Open-source support bot for Discord

Hacker News

Many companies have a public Discord where people ask questions. Sometimes those questions are dumb and can be easily answered by reading the support documents. So we built a bot that does 3 things: 1. Uses FAISS to index public support documents provided as URLs 2. Monitors a Discord channel for questions, prefixed with !help 3. Responds to questions with its best guess of the right answer and cites its source document(s), using Data Augmented Generation from LangChain It won’t be right all of the time, but if people find it helpful we’ll make it better. Take a look at the repo and give us some suggestions of how to improve it, or put up a PR! Repo: https://github.com/getbuff/Buff Website: https://www.getbuff.io/ Demo: https://www.youtube.com/shorts/JPKW9tX0K9Y

Visit product View on Hacker NewsJan 25, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, 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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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