什么是Agent的辩论（Debate）模式？如何通过多Agent辩论提升答案质量

publicinterfaceDebateAgent{
    /**
 * 根据问题和历史辩论记录生成当前轮次的论述
 * @paramquestion 待讨论的问题
 * @paramdebateHistory 历史辩论记录
 * @return 当前轮次的论述内容
 */
    StringgenerateArgument(String question,List<DebateRecord> debateHistory);

    /**
 * 获取Agent的角色定位
 */
    StringgetRoleName();

    /**
 * 获取Agent的关注维度
 */
    StringgetFocusDimension();
}

publicclassDebateRecord{
    privateString agentName;
    privateString argument;
    privateint round;
    privatelong timestamp;

    // 构造函数和getter/setter省略
}

publicclassPerformanceAgentimplementsDebateAgent{
    privatefinalLLMClient llmClient;
    privatefinalString rolePrompt;

    publicPerformanceAgent(LLMClient client){
        this.llmClient = client;
        this.rolePrompt ="""
        你是一位关注系统性能的架构师，需要从以下角度论证观点：
        - 系统响应时间和吞吐量
        - 资源利用效率
        - 可扩展性设计

        在辩论中你需要：
        1. 针对对方的论述找出性能相关的薄弱点
        2. 提供具体的性能数据或案例支撑你的观点
        3. 不要简单重复之前的论点，要基于对方的最新回应调整论述策略
        """;
    }

    @Override
    publicStringgenerateArgument(String question,List<DebateRecord> debateHistory){
        StringBuilder promptBuilder =newStringBuilder(rolePrompt)
        .append("\n\n待讨论问题：").append(question);

        // 如果有历史记录，加入上下文
        if(!debateHistory.isEmpty()){
            promptBuilder.append("\n\n辩论历史：");
            for(DebateRecordrecord: debateHistory){
            promptBuilder.append("\n[第").append(record.getRound())
            .append("轮-").append(record.getAgentName())
            .append("]: ").append(record.getArgument());
        }

        // 特别强调需要回应的最新论点
        DebateRecord lastRecord = debateHistory.get(debateHistory.size()-1);
        if(!lastRecord.getAgentName().equals(getRoleName())){
            promptBuilder.append("\n\n请重点回应上述")
            .append(lastRecord.getAgentName())
            .append("提出的观点，特别是与性能相关的部分。");
        }
    }

    return llmClient.generate(promptBuilder.toString());
}

@Override
publicStringgetRoleName(){
    return"性能架构师";
}

@Override
publicStringgetFocusDimension(){
    return"系统性能与可扩展性";
}
}

publicclassSecurityAgentimplementsDebateAgent{
privatefinalLLMClient llmClient;
privatefinalString rolePrompt;

publicSecurityAgent(LLMClient client){
    this.llmClient = client;
    this.rolePrompt ="""
    你是一位关注系统安全的工程师，需要从以下角度论证观点：
    - 潜在的安全漏洞和攻击面
    - 数据保护和隐私合规
    - 权限控制和访问安全

    在辩论中你需要：
    1. 识别对方方案中可能存在的安全风险
    2. 提出具体的安全加固建议
    3. 基于对方的回应调整你的质疑角度，避免重复论述
    """;
}

@Override
publicStringgenerateArgument(String question,List<DebateRecord> debateHistory){
    StringBuilder promptBuilder =newStringBuilder(rolePrompt)
    .append("\n\n待讨论问题：").append(question);

    if(!debateHistory.isEmpty()){
        promptBuilder.append("\n\n辩论历史：");
        for(DebateRecordrecord: debateHistory){
        promptBuilder.append("\n[第").append(record.getRound())
        .append("轮-").append(record.getAgentName())
        .append("]: ").append(record.getArgument());
    }

    DebateRecord lastRecord = debateHistory.get(debateHistory.size()-1);
    if(!lastRecord.getAgentName().equals(getRoleName())){
        promptBuilder.append("\n\n请从安全角度评估上述观点，指出可能的安全隐患。");
    }
}

return llmClient.generate(promptBuilder.toString());
}

@Override
publicStringgetRoleName(){
    return"安全工程师";
}

@Override
publicStringgetFocusDimension(){
    return"系统安全与合规";
}
}

publicclassDebateCoordinator{
    privatefinalList<DebateAgent> agents;
    privatefinalint maxRounds;
    privatefinaldouble convergenceThreshold;

    publicDebateCoordinator(List<DebateAgent> agents,int maxRounds){
        this.agents = agents;
        this.maxRounds = maxRounds;
        this.convergenceThreshold =0.15;// 论点变化小于15%认为收敛
    }

    publicDebateResultrunDebate(String question){
        List<DebateRecord> debateHistory =newArrayList<>();

        for(int round =1; round <= maxRounds; round++){
            System.out.println("=== 第 "+ round +" 轮辩论 ===");

            // 所有Agent依次发表观点
            for(DebateAgent agent : agents){
                String argument = agent.generateArgument(question, debateHistory);

                DebateRecordrecord=newDebateRecord(
                    agent.getRoleName(),
                    argument,
                    round,
                    System.currentTimeMillis()
                );
                debateHistory.add(record);

                System.out.println(agent.getRoleName()+": "+ argument);
            }

            // 检查是否应该提前终止
            if(round >=2&&shouldTerminateEarly(debateHistory, round)){
                System.out.println("检测到辩论已收敛，提前终止");
                break;
            }
        }

        // 生成最终结论
        returngenerateFinalResult(question, debateHistory);
    }

    privatebooleanshouldTerminateEarly(List<DebateRecord> history,int currentRound){
        // 比较最近两轮中各Agent的论点相似度
        Map<String,String> lastRound =newHashMap<>();
        Map<String,String> currentRoundArgs =newHashMap<>();

        for(DebateRecordrecord: history){
        if(record.getRound()== currentRound){
            currentRoundArgs.put(record.getAgentName(),record.getArgument());
        }elseif(record.getRound()== currentRound -1){
            lastRound.put(record.getAgentName(),record.getArgument());
        }
    }

    // 简化的相似度计算，实际应该用更精确的语义相似度
    for(String agentName : currentRoundArgs.keySet()){
        if(lastRound.containsKey(agentName)){
            double similarity =calculateSimilarity(
                lastRound.get(agentName),
                currentRoundArgs.get(agentName)
            );
            if(similarity <1- convergenceThreshold){
                returnfalse;// 还有Agent的观点在明显变化
            }
        }
    }

    returntrue;// 所有Agent的观点都趋于稳定
}

privatedoublecalculateSimilarity(String text1,String text2){
    // 这里使用简化的相似度计算
    // 实际应该使用embedding向量的余弦相似度
    Set<String> words1 =newHashSet<>(Arrays.asList(text1.split("\\s+")));
    Set<String> words2 =newHashSet<>(Arrays.asList(text2.split("\\s+")));

    Set<String> intersection =newHashSet<>(words1);
    intersection.retainAll(words2);

    Set<String> union =newHashSet<>(words1);
    union.addAll(words2);

    return union.isEmpty()?0:(double) intersection.size()/ union.size();
}

privateDebateResultgenerateFinalResult(String question,List<DebateRecord> history){
    // 使用总结Agent生成综合结论
    SummaryAgent summaryAgent =newSummaryAgent();
    String finalConclusion = summaryAgent.summarize(question, history);

    returnnewDebateResult(question, history, finalConclusion);
}
}

publicclassSummaryAgent{
privatefinalLLMClient llmClient;

publicSummaryAgent(){
    this.llmClient =newLLMClient();
}

publicStringsummarize(String question,List<DebateRecord> debateHistory){
    StringBuilder promptBuilder =newStringBuilder();
    promptBuilder.append("""
                         你是一位中立的总结专家，需要基于多方辩论内容生成综合结论。

                         要求：
                         1. 客观整合各方的有效论点，不偏袒任何一方
                         2. 指出各方观点的合理之处和局限性
                         3. 给出权衡后的综合建议
                         4. 明确指出哪些风险需要特别关注

                         """);

    promptBuilder.append("问题：").append(question).append("\n\n");
    promptBuilder.append("辩论记录：\n");

    for(DebateRecordrecord: debateHistory){
    promptBuilder.append("[第").append(record.getRound())
    .append("轮-").append(record.getAgentName())
    .append("]\n").append(record.getArgument())
    .append("\n\n");
}

promptBuilder.append("请基于以上辩论内容，给出综合结论：");

return llmClient.generate(promptBuilder.toString());
}
}