lowdefy VS LLMStack

I'm really enjoying reading through the docs and the tutorial. We've created Lowdefy, a config web-stack which makes it really simple to build quite advanced web apps. We're writing everything in YAML, but it has it's limitations, specifically when doing config type checking and IDE extensions that go beyond just YAML.

I've been looking for a way to have typed objects in the config to do config suggestions and type checking.. PKL looks like it can do this for us. And with the JSON output we might even be able to get there with minimal effort.

Is there anyone here with some PKL experience that would be willing to answer some technical questions re the use of PKL for more advanced, nested config?

See Lowdefy:

https://lowdefy.com/

https://github.com/lowdefy/lowdefy

Awsome! With Lowdefy we tried to build a low-code framework that works like code. We’ve developed a schema in which to define applications and we’ve built all kinds of apps for enterprise customers. Massive, advanced CRM systems, call centre solutions, ticketing systems, a light MRP, all kinds of survey apps and so many dashboards. Even our docs and our website are Lowdefy apps!

Give Lowdefy a try and reach out it you have any questions or want to see what is possible :) (We need to invest a lot more into content and examples, bootstapping is a grind!)

https://github.com/lowdefy/lowdefy

Also add Lowdefy onto the list https://github.com/lowdefy/lowdefy

co-founder here :)

100% this. And yes, good documentation takes a lot of investment but it pays off like compound interest. But with that done, it becomes even more important not to pull the carpet for no good reason, you are building a tower and documentation is at the foundation.

We’ve built Lowdefy [1] as an open source project and documented it with all effort, 200 pages of docs. I often forget why or how something works and then jump to the docs. This investment keeps on paying of as we use Lowdefy to build customer apps, new devs in the team typically take less than two week to get up to speed and start making contributions, the sharp ones, just a two or three days.

This year, we’re extended our documentation onto customer apps aswell, with flow diagrams, state machine definitions, detailed field level explication schema definitions, and end user test procedures. The key here for this documentation is detail. It should be easier to reach for the docs and the the answer, than to dive in the code and interpret it.

1 - https://github.com/lowdefy/lowdefy

https://github.com/lowdefy/lowdefy Co-Founder here.

Check out https://lowdefy.com/ they even have a sample survey app as one of their examples.

You can build an app that would do this

Lowdefy is a low code tool that uses AG Grid as a block component, allowing you to create apps which render data in AG Grid without a lot of coding knowledge. There is a Lowdefy example using AG Grid here.

Shameless plug, also check out Lowdefy - https://github.com/lowdefy/lowdefy

I’m also going to jump in here and say try Lowdefy https://github.com/lowdefy/lowdefy - co-founder here.

We take a different angle and believe that low code should still work like code. We focus on a developer first approach.

We have recently added support to query data from SingleStore to our agent framework, LLMStack (https://github.com/trypromptly/LLMStack). Out of the box performance performance when prompting with just the table schemas is pretty good with GPT-4.

The more domain specific knowledge needed for queries, the harder it has gotten in general. We've had good success `teaching` the model different concepts in relation to the dataset and giving it example questions and queries greatly improved performance.

This will hopefully improve the startup times for FFmpeg when streaming from virtual display buffers. We use FFmpeg in LLMStack (low-code framework to build and run LLM agents) to stream browser video. We use playwright to automate browser interactions and provide that as tool to the LLM. When this tool is invoked, we stream the video of these browser interactions with FFmpeg by streaming the virtual display buffer the browser is using.

There is a noticeable delay booting up this pipeline for each tool invoke right now. We are working on putting in some optimizations but improvements in FFmpeg will definitely help. https://github.com/trypromptly/LLMStack is the project repo for the curious.

We built https://github.com/trypromptly/LLMStack to serve exactly this persona. A low-code platform to quickly build RAG pipelines and other LLM applications.

Kudos to the team for a very detailed notebook going into things like pipeline evaluation wrt performance and costs etc. Even if we ignore the framework specific bits, it is a great guide to follow when building RAG systems in production.

We have been building RAG systems in production for a few months and have been tinkering with different strategies to get the most performance out of these pipelines. As others have pointed out, vector database may not be the right strategy for every problem. Similarly there are things like lost in the middle problems (https://arxiv.org/abs/2307.03172) that one may have to deal with. We put together our learnings building and optimizing these pipelines in a post at https://llmstack.ai/blog/retrieval-augmented-generation.

https://github.com/trypromptly/LLMStack is a low-code platform we open-sourced recently that ships these RAG pipelines out of the box with some app templates if anyone wants to try them out.

Django has been my go to framework for any new web project I start for more than a decade. Its batteries-included approach meant that one could go pretty far with just Django alone. Included admin interface and the views/templating setup was what first drew me to the project.

Django project itself has kept pace with recent developments in web development. I still remember migrations being an external project, getting merged in and the transition that followed. Ecosystem is pretty powerful too with projects like drf, channels, social-auth etc., covering most things we need to run in production.

https://github.com/trypromptly/LLMStack is a recent project I built entirely with Django. It uses django channels for websockets, drf for API and reactjs for the frontend.

We recently opensourced a similar platform for building workflows by chaining LLMs visually along with LocalAI support.

Check it out at https://github.com/trypromptly/LLMStack. Like you said, it was fairly easy to integrate LocalAI and is a great project.

Would you mind expanding why it was tough to get started with Retool?

We are building https://github.com/trypromptly/LLMStack, a low-code platform to build LLM apps with a goal of making it easy for non-tech people to leverage LLMs in their workflows. Would love to learn about your experience with retool and incorporate some of that feedback into LLMStack.

We built LLMStack for our internal purposes and pulled it out into its own repo and open sourced it at https://github.com/trypromptly/LLMStack.

LLMStack (https://github.com/trypromptly/LLMStack) is a no-code platform to build LLM apps that we have been working on for a few months and open-sourced recently. It comes with everything out of the box that one needs to build LLM apps locally or in an enterprise setting.