diff --git a/python/python/lancedb/embeddings/openai.py b/python/python/lancedb/embeddings/openai.py
index 4af0efce..b87faa73 100644
--- a/python/python/lancedb/embeddings/openai.py
+++ b/python/python/lancedb/embeddings/openai.py
@@ -10,16 +10,15 @@
 #  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.
-import os
 from functools import cached_property
-from typing import List, Optional, Union
-
-import numpy as np
+from typing import TYPE_CHECKING, List, Optional, Union
 
 from ..util import attempt_import_or_raise
 from .base import TextEmbeddingFunction
 from .registry import register
-from .utils import api_key_not_found_help
+
+if TYPE_CHECKING:
+    import numpy as np
 
 
 @register("openai")
@@ -28,10 +27,34 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
     An embedding function that uses the OpenAI API
 
     https://platform.openai.com/docs/guides/embeddings
+
+    This can also be used for open source models that
+    are compatible with the OpenAI API.
+
+    Notes
+    -----
+    If you're running an Ollama server locally,
+    you can just override the `base_url` parameter
+    and provide the Ollama embedding model you want
+    to use (https://ollama.com/library):
+
+    ```python
+    from lancedb.embeddings import get_registry
+    openai = get_registry().get("openai")
+    embedding_function = openai.create(
+        name="<ollama-embedding-model-name>",
+        base_url="http://localhost:11434",
+        )
+    ```
+
     """
 
     name: str = "text-embedding-ada-002"
     dim: Optional[int] = None
+    base_url: Optional[str] = None
+    default_headers: Optional[dict] = None
+    organization: Optional[str] = None
+    api_key: Optional[str] = None
 
     def ndims(self):
         return self._ndims
@@ -56,8 +79,8 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
             raise ValueError(f"Unknown model name {self.name}")
 
     def generate_embeddings(
-        self, texts: Union[List[str], np.ndarray]
-    ) -> List[np.array]:
+        self, texts: Union[List[str], "np.ndarray"]
+    ) -> List["np.array"]:
         """
         Get the embeddings for the given texts
 
@@ -70,15 +93,25 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
         if self.name == "text-embedding-ada-002":
             rs = self._openai_client.embeddings.create(input=texts, model=self.name)
         else:
-            rs = self._openai_client.embeddings.create(
-                input=texts, model=self.name, dimensions=self.ndims()
-            )
+            kwargs = {
+                "input": texts,
+                "model": self.name,
+            }
+            if self.dim:
+                kwargs["dimensions"] = self.dim
+            rs = self._openai_client.embeddings.create(**kwargs)
         return [v.embedding for v in rs.data]
 
     @cached_property
     def _openai_client(self):
         openai = attempt_import_or_raise("openai")
-
-        if not os.environ.get("OPENAI_API_KEY"):
-            api_key_not_found_help("openai")
-        return openai.OpenAI()
+        kwargs = {}
+        if self.base_url:
+            kwargs["base_url"] = self.base_url
+        if self.default_headers:
+            kwargs["default_headers"] = self.default_headers
+        if self.organization:
+            kwargs["organization"] = self.organization
+        if self.api_key:
+            kwargs["api_key"] = self
+        return openai.OpenAI(**kwargs)