[실습] Ollama를 활용한 채팅 API 환경 구축

JSCODE 시니

2026. 06. 13.

✅ 1. build.gradle 수정

dependencies {
	implementation 'org.springframework.boot:spring-boot-starter-webmvc'
	implementation 'org.springframework.ai:spring-ai-starter-model-ollama'
	// implementation 'org.springframework.ai:spring-ai-starter-model-openai'
	testImplementation 'org.springframework.boot:spring-boot-starter-webmvc-test'
	testRuntimeOnly 'org.junit.platform:junit-platform-launcher'
}

✅ 2. application.yml 수정

    #[2] Ollama 설정
    ollama:
      init:
        pull-model-strategy: when_missing
      chat:
        model: hf.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF

✅ 3. 모델 다운로드

ollama pull hf.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF

✅ 4. Controller 구현

Chat Client API :: Spring AI Reference

The ChatClient offers a fluent API for communicating with an AI Model. It supports both a synchronous and streaming programming model.

https://docs.spring.io/spring-ai/reference/api/chatclient.html

• 우리가 AI와 쉽게 소통할 수 있도록 Spring AI에서 제공하는 객체인 ChatClient

package com.jscode.chat.controller;

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class ChatController {

    private final ChatClient chatClient;

    public ChatController(ChatClient.Builder chatClientBuilder){
        this.chatClient = chatClientBuilder.build();
    }

    @GetMapping("/ai")
    public String generation(String userPrompt){
        return this.chatClient.prompt()
                .user(userPrompt)
                .call()
                .content(); // 받아온 응답 중 메타데이터는 버리고, 순수 content만 추출!
    }
}