spring boot简单运用ollama大模型（windows版本）

1、下载模型（windows为例）

　　打开官方网站https://ollama.com/download/windows。

　　打开exe文件，打开命令行工具，直接运行ollama run 要下载的模型（右上角的models能找到你想要的，例子以llama3.1展示，spring ai暂时非全支持，支持模型步骤2列出）

　　运行完后直接是这样显示

　　

　　至此，模型就安装完毕。

2、创建spring 项目

　　1、创建spring boot项目。以maven为例（spring ai 需要jdk17以上的版本）

    <properties>
        <java.version>17</java.version>
        <spring-ai.version>1.0.0-M1</spring-ai.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
        </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>
            </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>

　　2、修改配置（application.yml)

spring:
  application:
    name: demo
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: llama3.1
          temperature: 0.7

　　这个里面对应的模型要你下载的，我们是llama3.1，地址是本机的地址， spring ai 支持的模型仅下列

　

　　3、创建基于ollima聊天服务

import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;

import java.util.Map;

@RestController
public class ChatController {
    private final OllamaChatModel chatModel;

    @Autowired
    public ChatController(OllamaChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
    public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return chatModel.stream(prompt);
    }

}

　　4、创建embedding服务

import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.embedding.EmbeddingResponse;
import org.springframework.ai.ollama.OllamaEmbeddingModel;
import org.springframework.ai.ollama.api.OllamaApi;
import org.springframework.ai.ollama.api.OllamaModel;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

import java.util.List;
import java.util.Map;

@RestController
public class EmbeddingController {

    private EmbeddingModel getEmbeddingModel() {
        var ollamaApi = new OllamaApi();
        var embeddingModel = new OllamaEmbeddingModel(ollamaApi, OllamaOptions.builder().withModel(OllamaModel.LLAMA3_1.id()).build());
        return embeddingModel;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        var embeddingModel = getEmbeddingModel();
        EmbeddingResponse embeddingResponse = embeddingModel.embedForResponse(List.of(message));
        System.out.println(embeddingModel.dimensions());
        return Map.of("embedding", embeddingResponse);
    }
}

　　至此，基本的调用ollam服务就完成。