I built a widget to integrate document-based chatbots into your website

Hacker News

Hey HN, I’m excited to share a project I've been working on: the DocsChat Widget. It's a tool that allows you to integrate a chatbot trained on your documents directly into any website. Here’s what it can do: Document Support: The widget supports PDFs, DOCX, TXT, and Markdown files. OCR Capabilities: It can detect and process text from images, thanks to built-in OCR technology. Powered by GPT-40: Uses the latest OpenAI model for embedding and searching through documents. Framework-Agnostic: Built with Vue.js for easy integration into any web framework. Real-Time Querying: Users can interact with the chatbot to get real-time answers from your knowledge base. This widget aims to simplify the setup and troubleshooting process for product user manuals, support documents, and any other document-driven knowledge bases. I’ve also included an example use case where it helps users find setup instructions and troubleshooting steps quickly. Thanks for taking a look!

Visit product View on Hacker NewsMay 23, 2024

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

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

best fitHighest predicted score across all platforms for this description.

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: answers, users · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

9%9% 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.

Incorrect prediction on native model

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