joia VS lambdaprompt

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're using all sorts of different stacks and tooling. We made our own tooling at one point (https://github.com/approximatelabs/lambdaprompt/), but have more recently switched to just using the raw requests ourselves and writing out the logic ourselves in the product. For our main product, the code just lives in our next app, and deploys on vercel.

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)

This is great~ There's been some really rapid progress on Text2SQL in the last 6 months, and I really thinking this will have a real impact on the modern data stack ecosystem!

I had similar success with lambdaprompt for solving Text2SQL (https://github.com/approximatelabs/lambdaprompt/)

This is great! I love seeing how rapidly in the past 6 months these ideas are evolving. I've been internally calling these systems "prompt machines". I'm a strong believer that chaining together language model prompts is core to extracting real, and reproducible value from language models. I sometimes even wonder if systems like this are the path to AGI as well, and spent a full month 'stuck' on that hypothesis in October.

Specific to prompt-chaining: I've spent a lot of time ideating about where "prompts live" (are they best as API endpoint, as cli programs, as machines with internal state, treated as a single 'assembly instruction' -- where do "prompts" live naturally) and eventually decided on them being the most synonymous with functions (and api endpoints via the RPC concept)

mental model I've developed (sharing in case it resonates with anyone else)

a "chain" is `a = 'text'; b = p1(a); c = p2(b)` where p1 and p2 are LLM prompts.

What comes next (in my opinion) is other programming constructs: loops, conditionals, variables (memory), etc. (I think LangChain represents some of these concepts as their "areas" -> chain (function chaining), agents (loops), memory (variables))

To offer this code-style interface on top of LLMs, I made something similar to LangChain, but scoped what i made to only focus on the bare functional interface and the concept of a "prompt function", and leave the power of the "execution flow" up to the language interpreter itself (in this case python) so the user can make anything with it.

https://github.com/approximatelabs/lambdaprompt

I've had so much fun recently just playing with prompt chaining in general, it feels like the "new toy" in the AI space (orders of magnitude more fun than dall-e or chat-gpt for me). (I built sketch (posted the other day on HN) based on lambdaprompt)

My favorites have been things to test the inherent behaviors of language models using iterated prompts. I spent some time looking for "fractal" like behavior inside the functions, hoping that if I got the right starting point, an iterated function would avoid fixed points --> this has eluded me so far, so if anyone finds non-fixed points in LLMs, please let me know!

I'm a believer that the "next revolution" in machine-written code and behavior from LLMs will come when someone can tame LLM prompting to self-write prompt chains themselves (whether that is on lambdaprompt, langchain, or something else!)

All in all, I'm super hyped about LangChain, love the space they are in and the rapid attention they are getting~

From https://github.com/approximatelabs/sketch/blob/main/sketch/p... it appears that this library is calling a remote API, which obviates the utility of the demonstrated use case.

Upon closer inspection, it looks like https://github.com/approximatelabs/sketch interfaces with the model via https://github.com/approximatelabs/lambdaprompt, which is made by the same organization. This suggests to me that the former may be a toy demonstration of the latter.