Update：updata data fotmat rule

README.md

@@ -28,6 +28,7 @@
   <a href="https://deepwiki.com/MigoXLab/dingo"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
   <a href="https://archestra.ai/mcp-catalog/dataeval__dingo"><img src="https://archestra.ai/mcp-catalog/api/badge/quality/DataEval/dingo" alt="Trust Score"></a>
   <a href="https://clawhub.ai/e06084/dingo"><img src="https://img.shields.io/badge/ClawHub-Skill-orange?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCI+PHRleHQgeT0iMTgiIGZvbnQtc2l6ZT0iMTYiPvCfpp48L3RleHQ+PC9zdmc+" alt="ClawHub Skill"></a>
+  <a href="https://mcptoplist.com/server/glama%2FMigoXLab%2Fdingo"><img src="https://mcptoplist.com/badge/glama%2FMigoXLab%2Fdingo.svg" alt="MCP Toplist"></a>
 </p>
 
 </div>
@@ -96,7 +97,7 @@ Need a **production-grade data quality platform**? Try [Dingo SaaS](https://ding
 
 ### 📝 How to Get Free SaaS Code
 
-👉 **[Apply for Dingo SaaS Repository Access](https://aicarrier.feishu.cn/share/base/form/shrcnr19E0upfiA92Wm5i2eic7g)** 
+👉 **[Apply for Dingo SaaS Repository Access](https://aicarrier.feishu.cn/share/base/form/shrcn9RqYttByQ5H1np6Yrnmhuf)** 
 
 Review time: 1-5 business days | Suitable for enterprise data governance, team collaboration
 

README_ja.md

@@ -95,7 +95,7 @@
 
 ### 📝 無料 SaaS コードの入手方法
 
-👉 **[Dingo SaaS リポジトリアクセスを申請する](https://aicarrier.feishu.cn/share/base/form/shrcnr19E0upfiA92Wm5i2eic7g)** 
+👉 **[Dingo SaaS リポジトリアクセスを申請する](https://aicarrier.feishu.cn/share/base/form/shrcn9RqYttByQ5H1np6Yrnmhuf)** 
 
 審査時間：1-5営業日 | エンタープライズデータガバナンス、チームコラボレーションに最適
 

dingo/model/rule/rule_common.py

@@ -171,20 +171,19 @@ class RuleAudioDataFormat(BaseRule):
         "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleAudioDataFormat",
-        "description": "Check whether the audio data format is right",
+        "description": "Check whether the audio data format is right, include id, audio, text fields",
         "evaluation_results": ""
     }
 
-    _required_fields = [RequiredField.CONTENT]
     dynamic_config = EvaluatorRuleArgs()
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
         res = EvalDetail(metric=cls.__name__)
 
-        raw_data = input_data.raw_data
+        record = input_data.to_dict()
         key_list = ["id", "audio", "text"]
-        if all(key in raw_data for key in key_list):
+        if all(key in record for key in key_list):
             res.label = [QualityLabel.QUALITY_GOOD]
         else:
             res.status = True
@@ -1305,20 +1304,19 @@ class RuleImageDataFormat(BaseRule):
         "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleImageDataFormat",
-        "description": "Check whether the image data format is right",
+        "description": "Check whether the image data format is right, include img_id, image fields",
         "evaluation_results": ""
     }
 
-    _required_fields = [RequiredField.CONTENT]
     dynamic_config = EvaluatorRuleArgs()
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
         res = EvalDetail(metric=cls.__name__)
 
-        raw_data = input_data.raw_data
+        record = input_data.to_dict()
         key_list = ["img_id", "image"]
-        if all(key in raw_data for key in key_list):
+        if all(key in record for key in key_list):
             res.label = [QualityLabel.QUALITY_GOOD]
         else:
             res.status = True
@@ -1655,20 +1653,19 @@ class RuleNlpDataFormat(BaseRule):
         "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleNlpDataFormat",
-        "description": "Check whether the nlp data format is right",
+        "description": "Check whether the nlp data format is right, include track_id, content fields",
         "evaluation_results": ""
     }
 
-    _required_fields = [RequiredField.CONTENT]
     dynamic_config = EvaluatorRuleArgs()
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
         res = EvalDetail(metric=cls.__name__)
 
-        raw_data = input_data.raw_data
+        record = input_data.to_dict()
         key_list = ["track_id", "content"]
-        if all(key in raw_data for key in key_list):
+        if all(key in record for key in key_list):
             res.label = [QualityLabel.QUALITY_GOOD]
         else:
             res.status = True
@@ -1826,20 +1823,19 @@ class RuleSftDataFormat(BaseRule):
         "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleSftDataFormat",
-        "description": "Check whether the sft data format is right",
+        "description": "Check whether the sft data format is right, include track_id, type, prompt, completion fields",
         "evaluation_results": ""
     }
 
-    _required_fields = [RequiredField.CONTENT]
     dynamic_config = EvaluatorRuleArgs()
 
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
         res = EvalDetail(metric=cls.__name__)
 
-        raw_data = input_data.raw_data
+        record = input_data.to_dict()
         key_list = ["track_id", "type", "prompt", "completion"]
-        if all(key in raw_data for key in key_list):
+        if all(key in record for key in key_list):
             res.label = [QualityLabel.QUALITY_GOOD]
         else:
             res.status = True
@@ -2204,19 +2200,17 @@ class RuleVedioDataFormat(BaseRule):
         "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleVedioDataFormat",
-        "description": "Check whether the vedio data format is right",
+        "description": "Check whether the vedio data format is right, include id, video, text fields",
         "evaluation_results": ""
     }
     dynamic_config = EvaluatorRuleArgs()
 
-    _required_fields = [RequiredField.CONTENT]
-
     @classmethod
     def eval(cls, input_data: Data) -> EvalDetail:
         res = EvalDetail(metric=cls.__name__)
-        raw_data = input_data.raw_data
+        record = input_data.to_dict()
         key_list = ["id", "video", "text"]
-        if all(key in raw_data for key in key_list):
+        if all(key in record for key in key_list):
             res.label = [QualityLabel.QUALITY_GOOD]
         else:
             res.status = True