danswer VS llama.cpp

You might want to look at https://github.com/danswer-ai/danswer as well, as it sounds like their UI might be of suited for your use case.

There are already several RAG chat open source solutions available. Two that immediately come to mind are:

Danswer

https://github.com/danswer-ai/danswer

Khoj

https://github.com/khoj-ai/khoj

We have a connector interface and build guide for contributors: https://github.com/danswer-ai/danswer/blob/main/backend/dans...

Should be not too bad to build one out! Fun fact, more than half the connectors were built entirely by community members who needed them for their own teams and we're super grateful when they contribute it back to the repo.

Danswer is an MIT licensed project that can connect to a wide range of SaaS tools and provide a search/chat (RAG) functionality to help your team discover information and to turn that information into deeper understanding and actionable insights.

Code here: https://github.com/danswer-ai/danswer

Slack questions have always been a huge time sink for me. They’re a distraction that pulls me away from what I’m doing, and often requires digging up old knowledge. If I’m in the middle of something complex, I may take a while to context switch and get around to answering, which leaves the asker blocked for hours.

Addressing this seems simple: give an LLM your organizational context and plop it in Slack to answer things for you.

So that’s why we built DanswerBot! It’s MIT licensed (https://github.com/danswer-ai/danswer) and completely free to use. The bot can automatically sync with and back answers based on documents from Slack, Google Drive, GitHub, Confluence, Jira, Notion, local files, websites, and much more.

Quick demo vid: https://www.youtube.com/watch?v=5q35NeqsMnU

A quick note on hallucinations: in order to reduce their prevalence, all answers are backed by quotes. If the LLM-provided quotes don’t match any document or no quotes are given, we’ll warn the asker that something may have gone wrong. Additionally, all used documents are linked in case the asker wants to double check the answer. Answers can be thumbs-upped or thumbs-downed and all questions / answers are recorded in Postgres for easy future inspection / analysis.

For usability, we provide an admin dashboard where you can configure connectors (we have 14 currently). Once a connector is set up, we poll data sources every 10 minutes to keep answers up to date. Which LLM to use is also up to you - DanswerBot can be configured to use a locally hosted model, Azure OpenAI, or OpenAI directly.

Finally, if you aren’t a slack user (or if you just prefer a more tailored UI), there’s also a web interface to ask questions against your knowledge base. A short demo for that can be found at: https://youtu.be/cWWtnuVCUX0

Of course there’s a bunch more that I can’t cover in one post - happy to take questions in the comments (or in our Slack / Discord, which are linked on the Github repo).

If you’re interested in testing this out yourself, you can easily run everything locally with a single command. Docs to help you can be found at https://docs.danswer.dev/quickstart!

Code: https://github.com/danswer-ai/danswer

Sorry it's taking so long to review and for the radio silence on the PR.

We have been trying to figure out how to support more structured output formats without some of the side effects of grammars. With JSON mode (which uses grammars under the hood) there were originally quite a few issue reports namely around lower performance and cases where the model would infinitely generate whitespace causing requests to hang. This is an issue with OpenAI's JSON mode as well which requires the caller to "instruct the model to produce JSON" [1]. While it's possible to handle edge cases for a single grammar such as JSON (i.e. check for 'JSON' in the prompt), it's hard to generalize this to any format.

Supporting more structured output formats is definitely important. Fine-tuning for output formats is promising, and this thread [2] also has some great ideas and links.

[1] https://platform.openai.com/docs/guides/text-generation/json...

[2] https://github.com/ggerganov/llama.cpp/issues/4218

> Doing everything on-device would result in a horrible user experience. They might as well not participate in this generative AI rush at all if they hoped to keep it on-device.

On the contrary, I'm shocked over the last few months how "on device" on a Macbook Pro or Mac Studio competes plausibly with last year's early GPT-4, leveraging Llama 3 70b or Qwen2 72b.

There are surprisingly few things you "need" 128GB of so-called "unified RAM" for, but with M-series processors and the memory bandwidth, this is a use case that shines.

From this thread covering performance of llama.cpp on Apple Silicon M-series …

https://github.com/ggerganov/llama.cpp/discussions/4167

… "Buy as much memory as you can afford would be my bottom line!"

I'd love to use local models, but seems like most of the easy to use software out there (LM Studio, Backyard AI, koboldcpp) doesn't really play all that nicely with my Intel Arc GPU and it's painfully slow on my Ryzen 5 4500. Even my M1 MacBook isn't that fast at generating text with even 7B models.

I wonder if llama.cpp with SYCL could help, will have to try it out: https://github.com/ggerganov/llama.cpp/blob/master/README-sy...

But even if that worked, I'd still have the problem that IDEs and whatever else I have open already eats most of the 32 GB of RAM my desktop PC has. Whereas if I ran a small code model on the MacBook and connected to it through my PC, it'd still probably be too slow for autocomplete, when compared to GitHub Copilot and less accurate than ChatGPT or Phind for most stuff.

You're correct if you're focused exclusively on the work surrounding building foundation models to begin with. But if you take a broader view, having open models that we can legally fine tune and hack with locally has created a large and ever-growing community of builders and innovators that could not exist without these open models. Just take a look at projects like InvokeAI [0] in the image space or especially llama.cpp [1] in the text generation space. These projects are large, have lots of contributors, move very fast, and drive a lot of innovation and collaboration in applying AI to various domains in a way that simply wouldn't be possible without the open models.

[0] https://github.com/invoke-ai/InvokeAI

[1] https://github.com/ggerganov/llama.cpp

The first example will build an Embeddings database backed by llama.cpp vectorization.

and at some point (https://github.com/ggerganov/llama.cpp/issues/7444)

git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp LLAMA_CUDA=1 make -j

I focused on supporting Ollama, GPT4All, and LMStudio, all of which run smoothly on a Mac computer. Many of these tools are user-friendly wrappers around Llama.cpp, allowing easy model downloads and providing a REST interface to query the available models. Last week, I also added "👋🏼 Jan" support because HuggingFace has endorsed this provider out-of-the-box.

The output of the LLM is not just one token, but a statistical distribution across all possible output tokens. The tool you use to generate output will sample from this distribution with various techniques, and you can put constraints on it like not being too repetitive. Some of them support getting very specific about the allowed output format, e.g. https://github.com/ggerganov/llama.cpp/blob/master/grammars/... So even if the LLM says that an invalid token is the most likely next token, the tool will never select it for output. It will only sample from valid tokens.