🔍 搜索引擎查找Ollama公开API | DeepSeek免费使用教程+Fofa/Shodan实战指南 | AI模型远程调用技巧

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用户请求 → Ollama API (localhost:11434) → 加载模型 → 推理计算 → 返回结果

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{
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  "ip": "123.45.67.89",           // IP地址
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  "port": 11434,                   // 端口号
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  "protocol": "http",              // 协议类型
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  "country": "CN",                 // 国家代码
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  "region": "Beijing",             // 地区
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  "city": "Beijing",               // 城市
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  "isp": "China Telecom",          // ISP运营商
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  "last_update": "2025-02-10",     // 最后更新时间
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  "banner": "Ollama/0.1.29"        // 服务横幅信息
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}

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IP: 123.45.67.89
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Port: 11434
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Protocol: HTTP
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Product: Ollama
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Version: 0.1.29
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Country: China
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City: Shanghai
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ISP: China Unicom
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Last Update: 2025-02-10
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Tags: ai, llm, api

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import requests
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import json
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def check_ollama_api(base_url):
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    """检查Ollama API是否可用"""
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    try:
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        # 检查版本
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        version_resp = requests.get(f"{base_url}/api/version", timeout=5)
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        if version_resp.status_code == 200:
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            version = version_resp.json()
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            print(f"✅ Ollama版本: {version['version']}")
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        else:
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            print(f"❌ 版本查询失败: {version_resp.status_code}")
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            return False
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        # 检查模型列表
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        tags_resp = requests.get(f"{base_url}/api/tags", timeout=5)
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        if tags_resp.status_code == 200:
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            models = tags_resp.json().get('models', [])
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            print(f"✅ 可用模型数量: {len(models)}")
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            for model in models:
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                print(f"   - {model['name']}")
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            return True
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        else:
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            print(f"❌ 模型查询失败: {tags_resp.status_code}")
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            return False
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    except requests.exceptions.RequestException as e:
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        print(f"❌ 连接失败: {str(e)}")
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        return False
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# 使用示例
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if __name__ == "__main__":
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    base_url = "http://123.45.67.89:11434"
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    check_ollama_api(base_url)

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http://IP:11434
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http://IP:11434/api/version
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http://IP:11434/api/tags

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Step 1: 打开Chatbox
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Step 2: 点击左下角"设置"图标
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Step 3: 选择"模型提供商"
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Step 4: 点击"添加提供商"
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Step 5: 选择"Ollama"
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Step 6: 填写配置：
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  - Base URL: http://IP:11434
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  - Model: deepseek-r1:7b（或其他可用模型）
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Step 7: 点击"测试连接"
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Step 8: 保存配置

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{
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  "provider": "Ollama",
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  "baseUrl": "http://123.45.67.89:11434",
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  "apiKey": "",  // Ollama不需要API Key
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  "model": "deepseek-r1:7b",
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  "temperature": 0.7,
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  "maxTokens": 2048
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}

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import requests
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import json
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def chat_with_ollama(base_url, model, prompt):
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    """与Ollama模型对话"""
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    url = f"{base_url}/api/generate"
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    payload = {
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        "model": model,
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        "prompt": prompt,
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        "stream": False,
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        "options": {
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            "temperature": 0.7,
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            "top_p": 0.9,
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            "num_predict": 512
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        }
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    }
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    try:
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        response = requests.post(url, json=payload, timeout=30)
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        response.raise_for_status()
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        result = response.json()
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        return result.get('response', '')
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    except Exception as e:
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        return f"Error: {str(e)}"
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# 使用示例
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if __name__ == "__main__":
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    base_url = "http://123.45.67.89:11434"
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    model = "deepseek-r1:7b"
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    prompt = "请介绍一下你自己"
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    response = chat_with_ollama(base_url, model, prompt)
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    print(response)

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async function chatWithOllama(baseUrl, model, prompt) {
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    const url = `${baseUrl}/api/generate`;
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    const payload = {
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        model: model,
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        prompt: prompt,
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        stream: false,
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        options: {
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            temperature: 0.7,
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            top_p: 0.9,
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            num_predict: 512
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        }
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    };
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    try {
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        const response = await fetch(url, {
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            method: 'POST',
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            headers: {
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                'Content-Type': 'application/json'
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            },
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            body: JSON.stringify(payload)
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        });
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        if (!response.ok) {
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            throw new Error(`HTTP error! status: ${response.status}`);
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        }
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        const data = await response.json();
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        return data.response;
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    } catch (error) {
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        console.error('Error:', error);
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        return null;
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    }
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}
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// 使用示例
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const response = await chatWithOllama(
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    'http://123.45.67.89:11434',
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    'deepseek-r1:7b',
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    '请介绍一下你自己'
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);
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console.log(response);

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import requests
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import time
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import statistics
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def benchmark_ollama(base_url, model, num_requests=10):
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    """性能基准测试"""
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    prompt = "请用一句话介绍人工智能"
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    times = []
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    success_count = 0
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    print(f"开始性能测试 ({num_requests} 次请求)...")
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    for i in range(num_requests):
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        start_time = time.time()
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        try:
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            response = requests.post(
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                f"{base_url}/api/generate",
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                json={
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                    "model": model,
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                    "prompt": prompt,
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                    "stream": False
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                },
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                timeout=30
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            )
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            if response.status_code == 200:
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                success_count += 1
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                elapsed = time.time() - start_time
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                times.append(elapsed)
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                print(f"请求 {i+1}: {elapsed:.2f}s ✅")
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            else:
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                print(f"请求 {i+1}: 失败 ({response.status_code}) ❌")
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        except Exception as e:
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            print(f"请求 {i+1}: 异常 ({str(e)}) ❌")
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    # 统计结果
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    if times:
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        print("\n=== 测试结果 ===")
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        print(f"成功率: {success_count}/{num_requests} ({success_count/num_requests*100:.1f}%)")
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        print(f"平均响应时间: {statistics.mean(times):.2f}s")
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        print(f"中位数响应时间: {statistics.median(times):.2f}s")
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        print(f"最快响应: {min(times):.2f}s")
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        print(f"最慢响应: {max(times):.2f}s")
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        print(f"标准差: {statistics.stdev(times):.2f}s")
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# 运行测试
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benchmark_ollama("http://123.45.67.89:11434", "deepseek-r1:7b")

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# 1. 使用HTTPS（如果支持）
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base_url = "https://ollama.example.com"  # 而非http
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# 2. 脱敏敏感信息
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def sanitize_prompt(prompt):
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    """移除个人信息"""
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    import re
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    # 移除邮箱
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    prompt = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', '[EMAIL]', prompt)
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    # 移除手机号
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    prompt = re.sub(r'\b1[3-9]\d{9}\b', '[PHONE]', prompt)
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    # 移除身份证号
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    prompt = re.sub(r'\b\d{17}[\dXx]\b', '[ID]', prompt)
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    return prompt
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# 3. 使用代理
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proxies = {
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    'http': 'http://proxy.example.com:8080',
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    'https': 'https://proxy.example.com:8080'
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}
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response = requests.post(url, json=payload, proxies=proxies)
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# 4. 限制请求内容
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# 避免发送：密码、密钥、个人身份信息、商业机密

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主题：发现您的Ollama API暴露在公网
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您好，
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我在使用网络空间搜索引擎时，发现您的Ollama API服务（IP: xxx.xxx.xxx.xxx:11434）暴露在公网上，且未启用认证机制。
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这可能导致：
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1. 未经授权的访问和使用
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2. 算力资源被滥用
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3. 潜在的安全风险
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建议您：
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1. 配置防火墙，仅允许信任的IP访问
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2. 使用反向代理添加身份验证
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3. 或仅监听本地接口（127.0.0.1）
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如需帮助，可参考：https://github.com/ollama/ollama/blob/main/docs/faq.md
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祝好，
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[您的名字]

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from openai import OpenAI
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client = OpenAI(
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    api_key="your-api-key",
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    base_url="https://api.deepseek.com/v1"
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)
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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"},
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        {"role": "user", "content": "你好"}
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    ],
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    temperature=0.7,
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    max_tokens=1024
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)
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print(response.choices[0].message.content)

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#!/bin/bash
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# 一键部署多个模型
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echo "正在下载模型..."
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# DeepSeek
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ollama pull deepseek-r1:7b
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# Llama 3
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ollama pull llama3:8b
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# Qwen 2.5
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ollama pull qwen2.5:7b
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# ChatGLM
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ollama pull chatglm4:9b
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echo "下载完成！可用模型："
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ollama list