khoj VS danswer

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

I'm a fan of Khoj. Been using it for months. https://github.com/khoj-ai/khoj

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

This is the easiest I found, on here too.

Thanks for the feedback. Does your machine have a GPU? 32GB CPU RAM should be enough but GPU speeds up response time.

We have fixes for the seg fault[1] and improvement to the query speed[2] that should be released by end of day today[3].

Update khoj to version 0.10.1 with pip install --upgrade khoj-assistant to see if that improves your experience.

The number of documents/pages/entries doesn't scale memory utilization as quickly and doesn't affect the search, chat response time as much

[1]: The seg fault would occur when folks sent multiple chat queries at the same time. A lock and some UX improvements fixed that

[2]: The query time improvements are done by increasing batch size, to trade-off increased memory utilization for more speed

[3]: The relevant pull request for reference: https://github.com/khoj-ai/khoj/pull/393

We recently integrated Llama 2 into Khoj. I wanted to share a short real-world evaluation of using Llama 2 for the chat with docs use-cases and hear which models have worked best for you all. The standard benchmarks (ARC, HellaSwag, MMLU etc.) are not tuned for evaluating this

Self-host your Notion assistant using the instructions here. You'll need Python >= 3.8 to get started.

Here's an early example: https://github.com/khoj-ai/khoj

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