diff --git a/python/python/lancedb/embeddings/openai.py b/python/python/lancedb/embeddings/openai.py
index 7da8f2c3..2fca549d 100644
--- a/python/python/lancedb/embeddings/openai.py
+++ b/python/python/lancedb/embeddings/openai.py
@@ -1,17 +1,9 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
 from functools import cached_property
 from typing import TYPE_CHECKING, List, Optional, Union
+import logging
 
 from ..util import attempt_import_or_raise
 from .base import TextEmbeddingFunction
@@ -89,17 +81,26 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
         texts: list[str] or np.ndarray (of str)
             The texts to embed
         """
+        openai = attempt_import_or_raise("openai")
+
         # TODO retry, rate limit, token limit
-        if self.name == "text-embedding-ada-002":
-            rs = self._openai_client.embeddings.create(input=texts, model=self.name)
-        else:
-            kwargs = {
-                "input": texts,
-                "model": self.name,
-            }
-            if self.dim:
-                kwargs["dimensions"] = self.dim
-            rs = self._openai_client.embeddings.create(**kwargs)
+        try:
+            if self.name == "text-embedding-ada-002":
+                rs = self._openai_client.embeddings.create(input=texts, model=self.name)
+            else:
+                kwargs = {
+                    "input": texts,
+                    "model": self.name,
+                }
+                if self.dim:
+                    kwargs["dimensions"] = self.dim
+                rs = self._openai_client.embeddings.create(**kwargs)
+        except openai.BadRequestError:
+            logging.exception("Bad request: %s", texts)
+            return [None] * len(texts)
+        except Exception:
+            logging.exception("OpenAI embeddings error")
+            raise
         return [v.embedding for v in rs.data]
 
     @cached_property