ⌉⌊ 3.13.12 23:10
import requests
def chat_with_ollama(prompt):
    url="http://localhost:11434/api/generate"
    payload={
        "model":"qwen2.5:7b-instruct-q4_K_M",
        "prompt":prompt,
        "stream": False
    }
    try:
        response=requests.post(url,json=payload)
        response.raise_for_status()
        result=response.json()
        return result["response"]
    except Exception as e:
        return f"wront:{e}"
if __name__ =="__main__":
    answer=chat_with_ollama("用一句话解释什么是机器学习")
    print(answer)
    
⋈┈◎ python test.py                                                                             /tmp py ⌉⌊ 3.13.12 23:11
机器学习是让计算机在不进行明确编程的情况下从数据中学习并改进其性能的能力。

⋈┈◎ cat test.py                                                                                /tmp py ⌉⌊ 3.13.12 23:15
import ollama

response = ollama.chat(
    model='qwen2.5:7b-instruct-q4_K_M',
    messages=[{
        'role':'user',
        'content':'hello,做个自我介绍'
    }]
)
print(response['message']['content'])


⋈┈◎ python test.py                                                                             /tmp py ⌉⌊ 3.13.12 23:15
您好！我是Qwen，一个由阿里云开发的语言模型助手。我能够提供信息查询、语言翻译、创意写作等多方面的服务，旨在帮助用户更高效地获取所需的知识和完成日常任务。请问您有什么问题或需要帮助的吗？我会尽力提供支持。

◎ curl http://10.161.154.36:1234/v1/chat/completions \                                                          ⌂ 14:23
-H "Content-Type: application/json" \
-d '{
  "messages":[
    {"role":"user","content":"做一个简单的自我介绍"}
  ],
  "model":"qwen/qwen3.5-9b"
}'
{
  "id": "chatcmpl-7tbojw2c479laawnkddiyj",
  "object": "chat.completion",
  "created": 1777271043,
  "model": "qwen/qwen3.5-9b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\n\n你好呀！很高兴认识你！😊\n\n**如果是在问我（AI）的自我介绍：**\n我是人工智能助手，可以帮你处理文字、解答问题、提供创意灵感或者翻译资料。无论你需要什么帮助，随时都可以叫我。\n\n***\n\n**如果你是想要一段属于你自己的“自我介绍”模板，我为你准备了几个不同场景的版本，你可以直接套用或修改：**\n\n### 🌟 场景一：职场/面试（专业、稳重）\n> “大家好，我叫 [你的名字]，目前担任 [你的职位/职业]。我拥有 [X] 年的 [行业/领域] 经验，擅长 [核心技能 1] 和 [核心技能 2]。在上一份工作中，我主要负责 [简述主要成就]。我很期待能在这里学习新的知识，并贡献我的力量。”\n\n### 🎉 场景二：社交/聚会（轻松、有趣）\n> “哈喽！我是 [名字]，可以叫我 [昵称]。平时我喜欢 [爱好 1，如摄影] 和 [爱好 2，如健身]。不过我最大的特点是特别爱笑，如果你需要找话题或帮忙拍照，随时叫我！希望能和大家聊得来。”\n\n### 🎓 场景三：校园/班级（亲切、积极）\n> “大家好，我是来自 [学校/专业] 的 [名字]。性格比较开朗，擅长组织策划活动。加入这个集体，希望能和大家一起进步，如果有需要帮忙搬重物或整理资料的地方，请随时提！”\n\n### 🚀 场景四：一句话极简版（电梯演讲）\n> “我是 [名字]，一个对 [领域/兴趣] 充满热情的 [职业/身份]，致力于通过 [你的特长/价值] 解决问题。”\n\n---\n\n**💡 小建议：**\n1. **控制时长：** 一般控制在 30-60 秒读完。\n2. **突出亮点：** 用具体的成就或爱好代替笼统的形容词（比如“做事负责”不如“经手的项目零失误”）。\n3. **保持真诚：** 语气自然，眼神有交流会更好哦。\n\n如果你有更具体的场合或想强调的内容，可以告诉我，我帮你定制更精准的版本！",
        "reasoning_content": "Here's a thinking process that leads to the suggested self-introductions:\n\n1.  **Analyze the Request:**\n    *   **User:** \"做一个简单的自我介绍\" (Make a simple self-introduction).\n    *   **Intent:** The user wants me (the AI) or they want help writing a self-introduction for themselves?\n        *   *Scenario A:* They are asking *me* to introduce myself.\n        *   *Scenario B:* They want a template/prompt for *them* to use in an interview, social setting, or professional context.\n    *   **Constraint:** The request is very broad (\"simple\"). I need to cover both possibilities (introducing myself as AI OR helping them introduce themselves) to be most helpful.\n\n2.  **Determine the Best Approach:**\n    *   Since I am an AI assistant, usually when users say \"make a self-introduction\" without specifying context, they might expect me to introduce *me*. However, often people ask this when they want a *template* for themselves.\n    *   *Strategy:* I should primarily introduce myself as an AI first (clarifying my identity), but then offer templates for *their* use cases (professional, casual, etc.) because that's likely what they actually need to achieve.\n    *   *Tone:* Polite, clear, helpful, and versatile.\n\n3.  **Drafting - Part 1: Introducing Myself (The AI):**\n    *   Name: Qwen (or just \"Assistant\" depending on specific branding, but generally keep it friendly).\n    *   Function: AI assistant, text processing, problem-solving, etc.\n    *   Personality: Helpful, patient, accurate.\n    *   *Draft:* 你好！我是你的 AI 助手。我叫 Qwen（或者通义千问）。我可以帮你写文章、查资料、做翻译等。很高兴为你服务。 (Hello! I'm your AI assistant. My name is Qwen... I can help you write, search, translate... Happy to serve.)\n\n4.  **Drafting - Part 2: Templates for the User (The Human):**\n    *   Need different scenarios because \"simple\" varies.\n    *   *Scenario 1: Workplace/Interview (Professional)*\n        *   Key elements: Name, Role, Experience, Skills, Goal.\n        *   Tone: Confident, concise.\n    *   *Scenario 2: Social/Party (Casual)*\n        *   Key elements: Name, Hobbies, Fun fact.\n        *   Tone: Relaxed, friendly.\n    *   *Scenario 3: School/Classmate*\n        *   Key elements: Major, Interests, Open to cooperation.\n        *   Tone: Approachable.\n\n5.  **Refining the Content (Self-Correction/Optimization):**\n    *   *Correction:* Wait, simply introducing myself might be what they want, but usually, \"make a simple self-introduction\" implies \"help me write one\". I need to explicitly ask/offer templates.\n    *   *Better Strategy:* Start with a warm AI introduction, then immediately pivot to offering help for *them*. This covers all bases.\n    *   *Structure:*\n        1.  Greeting & My Identity (AI).\n        2.  Offer to help them write theirs.\n        3.  Provide examples/templates (Work, Social, General).\n        4.  Tips on how to write a good one.\n\n6.  **Finalizing the Output (in Chinese):**\n    *   *Greeting:* 你好呀！很高兴认识你。(Hello! Nice to meet you.)\n    *   *My Intro:* 我是通义千问 (Qwen)... capabilities...\n    *   *Transition:* 不过，我猜你可能也想需要一段**适合你自己的自我介绍**模板吧？(But, I guess you might need a template for yourself?) -> \"如果是你需要写一个自己的介绍...\"\n    *   *Templates:* Create distinct versions.\n    *   *Tips:* Add value (keep it short, highlight strengths, smile).\n\n7.  **Review against Constraints:**\n    *   Keep it simple? Yes, avoid overly technical jargon.\n    *   Be helpful? Yes, provide actionable text.\n\n8.  **Final Polish:** (Self-Correction during drafting)\n    *   Make sure the templates are customizable (use placeholders like [名字]).\n    *   Keep the tone encouraging.\n\n**(Self-Correction Note):** I need to make sure I don't confuse the user. If they just want me to introduce myself, giving them templates might feel like \"reading their mind\". But usually, this prompt means \"Help me write one\". I will address both to be safe.\n*Revised Plan:*\n1. Brief AI intro (in case).\n2. Main focus: Helping *them* write theirs (since that's the more common utility request).\n3. Provide 3 distinct scenarios.\n\nLet's write it.\n\n*Drafting the response...*\n- Start with friendly greeting.\n- Mention my identity briefly.\n- Ask if they want to introduce themselves in a specific context.\n- Give examples immediately as value-add.\n- End with an offer to customize.\n\n*Wait, one more thought:* The prompt is \"Do a simple self-introduction\". It could literally mean \"You (the AI) do it now\". Or \"Generate text for me\". I should prioritize the AI introduction first, then ask/offer the human version. This is safer.\nActually, looking at search history/common intent: Most users want to generate text for *themselves*. I will provide templates but frame it as \"Here are some versions for you.\"\nOkay, let's combine: \"Here I am (AI), and here is help for you.\"\n\nLet's structure the response clearly.\n1. My intro (The AI).\n2. Templates for them (Work, Casual, etc.).\n3. Tips.\n\n*Wait, keep it simple.* If I just say \"I am an AI\", that's too short. If I dump 3 templates, that's too long without context.\nLet's do this: \"Hello! I'm [AI Identity]. I can help you write one. Here are some examples.\"\n\nOkay, ready to generate.\n\n*(Final decision)*: I will provide a friendly AI intro first, then assume they want *templates* for themselves and provide those, categorized by scenario. This is the most comprehensive way to answer \"Simple self-intro\".\n\nLet's go.cw\n",
        "tool_calls": []
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 1874,
    "total_tokens": 1887,
    "completion_tokens_details": {
      "reasoning_tokens": 1405
    }
  },
  "stats": {},
  "system_fingerprint": "qwen/qwen3.5-9b"
}                                           

◎ cat test.py                                                                                  /tmp py ⌉⌊ 3.13.12 15:38
from openai import OpenAI

# 1. 初始化客户端，指向 LM Studio 的本地服务器
client = OpenAI(
    base_url="http://10.161.154.36:1234/v1", # 这是 LM Studio 的默认地址，如果你的端口不同请修改
    api_key="lm-studio"                  # 本地服务器不需要真实密钥，但参数不能为空
)

# 2. 发送对话请求
try:
    response = client.chat.completions.create(
        model="deepseek-r1-0528-qwen3-8b", # 必须与 LM Studio 中加载的模型名称一致
        messages=[
            {"role": "system", "content": "你是一个精通编程的 AI 助手。请用中文回答。"},
            {"role": "user", "content": "请解释一下什么是 Python 中的装饰器（Decorator）？"}
        ],
        temperature=0.7,  # 生成文本的随机性 (0.0 到 1.0)
        max_tokens=35380    # 限制生成的最大 token 数量（可选）
    )

    # 3. 打印模型的回复
    print("🤖 DeepSeek 的回复：\n")
    print(response.choices[0].message.content)

except Exception as e:
    print(f"调用失败，请检查 LM Studio 的服务器是否已启动。错误信息: {e}")


◎ python test.py                                                                               /tmp py ⌉⌊ 3.13.12 15:38
🤖 DeepSeek 的回复：


好的，我们来详细解释一下 Python 中的装饰器（Decorator）。

# 什么是 Python 装饰器？

在编程中，**装饰器是一种强大的函数类型的功能增强工具**，它允许你修改一个已存在的函数或类的行为，同时还能保持原有的代码不变。本质上，它是：

1.  **一个函数，接收另一个函数作为参数，并返回一个新的函数（替换原函数）**
2.  或者是一个可计算的类，用于动态地扩展对象的功能。

装饰器的核心思想是**“在不修改原始定义的情况下增加新功能”**，这通常通过组合和包裹的方式实现。Python 使用 `@` 符号来将装饰器应用到函数或方法上（或者也可以应用于类）。

## 为什么需要装饰器？

当开发者想要给现有的函数添加一些功能时，例如：

*   **权限验证：** 在调用某个需要特定权限的函数前，检查用户是否拥有该权限。
*   **日志记录：** 记录函数被调用的时间、参数和返回值等信息。
*   **性能计数/缓存：** 测量一个耗时函数执行时间，或者将计算结果缓存起来避免重复计算。
*   **输入验证：** 检查传入函数的参数类型或格式是否正确。

如果每次都要复制原始代码并加入这些新功能，代码会变得冗余且难以维护。装饰器提供了一种更优雅、更简洁的方式来复用代码，并集中地修改函数的行为。

## 如何理解它？

可以把装饰器想象成是：

*   **胶水：** 把两个或多个东西（在这里是函数）粘合在一起，赋予它们新的组合特性。
*   **外套/包装盒：** 给一个核心功能穿上一件“新衣服”，在不改变核心本身的情况下增加额外的功能。

### 用最简单的例子来理解

假设我们有一个需要权限才能访问的简单函数：

```python
def greet():
    print("你好！")
```

现在，我们想添加一个新的行为：只有管理员才允许执行这个函数。我们可以使用一个装饰器来做这件事。

首先，定义一个装饰器 `admin_required`，它接收被装饰的函数作为参数，并返回一个新的函数（包裹后的函数）：

```python
def admin_required(func):
    def wrapper():
        # 新增功能：检查用户是否为管理员
        if not is_admin():  # 假设有一个全局函数is_admin()可以判断当前用户权限
            print("你没有管理员权限，无法访问此功能。")
            return None

        # 如果是管理员，则调用原始的greet函数
        original_func = func
        result = original_func()
        return result

    # 返回包装后的函数（也就是新的“greet”）
    return wrapper
```

然后，我们使用 `@` 符号将这个装饰器应用到 `greet` 函数上：

```python
@admin_required  # 这个就是装饰器的语法糖！它会调用admin_required(greet)并将返回的新函数赋值给 greet_name
def greet():
    print("你好！")
```

现在，当我们调用 `greet()` 时，实际上是在调用由装饰器生成的那个新的函数（`wrapper` 函数）。这个新函数首先检查权限，如果通过了则执行原始的 `greet` 并返回其结果；如果没有，则打印错误信息并停止。

注意：在 Python 中，这相当于：

```python
def greet():
    print("你好！")

# 使用装饰器语法糖等同于：
greet = admin_required(greet)
```

### 装饰器的通用结构

一个典型的函数装饰器结构是这样的：

```python
def decorator(old_func):
    def new_func(*args, **kwargs):
        # 在调用旧函数之前添加的功能 (增强部分)
        result = old_func(*args, *kwargs)  # 执行被装饰的原始函数
        # 在调用之后添加的功能 (可能修改、记录或返回结果)

        return result

    return new_func
```

然后应用：

```python
@decorator
def my_function(...):
    ...
```

## 类装饰器和静态方法装饰器也是类似的原理

类装饰器也是一个函数，它接收一个类作为参数，并返回一个修改后的类。它可以用于添加到类的方法、改变类的结构或行为等。

### 总结一下关键点

*   **目的：** 增强或修改现有代码（通常是函数）的功能。
*   **本质：** 高阶函数，接收被装饰对象作为参数并返回一个新对象。
*   **语法糖：** `@` 符号使得应用装饰器非常简洁方便。
*   **工作方式：** 将原始函数包装在一个新的函数（或类）内部，在这个包裹层里可以添加任何代码，然后调用原始函数。

理解了装饰器的核心概念和工作原理后，你就可以开始使用它来编写更简洁、功能更强的 Python 代码了。这是一个非常重要的语言特性，尤其是在框架中很常用，如 Django 的 CBVs（基于类的视图）中的 `login_required` 装饰器等。

如果你想进一步学习，请尝试：
1. 编写自己的简单装饰器。
2. 查看标准库中的装饰器示例（比如 Flask, Django 中的常见装饰器）。
3. 研究如何使用多个装饰器修饰一个函数。