diff --git a/dbgpt/rag/evaluation/retriever.py b/dbgpt/rag/evaluation/retriever.py
index 1626f923f..c1d26fc41 100644
--- a/dbgpt/rag/evaluation/retriever.py
+++ b/dbgpt/rag/evaluation/retriever.py
@@ -79,9 +79,8 @@ class RetrieverMRRMetric(RetrieverEvaluationMetric):
 
     def sync_compute(
         self,
-        prediction: Optional[List[str]] = None,
-        contexts: Optional[List[str]] = None,
-        **kwargs: Any,
+        prediction: List[str],
+        contexts: Optional[Sequence[str]] = None,
     ) -> BaseEvaluationResult:
         """Compute MRR metric.
 
@@ -118,9 +117,8 @@ class RetrieverHitRateMetric(RetrieverEvaluationMetric):
 
     def sync_compute(
         self,
-        prediction: Optional[List[str]] = None,
-        contexts: Optional[List[str]] = None,
-        **kwargs: Any,
+        prediction: List[str],
+        contexts: Optional[Sequence[str]] = None,
     ) -> BaseEvaluationResult:
         """Compute HitRate metric.
 
diff --git a/dbgpt/storage/vector_store/oceanbase_store.py b/dbgpt/storage/vector_store/oceanbase_store.py
index fa58d7fdf..513a0bfc7 100644
--- a/dbgpt/storage/vector_store/oceanbase_store.py
+++ b/dbgpt/storage/vector_store/oceanbase_store.py
@@ -239,6 +239,7 @@ class OceanBase:
 
     def __init__(
         self,
+        database: str,
         connection_string: str,
         embedding_function: Embeddings,
         embedding_dimension: int = _OCEANBASE_DEFAULT_EMBEDDING_DIM,
@@ -254,6 +255,7 @@ class OceanBase:
         enable_normalize_vector: bool = False,
     ) -> None:
         """Create OceanBase Vector Store instance."""
+        self.database = database
         self.connection_string = connection_string
         self.embedding_function = embedding_function
         self.embedding_dimension = embedding_dimension
@@ -339,6 +341,24 @@ class OceanBase:
                 )
             conn.execute(text(create_index_query))
 
+    def check_table_exists(self) -> bool:
+        """Whether table `collection_name` exists."""
+        check_table_query = f"""
+            SELECT COUNT(*) as cnt
+            FROM information_schema.tables
+            WHERE table_schema='{self.database}' AND table_name='{self.collection_name}'
+        """
+        try:
+            with self.engine.connect() as conn, conn.begin(), ob_grwlock.reader_lock():
+                table_exists_res = conn.execute(text(check_table_query))
+                for row in table_exists_res:
+                    return row.cnt > 0
+                # No `cnt` rows? Just return False to pass `make mypy`
+                return False
+        except Exception as e:
+            logger.error(f"check_table_exists error: {e}")
+            return False
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -729,6 +749,7 @@ class OceanBaseStore(VectorStoreBase):
             self.collection_stat = ob_collection_stats[self.collection_name]
 
         self.vector_store_client = OceanBase(
+            database=self.OB_DATABASE,
             connection_string=self.connection_string,
             embedding_function=self.embeddings,
             collection_name=self.collection_name,
@@ -769,12 +790,7 @@ class OceanBaseStore(VectorStoreBase):
     def vector_name_exists(self):
         """Whether vector name exists."""
         self.logger.info("OceanBase: vector_name_exists..")
-        try:
-            self.vector_store_client.create_collection()
-            return True
-        except Exception as e:
-            logger.error("vector_name_exists error", e.message)
-            return False
+        return self.vector_store_client.check_table_exists()
 
     def load_document(self, chunks: List[Chunk]) -> List[str]:
         """Load document in vector database."""