chatbot-ui VS chatgpt-retrieval-plugin

One of the first things we did when GPT-4 became available was talk to our Azure rep and get access to the OpenAI models that they'd partnered with Microsoft to host in Azure. Now, we have our own private, not-datamined (so they claim, contractually) API endpoint and we use an OpenAI integration in VS Code[1] to connect to, allowing anyone in the company to use it to help them code.

I also spun up an internal chat UI[2] to replace ChatGPT so people can feel comfortable discussing proprietary data with the LLM endpoint.

The only thing that would make it more secure would be running inference engines internally, but I wouldn't have access to as good of models, and I'd need a _lot_ of hardware to match the speeds.

[1] - https://marketplace.visualstudio.com/items?itemName=AndrewBu...

[2] - https://github.com/mckaywrigley/chatbot-ui (legacy branch)

[3] https://github.com/mckaywrigley/chatbot-ui

Thank you for the work.

Please take this in a nice way: I can't see why I would use this over ChatbotUI+Ollama https://github.com/mckaywrigley/chatbot-ui

Seem the only advantage is having it as MacOS native app and only real distinction is maybe fast import and search - I've yet to try that though.

ChatbotUI (and other similar stuff) are cross-platform, customizable, private, debuggable. I'm easily able to see what it's trying to do.

You can make a privacy request for OpenAI to not train on your data here: https://privacy.openai.com/

Alternatively, you could also use your own UI/API token (API calls aren't trained on). Chatbot UI just got a major update released and has nice things like folders, and chat search: https://github.com/mckaywrigley/chatbot-ui

Also chatbotUI is great https://github.com/mckaywrigley/chatbot-ui it has a ui similar to chatgpt

> The comment I screenshotted is passive-aggressive at best, and there's no really good way to ask "is this repo dead" without being passive-aggressive. My day-to-day job that actually pays me a salary wouldn't ever provide a bulleted list of the reasons I suck, let alone a project I develop in my spare time.

There is nothing passive-aggressive about that comment. There is nothing problematic about it at all. Nobody's calling you slurs or making demands. I see one guy who might as well be a Mormon Boy Scout from Canada. "Is this repo dead" is not passive-aggressive, just ineloquent. Fuck my eyes until the jelly leaks out my ears if a courteous and professionally-written question constitutes "applying pressure and being rude" these days.

I don't know what a "bulleted list of the reasons [you] suck" has to do with anything (I don't see where anybody sent you one) but you're coming across as someone who invites people to your garage sale and then brandishes a shotgun and starts screaming when they set foot on your property.

> I’ve never seen any discussions or articles about whether it’s appropriate to ask if an open source repository is dead. Is there an implicit contract to actively maintain any open source software you publish? Are you obligated to provide free support if you hit a certain star amount on GitHub or ask for funding through GitHub Sponsorships/Patreon? After all, most permissive open source code licenses like the MIT License contain some variant of “the software is provided ‘as is’, without warranty of any kind.”

Here's an example of why everyone should ask if an open source project is dead:

https://github.com/mckaywrigley/chatbot-ui/issues

A number of issues complain about it leaking OpenAI keys. Nobody's figured out how, but it'd be nice to know if anybody's working on it, if it's worth submitting a PR, if it should be forked, if it's worth bothering with at all. This code is a massive liability in its current state. Its creator is absent. It warrants questions being asked about its future. Yeah, it's as-is software, but it's not an affront to your mother's virtue when someone asks if your shit still works or if you have plans to fix it.

> I’ve had an existential crisis about my work in open source AI on GitHub, particularly as there has been both increasingly toxic backlash against AI and because the AI industry has been evolving so rapidly that I flat-out don’t have enough bandwidth to keep up

Herein lies the problem? You sound overwhelmed. I've been there myself. I don't know what your year's been like but you genuinely might want to get away from the screen and get some fresh air. This is a good time of year to do it, since things generally slow down at work.

Many comments are criticizing the usage of LLM for this use case but I do believe this will become more common in the future. For example, OpenAI's retrieval plugin leverages LLM to do PII detection [1] instead of using the traditional libraries [2].

[1] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...

There is this

https://github.com/openai/chatgpt-retrieval-plugin

I just use SBERT which has models I can run locally

https://sbert.net/

should be yes, but even in examples from openai, they usually do splitting into chunks

For example in chatgpt-retrieval-plugin[0] repo default chunk size is just 200 tokens

[0] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...

this is anyway a limitation, no doubt, but chunking is pretty often used

To begin, visit the retrieval plugin repository.

You need to run the chatgpt-retrieval-plugin. The README has a quickstart guide for doing that: https://github.com/openai/chatgpt-retrieval-plugin

If the browser plugin for ChatGPT gets better to actually exceed what a web-scraper / summarizer can do, you could use GPT for the summarization part and store the results again in some vector database after creating embeddings. You could then use ChatGPT to "talk to your data", meaning when asked for specific information, it could query the database, retrieve the relevant knowledge and provide a meaningful answer based on your question. This could be done via OpenAIs own retrieval plugin (see https://github.com/openai/chatgpt-retrieval-plugin ) or by creating your own Chat-UI that performs the semantic search, feeds the result into the GPT4 API and returns the answer back to the user. David Shapiro made a nice video with step by step instructions on how to do that: https://www.youtube.com/watch?v=2xNzB7xq8nk&pp=ygUUZ3B0NCBtZW1vcnkgcGluZWNvbmU%3D

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For things like books or papers, I guess that embedding will stay relevant for a while.