feat: 为评估项新增超时时间与默认值配置

assistant-agent-autoconfigure/src/main/java/com/alibaba/assistant/agent/autoconfigure/subagent/node/CodeGeneratorNode.java

@@ -405,10 +405,11 @@ private String buildSystemPrompt(Language language, List<CodeactTool> codeactToo
 		sb.append("2. 函数名和参数必须与要求完全一致\n");
 		sb.append("3. 调用工具使用 实例名.方法名() 格式\n");
 		sb.append("4. 只返回纯代码，不要 ```python 标记\n");
+		sb.append("5. ⚠️【禁止生成注释】不要生成任何注释，包括：函数注释（docstring）、行注释（#开头）、多行注释。直接输出纯净的可执行代码。\n");
 		if (isCondition) {
-			sb.append("5. 条件函数必须返回 True 或 False\n");
+			sb.append("6. 条件函数必须返回 True 或 False\n");
 		} else {
-			sb.append("5. 【重要】每个函数必须有 return 语句返回结果\n");
+			sb.append("6. 【重要】每个函数必须有 return 语句返回结果\n");
 			sb.append("   - 查询/搜索类：return 搜索结果\n");
 			sb.append("   - 处理/计算类：return 处理结果\n");
 			sb.append("   - 通知/回复类：先执行操作，再 return 操作结果或状态\n");
@@ -616,15 +617,15 @@ private ReturnSchema getReturnSchema(CodeactTool tool) {
 
 		// 优先从 registry 获取（包含观测到的 schema）
 		if (returnSchemaRegistry != null) {
-			logger.info("CodeGeneratorNode#getReturnSchema - reason=开始查询schema, registryHashCode={}, toolName={}, allToolsWithSchema={}",
+			logger.debug("CodeGeneratorNode#getReturnSchema - reason=开始查询schema, registryHashCode={}, toolName={}, allToolsWithSchema={}",
 					System.identityHashCode(returnSchemaRegistry), toolName, returnSchemaRegistry.getToolsWithSchema());
 			ReturnSchema observed = returnSchemaRegistry.getSchema(toolName).orElse(null);
 			if (observed != null) {
-				logger.info("CodeGeneratorNode#getReturnSchema - reason=从registry获取到schema, toolName={}, sampleCount={}, hasSuccessShape={}",
+				logger.debug("CodeGeneratorNode#getReturnSchema - reason=从registry获取到schema, toolName={}, sampleCount={}, hasSuccessShape={}",
 						toolName, observed.getSampleCount(), observed.getSuccessShape() != null);
 				return observed;
 			} else {
-				logger.info("CodeGeneratorNode#getReturnSchema - reason=registry中未找到schema, toolName={}", toolName);
+				logger.debug("CodeGeneratorNode#getReturnSchema - reason=registry中未找到schema, toolName={}", toolName);
 			}
 		} else {
 			logger.warn("CodeGeneratorNode#getReturnSchema - reason=returnSchemaRegistry为null, toolName={}", toolName);

assistant-agent-core/src/main/java/com/alibaba/assistant/agent/core/executor/GraalCodeExecutor.java

@@ -41,6 +41,9 @@
 import org.springframework.ai.chat.model.ToolContext;
 import org.springframework.ai.tool.ToolCallback;
 
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
 import java.io.ByteArrayOutputStream;
 import java.io.PrintStream;
 import java.nio.charset.StandardCharsets;
@@ -438,8 +441,24 @@ private String generateCustomVariables(ToolContext toolContext) {
 		return sb.toString();
 	}
 
+	/**
+	 * 用于序列化复杂对象的 ObjectMapper
+	 */
+	private static final ObjectMapper JSON_MAPPER = new ObjectMapper();
+
 	/**
 	 * 将 Java 对象转换为 Python 字面量
+	 *
+	 * <p>支持的类型转换：
+	 * <ul>
+	 *   <li>null -> None</li>
+	 *   <li>String -> Python 字符串（支持多行）</li>
+	 *   <li>Number -> Python 数字</li>
+	 *   <li>Boolean -> True/False</li>
+	 *   <li>List -> Python 列表</li>
+	 *   <li>Map -> Python 字典</li>
+	 *   <li>其他对象 -> 尝试 JSON 序列化后解析为 Python 对象</li>
+	 * </ul>
 	 */
 	@SuppressWarnings("unchecked")
 	private String toPythonLiteral(Object value) {
@@ -486,8 +505,38 @@ private String toPythonLiteral(Object value) {
 			sb.append("}");
 			return sb.toString();
 		}
-		// 其他类型转为字符串
-		return "\"" + value.toString().replace("\\", "\\\\").replace("\"", "\\\"") + "\"";
+		// 其他复杂对象类型：尝试使用 Jackson 序列化为 JSON，然后在 Python 中解析
+		// 这样可以正确处理 Attachment 等 POJO 对象，而不是简单地调用 toString()
+		return convertComplexObjectToPythonLiteral(value);
+	}
+
+	/**
+	 * 将复杂对象转换为 Python 字面量
+	 *
+	 * <p>先尝试使用 Jackson 将对象序列化为 JSON，然后转换为 Python 字典/列表格式。
+	 * 如果序列化失败，则退化为字符串表示。
+	 *
+	 * @param value 要转换的对象
+	 * @return Python 字面量表示
+	 */
+	private String convertComplexObjectToPythonLiteral(Object value) {
+		try {
+			// 使用 Jackson 将对象序列化为 JSON 字符串
+			String jsonStr = JSON_MAPPER.writeValueAsString(value);
+
+			// 将 JSON 反序列化为 Map 或 List，然后递归转换为 Python 字面量
+			// 这样可以复用已有的 Map/List 处理逻辑
+			Object parsed = JSON_MAPPER.readValue(jsonStr, Object.class);
+
+			// 递归调用 toPythonLiteral 处理反序列化后的对象（Map 或 List）
+			return toPythonLiteral(parsed);
+		} catch (JsonProcessingException e) {
+			// JSON 序列化失败，退化为字符串表示
+			logger.warn("GraalCodeExecutor#convertComplexObjectToPythonLiteral - reason=JSON序列化失败, " +
+					"valueType={}, error={}, 退化为toString()", value.getClass().getName(), e.getMessage());
+			String strValue = value.toString();
+			return "\"" + strValue.replace("\\", "\\\\").replace("\"", "\\\"") + "\"";
+		}
 	}
 
 	/**

assistant-agent-core/src/main/java/com/alibaba/assistant/agent/core/tool/schema/DefaultReturnSchemaRegistry.java

@@ -137,7 +137,7 @@ public Optional<ReturnSchema> getSchema(String toolName) {
 			return Optional.empty();
 		}
 		ReturnSchema schema = mergedSchemas.get(toolName);
-		logger.info("DefaultReturnSchemaRegistry#getSchema - reason=查询schema, hashCode={}, toolName={}, found={}, allTools={}",
+		logger.debug("DefaultReturnSchemaRegistry#getSchema - reason=查询schema, hashCode={}, toolName={}, found={}, allTools={}",
 				System.identityHashCode(this), toolName, schema != null, mergedSchemas.keySet());
 		return Optional.ofNullable(schema);
 	}

assistant-agent-evaluation/src/main/java/com/alibaba/assistant/agent/evaluation/builder/EvaluationCriterionBuilder.java

@@ -223,6 +223,44 @@ public EvaluationCriterionBuilder multimodalForImages(String attachmentsPath, St
 		return this;
 	}
 
+	/**
+	 * Set the timeout for this criterion execution (in milliseconds).
+	 * When timeout occurs, the criterion returns TIMEOUT status with defaultValue.
+	 *
+	 * @param timeoutMs timeout in milliseconds
+	 * @return this builder
+	 */
+	public EvaluationCriterionBuilder timeoutMs(long timeoutMs) {
+		criterion.setTimeoutMs(timeoutMs);
+		return this;
+	}
+
+	/**
+	 * Set the default value to use when the criterion times out or errors.
+	 * This ensures evaluation can continue even when individual criteria fail.
+	 *
+	 * @param defaultValue the default value to use on timeout/error
+	 * @return this builder
+	 */
+	public EvaluationCriterionBuilder defaultValue(Object defaultValue) {
+		criterion.setDefaultValue(defaultValue);
+		return this;
+	}
+
+	/**
+	 * Configure timeout and default value together.
+	 * A convenience method for setting both timeout and fallback behavior.
+	 *
+	 * @param timeoutMs timeout in milliseconds
+	 * @param defaultValue the default value to use on timeout/error
+	 * @return this builder
+	 */
+	public EvaluationCriterionBuilder withTimeout(long timeoutMs, Object defaultValue) {
+		criterion.setTimeoutMs(timeoutMs);
+		criterion.setDefaultValue(defaultValue);
+		return this;
+	}
+
 	/**
 	 * Ensure the dependent criterion is in the dependsOn list
 	 */

assistant-agent-evaluation/src/main/java/com/alibaba/assistant/agent/evaluation/evaluator/LLMBasedEvaluator.java

@@ -27,6 +27,7 @@
 import org.slf4j.LoggerFactory;
 import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.prompt.Prompt;
 
 import java.util.HashMap;
@@ -48,11 +49,17 @@ public class LLMBasedEvaluator implements Evaluator {
 	private final ChatModel chatModel;
 	private final String evaluatorId;
 	private final ObjectMapper objectMapper;
+	private final ChatOptions chatOptions;
 
 	public LLMBasedEvaluator(ChatModel chatModel, String evaluatorId) {
+		this(chatModel, evaluatorId, null);
+	}
+
+	public LLMBasedEvaluator(ChatModel chatModel, String evaluatorId, ChatOptions chatOptions) {
 		this.chatModel = chatModel;
 		this.evaluatorId = evaluatorId;
 		this.objectMapper = new ObjectMapper();
+		this.chatOptions = chatOptions;
 	}
 
 	@Override
@@ -71,8 +78,8 @@ public CriterionResult evaluate(CriterionExecutionContext executionContext) {
 			logger.debug("Evaluating criterion {} with LLM, prompt: {}, chatModel: {} ({})",
 				executionContext.getCriterion().getName(), promptText, chatModel.getClass().getSimpleName(), chatModel);
 
-			// Call LLM
-			Prompt prompt = new Prompt(promptText);
+			// Call LLM with optional ChatOptions
+			Prompt prompt = chatOptions != null ? new Prompt(promptText, chatOptions) : new Prompt(promptText);
 			ChatResponse chatResponse = chatModel.call(prompt);
 			String response = chatResponse.getResult().getOutput().getText();
 

assistant-agent-evaluation/src/main/java/com/alibaba/assistant/agent/evaluation/evaluator/MultimodalLLMBasedEvaluator.java

@@ -29,6 +29,7 @@
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.ai.content.Media;
 
@@ -49,6 +50,7 @@ public class MultimodalLLMBasedEvaluator extends LLMBasedEvaluator {
     private static final Logger logger = LoggerFactory.getLogger(MultimodalLLMBasedEvaluator.class);
 
     private final ChatModel multimodalChatModel;
+    private final ChatOptions multimodalChatOptions;
 
     /**
      * 用于将 Map 转换为 MediaConvertible 实现类的 ObjectMapper
@@ -69,7 +71,21 @@ public class MultimodalLLMBasedEvaluator extends LLMBasedEvaluator {
      * @param evaluatorId 评估器ID
      */
     public MultimodalLLMBasedEvaluator(ChatModel textModel, ChatModel multimodalModel, String evaluatorId) {
-        this(textModel, multimodalModel, evaluatorId, createDefaultObjectMapper());
+        this(textModel, multimodalModel, evaluatorId, null, null, createDefaultObjectMapper());
+    }
+
+    /**
+     * 构造函数（带 ChatOptions）
+     *
+     * @param textModel 纯文本模型，用于普通评估
+     * @param multimodalModel 多模态模型，用于处理图片等多模态输入
+     * @param evaluatorId 评估器ID
+     * @param textChatOptions 纯文本模型的ChatOptions（可选）
+     * @param multimodalChatOptions 多模态模型的ChatOptions（可选）
+     */
+    public MultimodalLLMBasedEvaluator(ChatModel textModel, ChatModel multimodalModel, String evaluatorId,
+                                        ChatOptions textChatOptions, ChatOptions multimodalChatOptions) {
+        this(textModel, multimodalModel, evaluatorId, textChatOptions, multimodalChatOptions, createDefaultObjectMapper());
     }
 
     /**
@@ -93,8 +109,24 @@ private static ObjectMapper createDefaultObjectMapper() {
      */
     public MultimodalLLMBasedEvaluator(ChatModel textModel, ChatModel multimodalModel, 
                                         String evaluatorId, ObjectMapper objectMapper) {
-        super(textModel, evaluatorId);
+        this(textModel, multimodalModel, evaluatorId, null, null, objectMapper);
+    }
+
+    /**
+     * 构造函数（完整参数）
+     *
+     * @param textModel 纯文本模型，用于普通评估
+     * @param multimodalModel 多模态模型，用于处理图片等多模态输入
+     * @param evaluatorId 评估器ID
+     * @param textChatOptions 纯文本模型的ChatOptions（可选）
+     * @param multimodalChatOptions 多模态模型的ChatOptions（可选）
+     * @param objectMapper 用于类型转换的 ObjectMapper
+     */
+    public MultimodalLLMBasedEvaluator(ChatModel textModel, ChatModel multimodalModel, String evaluatorId,
+                                        ChatOptions textChatOptions, ChatOptions multimodalChatOptions, ObjectMapper objectMapper) {
+        super(textModel, evaluatorId, textChatOptions);
         this.multimodalChatModel = multimodalModel;
+        this.multimodalChatOptions = multimodalChatOptions;
         this.objectMapper = objectMapper != null ? objectMapper : new ObjectMapper();
     }
 
@@ -316,8 +348,10 @@ protected CriterionResult evaluateWithMultimodal(CriterionExecutionContext conte
                     .media(mediaList)
                     .build();
 
-            // 调用多模态模型
-            Prompt prompt = new Prompt(List.of(userMessage));
+            // 调用多模态模型（支持自定义ChatOptions）
+            Prompt prompt = multimodalChatOptions != null
+                    ? new Prompt(List.of(userMessage), multimodalChatOptions)
+                    : new Prompt(List.of(userMessage));
             ChatResponse chatResponse = multimodalChatModel.call(prompt);
             String responseText = chatResponse.getResult().getOutput().getText();