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⚡ Building applications with LLMs through composability ⚡
815bfa5408
**Description:** `langchain_huggingface` has a very large installation size of around 600 MB (on a Mac with Python 3.11). This is due to its dependency on `sentence-transformers`, which in turn depends on `torch`, which is 320 MB all by itself. Similarly, the depedency on `transformers` adds another set of heavy dependencies. With those dependencies removed, the installation of `langchain_huggingface` only takes up ~26 MB. This is only 5 % of the full installation! These libraries are not necessary to use `langchain_huggingface`'s API wrapper classes, only for local inferences/embeddings. All import statements for those two libraries already have import guards in place (try/catch with a helpful "please install x" message). This PR therefore moves those two libraries to an optional dependency group `full`. So a `pip install langchain_huggingface` will only install the lightweight version, and a `pip install "langchain_huggingface[full]"` will install all dependencies. I know this may break existing code, because `sentence-transformers` and `transformers` are now no longer installed by default. Given that users will see helpful error messages when that happens, and the major impact of this small change, I hope that you will still consider this PR. **Dependencies:** No new dependencies, but new optional grouping. |
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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.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.