evadb VS uniteai

Hey friends!

We have built an app for getting insights about your favorite GitHub community using large language models.

The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.

This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.

Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!

We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.

We’ve been working on this app for a month now and are excited to open source it today :)

Some useful links:

* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...

* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/

* EvaDB - https://github.com/georgia-tech-db/evadb

Please let us know what you think!

The discord link seems to be not working. Just a heads up.

The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....

Once the function is loaded, it can be used in queries in this way:

  SELECT id, Yolo(data)

Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?

[1] https://github.com/georgia-tech-db/eva

Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.

[1] https://github.com/georgia-tech-db/eva/tree/master/docker

EVA AI-Relational Database System; build simpler and faster AI-powered apps

I recently went through the same with UniteAI, and had to swap ctransformers back out for llama.cpp

I maintain the uniteai project, and have implemented a custom backend for serving transformers-compatible LLMs. (That file's actually a great ultra-light-weight server if transformers satisfies your needs; one clean file).

UniteAI brings together speech recognition and document / code search. The major difference is your UI is your preferred text editor.

https://github.com/freckletonj/uniteai

In answer to the same question I built UniteAI https://github.com/freckletonj/uniteai

It's local first, and ties many different AIs into one text editor, any arbitrary text editor in fact.

It does speech recognition, which isn't useful for writing code, but is useful for generating natural language LLM prompts and comments.

It does CodeLlama (and any HuggingFace-based language model)

It does ChatGPT

It does Retrieval Augmented Gen, which is where you have a query that searches through eg PDFs, Youtube transcripts, code bases, HTML, local or online files, Arxiv papers, etc. It then surfaces passages relevant to your query, that you can then further use in conjunction with an LLM.

I don't know how mainstream LLM-powered software looks, but for devs, I love this format of tying in the best models as they're released into one central repo where they can all play off each others' strengths.

I'm working on UniteAI, a project to tie different AI capabilities into the editor, and it has a clean LSP Server.

*TL;DR*: chat with AI, code with AI, speak to AI (voice-to-text + vice versa), have AI search huge corpora or websites for you, all via an interface of collaborating on a text doc together in the editor you use now.

*Motivation*

I find the last year of AI incredibly heartening. Researchers are still regularly releasing SoTA models in disparate domains. Meta is releasing powerful Llama under generous provisions (As is the UAE with Falcon?!). And the Open Source community has shown a tidal wave of interest and effort into building things out of these tools (112k repos on GH mentioning ML!).

Facing this deluge of valuable things that communities are shepherding into the world, I wanted to incorporate them into my workflows, which as a software engineer, means my text editor.

*UniteAI*

So I started *UniteAI* https://github.com/freckletonj/uniteai, an Apache-2.0 licensed tool.

Check out the screencasts: https://github.com/freckletonj/uniteai#some-core-features

This project:

* Ties in to *any editor* via Language Server Protocol. Like collaborating in G-Docs, you collab with whatever AI directly in the document, all of you writing alongside each other concurrently.

* Like Copilot / Cursor, it can write code/text right in your doc.

* It supports *any Locally runnable model* (Llama family, Falcon, Finetunes, the 21k available models on HF, etc.)

* It supports *OpenAI/ChatGPT* via API key.

* *Speech-to-Text*, useful for writing prompts to your LLM

* You can do *Semantic Search* (Retrieval Augmented Generation) on many sources: local files, Arxiv, youtube transcripts, Project Gutenberg books, any online HTML, basically if you give it a URI, it can probably use it.

* You can trigger features easily via [key combos](https://github.com/freckletonj/uniteai#keycombos).

* Written in Python, so, much more generic than writing a bespoke `some_specific_editor` plugin.

*Caveat*

Since it always comes up, *AI is not perfect*. AI is a tool to augment your time, not replace it. It hallucinates, it lies, it bullshits, it writes bad code, it gives dangerous advice.

But can still do many useful things, and for me it is a *huge force multiplier.*

*You need a Human In The Loop*, which is why it's nice to work together iteratively on a text document, per, this project. You keep it on track.

*Why is this interesting*

These tools play well when used together:

* *Code example:* I can Voice-to-Text a function comment then send that to an LLM to write the function.

* *Code example 2:* I can chit chat about project architecture plans, and strategies, and libraries I should consider.

* *Documentation example:* I can retrieve relevant sections of my city's building code with a natural language query, then send that to an LLM to expound upon.

* *Authorship example*: I can have my story arcs and character dossiers in some markdown file, and use that guidance to contextualize an AI as it works with me for writing a story.

* *Entertainment example*: I told my AI it was a Dungeon Master, then over breakfast with friends, used Voice-to-Text and Text-to-Wizened-Wizard-Voice, and played a hillarious game. I still had to drive all this via a text doc, and handy key combos.

*RFC*

Installation instructions are on the repo: https://github.com/freckletonj/uniteai#quickstart-installing...

This is still nascent, and I welcome all feedback, positive or critical.

We have a community linked on the repo which you're invited to join.

I'd love love to chat with people who like this idea, use it, want to see other features, want to contribute their effort, want to file bug reports, etc.

A big part of my motivation in this is to socialize with like-minds, and build something cool.

*Thanks for checking this out!*

[UniteAI](https://github.com/freckletonj/uniteai) I think fits the bill for you.

This is my project, where the goal is to Unite your AI-stack inside your editor (so, Speech-to-text, Local LLMs, Chat GPT, Retrieval Augmented Gen, etc).

It's built atop a Language Server, so, while no one has made an IntelliJ client yet, it's simple to. I'll help you do it if you make a GH Issue!

UniteAI (github)