joia VS sketch

I've built an open-source ChatGPT UI designed for team collaboration.

Github Link: https://github.com/joiahq/joia

Benefits vs the original:

Over the past few months, we've been building Joia, an open-source alternative to "ChatGPT for Teams" that allows organizations to provide their people with access to Large Language Models. Joia's Chat UI connects not only to OpenAI but also to any LLM, including popular open-source ones like Llama 2, Mixtral, and Zephyr.

We're thrilled to share our beta with you at https://github.com/joiahq/joia

Why We Built This:

We believe that all organizations, from startups to enterprises, will soon integrate LLMs into their day-to-day operations. And while the AI ecosystem is flourishing with a wide range of models, the current providers like ChatGPT, Bard or Copilot are creating closed environments that limit interactions to their owned models only.

We envision a future where AI is interoperable, and where people will choose the pick best model for their job. We are building Joia to make this freedom of choice a reality, offering an open-source product that is easy to use, organizations-ready and built for collaboration.

Features of Our Beta Version:

User Management: Easily provide user access to LLMs by adding them to a collaborative workspace.

Multi-LLM Access: Interact smoothly with models like GPT-4, Llama 2, Mixtral, Zephyr, Perplexity, Gemini, and others. More to be added soon.

Reusable Chatbots: Create pre-configured chatbots (also known as GPTs within the OpenAI ecosystem) and share them with your teammates.

Bring Your Own API Keys: Connect directly with the LLM provider of your choice by simply inserting their API keys. We currently support OpenAI, Openrouter, and Amazon Bedrock; with more to come soon.

Purchase Credits with Us and Consolidate Payments: In our cloud version, you can buy credits and centralize the billing for all users in the organization.

Open Source License: We offer the software under the Affero license, allowing anyone to self-host the platform.

We're still in the early stages and eagerly welcome your feedback and suggestions. Please feel free to try it out at no cost and tell us what you think.

Our main website: https://joia.so

GitHub: https://github.com/joiahq/joia

[Coming soon] Open source version: We are just preparing the code to make it available to anyone who wants to self host it. We'll also welcome anyone willing to contribute. Check out our Github Repository and star it if you're interested in the project!

We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product.

1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch

2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm)

3. Our main product: julyp. https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut)

For your next question, about the stack and deploy:

This morning I added a "Related Projects" [3] Section to the Buckaroo docs. If Buckaroo doesn't solve your problem, look at one of the other linked projects (like Mito).

[1] https://github.com/approximatelabs/sketch

[2] https://github.com/paddymul/buckaroo

[3] https://buckaroo-data.readthedocs.io/en/latest/FAQ.html

For GPT/Copilot style help for pandas, in notebooks REPL flow (without needing to install plugins), I built sketch. I genuinely use it every-time I'm working on pandas dataframes for a quick one-off analysis. Just makes the iteration loop so much faster. (Specifically the `.sketch.howto`, anecdotally I actually don't use `.sketch.ask` anymore)

https://github.com/approximatelabs/sketch

https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://github.com/approximatelabs/sketch

Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs.

All together its only ~600 lines of python.

I haven't had a chance to really push all the different examples out there, but most "complex behaviors", so there aren't many patterns to copy. But if you're comfortable in python, then I think it offers a pretty good interface.

I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)

Please look at sketch and langchain pandas/SQL plugins. I have seen excellent results with both of these approaches. Both of these approaches will require you to send metadata to openAI.

👉 Understand your data through questions 👉 Create code from plain text Quick Read: https://www.marktechpost.com/2023/02/01/meet-sketch-an-ai-code-writing-assistant-for-pandas/ Github: https://github.com/approximatelabs/sketch

(3) Asking for re-writes of failed queries (happens occasionally) also helps

The main challenge I think with a lot of these "look it works" tools for data applications, is how do you get an interface that actually will be easy to adopt. The chat-bot style shown here (discord and slack integration) I can see being really valuable, as I believe there has been some traction with these style integrations with data catalog systems recently. People like to ask data questions to other people in slack, adding a bot that tries to answer might short-circuit a lot of this!

We built a prototype where we applied similar techniques to the pandas-code-writing part of the stack, trying to help keep data scientists / data analysts "in flow", integrating the code answers in notebooks (similar to how co-pilot puts suggestions in-line) -- and released https://github.com/approximatelabs/sketch a little while ago.

Python AI Helper https://github.com/approximatelabs/sketch