// 1. 创建智能体importio.leavesfly.tinyai.agent.AdvancedAgent; AdvancedAgentagent = newAdvancedAgent("我的助手", "你是一个智能助手"); agent.addKnowledge("Java是一种面向对象的编程语言", "java_info"); Stringresponse = agent.processMessage("什么是Java？"); // 2. 使用具身智能体（自动驾驶）importio.leavesfly.tinyai.agent.embodied.*; EnvironmentConfigconfig = EnvironmentConfig.createHighwayConfig(); EmbodiedAgentembodiedAgent = newEmbodiedAgent(config); Episodeepisode = embodiedAgent.runEpisode(200); System.out.println("总奖励: " + episode.getTotalReward()); // 3. 使用AI编程助手importio.leavesfly.tinyai.agent.cursor.AICodingCursor; AICodingCursorcursor = newAICodingCursor("编程助手"); Map<String, Object> analysis = cursor.analyzeCode(sourceCode); StringgeneratedCode = cursor.generateCode("method validateEmail"); // 4. 使用深度学习模型importio.leavesfly.tinyai.ml.*; importio.leavesfly.tinyai.nnet.block.MlpBlock; MlpBlockmlpBlock = newMlpBlock("classifier", 784, newint[]{128, 64, 10}); Modelmodel = newModel("mnist_classifier", mlpBlock); // 5. 使用大语言模型importio.leavesfly.tinyai.gpt2.GPT2Model; GPT2Modelgpt = GPT2Model.createSmallModel("gpt2-small"); List<Integer> prompt = Arrays.asList(100, 200, 300); List<Integer> generated = gpt.generateText(prompt, 50);

// 创建和配置智能体AdvancedAgentagent = newAdvancedAgent("助手名称", "系统提示"); agent.getToolRegistry().registerTool("calculator", calculatorTool, "计算器工具"); agent.addKnowledge("知识内容", "knowledge_id"); // 对话处理Stringresponse = agent.processMessage("用户问题"); // 自进化智能体SelfEvolvingAgentevolAgent = newSelfEvolvingAgent("学习助手"); TaskResultresult = evolAgent.processTask("任务名称", context); evolAgent.selfEvolve(); // 触发自我进化// 手稿智能体（OpenManus架构）Manusmanus = newManus("Manus助手"); manus.setExecutionMode(ExecutionMode.DIRECT_AGENT); manus.setPlanningEnabled(true); // 启用计划驱动模式Messageresponse = manus.processMessage(newMessage("user", "完成复杂任务")); // 具身智能体（自动驾驶）EmbodiedAgentembodiedAgent = newEmbodiedAgent(config); StepResultresult = embodiedAgent.step(); Episodeepisode = embodiedAgent.runEpisode(200); // AI编程助手AICodingCursorcursor = newAICodingCursor("编程助手"); Map<String, Object> analysis = cursor.analyzeCode(code); List<RefactorSuggestion> suggestions = cursor.suggestRefactor(code); Map<String, Object> debugResult = cursor.debugCode(code);

// 模型训练DataSetdataSet = newArrayDataset(xData, yData); Modelmodel = newModel("classifier", mlpBlock); Trainertrainer = newTrainer(epochs, monitor, evaluator, true, 4); // 并行训练trainer.init(dataSet, model, loss, optimizer); trainer.train(true); // 强化学习DQNAgentagent = newDQNAgent("CartPole-DQN", stateDim, actionDim, hiddenSizes, learningRate, epsilon, gamma, batchSize, bufferSize, updateFreq); CartPoleEnvironmentenv = newCartPoleEnvironment(); // 训练循环...

// GPT模型GPT2Modelmodel = GPT2Model.createMediumModel("gpt2-medium"); NdArraygenerated = model.generateSequence(inputTokens, maxLength); // LoRA微调LoraConfigconfig = LoraConfig.createMediumRank(); LoraLinearLayerlayer = newLoraLinearLayer("lora_layer", 512, 256, config, true); layer.enableLora(); // 启用LoRA微调

publicclassCustomAgentextendsBaseAgent{@OverrideprotectedObjectperformTask(AgentTasktask) throwsException{// 实现自定义任务处理逻辑returnprocessCustomTask(task)} @OverrideprotectedvoidinitializeCapabilities(){capabilities.add("自定义能力")} }

publicclassCustomLayerextendsLayer{@OverridepublicVariablelayerForward(Variable... inputs){// 实现自定义前向传播returncustomComputation(inputs[0])} @Overridepublicvoidinit(){// 参数初始化addParameter("weight", NdArray.randn(inputShape, outputShape))} }