Ollama【部署 02】Linux本地化部署及SpringBoot2.X集成Ollama（ollama-linux-amd64.tgz最新版本 0.6.2）

原创

yuanzhengme

发布于 2025-08-22 18:39:11

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文章被收录于专栏：Linux相关Linux相关

安装资源分享：

百度网盘链接: https://pan.baidu.com/s/17qK0Nx73bFOsicLgLmA8-A?pwd=tc61 提取码: tc61

包含文件：

Windows OllamaStep.exe（版本 0.5.7）
Linux ollama-linux-amd64-0.3.9.tgz
Linux ollama-linux-amd64-0.5.11.tgz
Linux ollama-linux-amd64-0.6.2.tgz
Chatbox-1.9.8-Step.exe（实现客户端操作）
AnythingLLMDesktop-v1.7.4.exe（实现客户端操作）

1.本地部署

1.1 软件安装

Ollama 官网

1.1.1 脚本安装

curl -fsSL https://ollama.com/install.sh | sh

脚本安装默认路径是 /usr/local，如果此目录挂载空间不大，建议手动安装。

1.1.2 手动安装

点击 Manual install instructions可查看《手动安装文档》包含：

一般 Linux 安装
AMD GPU install
ARM64 install
Adding Ollama as a startup service (recommended)
Install CUDA drivers (optional)
Install AMD ROCm drivers (optional)
Customizing
Updating
Installing specific versions
Viewing logs
Uninstall

本次进行一般安装，流程如下：

curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz

下载非常慢，文件1604M显示需要54 小时有点难绷，网盘上也有分享，版本是 0.3.9。

下载：

# 使用后台下载
nohup curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz > download.log 2>&1 &
# 使用 wget 进行断点续传
nohup wget -c https://ollama.com/download/ollama-linux-amd64.tgz > download.log 2>&1 &
# wget跟随的最新url
https://github.com/ollama/ollama/releases/latest/download/ollama-linux-amd64.tgz

下载失败后可以续传，虽然还是很慢，但是之前下载的数据没有白费：

解压、启动：

# 解压
sudo tar -C /usr -xzf ollama-linux-amd64.tgz
# 启动
ollama serve
# 验证
ollama -v

# 使用后台启动
nohup ./ollama serve >> serve.log 2>&1 &

# CPU
msg="inference compute" id=0 library=cpu variant=avx2 compute="" driver=0.0 name="" total="156.1 GiB" available="105.4 GiB"
# GPU
msg="inference compute" id=GPU-5892a465-7090-90e9-d072-f04f3e56380a library=cuda variant=v11 compute=7.5 driver=0.0 name="" total="14.8 GiB" available="11.9 GiB"'
# 启动后 调用时的信息【1.5b】
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04    Driver Version: 460.27.04    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:2F:00.0 Off |                    0 |
| N/A   77C    P0    34W /  70W |   2831MiB / 15109MiB |     11%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A    281537      C   ...a_v11/ollama_llama_server     1809MiB |
+-----------------------------------------------------------------------------+

1.2 模型安装

安装命令跟 Windows 版本的一致：

ollama run deepseek-r1:1.5b

我使用的是 root 用户，模型安装在 /root/.ollama/models目录下。

1.3 端口映射

由于默认绑定的是 127.0.0.1使用 Nginx 将 Ollama 服务的端口映射出来：

server {
  listen 11435;
  server_name localhost;

  location / {
    proxy_pass http://127.0.0.1:11434;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Forwarded-Proto $scheme;
  }
}

要注意防火墙。

也可以设置 Ollama 的环境变量 OLLAMA_HOST=0.0.0.0 监听所有可用的网络接口，从而允许外部网络访问：

export OLLAMA_HOST=0.0.0.0:11434
nohup ./ollama serve >> serve.log 2>&1 &

Ollama 的环境变量：

Usage:
  ollama serve [flags]

Aliases:
  serve, start

Flags:
  -h, --help   help for serve

Environment Variables:
      OLLAMA_DEBUG               Show additional debug information (e.g. OLLAMA_DEBUG=1)
      OLLAMA_HOST                IP Address for the ollama server (default 127.0.0.1:11434)
      OLLAMA_KEEP_ALIVE          The duration that models stay loaded in memory (default "5m")
      OLLAMA_MAX_LOADED_MODELS   Maximum number of loaded models per GPU
      OLLAMA_MAX_QUEUE           Maximum number of queued requests
      OLLAMA_MODELS              The path to the models directory
      OLLAMA_NUM_PARALLEL        Maximum number of parallel requests
      OLLAMA_NOPRUNE             Do not prune model blobs on startup
      OLLAMA_ORIGINS             A comma separated list of allowed origins
      OLLAMA_SCHED_SPREAD        Always schedule model across all GPUs
      OLLAMA_TMPDIR              Location for temporary files
      OLLAMA_FLASH_ATTENTION     Enabled flash attention
      OLLAMA_LLM_LIBRARY         Set LLM library to bypass autodetection

2.SpringBoot 集成

Spring AI 官方文档 Ollama 的 API 说明文档。

<font style="color:rgb(190, 184, 176);background-color:rgb(31, 35, 41);">Spring AI supports Spring Boot 3.2.x and 3.3.x</font>
<font style="color:rgb(190, 184, 176);background-color:rgb(31, 35, 41);">最低 JDK 要求为 17</font>

由于项目使用的依然是 JDK8 这里自行封装 API。

2.1 配置信息及配置类

spring:
  ai:
    ollama:
      baseUrl: http://192.168.0.1:11434
      temperature: 0.8
      maxTokens: 4096
      stream: false

@Data
@Component
@ConfigurationProperties(prefix = "spring.ai.ollama")
public class OllamaConfig {
    /**
     * 调用路径
     */
    public String baseUrl;
    /**
     * 严谨与想象【0-2】
     */
    public double temperature = 0.8;
    /**
     * 最大token量
     */
    public int maxTokens = 4096;
    /**
     * 是否流输出
     */
    public boolean stream = false;
}

2.2 请求及响应对象

公共的信息类：

@Data
@NoArgsConstructor
@AllArgsConstructor
public class Message {

    @ApiModelProperty("角色")
    private String role;

    @ApiModelProperty("内容")
    private String content;

}

请求对象封装，两个请求对象属性很相似：

@Data
@NoArgsConstructor
@AllArgsConstructor
public class GenerateReq {

    @ApiModelProperty("模型")
    private String model;

    @ApiModelProperty("提示")
    private String prompt;

    @ApiModelProperty("是否流输出")
    private boolean stream = false;

}

@Data
@NoArgsConstructor
@AllArgsConstructor
public class ChatReq {

    @ApiModelProperty("模型")
    private String model;

    @ApiModelProperty("提示")
    private List<Message> messages;

    @ApiModelProperty("是否流输出")
    private boolean stream = false;

}

响应对象封装，也是比较相似的：

@Data
@NoArgsConstructor
@AllArgsConstructor
public class GenerateRes {

    private String model;
    private String created_at;
    private String response;
    private String done_reason;
    private boolean done;
    private int total_duration;
    private int load_duration;
    private int prompt_eval_count;
    private int prompt_eval_duration;
    private int eval_count;
    private int eval_duration;

}

@Data
@NoArgsConstructor
@AllArgsConstructor
public class ChatRes {
    
    private String model;
    private String created_at;
    private Message message;
    private String done_reason;
    private boolean done;
    private long total_duration;
    private long load_duration;
    private long prompt_eval_count;
    private long prompt_eval_duration;
    private long eval_count;
    private long eval_duration;

}

2.3 OllamaController

简单弄两个接口进行测试：

@Slf4j
@RestController
@RequestMapping("/api")
public class OllamaController {

    @Resource
    private OllamaComponent ollamaComponent;

    @PostMapping("/generate")
    @ApiOperation(value = "生成补全", tags = {"Chat"})
    public R<Object> generate(@RequestBody GenerateReq req) {
        return ollamaComponent.generate(req);
    }

    @PostMapping("/chat")
    @ApiOperation(value = "生成聊天补全", tags = {"Chat"})
    public R<Object> chat(@RequestBody ChatReq req) {
        return ollamaComponent.chat(req);
    }

}

2.4 OllamaComponent

调用服务的类：

@Slf4j
@Component
public class OllamaComponent {

    @Resource
    private OllamaUtil ollamaUtil;

    public R<Object> generate(GenerateReq req) {
        GenerateRes generateRes = null;
        try {
            generateRes = ollamaUtil.generate(req);
        } catch (Exception e) {
            e.printStackTrace();
            log.error("generate Failed!");
        }
        return R.ok(generateRes);
    }

    public R<Object> chat(ChatReq req) {
        ChatRes chatRes = null;
        try {
            chatRes = ollamaUtil.chat(req);
        } catch (Exception e) {
            e.printStackTrace();
            log.error("chat Failed!");
        }
        return R.ok(chatRes);
    }
}

2.5 OllamaUtil

发送接口请求的工具类：

@Slf4j
@Component
public class OllamaUtil {

    @Resource
    private OllamaConfig ollamaConfig;

    private static final int CODE200 = 200;

    public GenerateRes generate(GenerateReq req) {
        try {
            String bodyStr = ollamaRemoteApi("/api/generate", req);
            return JSON.parseObject(bodyStr, GenerateRes.class);
        } catch (IOException e) {
            e.printStackTrace();
        }
        return null;
    }

    public ChatRes chat(ChatReq req) {
        try {
            String bodyStr = ollamaRemoteApi("/api/chat", req);
            return JSON.parseObject(bodyStr, ChatRes.class);
        } catch (IOException e) {
            e.printStackTrace();
        }
        return null;
    }

    private String ollamaRemoteApi(String url, Object req) throws IOException {
        String bodyStr = "";
        OkHttpClient client = OkHttpClientConfig.getUnsafeOkHttpClient();
        MediaType mediaType = MediaType.parse("application/json");
        RequestBody body = RequestBody.create(mediaType, JSON.toJSONString(req));
        Request request = new Request.Builder()
                .url(ollamaConfig.baseUrl + url)
                .method("POST", body)
                .addHeader("User-Agent", "Apifox/1.0.0 (https://apifox.com)")
                .addHeader("Content-Type", "application/json")
                .addHeader("Accept", "*/*")
                .addHeader("Connection", "keep-alive")
                .build();
        Response response = client.newCall(request).execute();
        if (response.code() == CODE200) {
            bodyStr = response.body().string();
        }
        return bodyStr;
    }
}

2.6 OkHttpClientConfig

这个类主要是为了绕过 https，一般的请求也适用，由开发工程师 dongliang7贡献，特此感谢：

/**
 * 创建 OkHttpClient 不进行SSL（证书）验证
 *
 * @author dongliang7
 * @date  2021年11月19日 09:50:00
 */
@Slf4j
public class OkHttpClientConfig {

    public static OkHttpClient getUnsafeOkHttpClient() {
        try {
            // 创建不验证证书链的信任管理器
            final TrustManager[] trustAllCerts = new TrustManager[]{
                    new X509TrustManager() {
                        @Override
                        public void checkClientTrusted(java.security.cert.X509Certificate[] chain, String authType) {
                        }

                        @Override
                        public void checkServerTrusted(java.security.cert.X509Certificate[] chain, String authType) {
                        }

                        @Override
                        public java.security.cert.X509Certificate[] getAcceptedIssuers() {
                            return new java.security.cert.X509Certificate[]{};
                        }
                    }
            };
            // 安装全信任信任管理器
            final SSLContext sslContext = SSLContext.getInstance("SSL");
            sslContext.init(null, trustAllCerts, new java.security.SecureRandom());
            // 使用我们完全信任的管理器创建 ssl 套接字工厂
            final SSLSocketFactory sslSocketFactory = sslContext.getSocketFactory();
            OkHttpClient.Builder builder = new OkHttpClient.Builder()
                    .connectTimeout(60, TimeUnit.SECONDS)
                    .readTimeout(60, TimeUnit.SECONDS)
                    .writeTimeout(120, TimeUnit.SECONDS);
            builder.sslSocketFactory(sslSocketFactory);
            builder.hostnameVerifier((hostname, session) -> true);
            return builder.build();
        } catch (Exception e) {
            log.error("创建OkHttpClient不进行SSL（证书）验证失败：{}", e.getMessage());
            throw new RuntimeException(e);
        }
    }
}

3.更新日志

20250326 网盘添加 ollama-linux-amd64-0.5.11.tgz 0.5.11版本资源

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

人工智能

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

人工智能

#ollama

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