Spring-ai ollama + tools基础使用

spring-ai 使用版本与坐标

<ai.version>1.0.0-M6</ai.version>

		<dependency>
			<groupId>org.springframework.ai</groupId>
			<artifactId>spring-ai-core</artifactId>
			<version>${ai.version}</version>
		</dependency>
		<dependency>
			<groupId>org.springframework.ai</groupId>
			<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
			<version>${ai.version}</version>
		</dependency>

Controller 分别使用Ollama中chatModel 与Core中chatModel+Tools

package com.vecter.mini.program.web.controller;

import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
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.*;

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

    @Resource
    private OllamaChatModel chatModel;

    @Resource
    private ChatClient chatModelClient;

    /**
     * 本地调用ollama
     * @param message
     * @return
     */
    @PostMapping("/ollam")
    public String ollam(@RequestParam String message){
        message = "请使用中文简体回答：" + message;
        Prompt prompt = new Prompt(new UserMessage(message));
        ChatResponse call = chatModel.call(prompt);
        String callstr = call.toString();
//        String call = ollamaChatModel.call(message);
        log.info(callstr);
        return message;
    }

    /**
     * 挂载tools调用Ollama
     * @param message
     * @return
     */
    @GetMapping("/mcpServer")
    public String mcpServer(@RequestParam String message){
        ChatClient.CallResponseSpec call = chatModelClient.prompt(message).call();
        log.info(call.content());
        return call.content();
    }


}

Tools服务

package com.vecter.mini.program.business.service;

import org.springframework.ai.tool.annotation.Tool;
import org.springframework.ai.tool.annotation.ToolParam;
import org.springframework.stereotype.Service;

import java.util.Random;

@Service
public class AiWebfluxService {

    @Tool(description = "通给出的物料编码获取物料库存信息")
    public String getWeather(@ToolParam(description = "物料编码") String productName) {
        // 创建 Random 对象
        Random random = new Random();
        // 生成一个随机整数
        int randomInt = random.nextInt();
        // 生成一个 0 到 99 之间的随机整数
        int randomIntInRange = random.nextInt(100);

        return "物料编码" +productName +  "库存是" + randomIntInRange+"PCS";
    }
}

装载Tools入Chat

package com.vecter.mini.program.business.config;

import com.vecter.mini.program.business.service.AiWebfluxService;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.tool.method.MethodToolCallbackProvider;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class McpToolsConfig {

    @Resource
    private ChatModel chatModel;

    @Resource
    private AiWebfluxService aiWebfluxService;
    @Bean
    public ChatClient chatModelClient() {
        return ChatClient
                .builder(chatModel)
                .defaultTools(MethodToolCallbackProvider.builder().toolObjects(aiWebfluxService).build())
                .build();
    }

}

源码地址: github.com/Vecter8357/…