docs: update README

README.md

@@ -263,8 +263,19 @@ Clean plugin architecture for custom rules, prompts, and models:
 class MyCustomRule(BaseRule):
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
-        # Your logic here
-        return EvalDetail(status=False, label=['QUALITY_GOOD'])
+        # Example: check if content is empty
+        if not input_data.content:
+            return EvalDetail(
+                metric=cls.__name__,
+                status=True,  # Found an issue
+                label=[f'{cls.metric_type}.{cls.__name__}'],
+                reason=["Content is empty"]
+            )
+        return EvalDetail(
+            metric=cls.__name__,
+            status=False,  # No issue found
+            label=['QUALITY_GOOD']
+        )
 ```
 **Why It Matters**: Adapt to domain-specific requirements without forking the codebase.
 
@@ -287,9 +298,9 @@ Dingo provides **70+ evaluation metrics** across multiple dimensions, combining
 | **Security** | PII detection, Perspective API toxicity | Privacy and safety |
 
 📊 **[View Complete Metrics Documentation →](docs/metrics.md)**
-📖 **[RAG Evaluation Guide →](docs/rag_evaluation_metrics_zh.md)**
-🔍 **[Hallucination Detection Guide →](docs/hallucination_guide.md)**
-✅ **[Factuality Assessment Guide →](docs/factcheck_guide.md)**
+📖 **[RAG Evaluation Guide (中文) →](docs/rag_evaluation_metrics_zh.md)**
+🔍 **[Hallucination Detection Guide (中文) →](docs/hallucination_guide.md)**
+✅ **[Factuality Assessment Guide (中文) →](docs/factcheck_guide.md)**
 
 Most metrics are backed by academic research to ensure scientific rigor.
 
@@ -451,6 +462,7 @@ class DomainSpecificRule(BaseRule):
         is_valid = your_validation_logic(text)
 
         return EvalDetail(
+            metric=cls.__name__,
             status=not is_valid,  # False = good, True = bad
             label=['QUALITY_GOOD' if is_valid else 'QUALITY_BAD_CUSTOM'],
             reason=["Validation details..."]

README_ja.md

@@ -260,8 +260,19 @@ outputs/
 class MyCustomRule(BaseRule):
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
-        # あなたのロジック
-        return EvalDetail(status=False, label=['QUALITY_GOOD'])
+        # 例：コンテンツが空かチェック
+        if not input_data.content:
+            return EvalDetail(
+                metric=cls.__name__,
+                status=True,  # 問題を発見
+                label=[f'{cls.metric_type}.{cls.__name__}'],
+                reason=["コンテンツが空です"]
+            )
+        return EvalDetail(
+            metric=cls.__name__,
+            status=False,  # 問題なし
+            label=['QUALITY_GOOD']
+        )
 ```
 **重要性**：コードベースをフォークせずにドメイン固有のニーズに適応。
 
@@ -435,22 +446,26 @@ Dingo はドメイン固有のニーズに対応する柔軟な拡張メカニ
 ```python
 from dingo.model import Model
 from dingo.model.rule.base import BaseRule
-from dingo.config.input_args import EvaluatorRuleArgs
 from dingo.io import Data
 from dingo.io.output.eval_detail import EvalDetail
 
-
-@Model.rule_register('QUALITY_BAD_RELEVANCE', ['default'])
-class MyCustomRule(BaseRule):
-    """テキスト内のカスタムパターンをチェック"""
-
-    dynamic_config = EvaluatorRuleArgs(pattern=r'your_pattern_here')
+@Model.rule_register('QUALITY_BAD_CUSTOM', ['default'])
+class DomainSpecificRule(BaseRule):
+    """ドメイン固有のパターンをチェック"""
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
-        res = EvalDetail()
-        # ここにルール実装
-        return res
+        text = input_data.content
+
+        # あなたのカスタムロジック
+        is_valid = your_validation_logic(text)
+
+        return EvalDetail(
+            metric=cls.__name__,
+            status=not is_valid,  # False = 良好, True = 問題あり
+            label=['QUALITY_GOOD' if is_valid else 'QUALITY_BAD_CUSTOM'],
+            reason=["検証の詳細..."]
+        )
 ```
 
 ### カスタムLLM統合

README_zh-CN.md

@@ -262,8 +262,19 @@ outputs/
 class MyCustomRule(BaseRule):
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
-        # 你的逻辑
-        return EvalDetail(status=False, label=['QUALITY_GOOD'])
+        # 示例：检查内容是否为空
+        if not input_data.content:
+            return EvalDetail(
+                metric=cls.__name__,
+                status=True,  # 发现问题
+                label=[f'{cls.metric_type}.{cls.__name__}'],
+                reason=["内容为空"]
+            )
+        return EvalDetail(
+            metric=cls.__name__,
+            status=False,  # 未发现问题
+            label=['QUALITY_GOOD']
+        )
 ```
 **为什么重要**：适应特定领域需求而无需分叉代码库。
 
@@ -437,22 +448,26 @@ Dingo 提供灵活的扩展机制来满足特定领域需求。
 ```python
 from dingo.model import Model
 from dingo.model.rule.base import BaseRule
-from dingo.config.input_args import EvaluatorRuleArgs
 from dingo.io import Data
 from dingo.io.output.eval_detail import EvalDetail
 
-
-@Model.rule_register('QUALITY_BAD_RELEVANCE', ['default'])
-class MyCustomRule(BaseRule):
-    """检查文本中的自定义模式"""
-
-    dynamic_config = EvaluatorRuleArgs(pattern=r'your_pattern_here')
+@Model.rule_register('QUALITY_BAD_CUSTOM', ['default'])
+class DomainSpecificRule(BaseRule):
+    """检查特定领域的模式"""
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
-        res = EvalDetail()
-        # 您的规则实现
-        return res
+        text = input_data.content
+
+        # 你的自定义逻辑
+        is_valid = your_validation_logic(text)
+
+        return EvalDetail(
+            metric=cls.__name__,
+            status=not is_valid,  # False = 良好, True = 有问题
+            label=['QUALITY_GOOD' if is_valid else 'QUALITY_BAD_CUSTOM'],
+            reason=["验证详情..."]
+        )
 ```
 
 ### 自定义LLM集成