Ollama是一个用于大数据和机器学习的平台，它可以帮助企业进行数据处理、分析和决策制定。
１、在Spring Boot项目pom.xml中添加Ollama客户端库依赖

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

２、在yaml文件中配置ollama的地址和模型

spring:
  ai:
    ollama:
      base-url: http://127.0.0.1:11434
      chat:
        model: qwen:14b

配置文件指定了 Ollama API 地址和端口，同时指定了默认模型qwen:14b
3、 创建Controller，使用OllamaChatClient进行文字生成或者对话

 
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.beans.factory.annotation.Qualifier;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
 
@RestController
public class ChatClientController {
 
    @Autowired
    @Qualifier("ollamaChatClient")
    private OllamaChatClient ollamaChatClient;
 
    /**
     * 
     */
    @GetMapping("/ollama/chat/v1")
    public String ollamaChat(@RequestParam String msg) {
        return this.ollamaChatClient.call(msg);
    }
 
    /**
     * prompt模板功能
     */
    @GetMapping("/ollama/chat/v2")
    public Object ollamaChatV2(@RequestParam String msg) {
        Prompt prompt = new Prompt(msg);
        ChatResponse chatResponse = ollamaChatClient.call(prompt);
        return chatResponse;
    }
 
    /**
     * 
     */
    @GetMapping("/ollama/chat/v3")
    public Object ollamaChatV3(@RequestParam String msg) {
        Prompt prompt = new Prompt(
                msg,
                OllamaOptions.create()
                        .withModel("qwen:14b")
                        .withTemperature(0.4F));
        ChatResponse chatResponse = ollamaChatClient.call(prompt);
        return chatResponse.getResult().getOutput().getContent();
    }
}