Building applications with LLMs through composability
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Really Him fbd2e10703
docs: hide jsx in llm chain tutorial (#30187)
## **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`):

![Screenshot 2025-03-07 at 2 07
15 PM](https://github.com/user-attachments/assets/a528d618-f5a0-4d2e-9aed-16d4b8148b5a)

Now, here is the same notebook viewed in colab:

![Screenshot 2025-03-07 at 2 08
41 PM](https://github.com/user-attachments/assets/87acf5b7-a3e0-46ac-8126-6cac6eb93586)

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>
2025-03-26 14:22:33 -04:00
.devcontainer community[minor]: Add ApertureDB as a vectorstore (#24088) 2024-07-16 09:32:59 -07:00
.github infra(GHA): description is required based on schema definition (#30305) 2025-03-17 18:42:42 +00:00
cookbook Docs: Fix typo in cookbook (#30485) 2025-03-25 18:15:29 -04:00
docs docs: hide jsx in llm chain tutorial (#30187) 2025-03-26 14:22:33 -04:00
libs community[patch]: update PyPDFParser to take into account filters returned as arrays (#30489) 2025-03-26 14:16:54 -04:00
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.pre-commit-config.yaml docs: fix builds (#29890) 2025-02-19 13:35:59 -05:00
.readthedocs.yaml docs(readthedocs): streamline config (#30307) 2025-03-18 11:47:45 -04:00
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poetry.toml multiple: use modern installer in poetry (#23998) 2024-07-08 18:50:48 -07:00
pyproject.toml langchain: clean pyproject ruff section (#30070) 2025-03-09 15:06:02 -04:00
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uv.lock openai[patch]: support Responses API (#30231) 2025-03-12 12:25:46 -04:00
yarn.lock box: add langchain box package and DocumentLoader (#25506) 2024-08-21 02:23:43 +00:00

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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 youre 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 LangChains 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 applications needs. As the industry frontier evolves, adapt quickly — LangChains abstractions keep you moving without losing momentum.

LangChains 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.