Remove dead code (#17899)

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -228,7 +228,7 @@ class OracleVector(BaseVector):
 
         top_k = kwargs.get("top_k", 5)
         # just not implement fetch by score_threshold now, may be later
-        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        # score_threshold = float(kwargs.get("score_threshold") or 0.0)
         if len(query) > 0:
             # Check which language the query is in
             zh_pattern = re.compile("[\u4e00-\u9fa5]+")

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -65,8 +65,6 @@ class RelytVector(BaseVector):
         return VectorType.RELYT
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs) -> None:
-        index_params: dict[str, Any] = {}
-        metadatas = [d.metadata for d in texts]
         self.create_collection(len(embeddings[0]))
         self.embedding_dimension = len(embeddings[0])
         self.add_texts(texts, embeddings)

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -187,7 +187,6 @@ class TiDBVector(BaseVector):
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 4)
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
-        filter = kwargs.get("filter")
         distance = 1 - score_threshold
 
         query_vector_str = ", ".join(format(x) for x in query_vector)

api/core/workflow/graph_engine/entities/next_graph_node.py

@@ -1,13 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel
-
-from core.workflow.graph_engine.entities.graph import GraphParallel
-
-
-class NextGraphNode(BaseModel):
-    node_id: str
-    """next node id"""
-
-    parallel: Optional[GraphParallel] = None
-    """parallel"""

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -597,7 +597,6 @@ class KnowledgeRetrievalNode(LLMNode):
     def _get_prompt_template(self, node_data: KnowledgeRetrievalNodeData, metadata_fields: list, query: str):
         model_mode = ModelMode.value_of(node_data.metadata_model_config.mode)  # type: ignore
         input_text = query
-        memory_str = ""
 
         prompt_messages: list[LLMNodeChatModelMessage] = []
         if model_mode == ModelMode.CHAT: