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⚡ Building applications with LLMs through composability ⚡
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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Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.