AI-学习记录

FANSEA2025-04-032025-07-28

AI-Learning

AI的应用方面

Agent+Function Calling

Rag Embeddings

开发框架选择

对话机器人

基本调用

引入依赖

<dependency>
			<groupId>org.springframework.ai</groupId>
			<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
		</dependency>
		
<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>

配置Client

@Configuration
public class CommonConfiguration {
    @Bean
    public ChatClient chatClient(OpenAiChatModel model){
        return ChatClient
                .builder(model)
                .defaultSystem("你是一个AI助手，名字叫帆帆，你要热心回答用户的问题")
                .build();
    }
}

生成会话

public class AiController {

    private final ChatClient chatClient;

    // 指定字符编码否则无法正确展示
    @RequestMapping(value = "/chat",produces = "text/html;charset=utf-8")
    public Flux<String> chat(String prompt){
        return chatClient.prompt()
                .user(prompt)
                .stream()
                .content();
    }
}

会话日志/记忆

SimpleLoggerAdvisor：会话日志控制台记录

需要配置日志记录级别

1
2
3

logging:
  level:
    org.springframework.ai.chat.client.advisor: debug

MessageChatMemoryAdvisor：会话记录

配置Advisor

@Configuration
public class CommonConfiguration {

    // 配置会话记录advisor
    @Bean
    public ChatMemory chatMemory(){
        // 基于map的内存会话保存
        return new InMemoryChatMemory();
    }


    @Bean
    public ChatClient chatClient(OpenAiChatModel model, ChatMemory chatMemory){
        return ChatClient
                .builder(model)
                .defaultAdvisors(
                        new MessageChatMemoryAdvisor(chatMemory),
                        new SimpleLoggerAdvisor()
                )
                .defaultSystem("你是一个可爱的AI助手，名字叫帆帆，你要热心回答用户的问题")
                .build();
    }
}

在对话回复API中加入会话ID

// 指定字符编码否则无法正确展示
    @RequestMapping(value = "/chat",produces = "text/html;charset=utf-8")
    public Flux<String> chat(String prompt,String chatId){
        return chatClient.prompt()
                .user(prompt)
                .advisors(a -> a.param(CHAT_MEMORY_CONVERSATION_ID_KEY,chatId)) // 加入会话id
                .stream()
                .content();
    }

会话历史

会话历史其实保存在InMemoryChatMemory，但是他包含其他不需要的消息类型，需要过滤处理

@Data
public class MessageVo {
    private String role;
    private String content;

    // 构造器
    public MessageVo(Message message) {
        switch (message.getMessageType()) {
            case USER:
                this.role = "user";break;
            case ASSISTANT:
                this.role = "assistant";break;
            default:
                this.role = "";break;
        }
        this.content = message.getText();
    }
}

@GetMapping("/{type}/{chatId}")
    public List<MessageVo> getHistoryChat(@PathVariable String type, @PathVariable String chatId){
        List<Message> messages = chatMemory.get(chatId, Integer.MAX_VALUE);
        return messages.stream().map(MessageVo::new).toList();
}

SDK

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.JsonUtils;

public class Main {
    private static final String modelName = "qwen-audio-turbo-latest";
    public static void MultiRoundConversationCall() throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage systemMessage = MultiModalMessage.builder().role(Role.SYSTEM.getValue())
                .content(Arrays.asList(Collections.singletonMap("text", "You are a helpful assistant."))).build();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("audio", "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3"),
                        Collections.singletonMap("text", "这段音频在说什么?"))).build();
        List<MultiModalMessage> messages = new ArrayList<>();
        messages.add(systemMessage);
        messages.add(userMessage);
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                .model(modelName)
                .messages(messages)
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println("第一轮回复："+JsonUtils.toJson(result));
        // add the result to conversation
        messages.add(result.getOutput().getChoices().get(0).getMessage());
        MultiModalMessage msg = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("text", "简单介绍这家公司。"))).build();
        messages.add(msg);
        // new messages
        param.setMessages((List)messages);
        result = conv.call(param);
        System.out.println("第二轮回复："+JsonUtils.toJson(result));
    }
    public static void main(String[] args) {
        try {
            MultiRoundConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

FuctionCall

Tool 主要用于：

信息检索。此类别中的工具可用于从外部源（如数据库、Web 服务、文件系统或 Web 搜索引擎）检索信息。目标是增强模型的知识，使其能够回答其他方式无法回答的问题。因此，它们可用于检索增强生成（RAG）方案。例如，工具可用于检索给定位置的当前天气、检索最新的新闻文章或查询数据库以获取特定记录。
采取行动。此类别中的工具可用于在软件系统中执行作，例如发送电子邮件、在数据库中创建新记录、提交表单或触发工作流。目标是自动执行原本需要人工干预或显式编程的任务。例如，可以使用工具为与聊天机器人交互的客户预订航班，在网页上填写表单，或在代码生成场景中实现基于自动测试（TDD）的 Java 类。