Ollama使用

Ollama是一个用于在本地计算机上运行大模型的软件
软件运行后监听11434端口，自己写的程序要调大模型就用这个端口

ollama命令
ollama list：显示模型列表
ollama show：显示模型的信息
ollama pull：拉取模型
ollama push：推送模型
ollama cp：拷贝一个模型
ollama rm：删除一个模型
ollama run：运行一个模型

ollama全是命令行下操作，所以结合web客户端界面使用【安装可选】
主流的web工具
1 Openwebui
2 LobeChat,功能强大，可调用Ollama的模型，也可调用openai,google的等，在设置界面中配置url和key即可

spring Ai框架调用

1 pom.xml，注意添加的依赖和配置了仓库

<?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>3.2.5</version>
		<relativePath/> <!-- lookup parent from repository -->
	</parent>
	<groupId>com.example</groupId>
	<artifactId>spring-ai-ollama</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<name>spring-ai-ollama</name>
	<description>spring-ai-ollama</description>
	<properties>
		<java.version>17</java.version>
		<spring-ai.version>0.8.1</spring-ai.version>
	</properties>
	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-web</artifactId>
		</dependency>

		<dependency>
			<groupId>io.springboot.ai</groupId>
			<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
			<version>1.0.0</version>
		</dependency>

		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-devtools</artifactId>
			<scope>runtime</scope>
			<optional>true</optional>
		</dependency>
		<dependency>
			<groupId>org.projectlombok</groupId>
			<artifactId>lombok</artifactId>
			<optional>true</optional>
		</dependency>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-test</artifactId>
			<scope>test</scope>
		</dependency>
	</dependencies>
	<dependencyManagement>
		<dependencies>
			<dependency>
				<groupId>org.springframework.ai</groupId>
				<artifactId>spring-ai-bom</artifactId>
				<version>${spring-ai.version}</version>
				<type>pom</type>
				<scope>import</scope>
			</dependency>
		</dependencies>
	</dependencyManagement>

	<build>
		<plugins>
			<plugin>
				<groupId>org.springframework.boot</groupId>
				<artifactId>spring-boot-maven-plugin</artifactId>
				<configuration>
					<excludes>
						<exclude>
							<groupId>org.projectlombok</groupId>
							<artifactId>lombok</artifactId>
						</exclude>
					</excludes>
				</configuration>
			</plugin>
		</plugins>
	</build>
	<repositories>
		<repository>
			<id>spring-milestones</id>
			<name>Spring Milestones</name>
			<url>https://repo.spring.io/milestone</url>
			<snapshots>
				<enabled>false</enabled>
			</snapshots>
		</repository>
	</repositories>

</project>

2 yml配置，写自己的 Ollama 地址，模型用哪个，先用Ollama去下载

spring:
  application:
    name: spring-ai-ollama

  ai:
    ollama:
      base-url: http://120.55.99.218:11434
      chat:
        options:
          model: gemma:7b

3 测试

import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.messages.AssistantMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.PromptTemplate;
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.*;

@RestController
public class AiController {

    @Autowired
    private OllamaChatClient ollamaChatClient;

    @GetMapping(value = "/chat_1")
    public String chat_1(@RequestParam(value = "message") String message) {
        String call = ollamaChatClient.call(message);
        System.out.println("模型回答 = " + call);
        return call;
    }

    @GetMapping(value = "/chat_2")
    public Object chat_2(@RequestParam(value = "message") String message) {
        Prompt prompt = new Prompt(
                message,
                OllamaOptions.create()
                        //代码中配置，会覆盖application.yml中的配置
                        .withModel("gemma:7b") //使用什么大模型
                        .withTemperature(0.9F) //温度高，更发散,准确性降低，温度低，更保守，准确性高
        );

        ChatResponse call = ollamaChatClient.call(prompt);
        AssistantMessage output = call.getResult().getOutput();
        System.out.println("模型回答 = " + output.getContent());
        return output;
    }

    @GetMapping("/chat_3/{size}")
    public String chatYear(@PathVariable("size") Integer size) {
        String message = "随便写一句话,{size} 字以内";
        PromptTemplate promptTemplate = new PromptTemplate(message);
        promptTemplate.add("size", size);
        System.out.println(promptTemplate.render());
        return ollamaChatClient.call(promptTemplate.render());
    }
}