diff --git a/apis/python/src/tiledb/vector_search/embeddings/__init__.py b/apis/python/src/tiledb/vector_search/embeddings/__init__.py
index 5d3538370..b9254f848 100644
--- a/apis/python/src/tiledb/vector_search/embeddings/__init__.py
+++ b/apis/python/src/tiledb/vector_search/embeddings/__init__.py
@@ -2,6 +2,7 @@
 from .image_resnetv2_embedding import ImageResNetV2Embedding
 from .langchain_embedding import LangChainEmbedding
 from .object_embedding import ObjectEmbedding
+from .ollama_embedding import OllamaEmbedding
 from .random_embedding import RandomEmbedding
 from .sentence_transformers_embedding import SentenceTransformersEmbedding
 from .soma_geneptw_embedding import SomaGenePTwEmbedding
@@ -18,4 +19,5 @@
     "LangChainEmbedding",
     "SomaScGPTEmbedding",
     "SomaSCVIEmbedding",
+    "OllamaEmbedding",
 ]
diff --git a/apis/python/src/tiledb/vector_search/embeddings/ollama_embedding.py b/apis/python/src/tiledb/vector_search/embeddings/ollama_embedding.py
new file mode 100644
index 000000000..dabefead1
--- /dev/null
+++ b/apis/python/src/tiledb/vector_search/embeddings/ollama_embedding.py
@@ -0,0 +1,49 @@
+from typing import Dict, Optional, OrderedDict, Sequence, Union
+
+import numpy as np
+
+# from tiledb.vector_search.embeddings import ObjectEmbedding
+
+
+class OllamaEmbedding:
+    """
+    Embedding functions from Ollama.
+
+    This attempts to import the embedding_class from the ollama module.
+    """
+
+    def __init__(
+        self,
+        dimensions: int,
+        embedding_class: str = "embed",  # really it's the method
+        embedding_kwargs: Optional[Dict] = None,
+    ):
+        self.dim_num = dimensions
+        self.embedding_class = embedding_class
+        self.embedding_kwargs = embedding_kwargs
+
+    def init_kwargs(self) -> Dict:
+        return {
+            "dimensions": self.dim_num,
+            "embedding_class": self.embedding_class,
+            "embedding_kwargs": self.embedding_kwargs,
+        }
+
+    def dimensions(self) -> int:
+        return self.dim_num
+
+    def vector_type(self) -> np.dtype:
+        return np.float32
+
+    def load(self) -> None:
+        import importlib
+
+        try:
+            embeddings_module = importlib.import_module("ollama")
+            embedding_method_ = getattr(embeddings_module, self.embedding_class)
+            self.embedding = embedding_method_(**self.embedding_kwargs)
+        except ImportError as e:
+            print(e)
+
+    def embed(self, objects: Union[str, Sequence[str]]) -> np.ndarray:
+        return np.array(self.embedding(input=objects).embeddings, dtype=np.float32)
diff --git a/apis/python/test/test_ingestion.py b/apis/python/test/test_ingestion.py
index 935482162..172d6c039 100644
--- a/apis/python/test/test_ingestion.py
+++ b/apis/python/test/test_ingestion.py
@@ -2012,6 +2012,74 @@ def test_ivf_flat_taskgraph_query(tmp_path):
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
 
 
+def test_ollama_embedding():
+    """Test OllamaEmbedding class with mocked ollama library."""
+    from unittest.mock import MagicMock
+    from unittest.mock import Mock
+    from unittest.mock import patch
+
+    from tiledb.vector_search.embeddings import OllamaEmbedding
+
+    # Test initialization
+    dimensions = 384
+    embedding_class = "embed"
+    embedding_kwargs = {"model": "nomic-embed-text"}
+
+    embedding = OllamaEmbedding(
+        dimensions=dimensions,
+        embedding_class=embedding_class,
+        embedding_kwargs=embedding_kwargs,
+    )
+
+    # Test dimensions() method
+    assert embedding.dimensions() == dimensions
+
+    # Test vector_type() method
+    assert embedding.vector_type() == np.float32
+
+    # Test init_kwargs() method
+    init_kwargs = embedding.init_kwargs()
+    assert init_kwargs["dimensions"] == dimensions
+    assert init_kwargs["embedding_class"] == embedding_class
+    assert init_kwargs["embedding_kwargs"] == embedding_kwargs
+
+    # Mock the ollama module
+    mock_ollama = MagicMock()
+
+    # Create a mock embedding result with the expected structure
+    mock_embed_result = Mock()
+    mock_embed_result.embeddings = [
+        [0.1] * dimensions,  # 384 dimensions for first text
+        [0.2] * dimensions,  # 384 dimensions for second text
+    ]
+
+    # Create a mock callable that will be returned by embed(**kwargs)
+    mock_callable = Mock(return_value=mock_embed_result)
+
+    # Mock the embed function to return our callable when called with **embedding_kwargs
+    mock_ollama.embed = Mock(return_value=mock_callable)
+
+    # Patch the importlib.import_module to return our mock
+    with patch("importlib.import_module", return_value=mock_ollama):
+        # Test load() method
+        embedding.load()
+
+        # Test embed() method with multiple texts
+        test_texts = ["hello world", "test document"]
+        result = embedding.embed(test_texts)
+
+        # Verify the result
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == np.float32
+        assert result.shape == (2, dimensions)
+
+        # Verify embed was called with correct kwargs during load
+        mock_ollama.embed.assert_called_once_with(model="nomic-embed-text")
+
+        # Verify the callable was called with correct input parameter
+        mock_callable.assert_called_once_with(input=test_texts)
+
+
 def test_dimensions_parameter_override(tmp_path):
     """
     Test the dimensions parameter functionality with TileDB array input.