diff --git a/api/core/rag/datasource/vdb/chroma/chroma_vector.py b/api/core/rag/datasource/vdb/chroma/chroma_vector.py
index de1572410c..cbc846f716 100644
--- a/api/core/rag/datasource/vdb/chroma/chroma_vector.py
+++ b/api/core/rag/datasource/vdb/chroma/chroma_vector.py
@@ -65,7 +65,7 @@ class ChromaVector(BaseVector):
             self._client.get_or_create_collection(collection_name)
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
-    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs) -> list[str]:
         uuids = self._get_uuids(documents)
         texts = [d.page_content for d in documents]
         metadatas = [d.metadata for d in documents]
@@ -73,6 +73,7 @@ class ChromaVector(BaseVector):
         collection = self._client.get_or_create_collection(self._collection_name)
         # FIXME: chromadb using numpy array, fix the type error later
         collection.upsert(ids=uuids, documents=texts, embeddings=embeddings, metadatas=metadatas)  # type: ignore
+        return uuids
 
     def delete_by_metadata_field(self, key: str, value: str):
         collection = self._client.get_or_create_collection(self._collection_name)
diff --git a/api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py b/api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py
index 91bb71bfa6..8e8120fc10 100644
--- a/api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py
+++ b/api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py
@@ -605,25 +605,36 @@ class ClickzettaVector(BaseVector):
                 logger.warning("Failed to create inverted index: %s", e)
                 # Continue without inverted index - full-text search will fall back to LIKE
 
-    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs) -> list[str]:
         """Add documents with embeddings to the collection."""
         if not documents:
-            return
+            return []
 
         batch_size = self._config.batch_size
         total_batches = (len(documents) + batch_size - 1) // batch_size
+        added_ids = []
 
         for i in range(0, len(documents), batch_size):
             batch_docs = documents[i : i + batch_size]
             batch_embeddings = embeddings[i : i + batch_size]
+            batch_doc_ids = []
+            for doc in batch_docs:
+                metadata = doc.metadata if isinstance(doc.metadata, dict) else {}
+                batch_doc_ids.append(self._safe_doc_id(metadata.get("doc_id", str(uuid.uuid4()))))
+            added_ids.extend(batch_doc_ids)
 
             # Execute batch insert through write queue
-            self._execute_write(self._insert_batch, batch_docs, batch_embeddings, i, batch_size, total_batches)
+            self._execute_write(
+                self._insert_batch, batch_docs, batch_embeddings, batch_doc_ids, i, batch_size, total_batches
+            )
+
+        return added_ids
 
     def _insert_batch(
         self,
         batch_docs: list[Document],
         batch_embeddings: list[list[float]],
+        batch_doc_ids: list[str],
         batch_index: int,
         batch_size: int,
         total_batches: int,
@@ -641,14 +652,9 @@ class ClickzettaVector(BaseVector):
         data_rows = []
         vector_dimension = len(batch_embeddings[0]) if batch_embeddings and batch_embeddings[0] else 768
 
-        for doc, embedding in zip(batch_docs, batch_embeddings):
+        for doc, embedding, doc_id in zip(batch_docs, batch_embeddings, batch_doc_ids):
             # Optimized: minimal checks for common case, fallback for edge cases
-            metadata = doc.metadata or {}
-
-            if not isinstance(metadata, dict):
-                metadata = {}
-
-            doc_id = self._safe_doc_id(metadata.get("doc_id", str(uuid.uuid4())))
+            metadata = doc.metadata if isinstance(doc.metadata, dict) else {}
 
             # Fast path for JSON serialization
             try: