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⚡ Building applications with LLMs through composability ⚡
fbd2e10703
## **Description:** The Jupyter notebooks in the docs section are extremely useful and critical for widespread adoption of LangChain amongst new developers. However, because they are also converted to MDX and used to build the HTML for the Docusaurus site, they contain JSX code that degrades readability when opened in a "notebook" setting (local notebook server, google colab, etc.). For instance, here we see the website, with a nice React tab component for installation instructions (`pip` vs `conda`):  Now, here is the same notebook viewed in colab:  Note that the text following "To install LangChain run:" contains snippets of JSX code that is (i) confusing, (ii) bad for readability, (iii) potentially misleading for a novice developer, who might take it literally to mean that "to install LangChain I should run `import Tabs from...`" and then an ill-formed command which mixes the `pip` and `conda` installation instructions. Ideally, we would like to have a system that presents a similar/equivalent UI when viewing the notebooks on the documentation site, or when interacting with them in a notebook setting - or, at a minimum, we should not present ill-formed JSX snippets to someone trying to execute the notebooks. As the documentation itself states, running the notebooks yourself is a great way to learn the tools. Therefore, these distracting and ill-formed snippets are contrary to that goal. ## **Fixes:** * Comment out the JSX code inside the notebook `docs/tutorials/llm_chain` with a special directive `<!-- HIDE_IN_NB` (closed with `HIDE_IN_NB -->`). This makes the JSX code "invisible" when viewed in a notebook setting. * Add a custom preprocessor that runs process_cell and just erases these comment strings. This makes sure they are rendered when converted to MDX. * Minor tweak: Refactor some of the Markdown instructions into an executable codeblock for better experience when running as a notebook. * Minor tweak: Optionally try to get the environment variables from a `.env` file in the repo so the user doesn't have to enter it every time. Depends on the user installing `python-dotenv` and adding their own `.env` file. * Add an environment variable for "LANGSMITH_PROJECT" (default="default"), per the LangSmith docs, so a local user can target a specific project in their LangSmith account. **NOTE:** If this PR is approved, and the maintainers agree with the general goal of aligning the notebook execution experience and the doc site UI, I would plan to implement this on the rest of the JSX snippets that are littered in the notebooks. **NOTE:** I wasn't able to/don't know how to run the linkcheck Makefile commands. - [X] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ --------- Co-authored-by: Really Him <hesereallyhim@proton.me> |
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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.