CX_DB8 VS joia

I was working on this stuff before it was cool, so in the sense of the precursor to LLMs (and sometimes supporting LLMs still) I've built many things:

1. Games you can play with word2vec or related models (could be drop in replaced with sentence transformer). It's crazy that this is 5 years old now: https://github.com/Hellisotherpeople/Language-games

2. "Constrained Text Generation Studio" - A research project I wrote when I was trying to solve LLM's inability to follow syntactic, phonetic, or semantic constraints: https://github.com/Hellisotherpeople/Constrained-Text-Genera...

3. DebateKG - A bunch of "Semantic Knowledge Graphs" built on my pet debate evidence dataset (LLM backed embeddings indexes synchronized with a graphDB and a sqlDB via txtai). Can create compelling policy debate cases https://github.com/Hellisotherpeople/DebateKG

4. My failed attempt at a good extractive summarizer. My life work is dedicated to one day solving the problems I tried to fix with this project: https://github.com/Hellisotherpeople/CX_DB8

I really missed out on this thread despite being likely one of the most important folks to post on it (I turned my time in Policy Debate into an NLP career - see DebateSum: https://huggingface.co/datasets/Hellisotherpeople/DebateSum and CX_DB8: https://github.com/Hellisotherpeople/CX_DB8)

For those who are interested in the intersection of AI and Debate Evidence, there's a lot more work being done right now. We have a follow-up dataset to DebateSum on its way to a paper at some conference called OpenCaseList: https://huggingface.co/datasets/Yusuf5/OpenCaselist which is basically DebateSum but 40x better in every way. This is also likely the largest and best quality argument mining dataset ever gathered.

Fun anecdote, when I tried to introduce automatic extractive summarization tools to the debate community, I had parent/judge/teacher groups who were FLIPPING out about this. They were not happy at the idea of automatic debating or computer assisted debating systems.

This has deep connections with my attempt to implement an effective queryable word-level grammatically correct extractive text summarizer (AKA: The way most people actually summarize documents) - https://github.com/Hellisotherpeople/CX_DB8

I will try to implement this with the necessary changes to actually make this work properly, where instead of generating a new answer, it simply highlights the most likely text spans.

Is there any path forward to make Haystack do word-level extractive summarization? e.g. like this: https://github.com/Hellisotherpeople/CX_DB8

or like this: https://huggingface.co/spaces/Hellisotherpeople/Unsupervised...

I am trying to find anything better than these two for this task. I feel like Haystack could be an option - but I am not sure.

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!