langchainrb

> Although there’s no dedicated gem for Ollama yet https://rubygems.org/gems/ollama-ai https://github.com/gbaptista/ollama-ai > A Ruby gem for interacting with Ollama's API that allows you to run open source AI LLMs (Large Language Models) locally To be fair, this just depends on Faraday and wrap the http API - it still doesn't automate ollama install etc. Ed: also https://github.com/andreibondarev/langchainrb https://github.com/andreibondarev/langchainrb_rails Ed2: anyone have some insight as to why it doesn't support summarize for ollama?

I've been frustrated by the fact that people still believe that python is some sort of magical default to build LLM apps. It's not. English is the new programming language. White paper for reference: https://arxiv.org/abs/2210.03629 Notice how the prompt used in Langchain (python) https://github.com/langchain-ai/langchain/blob/d21a494a27639... Is the same as it is in Langchain.rb (ruby) https://github.com/andreibondarev/langchainrb/blob/6fc2b962c...

Is the emerging architecture made out to be more complicated than what most of the companies are currently building? Perhaps! But this is most likely the general direction where things will start trending towards as the auxiliary ecosystem matures. Shameless plug: For fellow Ruby-ists we're building an orchestration layer for building LLM applications, inspired by the original, Langchain.rb: https://github.com/andreibondarev/langchainrb

We’re building “Langchain for Ruby” under the current working name of “Langchain.rb”: https://github.com/andreibondarev/langchainrb People that have contributed on the project thus far each have at least a decade of experience programming in Ruby. We’re trying our best to build an abstraction layer on top all of the common emerging AI/ML techniques, tools, and providers. We’re also focusbig on building an excellent developer experience that Ruby developers love and have gotten to expect. Unlike the Python project, as it’s been pointed out here a countless number of times, we’d like to avoid deeply nested class structures that make it incredibly difficult to track and extend. We’ve been pondering over the “what does Rails for Machine Learning look like?” question, and we’re taking a stab at answering this question. We’re hyper-focused on t