ricardoagzz

Thanks for the feedback!

I wrote an example on how you can use it to create a simple pipeline to scrape wikipedia's daily featured article, chunk it, and store it in a vector DB using the scheduled cells, then use the HTTP cells to expose an endpoint calling GPT with RAG using the vector DB (https://docs.neutrinolabs.dev/example-up-to-date-chatbot). Any specific use cases you had in mind?

And I agree, the deployment point is super important, its in the works!

Of course, we're working on better support for deployment soon, but since its just a FastAPI app with a dockerfile you could deploy it anywhere. Also depends on your use case though, for example, if you have auth requirements you could add a middleware to your endpoints to verify a jwt token or call the endpoints from another backend service.

Super cool! And Llmflows looks really interesting, how does it differentiate from Langchain?

A simple tutorial on how to build a Node, Express, MongoDB backend for a simplified HackerNews app in only a few minutes using Neutrino.