LangChain is short for Language Chain. An LLM, or Large Language Model, is the “Language” part. This library makes it easier for Elixir applications to “chain” or connect different processes, integrations, libraries, services, or functionality together with an LLM.

LangChain is a framework for developing applications powered by language models. It enables applications that are:

• Data-aware: connect a language model to other sources of data
• Agentic: allow a language model to interact with its environment

The main value props of LangChain are:

1. Components: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
2. Off-the-shelf chains: a structured assembly of components for accomplishing specific higher-level tasks

Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones.

Announcement post for the initial release that explains and gives an overview:

Library on Github:
https://github.com/brainlid/langchain

The library helps to integrate an Elixir app with an LLM (Large Language Model) like ChatGPT. It includes Livebook notebooks for easy experimental play. This supports defining your own functions to expose to an LLM which it can call, allowing your app to extend the functionality in interesting new ways.