hack/tools/scripts/llm-key-hunter/results/_cache/content/312527814_java-practice.json

{"repo": "312527814/java-practice", "files": {"llm/spring-ai-alibaba/src/main/java/org/example/apptest/TestInterceptor.java": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1785745665.978871, "n": 2214, "h": "d3ca168a135a7071", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.agent.interceptor.contextediting.ContextEditingInterceptor;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\n\npublic class TestInterceptor {\n    public static void main(String[] args) throws GraphRunnerException {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        ModelHook loggingHook = new LoggingModelHook();\n        // 使用\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"resilient_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook,new LoggingModelHook2())\n//                .interceptors(new MyModelInterceptor(),new MyToolInterceptor())\n                .build();\n\n        AssistantMessage response = agent.call(\"what is the weather in San Francisco\");\n        System.out.println(response.getText());\n    }\n}\n"}, "7c896eb7a4f9495726061d690d27124e647beca3": {"t": 1785755544.7406855, "n": 2214, "h": "d3ca168a135a7071", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.agent.interceptor.contextediting.ContextEditingInterceptor;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\n\npublic class TestInterceptor {\n    public static void main(String[] args) throws GraphRunnerException {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        ModelHook loggingHook = new LoggingModelHook();\n        // 使用\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"resilient_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook,new LoggingModelHook2())\n//                .interceptors(new MyModelInterceptor(),new MyToolInterceptor())\n                .build();\n\n        AssistantMessage response = agent.call(\"what is the weather in San Francisco\");\n        System.out.println(response.getText());\n    }\n}\n"}, "82101e97e125e52e409a738fb8e891fe167e64e9": {"t": 1785755546.1534293, "n": 2212, "h": "1bfbec85a50dacac", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.agent.interceptor.contextediting.ContextEditingInterceptor;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\n\npublic class TestInterceptor {\n    public static void main(String[] args) throws GraphRunnerException {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        ModelHook loggingHook = new LoggingModelHook();\n        // 使用\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"resilient_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook,new LoggingModelHook2())\n                .interceptors(new MyModelInterceptor(),new MyToolInterceptor())\n                .build();\n\n        AssistantMessage response = agent.call(\"what is the weather in San Francisco\");\n        System.out.println(response.getText());\n    }\n}\n"}}, "llm/spring-ai-alibaba/src/main/java/org/example/apptest/TestHumanInTheLoop.java": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1785745666.1656413, "n": 5537, "h": "5de63f27fc28b446", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.NodeOutput;\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.action.InterruptionMetadata;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.hip.HumanInTheLoopHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.hip.ToolConfig;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\n\nimport java.util.List;\nimport java.util.Optional;\nimport java.util.Scanner;\n\npublic class TestHumanInTheLoop {\n    public static void main(String[] args) throws GraphRunnerException {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n\n        MemorySaver memorySaver = new MemorySaver();\n\n        HumanInTheLoopHook humanInTheLoopHook = HumanInTheLoopHook.builder()\n                .approvalOn(\"get_weather\", ToolConfig.builder()\n                        .description(\"查询天气需要审批\")\n                        .build())\n                .build();\n\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"approval_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(List.of(humanInTheLoopHook))\n                .interceptors(new MyModelInterceptor())\n                .saver(memorySaver)\n                .enableLogging(true)\n                .build();\n\n        // HITL 必须指定 threadId，用于 checkpoint 恢复\n        String threadId = \"hitl-test-thread\";\n        RunnableConfig config = RunnableConfig.builder()\n                .threadId(threadId)\n                .build();\n\n        // 第一次调用：触发工具审批中断（不能用 call()，要用 invokeAndGetOutput 检测中断）\n        System.out.println(\"=== 第一次调用：期望触发人工审批中断 ===\");\n        Optional<NodeOutput> result = agent.invokeAndGetOutput(\n                \"what is the weather in San Francisco\",\n                config);\n\n        if (result.isEmpty()) {\n            System.out.println(\"第一次调用无返回，请检查 DEEPSEEK_API_KEY 或模型是否调用了 get_weather\");\n            return;\n        }\n\n        if (result.get() instanceof InterruptionMetadata interruption) {\n            System.out.println(\"检测到人工审批中断，待审批工具：\");\n            interruption.toolFeedbacks().forEach(feedback -> {\n                System.out.println(\"  工具: \" + feedback.getName());\n                System.out.println(\"  参数: \" + feedback.getArguments());\n                System.out.println(\"  说明: \" + feedback.getDescription());\n            });\n            // 构建批准反馈\n            InterruptionMetadata.Builder feedbackBuilder = InterruptionMetadata.builder()\n                    .nodeId(interruption.node())\n                    .state(interruption.state());\n            System.out.print(\"是否调用该工具？(yes/no): \");\n            System.out.flush();\n            Scanner scanner = new Scanner(System.in);\n            String name = scanner.nextLine().trim();\n\n            if(name.equals(\"yes\")){\n                interruption.toolFeedbacks().forEach(toolFeedback ->\n                        feedbackBuilder.addToolFeedback(\n                                InterruptionMetadata.ToolFeedback.builder(toolFeedback)\n                                        .result(InterruptionMetadata.ToolFeedback.FeedbackResult.APPROVED)\n                                        .build()));\n                // 第二次调用：resumeConfig + 空消息恢复执行\n                System.out.println(\"\\n=== 第二次调用：批准并恢复执行 ===\");\n            }else {\n                interruption.toolFeedbacks().forEach(toolFeedback ->\n                        feedbackBuilder.addToolFeedback(\n                                InterruptionMetadata.ToolFeedback.builder(toolFeedback)\n                                        .result(InterruptionMetadata.ToolFeedback.FeedbackResult.REJECTED)\n                                        .build()));\n                // 第二次调用：resumeConfig + 空消息恢复执行\n                System.out.println(\"\\n=== 第二次调用：拒绝执行 ===\");\n            }\n\n\n            InterruptionMetadata approvalMetadata = feedbackBuilder.build();\n            RunnableConfig resumeConfig = RunnableConfig.builder()\n                    .threadId(threadId)\n                    .addMetadata(RunnableConfig.HUMAN_FEEDBACK_METADATA_KEY, approvalMetadata)\n                    .build();\n\n\n\n            AssistantMessage response = agent.call(\"\", resumeConfig);\n            System.out.println(\"最终结果: \" + response.getText());\n            return;\n        }\n        System.out.println(\"未触发人工审批，首次返回类型: \" + result.get().getClass().getSimpleName());\n    }\n}\n"}, "82101e97e125e52e409a738fb8e891fe167e64e9": {"t": 1785755544.6424208, "n": 5537, "h": "5de63f27fc28b446", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.NodeOutput;\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.action.InterruptionMetadata;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.hip.HumanInTheLoopHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.hip.ToolConfig;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\n\nimport java.util.List;\nimport java.util.Optional;\nimport java.util.Scanner;\n\npublic class TestHumanInTheLoop {\n    public static void main(String[] args) throws GraphRunnerException {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n\n        MemorySaver memorySaver = new MemorySaver();\n\n        HumanInTheLoopHook humanInTheLoopHook = HumanInTheLoopHook.builder()\n                .approvalOn(\"get_weather\", ToolConfig.builder()\n                        .description(\"查询天气需要审批\")\n                        .build())\n                .build();\n\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"approval_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(List.of(humanInTheLoopHook))\n                .interceptors(new MyModelInterceptor())\n                .saver(memorySaver)\n                .enableLogging(true)\n                .build();\n\n        // HITL 必须指定 threadId，用于 checkpoint 恢复\n        String threadId = \"hitl-test-thread\";\n        RunnableConfig config = RunnableConfig.builder()\n                .threadId(threadId)\n                .build();\n\n        // 第一次调用：触发工具审批中断（不能用 call()，要用 invokeAndGetOutput 检测中断）\n        System.out.println(\"=== 第一次调用：期望触发人工审批中断 ===\");\n        Optional<NodeOutput> result = agent.invokeAndGetOutput(\n                \"what is the weather in San Francisco\",\n                config);\n\n        if (result.isEmpty()) {\n            System.out.println(\"第一次调用无返回，请检查 DEEPSEEK_API_KEY 或模型是否调用了 get_weather\");\n            return;\n        }\n\n        if (result.get() instanceof InterruptionMetadata interruption) {\n            System.out.println(\"检测到人工审批中断，待审批工具：\");\n            interruption.toolFeedbacks().forEach(feedback -> {\n                System.out.println(\"  工具: \" + feedback.getName());\n                System.out.println(\"  参数: \" + feedback.getArguments());\n                System.out.println(\"  说明: \" + feedback.getDescription());\n            });\n            // 构建批准反馈\n            InterruptionMetadata.Builder feedbackBuilder = InterruptionMetadata.builder()\n                    .nodeId(interruption.node())\n                    .state(interruption.state());\n            System.out.print(\"是否调用该工具？(yes/no): \");\n            System.out.flush();\n            Scanner scanner = new Scanner(System.in);\n            String name = scanner.nextLine().trim();\n\n            if(name.equals(\"yes\")){\n                interruption.toolFeedbacks().forEach(toolFeedback ->\n                        feedbackBuilder.addToolFeedback(\n                                InterruptionMetadata.ToolFeedback.builder(toolFeedback)\n                                        .result(InterruptionMetadata.ToolFeedback.FeedbackResult.APPROVED)\n                                        .build()));\n                // 第二次调用：resumeConfig + 空消息恢复执行\n                System.out.println(\"\\n=== 第二次调用：批准并恢复执行 ===\");\n            }else {\n                interruption.toolFeedbacks().forEach(toolFeedback ->\n                        feedbackBuilder.addToolFeedback(\n                                InterruptionMetadata.ToolFeedback.builder(toolFeedback)\n                                        .result(InterruptionMetadata.ToolFeedback.FeedbackResult.REJECTED)\n                                        .build()));\n                // 第二次调用：resumeConfig + 空消息恢复执行\n                System.out.println(\"\\n=== 第二次调用：拒绝执行 ===\");\n            }\n\n\n            InterruptionMetadata approvalMetadata = feedbackBuilder.build();\n            RunnableConfig resumeConfig = RunnableConfig.builder()\n                    .threadId(threadId)\n                    .addMetadata(RunnableConfig.HUMAN_FEEDBACK_METADATA_KEY, approvalMetadata)\n                    .build();\n\n\n\n            AssistantMessage response = agent.call(\"\", resumeConfig);\n            System.out.println(\"最终结果: \" + response.getText());\n            return;\n        }\n        System.out.println(\"未触发人工审批，首次返回类型: \" + result.get().getClass().getSimpleName());\n    }\n}\n"}}, "llm/spring-ai-alibaba/src/main/java/org/example/apptest/TestHook.java": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1785745700.6606915, "n": 3396, "h": "e94216387c8f6c91", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.dashscope.api.DashScopeApi;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.example.hooks.RAGMessagesHook;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.content.Media;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\nimport org.springframework.core.io.FileSystemResource;\nimport org.springframework.util.MimeTypeUtils;\n\npublic class TestHook {\n    public static void main(String[] args) throws GraphRunnerException {\n// 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        String customSchema = \"请按照以下JSON格式输出：\\n\"\n                + \"{\\n\"\n                + \"    \\\"title\\\": \\\"标题\\\",\\n\"\n                + \"    \\\"content\\\": \\\"内容\\\",\\n\"\n                + \"    \\\"style\\\": \\\"风格\\\"\\n\"\n                + \"}\";\n\n        ModelHook loggingHook = new LoggingModelHook();\n\n        ModelCallLimitHook hook = ModelCallLimitHook.builder()\n                .runLimit(5)  // 限制最多调用 5 次\n                .exitBehavior(ModelCallLimitHook.ExitBehavior.ERROR)  // 超出限制时抛出异常\n                .build();\n\n        // threadId 是给定对话的唯一标识符\n        RunnableConfig runnableConfig = RunnableConfig.builder().threadId(\"threadId\").addMetadata(\"user_id\", \"1\").build();\n        runnableConfig.context().put(\"1\",\"222\");\n        // 创建 agent\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"weather_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook)\n                .systemPrompt(\"You are a helpful assistant\")\n                .saver(new MemorySaver())\n                .outputSchema(customSchema)\n                .build();\n        AssistantMessage response=null;\n//      response = agent.call(\"你是什么模型？\",runnableConfig);\n        response = agent.call(\"what is the weather in San Francisco?\", runnableConfig);\n        System.out.println(response.getText());\n    }\n}\n"}, "7c896eb7a4f9495726061d690d27124e647beca3": {"t": 1785755543.5079172, "n": 3396, "h": "e94216387c8f6c91", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.dashscope.api.DashScopeApi;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.example.hooks.RAGMessagesHook;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.content.Media;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\nimport org.springframework.core.io.FileSystemResource;\nimport org.springframework.util.MimeTypeUtils;\n\npublic class TestHook {\n    public static void main(String[] args) throws GraphRunnerException {\n// 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        String customSchema = \"请按照以下JSON格式输出：\\n\"\n                + \"{\\n\"\n                + \"    \\\"title\\\": \\\"标题\\\",\\n\"\n                + \"    \\\"content\\\": \\\"内容\\\",\\n\"\n                + \"    \\\"style\\\": \\\"风格\\\"\\n\"\n                + \"}\";\n\n        ModelHook loggingHook = new LoggingModelHook();\n\n        ModelCallLimitHook hook = ModelCallLimitHook.builder()\n                .runLimit(5)  // 限制最多调用 5 次\n                .exitBehavior(ModelCallLimitHook.ExitBehavior.ERROR)  // 超出限制时抛出异常\n                .build();\n\n        // threadId 是给定对话的唯一标识符\n        RunnableConfig runnableConfig = RunnableConfig.builder().threadId(\"threadId\").addMetadata(\"user_id\", \"1\").build();\n        runnableConfig.context().put(\"1\",\"222\");\n        // 创建 agent\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"weather_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook)\n                .systemPrompt(\"You are a helpful assistant\")\n                .saver(new MemorySaver())\n                .outputSchema(customSchema)\n                .build();\n        AssistantMessage response=null;\n//      response = agent.call(\"你是什么模型？\",runnableConfig);\n        response = agent.call(\"what is the weather in San Francisco?\", runnableConfig);\n        System.out.println(response.getText());\n    }\n}\n"}, "82101e97e125e52e409a738fb8e891fe167e64e9": {"t": 1785755545.091831, "n": 3337, "h": "e688ae5ec078cede", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.dashscope.api.DashScopeApi;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;\nimport com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;\nimport com.alibaba.cloud.ai.graph.RunnableConfig;\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.ModelHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.modelcalllimit.ModelCallLimitHook;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.exception.GraphRunnerException;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.content.Media;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.ai.tool.ToolCallback;\nimport org.springframework.ai.tool.function.FunctionToolCallback;\nimport org.springframework.core.io.FileSystemResource;\nimport org.springframework.util.MimeTypeUtils;\n\npublic class TestHook {\n    public static void main(String[] args) throws GraphRunnerException {\n// 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        // 定义天气查询工具\n        ToolCallback weatherTool = FunctionToolCallback.builder(\"get_weather\", new WeatherTool())\n                .description(\"Get weather for a given city\")\n                .inputType(WeatherRequest.class)\n                .build();\n        String customSchema = \"请按照以下JSON格式输出：\\n\"\n                + \"{\\n\"\n                + \"    \\\"title\\\": \\\"标题\\\",\\n\"\n                + \"    \\\"content\\\": \\\"内容\\\",\\n\"\n                + \"    \\\"style\\\": \\\"风格\\\"\\n\"\n                + \"}\";\n\n        ModelHook loggingHook = new LoggingModelHook();\n\n        ModelCallLimitHook hook = ModelCallLimitHook.builder()\n                .runLimit(5)  // 限制最多调用 5 次\n                .exitBehavior(ModelCallLimitHook.ExitBehavior.ERROR)  // 超出限制时抛出异常\n                .build();\n\n        // threadId 是给定对话的唯一标识符\n        RunnableConfig runnableConfig = RunnableConfig.builder().threadId(\"threadId\").addMetadata(\"user_id\", \"1\").build();\n        runnableConfig.context().put(\"1\",\"222\");\n        // 创建 agent\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"weather_agent\")\n                .model(chatModel)\n                .tools(weatherTool)\n                .hooks(loggingHook,hook)\n                .systemPrompt(\"You are a helpful assistant\")\n                .saver(new MemorySaver())\n                .outputSchema(customSchema)\n                .build();\n\n        AssistantMessage response = agent.call(\"你是什么模型？\",runnableConfig);\n        response = agent.call(\"what is the weather in San Francisco?\", runnableConfig);\n        System.out.println(response.getText());\n    }\n}\n"}}, "llm/spring-ai-alibaba/src/main/java/org/example/apptest/TestSkills.java": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1785745706.2894678, "n": 3703, "h": "9512b0fa8367c0eb", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.shelltool.ShellToolAgentHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.skills.SkillsAgentHook;\nimport com.alibaba.cloud.ai.graph.agent.interceptor.contextediting.ContextEditingInterceptor;\nimport com.alibaba.cloud.ai.graph.agent.tools.ShellTool2;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.skills.registry.SkillRegistry;\nimport com.alibaba.cloud.ai.graph.skills.registry.filesystem.FileSystemSkillRegistry;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.core.io.ClassPathResource;\nimport org.springframework.core.io.Resource;\n\nimport java.net.URL;\nimport java.nio.file.Path;\nimport java.util.List;\n\npublic class TestSkills {\n    public static void main(String[] args) throws Exception {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        String skillsDirectory = getTestSkillsDirectory();\n\n        // 1. 技能注册表：从 src/test/resources/skills 加载\n        SkillRegistry registry = FileSystemSkillRegistry.builder()\n                .projectSkillsDirectory(skillsDirectory)\n                .build();\n\n        // 2. Skills Hook：注册 read_skill 工具并注入技能列表到系统提示\n        SkillsAgentHook skillsHook = SkillsAgentHook.builder()\n                .skillRegistry(registry)\n                .build();\n\n        // 3. Shell Hook：工作目录设为技能根目录，便于执行 scripts/extract_pdf.py\n        ShellToolAgentHook shellHook = ShellToolAgentHook.builder()\n                .shellTool2(ShellTool2.builder(skillsDirectory + \"/skills/pdf-extractor\").build())\n                .build();\n\n        ContextEditingInterceptor build1 = ContextEditingInterceptor.builder().trigger(120000).clearAtLeast(60000).build();\n\n        // 4. 构建 Agent\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"skills-integration-agent\")\n                .model(chatModel)\n                .saver(new MemorySaver())\n                .interceptors(build1)\n                .hooks(List.of(skillsHook, shellHook))\n                .enableLogging(true)\n                .build();\n\n        // 5. 使用真实 PDF 路径（与官方 AgentSkillsTest 一致）\n        String pdfPath = skillsDirectory + \"/skills/pdf-extractor/saa-roadmap.pdf\";\n        AssistantMessage response = agent.call(\"请从 \" + pdfPath + \" 文件中提取关键信息。\");\n\n        System.out.println(\"回复内容：\");\n        System.out.println(response.getText());\n    }\n\n    private static String getTestSkillsDirectory() throws Exception {\n        Resource resource = new ClassPathResource(\"skills\");\n        if (resource.exists() && resource.getFile().isDirectory()) {\n            return resource.getFile().getAbsolutePath();\n        }\n        URL url = TestSkills.class.getClassLoader().getResource(\"skills\");\n        if (url != null && \"file\".equals(url.getProtocol())) {\n            return Path.of(url.toURI()).toString();\n        }\n        throw new IllegalStateException(\"Cannot find test skills directory under src/test/resources/skills\");\n    }\n}\n"}, "82101e97e125e52e409a738fb8e891fe167e64e9": {"t": 1785755543.3313437, "n": 3703, "h": "9512b0fa8367c0eb", "c": "package org.example.apptest;\n\nimport com.alibaba.cloud.ai.graph.agent.ReactAgent;\nimport com.alibaba.cloud.ai.graph.agent.hook.shelltool.ShellToolAgentHook;\nimport com.alibaba.cloud.ai.graph.agent.hook.skills.SkillsAgentHook;\nimport com.alibaba.cloud.ai.graph.agent.interceptor.contextediting.ContextEditingInterceptor;\nimport com.alibaba.cloud.ai.graph.agent.tools.ShellTool2;\nimport com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;\nimport com.alibaba.cloud.ai.graph.skills.registry.SkillRegistry;\nimport com.alibaba.cloud.ai.graph.skills.registry.filesystem.FileSystemSkillRegistry;\nimport org.springframework.ai.chat.messages.AssistantMessage;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatModel;\nimport org.springframework.ai.deepseek.DeepSeekChatOptions;\nimport org.springframework.ai.deepseek.api.DeepSeekApi;\nimport org.springframework.core.io.ClassPathResource;\nimport org.springframework.core.io.Resource;\n\nimport java.net.URL;\nimport java.nio.file.Path;\nimport java.util.List;\n\npublic class TestSkills {\n    public static void main(String[] args) throws Exception {\n        // 初始化 ChatModel\n        DeepSeekApi deepSeekApi = DeepSeekApi.builder()\n                .apiKey(System.getenv(\"DEEPSEEK_API_KEY\"))\n                .build();\n\n        ChatModel chatModel = DeepSeekChatModel.builder()\n                .deepSeekApi(deepSeekApi)\n                .defaultOptions(DeepSeekChatOptions.builder()\n                        .model(\"deepseek-v4-pro\")\n                        .temperature(0.7)\n                        .build())\n                .build();\n\n        String skillsDirectory = getTestSkillsDirectory();\n\n        // 1. 技能注册表：从 src/test/resources/skills 加载\n        SkillRegistry registry = FileSystemSkillRegistry.builder()\n                .projectSkillsDirectory(skillsDirectory)\n                .build();\n\n        // 2. Skills Hook：注册 read_skill 工具并注入技能列表到系统提示\n        SkillsAgentHook skillsHook = SkillsAgentHook.builder()\n                .skillRegistry(registry)\n                .build();\n\n        // 3. Shell Hook：工作目录设为技能根目录，便于执行 scripts/extract_pdf.py\n        ShellToolAgentHook shellHook = ShellToolAgentHook.builder()\n                .shellTool2(ShellTool2.builder(skillsDirectory + \"/skills/pdf-extractor\").build())\n                .build();\n\n        ContextEditingInterceptor build1 = ContextEditingInterceptor.builder().trigger(120000).clearAtLeast(60000).build();\n\n        // 4. 构建 Agent\n        ReactAgent agent = ReactAgent.builder()\n                .name(\"skills-integration-agent\")\n                .model(chatModel)\n                .saver(new MemorySaver())\n                .interceptors(build1)\n                .hooks(List.of(skillsHook, shellHook))\n                .enableLogging(true)\n                .build();\n\n        // 5. 使用真实 PDF 路径（与官方 AgentSkillsTest 一致）\n        String pdfPath = skillsDirectory + \"/skills/pdf-extractor/saa-roadmap.pdf\";\n        AssistantMessage response = agent.call(\"请从 \" + pdfPath + \" 文件中提取关键信息。\");\n\n        System.out.println(\"回复内容：\");\n        System.out.println(response.getText());\n    }\n\n    private static String getTestSkillsDirectory() throws Exception {\n        Resource resource = new ClassPathResource(\"skills\");\n        if (resource.exists() && resource.getFile().isDirectory()) {\n            return resource.getFile().getAbsolutePath();\n        }\n        URL url = TestSkills.class.getClassLoader().getResource(\"skills\");\n        if (url != null && \"file\".equals(url.getProtocol())) {\n            return Path.of(url.toURI()).toString();\n        }\n        throw new IllegalStateException(\"Cannot find test skills directory under src/test/resources/skills\");\n    }\n}\n"}}, "llm/langchain4j-app/src/main/java/org/example/Langchain4jApp.java": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1785746124.3267722, "n": 4713, "h": "b91815a48f5a3a06", "c": "package org.example;\n\nimport dev.langchain4j.community.model.dashscope.QwenChatModel;\nimport dev.langchain4j.community.model.dashscope.WanxImageModel;\nimport dev.langchain4j.data.image.Image;\nimport dev.langchain4j.data.message.AiMessage;\nimport dev.langchain4j.data.message.ChatMessage;\nimport dev.langchain4j.data.message.UserMessage;\nimport dev.langchain4j.model.chat.ChatModel;\nimport dev.langchain4j.model.chat.response.ChatResponse;\nimport dev.langchain4j.model.ollama.OllamaChatModel;\nimport dev.langchain4j.model.openai.OpenAiChatModel;\nimport dev.langchain4j.model.output.Response;\nimport dev.langchain4j.service.AiServices;\nimport dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;\nimport org.example.tool.Calculator;\nimport org.junit.Test;\n\nimport java.util.Arrays;\nimport java.util.List;\n\n/**\n * Hello world!\n */\npublic class Langchain4jApp {\n    public static void main(String[] args) {\n        deepseek();\n    }\n\n\n    public static void deepseek() {\n\n        ChatModel model = OpenAiChatModel.builder()\n                .baseUrl(\"https://api.deepseek.com/v1\")\n                .apiKey(System.getenv(\"deepseek.api.key\"))\n                .modelName(\"deepseek-reasoner\")\n                .returnThinking(false)\n                .build();\n        InMemoryChatMemoryStore store = new InMemoryChatMemoryStore();\n        String memoryId=\"foo\";\n        List<ChatMessage> messages = store.getMessages(memoryId);\n        messages.add(new UserMessage(\"请记住我叫刘德华，回答不要超过10个字\"));\n        store.updateMessages(memoryId,messages);\n\n        ChatResponse chat = model.chat(store.getMessages(memoryId));\n        messages.add(chat.aiMessage());\n\n        store.updateMessages(memoryId,messages);\n\n        messages.add(new UserMessage(\"我叫什么名字？回答不要超过10个字\"));\n        store.updateMessages(memoryId,store.getMessages(memoryId));\n        System.out.println(store.getMessages(memoryId));\n\n        System.out.println(\"....................\");\n        ChatResponse chat2 = model.chat(store.getMessages(memoryId));\n        System.out.println(chat2);\n\n\n\n    }\n\n    @Test\n    public  void ollama() {\n\n        ChatModel model = OllamaChatModel.builder()\n                .baseUrl(\"https://api.deepseek.com/v1\")\n                .temperature(0.0)\n                .logRequests(true)\n                .logResponses(true)\n                .modelName(\"TINY_DOLPHIN_MODEL\")\n                .build();\n        String result = model.chat(\"介绍一下你自己,不要超过10个字\");\n        System.out.println(result);\n    }\n    @Test\n    public void MemoryStore() {\n        InMemoryChatMemoryStore store = new InMemoryChatMemoryStore();\n        store.updateMessages(\"foo\", Arrays.asList(new UserMessage(\"abc def\"), new AiMessage(\"ghi jkl\")));\n\n        List<ChatMessage> foo = store.getMessages(\"foo\");\n        System.out.println(foo);\n        store.deleteMessages(\"foo\");\n        System.out.println(foo);\n\n    }\n\n    @Test\n    public void wanxImageModel() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        WanxImageModel wanxImageModel = WanxImageModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"wanx2.1-t2i-plus\")\n                .apiKey(getenv)\n                .build();\n\n        Response<Image> response = wanxImageModel.generate(\"美女\");\n        System.out.println(response.content().url());\n    }\n    @Test\n    public void QwenChatModel() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        QwenChatModel qwenChatModel = QwenChatModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"qwen3-vl-plus\")\n                .apiKey(getenv)\n                .build();\n\n//        String result = qwenChatModel.chat(\"请介绍一下你自己，不要超过10个字\");\n        String result = qwenChatModel.chat(\"langchain4j是由谁开发出来的？\");\n        System.out.println(result);\n    }\n    interface Assistant {\n\n        String chat(String userMessage);\n    }\n\n    @Test\n    public void Tool() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        QwenChatModel model = QwenChatModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"qwen-max\")\n                .apiKey(getenv)\n                .build();\n\n        Assistant assistant = AiServices.builder(Assistant.class)\n                .chatModel(model)\n                .tools(new Calculator())\n                .build();\n\n//        String question = \"What is the square root of the sum of the numbers of letters in the words \\\"hello\\\" and \\\"world\\\"?\";\n//        String question = \"请计算3和4的和是多少？\";\n        String question = \"请将'redis'和‘mysql’拼接一下，直接返回结果，不要有多余的补充。\";\n\n        String answer = assistant.chat(question);\n\n        System.out.println(answer);\n    }\n}\n"}, "ecd12b8290930b71a5925019b43f91a534521f86": {"t": 1785755544.67521, "n": 4713, "h": "b91815a48f5a3a06", "c": "package org.example;\n\nimport dev.langchain4j.community.model.dashscope.QwenChatModel;\nimport dev.langchain4j.community.model.dashscope.WanxImageModel;\nimport dev.langchain4j.data.image.Image;\nimport dev.langchain4j.data.message.AiMessage;\nimport dev.langchain4j.data.message.ChatMessage;\nimport dev.langchain4j.data.message.UserMessage;\nimport dev.langchain4j.model.chat.ChatModel;\nimport dev.langchain4j.model.chat.response.ChatResponse;\nimport dev.langchain4j.model.ollama.OllamaChatModel;\nimport dev.langchain4j.model.openai.OpenAiChatModel;\nimport dev.langchain4j.model.output.Response;\nimport dev.langchain4j.service.AiServices;\nimport dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;\nimport org.example.tool.Calculator;\nimport org.junit.Test;\n\nimport java.util.Arrays;\nimport java.util.List;\n\n/**\n * Hello world!\n */\npublic class Langchain4jApp {\n    public static void main(String[] args) {\n        deepseek();\n    }\n\n\n    public static void deepseek() {\n\n        ChatModel model = OpenAiChatModel.builder()\n                .baseUrl(\"https://api.deepseek.com/v1\")\n                .apiKey(System.getenv(\"deepseek.api.key\"))\n                .modelName(\"deepseek-reasoner\")\n                .returnThinking(false)\n                .build();\n        InMemoryChatMemoryStore store = new InMemoryChatMemoryStore();\n        String memoryId=\"foo\";\n        List<ChatMessage> messages = store.getMessages(memoryId);\n        messages.add(new UserMessage(\"请记住我叫刘德华，回答不要超过10个字\"));\n        store.updateMessages(memoryId,messages);\n\n        ChatResponse chat = model.chat(store.getMessages(memoryId));\n        messages.add(chat.aiMessage());\n\n        store.updateMessages(memoryId,messages);\n\n        messages.add(new UserMessage(\"我叫什么名字？回答不要超过10个字\"));\n        store.updateMessages(memoryId,store.getMessages(memoryId));\n        System.out.println(store.getMessages(memoryId));\n\n        System.out.println(\"....................\");\n        ChatResponse chat2 = model.chat(store.getMessages(memoryId));\n        System.out.println(chat2);\n\n\n\n    }\n\n    @Test\n    public  void ollama() {\n\n        ChatModel model = OllamaChatModel.builder()\n                .baseUrl(\"https://api.deepseek.com/v1\")\n                .temperature(0.0)\n                .logRequests(true)\n                .logResponses(true)\n                .modelName(\"TINY_DOLPHIN_MODEL\")\n                .build();\n        String result = model.chat(\"介绍一下你自己,不要超过10个字\");\n        System.out.println(result);\n    }\n    @Test\n    public void MemoryStore() {\n        InMemoryChatMemoryStore store = new InMemoryChatMemoryStore();\n        store.updateMessages(\"foo\", Arrays.asList(new UserMessage(\"abc def\"), new AiMessage(\"ghi jkl\")));\n\n        List<ChatMessage> foo = store.getMessages(\"foo\");\n        System.out.println(foo);\n        store.deleteMessages(\"foo\");\n        System.out.println(foo);\n\n    }\n\n    @Test\n    public void wanxImageModel() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        WanxImageModel wanxImageModel = WanxImageModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"wanx2.1-t2i-plus\")\n                .apiKey(getenv)\n                .build();\n\n        Response<Image> response = wanxImageModel.generate(\"美女\");\n        System.out.println(response.content().url());\n    }\n    @Test\n    public void QwenChatModel() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        QwenChatModel qwenChatModel = QwenChatModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"qwen3-vl-plus\")\n                .apiKey(getenv)\n                .build();\n\n//        String result = qwenChatModel.chat(\"请介绍一下你自己，不要超过10个字\");\n        String result = qwenChatModel.chat(\"langchain4j是由谁开发出来的？\");\n        System.out.println(result);\n    }\n    interface Assistant {\n\n        String chat(String userMessage);\n    }\n\n    @Test\n    public void Tool() {\n        String getenv = System.getenv(\"ALI_AI_KEY\");\n        QwenChatModel model = QwenChatModel.builder()\n                .baseUrl(\"https://dashscope.aliyuncs.com/api/v1\")\n                .modelName(\"qwen-max\")\n                .apiKey(getenv)\n                .build();\n\n        Assistant assistant = AiServices.builder(Assistant.class)\n                .chatModel(model)\n                .tools(new Calculator())\n                .build();\n\n//        String question = \"What is the square root of the sum of the numbers of letters in the words \\\"hello\\\" and \\\"world\\\"?\";\n//        String question = \"请计算3和4的和是多少？\";\n        String question = \"请将'redis'和‘mysql’拼接一下，直接返回结果，不要有多余的补充。\";\n\n        String answer = assistant.chat(question);\n\n        System.out.println(answer);\n    }\n}\n"}}, "llm/mcp-client/src/main/resources/application.properties": {"30bb0f3406775108e5df79eadda3cf777a0f0f49": {"t": 1786068461.7759323, "n": 411, "h": "0e93db404a3cb2d8", "c": "server.port=8088\n\nspring.ai.openai.api-key=${OPENAI_API_KEY}\nspring.ai.openai.base-url=https://api.deepseek.com\nspring.ai.openai.chat.options.model=deepseek-reasoner\n\nspring.ai.dashscope.api-key=${ALI_AI_KEY}\nspring.ai.dashscope.chat.options.model=qwen-max\n#spring.ai.mcp.client.sse.name=LocationNameService\n#spring.ai.mcp.client.type=ASYNC\nspring.ai.mcp.client.sse.connections.server.url=http://localhost:8080\n"}}}}