feat(ollama): add dimensions to OllamaEmbeddings (#36543)

Fixes #34623

Add `dimensions` field to `OllamaEmbeddings` to allow users to specify 
output embedding size for models that support variable dimensions . The
field is passed
directly to the Ollama client's `embed()` call for both sync and async
methods.

**How I verified it works:**
- Ran unit tests: `python -m pytest tests/unit_tests/ -v`
- Ran integration tests against a live Ollama instance:
`OLLAMA_HOST=http://ollama:11434 python -m pytest
tests/integration_tests/ -v`
- Confirmed that passing `dimensions=768` no longer raises
`extra_forbidden`
  Pydantic validation error and returns embeddings of the expected size.

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>

libs/partners/ollama/tests/unit_tests/test_embeddings.py

@@ -1,7 +1,7 @@
 """Test embedding model integration."""
 
 from typing import Any
-from unittest.mock import MagicMock, Mock, patch
+from unittest.mock import AsyncMock, MagicMock, Mock, patch
 
 import pytest
 
@@ -54,6 +54,60 @@ def test_embed_documents_passes_options(mock_client_class: Any) -> None:
     assert options["temperature"] == 0.5
 
 
+@patch("langchain_ollama.embeddings.Client")
+def test_embed_documents_passes_dimensions(mock_client_class: Any) -> None:
+    """Test that embed_documents passes dimensions to the embed call."""
+    mock_client = Mock()
+    mock_client_class.return_value = mock_client
+    mock_client.embed.return_value = {"embeddings": [[0.1, 0.2, 0.3]]}
+
+    embeddings = OllamaEmbeddings(model=MODEL_NAME, dimensions=512)
+    embeddings.embed_documents(["test text"])
+
+    call_args = mock_client.embed.call_args
+    assert call_args.kwargs["dimensions"] == 512
+
+
+@patch("langchain_ollama.embeddings.Client")
+def test_embed_documents_dimensions_none_by_default(mock_client_class: Any) -> None:
+    """Test that dimensions defaults to None when not specified."""
+    mock_client = Mock()
+    mock_client_class.return_value = mock_client
+    mock_client.embed.return_value = {"embeddings": [[0.1, 0.2, 0.3]]}
+
+    embeddings = OllamaEmbeddings(model=MODEL_NAME)
+    embeddings.embed_documents(["test text"])
+
+    call_args = mock_client.embed.call_args
+    assert call_args.kwargs["dimensions"] is None
+
+
+@patch("langchain_ollama.embeddings.AsyncClient")
+@patch("langchain_ollama.embeddings.Client")
+async def test_aembed_documents_passes_dimensions(
+    mock_client_class: Any, mock_async_client_class: Any
+) -> None:
+    """Test that aembed_documents passes dimensions to the async embed call."""
+    mock_async_client = AsyncMock()
+    mock_async_client_class.return_value = mock_async_client
+    mock_async_client.embed.return_value = {"embeddings": [[0.1, 0.2, 0.3]]}
+
+    embeddings = OllamaEmbeddings(model=MODEL_NAME, dimensions=512)
+    await embeddings.aembed_documents(["test text"])
+
+    call_args = mock_async_client.embed.call_args
+    assert call_args.kwargs["dimensions"] == 512
+
+
+def test_dimensions_validation() -> None:
+    """Test that dimensions must be a positive integer."""
+    with pytest.raises(ValueError, match="must be a positive integer"):
+        OllamaEmbeddings(model=MODEL_NAME, dimensions=0)
+
+    with pytest.raises(ValueError, match="must be a positive integer"):
+        OllamaEmbeddings(model=MODEL_NAME, dimensions=-1)
+
+
 def test_embed_documents_raises_when_client_none() -> None:
     """Test that embed_documents raises RuntimeError when client is None."""
     with patch("langchain_ollama.embeddings.Client") as mock_client_class: