r2 transform

api/services/entities/knowledge_entities/rag_pipeline_entities.py

@@ -30,8 +30,8 @@ class RerankingModelConfig(BaseModel):
     Reranking Model Config.
     """
 
-    reranking_provider_name: str
-    reranking_model_name: str
+    reranking_provider_name: Optional[str] = ""
+    reranking_model_name: Optional[str] = ""
 
 
 class VectorSetting(BaseModel):
@@ -57,8 +57,8 @@ class WeightedScoreConfig(BaseModel):
     Weighted score Config.
     """
 
-    vector_setting: VectorSetting
-    keyword_setting: KeywordSetting
+    vector_setting: Optional[VectorSetting]
+    keyword_setting: Optional[KeywordSetting]
 
 
 class EmbeddingSetting(BaseModel):

api/services/rag_pipeline/rag_pipeline_transform_service.py

@@ -136,8 +136,8 @@ class RagPipelineTransformService:
     def _deal_knowledge_index(
         self, dataset: Dataset, doc_form: str, indexing_technique: str, retrieval_model: dict, node: dict
     ):
-        knowledge_configuration = node.get("data", {})
-        knowledge_configuration = KnowledgeConfiguration(**knowledge_configuration)
+        knowledge_configuration_dict = node.get("data", {})
+        knowledge_configuration = KnowledgeConfiguration(**knowledge_configuration_dict)
 
         if indexing_technique == "high_quality":
             knowledge_configuration.embedding_model = dataset.embedding_model
@@ -150,7 +150,9 @@ class RagPipelineTransformService:
         else:
             dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
 
-        return knowledge_configuration.model_dump()
+        knowledge_configuration_dict.update(knowledge_configuration.model_dump())
+        node["data"] = knowledge_configuration_dict
+        return node
 
     def _create_pipeline(
         self,