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98c3bbbaf0
The `num_gpu` parameter in `OllamaEmbeddings` was not being passed to
the Ollama client in the async embedding method, causing GPU
acceleration settings to be ignored when using async operations.
## Problem
The issue was in the `aembed_documents` method where the `options`
parameter (containing `num_gpu` and other configuration) was missing:
```python
# Sync method (working correctly)
return self._client.embed(
self.model, texts, options=self._default_params, keep_alive=self.keep_alive
)["embeddings"]
# Async method (missing options parameter)
return (
await self._async_client.embed(
self.model, texts, keep_alive=self.keep_alive # ❌ No options!
)
)["embeddings"]
```
This meant that when users specified `num_gpu=4` (or any other GPU
configuration), it would work with sync calls but be ignored with async
calls.
## Solution
Added the missing `options=self._default_params` parameter to the async
embed call to match the sync version:
```python
# Fixed async method
return (
await self._async_client.embed(
self.model,
texts,
options=self._default_params, # ✅ Now includes num_gpu!
keep_alive=self.keep_alive,
)
)["embeddings"]
```
## Validation
- ✅ Added unit test to verify options are correctly passed in both sync
and async methods
- ✅ All existing tests continue to pass
- ✅ Manual testing confirms `num_gpu` parameter now works correctly
- ✅ Code passes linting and formatting checks
The fix ensures that GPU configuration works consistently across both
synchronous and asynchronous embedding operations.
Fixes #32059.
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64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
"""Test embedding model integration."""
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from typing import Any
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from unittest.mock import Mock, patch
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from langchain_ollama.embeddings import OllamaEmbeddings
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MODEL_NAME = "llama3.1"
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def test_initialization() -> None:
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"""Test embedding model initialization."""
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OllamaEmbeddings(model=MODEL_NAME, keep_alive=1)
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@patch("langchain_ollama.embeddings.validate_model")
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def test_validate_model_on_init(mock_validate_model: Any) -> None:
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"""Test that the model is validated on initialization when requested."""
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# Test that validate_model is called when validate_model_on_init=True
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OllamaEmbeddings(model=MODEL_NAME, validate_model_on_init=True)
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mock_validate_model.assert_called_once()
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mock_validate_model.reset_mock()
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# Test that validate_model is NOT called when validate_model_on_init=False
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OllamaEmbeddings(model=MODEL_NAME, validate_model_on_init=False)
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mock_validate_model.assert_not_called()
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# Test that validate_model is NOT called by default
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OllamaEmbeddings(model=MODEL_NAME)
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mock_validate_model.assert_not_called()
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@patch("langchain_ollama.embeddings.Client")
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def test_embed_documents_passes_options(mock_client_class: Any) -> None:
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"""Test that embed_documents method passes options including num_gpu."""
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# Create a mock client instance
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mock_client = Mock()
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mock_client_class.return_value = mock_client
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# Mock the embed method response
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mock_client.embed.return_value = {"embeddings": [[0.1, 0.2, 0.3]]}
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# Create embeddings with num_gpu parameter
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embeddings = OllamaEmbeddings(model=MODEL_NAME, num_gpu=4, temperature=0.5)
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# Call embed_documents
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result = embeddings.embed_documents(["test text"])
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# Verify the result
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assert result == [[0.1, 0.2, 0.3]]
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# Check that embed was called with correct arguments
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mock_client.embed.assert_called_once()
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call_args = mock_client.embed.call_args
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# Verify the keyword arguments
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assert "options" in call_args.kwargs
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assert "keep_alive" in call_args.kwargs
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# Verify options contain num_gpu and temperature
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options = call_args.kwargs["options"]
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assert options["num_gpu"] == 4
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assert options["temperature"] == 0.5
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