SpringAI项目之Ollama大模型工具【聊天机器人】

备注：

（1）大模型Ollama工具 安装 千问 或 llama3大模型

（2）SpringAI是新的Spring依赖，JDK17以上，Springboot3.2以上版本。

SpringAI项目开发【聊天机器人】

1、pom.xml依赖引入：

（1）首先构建一个springboot的web项目：

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

（2）引入SpringAI大模型依赖 -- spring-ai-ollama

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
    <version>1.0.0-SNAPSHOT</version>
</dependency>

<!-- 因为maven暂时下载不了SpringAIOllama依赖，引用Spring快照依赖包 -->
<repositories>
    <repository>
        <id>spring-snapshots</id>
        <name>Spring Snapshots</name>
        <url>https://repo.spring.io/snapshot</url>
        <releases>
            <enabled>false</enabled>
        </releases>
    </repository>
</repositories>

总结：完整的pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.2.4</version>
        <relativePath/> <!-- lookup parent from repository -->
    </parent>
    <groupId>com.new3s</groupId>
    <artifactId>springAiTest428</artifactId>
    <version>1.0-SNAPSHOT</version>
    <name>springAiTest428</name>
    <description>springAiTest428</description>
 
    <properties>
        <java.version>22</java.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
 
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
            <version>1.0.0-SNAPSHOT</version>
        </dependency>
 
    </dependencies>
 
    <repositories>
        <repository>
            <id>spring-snapshots</id>
            <name>Spring Snapshots</name>
            <url>https://repo.spring.io/snapshot</url>
            <releases>
                <enabled>false</enabled>
            </releases>
        </repository>
    </repositories>
</project>

2、创建一个启动类

package com.company;
 
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
 
@SpringBootApplication
public class OllamaApplcation {
 
    public static void main(String[] args) {
        SpringApplication.run(OllamaApplcation.class, args);
        System.out.println("Hi, Spring AI Ollama!");
    }
 
}

3、创建Controller

1> 千问的Controller

package com.company.controller;
 
import jakarta.annotation.Resource;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatClient;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
 
 
@RestController
public class QianWenController {
 
    @Resource
    private OllamaChatClient ollamaChatClient;
 
    @RequestMapping(value = "/qianwen-ai")
    private Object ollama(@RequestParam(value = "msg") String msg) {
        String called = ollamaChatClient.call(msg);
        System.out.println(called);
        return called;
    }
 
    @RequestMapping(value = "/qianwen-ai2")
    private Object ollama2(@RequestParam(value = "msg") String msg) {
        ChatResponse chatResponse = ollamaChatClient.call(new Prompt(msg, OllamaOptions.create()
                // 使用哪个模型：qwen:0.5b-chat模型
                .withModel("qwen:0.5b-chat")
                // 温度：温度值越高，准确率下降；温度值越低，准确率提高了
                .withTemperature(0.4F)
        ));
 
        //chatResponse.getResult().getOutput().getContent();
        System.out.println(chatResponse.getResult());
        return chatResponse.getResult();
    }
 
}

2> llama3的Controller

package com.company.controller;
 
import jakarta.annotation.Resource;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatClient;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
 
@RestController
public class LlamaController {
 
    @Resource
    private OllamaChatClient ollamaChatClient;
 
    @RequestMapping(value = "/llama-ai")
    private Object ollama(@RequestParam(value = "msg") String msg) {
        String called = ollamaChatClient.call(msg);
        System.out.println(called);
        return called;
    }
 
    @RequestMapping(value = "/llama-ai2")
    private Object ollama2(@RequestParam(value = "msg") String msg) {
        ChatResponse chatResponse = ollamaChatClient.call(new Prompt(msg, OllamaOptions.create()
                // 使用哪个模型：llama3模型
                .withModel("llama3:8b")
                // 温度：温度值越高，准确率下降；温度值越低，准确率提高了
                .withTemperature(0.4F)
        ));
 
        System.out.println(chatResponse.getResult().getOutput());
        return chatResponse.getResult().getOutput();
    }
 
}

4、资源配置【注意：Ollama的默认端口是11434】

spring:
  ai:
    ollma:
      base-url: http://localhost:11434
      chat:
        options:
# 配置文件指定时，现在程序中指定的模型，程序没有指定模型在对应查找配置中的模型
#          model: qwen:0.5b-chat
          model: llama3:8b
 
 

5、模型的运行结果

1> 调用llama3模型的对话接口

2> 调用qianwen模型的对话接口