每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 (#2480)

server/knowledge_base/kb_service/pg_kb_service.py

@@ -11,22 +11,27 @@ from server.knowledge_base.kb_service.base import SupportedVSType, KBService, Em
     score_threshold_process
 from server.knowledge_base.utils import KnowledgeFile
 import shutil
+import sqlalchemy
+from sqlalchemy.engine.base import Engine
+from sqlalchemy.orm import Session
 
 
 class PGKBService(KBService):
-    pg_vector: PGVector
+    engine: Engine = sqlalchemy.create_engine(kbs_config.get("pg").get("connection_uri"), pool_size=10)
 
     def _load_pg_vector(self):
+        self.connection = PGKBService.engine.connect()
         self.pg_vector = PGVector(embedding_function=EmbeddingsFunAdapter(self.embed_model),
                                   collection_name=self.kb_name,
                                   distance_strategy=DistanceStrategy.EUCLIDEAN,
+                                  connection=self.connection,
                                   connection_string=kbs_config.get("pg").get("connection_uri"))
 
     def get_doc_by_ids(self, ids: List[str]) -> List[Document]:
-        with self.pg_vector._create_engine().connect() as connect:
+        with Session(self.connection) as session:
             stmt = text("SELECT document, cmetadata FROM langchain_pg_embedding WHERE collection_id in :ids")
             results = [Document(page_content=row[0], metadata=row[1]) for row in
-                       connect.execute(stmt, parameters={'ids': ids}).fetchall()]
+                       session.execute(stmt, {'ids': ids}).fetchall()]
             return results
 
     # TODO:
@@ -43,8 +48,8 @@ class PGKBService(KBService):
         return SupportedVSType.PG
 
     def do_drop_kb(self):
-        with self.pg_vector._create_engine().connect() as connect:
+        with Session(self.connection) as session:
-            connect.execute(text(f'''
+            session.execute(text(f'''
                     -- 删除 langchain_pg_embedding 表中关联到 langchain_pg_collection 表中 的记录
                     DELETE FROM langchain_pg_embedding
                     WHERE collection_id IN (
@@ -53,11 +58,10 @@ class PGKBService(KBService):
                     -- 删除 langchain_pg_collection 表中 记录
                     DELETE FROM langchain_pg_collection WHERE name = '{self.kb_name}';
             '''))
-            connect.commit()
+            session.commit()
             shutil.rmtree(self.kb_path)
 
     def do_search(self, query: str, top_k: int, score_threshold: float):
-        self._load_pg_vector()
         embed_func = EmbeddingsFunAdapter(self.embed_model)
         embeddings = embed_func.embed_query(query)
         docs = self.pg_vector.similarity_search_with_score_by_vector(embeddings, top_k)
@@ -69,13 +73,13 @@ class PGKBService(KBService):
         return doc_infos
 
     def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
-        with self.pg_vector._create_engine().connect() as connect:
+        with Session(self.connection) as session:
             filepath = kb_file.filepath.replace('\\', '\\\\')
-            connect.execute(
+            session.execute(
                 text(
                     ''' DELETE FROM langchain_pg_embedding WHERE cmetadata::jsonb @> '{"source": "filepath"}'::jsonb;'''.replace(
                         "filepath", filepath)))
-            connect.commit()
+            session.commit()
 
     def do_clear_vs(self):
         self.pg_vector.delete_collection()