diff --git a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
index 8e7b91631f..6c4c4a7eb9 100644
--- a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
@@ -68,7 +68,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
 
         for i in _iter:
             # call embedding model
-            embeddings, embedding_used_tokens = self._embedding_invoke(
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                 model=model,
                 client=client,
                 texts=tokens[i: i + max_chunks],
@@ -76,7 +76,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             )
 
             used_tokens += embedding_used_tokens
-            batched_embeddings += [data for data in embeddings]
+            batched_embeddings += embeddings_batch
 
         results: list[list[list[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: list[list[int]] = [[] for _ in range(len(texts))]
@@ -87,7 +87,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
         for i in range(len(texts)):
             _result = results[i]
             if len(_result) == 0:
-                embeddings, embedding_used_tokens = self._embedding_invoke(
+                embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                     model=model,
                     client=client,
                     texts=[""],
@@ -95,7 +95,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
                 )
 
                 used_tokens += embedding_used_tokens
-                average = embeddings[0]
+                average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()