I came across an interesting library called Train that implements some of the core concepts from LangChain in Elixir.

I think it would be fine to take the python library as a dependency and provide basic interop by copying the API. I know Google used some kind of code generation to create their Elixir deps for the massive Google API - not sure how they went about it, but in theory one could use codegen to do this.

Or put ChatGPT on the job; use AutoGPT to generate an Elixir library. (Though in my experience ChatGPT isn’t the best with Elixir.)

I recently completed the langchain course on deepLearning.ai - while to me, the API seemed unnecessarily tedious and complex, but use-cases it solves are essential. In fact, I have been asked for a few similar things in a product I’m building on top of openai.

What is the community’s take on a library like langchain and what’s Elixir’s way of implementing the API?

(I did see langchainex project on github - it seems promising, however, I felt it to be very Pythonish)

It would be cool to have such an initiative but it seems like a lot of work to have feature parity.
Do you have any ideas how it could be achieved faster and efficiently?

I had spent a few weeks/months learning Elixir in 2022. After the release of ChatGPT, I started developing Large Language Model (LLM) based AI applications. Given that my knowledge of Elixir was limited, I chose to use a Python based library called LangChain.

LangChain is a reasonably well-designed wrapper or abstraction around existing Python libraries that makes developing LLM based applications easier. The difficult/tricky part of creating LangChain is the various prompts baked into its various functions.

I believe creating/releasing a port of LangChain in Elixir could greatly help the ecosystem.

Any thoughts?