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DocAsker – Use LLMs to ask documentation questions

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DocAsker – Use LLMs to ask documentation questions

We've built this over the last few weeks to leverage vector search and LLMs (this is backed by GPT-3.5, though we're also testing Flan-T5) to answer question over large sets of documents with references. Currently, we've ingested the documentation for React and some key adjacent libraries (Redux, React-Redux, React-Router, MUI). This allows you to ask various natural language questions and the output is hopefully a relevant answer with code examples if applicable, while sourcing the original docs whenever possible. We're working on adding up more documentations and have more "general" questions (e.g., query your own notion documentation). Any feedback is appreciated at this stage, let us know what you think and if there are any libs you'd like to see added!

Visit productView on Hacker NewsJan 31, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
92%92% 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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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 · 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
13%13% 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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