DanswerChat – open-source GPTs with access to your org's knowledge

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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. Demo video: https://www.youtube.com/watch?v=6cDiv-TShh4 Code here: https://github.com/danswer-ai/danswer Setup docs: https://docs.danswer.dev/quickstart Danswer is self-hostable and comes ready to use out of the box with a UI, admin features, 20+ auto-syncing connectors, and user/access management. All your data is persisted on-prem and you can easily customize it to hook up to any LLM provider of your choice (though we recommend GPT-4 for best results). As you ask questions, the system runs retrieval as needed to fetch relevant documents and uses GenAI to help you find the answers you’re seeking. You can also select specific documents to chat with at any point and deselect them when you want the system to run more retrievals. You can further refine your search by applying time and source type filters. With Danswer you can also create document-sets for specific use cases. You can easily group together knowledge for different purposes like onboarding, HR answers, team specific knowledge, finance, etc. You can combine different sources at different granularities such as creating a document-set which is a combination of Slack channels + some set of Google Drive Folders + some workspaces from Confluence, etc. You get the picture. Finally, just like GPTs, you can configure custom prompts and create different assistants for varied tasks like debugging, summarizing, generating content, all while pulling in the context documents you want to include. Out of the box, Danswer comes with several pre-configured general use assistants. You can use Danswer to improve your operational efficiencies for any team that needs it. We’ve seen users use it for onboarding, customer support, cross-team communications, sales-call prep, and as an engineering assistant. We also have a Slack ( https://join.slack.com/t/danswer/shared_invite/zt-1u3h3ke3b-... ) and a Discord ( https://discord.gg/TDJ59cGV2X ) community. Always looking forward to user feedback as this helps us improve our software! See you there :)

Visit product View on Hacker NewsDec 18, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, google, user · Missing: mac, agents, macos

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video, google, answers · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users, communications · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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