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Danswer APIs – Open-source APIs for building RAG apps over company docs

Hacker News

Danswer APIs – Open-source APIs for building RAG apps over company docs

Hi HN! I’m one of the creators of Danswer, the open-source enterprise search project that you may have seen on HN a bit under a year ago ( https://news.ycombinator.com/item?id=39467413 ). Really excited to be back to post about our new APIs :) Over the last half-year the #1 thing we’ve heard from our open-source community is the desire to build custom applications on top of the core connections with common apps + search + assistants. While this has always technically been possible, it hasn’t been easy for two main reasons: (1) inability to use APIs with auth-enabled w/o grabbing cookies from the browser and (2) a lack of simple APIs. Well no longer! For (1), we now provide a UI to generate API keys and for (2) we provide a pure “search” endpoint, a “chat” endpoint, and an implementation of OpenAI's Assistants API. With these, we take care of all the boring stuff (integrating with 35+ tools, constantly keeping docs up-to-date, and handling permissions) and provide an opinionated starting point for the generic hard stuff (really good hybrid-search + factoring in metadata, recency, etc.), so that you can focus on the specific business logic of the app. Back when I was at Alation, on-call for our team could have been made so much easier by a small, custom app that auto-responded to every alert based on Confluence, Slack, and a few Drive folders. Danswer’s APIs would power the core of this, and all I’d be left with are app-specific things like routing alerts to the right team member based on the document ownership of the cited documents. For an example of what it looks like to use the APIs, check out this script here: https://github.com/danswer-ai/danswer/blob/main/examples/ass... ! If you're curious what's happening under the hood, check out our docs: https://docs.danswer.dev/system_overview . Thanks, excited to hear your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, apps, new · 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
47%47% 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
31%31% 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
21%21% 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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