Awesome NLP with Ruby VS langchainrb

Glimmer has been around for a while and is in active development.

Here is a extensive list about NLP in ruby: https://github.com/arbox/nlp-with-ruby.

If you want a fast running and fast starting GUI you should take a look at GraalVM from oracle and its Ruby implementation called TruffleRuby, it translates Ruby code to native code and optimizes C and Ruby code at compile and runtime to make it faster: https://www.graalvm.org/ruby/

8. LangChain RB | Github | tutorial

So I hooked up the Ruby on Rails langchainrb gem (https://github.com/andreibondarev/langchainrb) and it seems like the approach is to store the plane text entries as meta data on pinecone. I definitely DO NOT want to do this as the data is private and secure on my own DB.

langchain

I would suggest taking a look at Langchain.rb as well. Disclosure: I'm the core maintainer.

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

I'm the author of Langchain.rb.

@dliteful23 I loved your super detailed lessons-learned article! I'm the author of Langchain.rb, I would love to hear what you think of it if you get a chance to check it out. If there's anything that you'd like to see in the framework, please do let us know and we'll make sure to build it out if it aligns with the vision.

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 the open source community and the developer community at large. All feedback/ideas/contributions/criticism are welcome and encouraged!