chainlit VS danswer

chainlit is open source project that makes it very easy to build frontend interfaces like chatgpt and other features that are required for conversational ai app, so we can focus on the core part and don't need to worry about basic things, and it is dead simple to work with

I have created a sample chatbot application that uses Chainlit and LangChain to showcase Amazon Bedrock.

For the interface, I'm using chainlit, a new UI library for building LLM apps, with an integration with Langchain.

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