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詹学伟
Published on 2025-11-23 / 22 Visits
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saa-提示词及提示词模板

#spring ai alibaba

一、说明

提示词在大模型使用中有着举足轻重的作用，好的提示词会让结果非常精准。今天要讲解的是spring-ai-alibaba中的提示词的使用。

另外，本章节统一使用之前的父工程，同时本章节使用的是chatClient（非下面文档中的chatModel）

二、官方文档

https://java2ai.com/docs/frameworks/agent-framework/tutorials/messages

三、代码

代码结构：

POM文件

<?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>com.saa</groupId>
		<artifactId>saa-parent</artifactId>
		<version>0.0.1-SNAPSHOT</version>
	</parent>
	<groupId>com.saa</groupId>
	<artifactId>saa-prompt-template</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<name>saa-prompt-template</name>
	<description>saa-prompt-template</description>
	<properties>
		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
		<maven.compiler.source>17</maven.compiler.source>
		<maven.compiler.target>17</maven.compiler.target>
	</properties>
	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-web</artifactId>
		</dependency>
		<dependency>
			<groupId>com.alibaba.cloud.ai</groupId>
			<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
		</dependency>

		<dependency>
			<groupId>org.projectlombok</groupId>
			<artifactId>lombok</artifactId>
			<version>1.18.34</version>
			<optional>true</optional>
		</dependency>

		<dependency>
			<groupId>cn.hutool</groupId>
			<artifactId>hutool-all</artifactId>
			<version>5.8.16</version>
		</dependency>
		<dependency>
			<groupId>junit</groupId>
			<artifactId>junit</artifactId>
			<scope>test</scope>
		</dependency>

		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-test</artifactId>
		</dependency>
	</dependencies>


	<build>
		<plugins>
			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-compiler-plugin</artifactId>
				<version>3.8.1</version>
				<configuration>
					<source>17</source>
					<target>17</target>
					<encoding>UTF-8</encoding>
				</configuration>
			</plugin>
			<plugin>
				<groupId>org.springframework.boot</groupId>
				<artifactId>spring-boot-maven-plugin</artifactId>
				<version>${spring-boot.version}</version>
			</plugin>
		</plugins>
	</build>

</project>

YML文件：

spring:
  application:
    name: saa-prompt-template
  ai:
    dashscope:
      api-key: sk-09c7b571687b46d5a2e25a03fbddxxxx
      base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
      chat:
        options:
          model: qwen3-max

server:
  port: 8084
  servlet:
    encoding:
      enabled: true
      force: true
      charset: UTF-8

提示词模板文件：

讲一个关于{topic}的故事，并以{output_format}格式输出，字数在{wordCount}左右

配置类：

package com.saa.prompt.template.config;

import com.alibaba.cloud.ai.dashscope.api.DashScopeApi;
import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.prompt.ChatOptions;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

/**
 * @author zhanxuewei
 */
@Configuration
public class SsaLLMConfig {


    @Value("${spring.ai.dashscope.api-key}")
    private String apiKey;

    private static final String MODEL_DEEPSEEK = "deepseek-v3";
    private static final String MODEL_QWEN = "qwen-max";

    @Bean(name = "deepseek")
    public ChatModel deepseek() {
        return DashScopeChatModel.builder()
                .dashScopeApi(DashScopeApi.builder().apiKey(apiKey).build())
                .defaultOptions(DashScopeChatOptions.builder().withModel(MODEL_DEEPSEEK).build())
                .build();
    }

    @Bean(name = "qwen")
    public ChatModel qwen() {
        return DashScopeChatModel.builder()
                .dashScopeApi(DashScopeApi.builder().apiKey(apiKey).build())
                .defaultOptions(DashScopeChatOptions.builder().withModel(MODEL_QWEN).build())
                .build();
    }

    @Bean(name = "deepseekChatClient")
    public ChatClient deepeekChatClient(@Qualifier("deepseek") ChatModel deepseek) {
        return ChatClient.builder(deepseek)
                .defaultOptions(ChatOptions.builder().model(MODEL_DEEPSEEK).build())
                .build();
    }

    @Bean(name = "qwenChatClient")
    public ChatClient qwenChatClient(@Qualifier("qwen") ChatModel qwen) {
        return ChatClient.builder(qwen)
                .defaultOptions(ChatOptions.builder().model(MODEL_QWEN).build())
                .build();
    }

}

控制器：

package com.saa.prompt.template.controller;

import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.PromptTemplate;
import org.springframework.ai.chat.prompt.SystemPromptTemplate;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;

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

@Slf4j
@RequestMapping("/prompt/template")
@RestController
public class PromptTemplateController {

    @Resource(name = "deepseek")
    private ChatModel deepseekChatModel;

    @Resource(name = "qwen")
    private ChatModel qwenChatModel;

    @Resource(name = "deepseekChatClient")
    private ChatClient deepseekChatClient;

    @Resource(name = "qwenChatClient")
    private ChatClient qwenChatClient;

    @Value("classpath:/prompttemplate/story.txt")
    private org.springframework.core.io.Resource userTemplate;

    @GetMapping("/chat")
    public Flux<String> chat(@RequestParam("topic") String topic,
                             @RequestParam("output_format") String output_format,
                             @RequestParam("wordCount") String wordCount) {
        PromptTemplate promptTemplate = new PromptTemplate("讲一个关于{topic}的故事，并以{output_format}格式输出，字数在{wordCount}左右");

        Prompt prompt = promptTemplate.create(Map.of(
                "topic", topic,
                "output_format", output_format,
                "wordCount", wordCount
        ));
        Flux<String> content = deepseekChatClient.prompt(prompt).stream().content();
        return content;
    }

    @GetMapping("/chat2")
    public Flux<String> chat2(@RequestParam("topic") String topic,
                              @RequestParam("output_format") String output_format,
                              @RequestParam("wordCount") String wordCount) {
        PromptTemplate promptTemplate = new PromptTemplate(userTemplate);

        Prompt prompt = promptTemplate.create(Map.of(
                "topic", topic,
                "output_format", output_format,
                "wordCount", wordCount
        ));
        Flux<String> content = deepseekChatClient.prompt(prompt).stream().content();
        return content;
    }

    @GetMapping("/chat3")
    public Flux<String> chat3(@RequestParam("systemTopic") String systemTopic,
                              @RequestParam("userTopic") String userTopic) {
        SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate("你是{systemTopic}助手，是回答{systemTopic},其它无可奉告，并以html格式返回结果");
        Message systemPromptTemplateMessage = systemPromptTemplate.createMessage(Map.of("systemTopic", systemTopic));

        PromptTemplate promptTemplate = new PromptTemplate("解释一下{userTopic}");
        Message userMessage = promptTemplate.createMessage(Map.of("userTopic", userTopic));

        Prompt prompt = new Prompt(List.of(systemPromptTemplateMessage, userMessage));

       return deepseekChatClient.prompt(prompt).stream().content();

    }
}

四、测试结果

SAA-多模型共存
saa-会话持久化

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