perf typing

server/db/repository/knowledge_file_repository.py

@@ -83,7 +83,7 @@ def add_file_to_db(session,
                 kb_file: KnowledgeFile,
                 docs_count: int = 0,
                 custom_docs: bool = False,
-                doc_infos: List[str] = [], # 形式：[{"id": str, "metadata": dict}, ...]
+                doc_infos: List[Dict] = [], # 形式：[{"id": str, "metadata": dict}, ...]
                 ):
     kb = session.query(KnowledgeBaseModel).filter_by(kb_name=kb_file.kb_name).first()
     if kb:

server/knowledge_base/kb_service/base.py

@@ -24,7 +24,7 @@ from server.knowledge_base.utils import (
     list_kbs_from_folder, list_files_from_folder,
 )
 
-from typing import List, Union, Dict, Optional
+from typing import List, Union, Dict, Optional, Tuple
 
 from server.embeddings_api import embed_texts, aembed_texts, embed_documents
 from server.knowledge_base.model.kb_document_model import DocumentWithVSId
@@ -261,7 +261,7 @@ class KBService(ABC):
                   query: str,
                   top_k: int,
                   score_threshold: float,
-                  ) -> List[Document]:
+                  ) -> List[Tuple[Document, float]]:
         """
         搜索知识库子类实自己逻辑
         """

server/knowledge_base/kb_service/faiss_kb_service.py

@@ -61,7 +61,7 @@ class FaissKBService(KBService):
                   query: str,
                   top_k: int,
                   score_threshold: float = SCORE_THRESHOLD,
-                  ) -> List[Document]:
+                  ) -> List[Tuple[Document, float]]:
         embed_func = EmbeddingsFunAdapter(self.embed_model)
         embeddings = embed_func.embed_query(query)
         with self.load_vector_store().acquire() as vs: