feat(python): batch Ollama embed calls (#2453)

Other embedding integrations such as Cohere and OpenAI already send
requests in batches. We should do that for Ollama too to improve
throughput.

The Ollama [`.embed`
API](63ca747622/ollama/_client.py (L359-L378))
was added in version 0.3.0 (almost a year ago) so I updated the version
requirement in pyproject.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
- Improved compatibility with newer versions of the "ollama" package by
requiring version 0.3.0 or higher.
- Enhanced embedding generation to process batches of texts more
efficiently and reliably.
- **Refactor**
	- Improved type consistency and clarity for embedding-related methods.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

python/python/lancedb/embeddings/ollama.py

@@ -2,14 +2,15 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from functools import cached_property
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import TYPE_CHECKING, List, Optional, Sequence, Union
+
+import numpy as np
 
 from ..util import attempt_import_or_raise
 from .base import TextEmbeddingFunction
 from .registry import register
 
 if TYPE_CHECKING:
-    import numpy as np
     import ollama
 
 
@@ -28,23 +29,21 @@ class OllamaEmbeddings(TextEmbeddingFunction):
     keep_alive: Optional[Union[float, str]] = None
     ollama_client_kwargs: Optional[dict] = {}
 
-    def ndims(self):
+    def ndims(self) -> int:
         return len(self.generate_embeddings(["foo"])[0])
 
-    def _compute_embedding(self, text) -> Union["np.array", None]:
-        return (
-            self._ollama_client.embeddings(
-                model=self.name,
-                prompt=text,
-                options=self.options,
-                keep_alive=self.keep_alive,
-            )["embedding"]
-            or None
+    def _compute_embedding(self, text: Sequence[str]) -> Sequence[Sequence[float]]:
+        response = self._ollama_client.embed(
+            model=self.name,
+            input=text,
+            options=self.options,
+            keep_alive=self.keep_alive,
         )
+        return response.embeddings
 
     def generate_embeddings(
-        self, texts: Union[List[str], "np.ndarray"]
-    ) -> list[Union["np.array", None]]:
+        self, texts: Union[List[str], np.ndarray]
+    ) -> list[Union[np.array, None]]:
         """
         Get the embeddings for the given texts
 
@@ -54,8 +53,8 @@ class OllamaEmbeddings(TextEmbeddingFunction):
             The texts to embed
         """
         # TODO retry, rate limit, token limit
-        embeddings = [self._compute_embedding(text) for text in texts]
-        return embeddings
+        embeddings = self._compute_embedding(texts)
+        return list(embeddings)
 
     @cached_property
     def _ollama_client(self) -> "ollama.Client":