Add RAG to any app in minutes – Dabarqus

Hacker News

If you're a developer, building a basic RAG solution is pretty straightforward. There are tons of tutorials and how-tos, as well as Python code to reuse. But, if you're deploying your RAG solution within a company, or on end-user PCs, you will also have to figure out some potentially tricky deployment and maintenance issues. That also means deploying Python, a vector database, the right embedding AI model, and possibly dealing with licensing challenges. Dabarqus was created to address these issues with a stand-alone, all-in-one solution with no runtime dependencies. It's written in C++ and has built-in vector search, an industry-standard embedding model, and a REST API for easy development integration. I made an example python chatbot that uses Dabarqus with Ollama, and put it in the Github repo. I'd love your feedback – is anything missing? What would make Dabarqus more useful? Thanks for checking this out. Looking forward to your thoughts.

Visit product View on Hacker NewsOct 29, 2024

Share card

Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

87%87% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% 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

32%32% 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

16%16% 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

Similar products

Add texting to your landline in ~5 minutes43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Add texting to your landline in ~5 minutes

Hacker News3

Vectorless RAG48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Vectorless RAG

Hacker News11

PageIndex – Vectorless RAG66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PageIndex – Vectorless RAG

Hacker News192

Preprocess65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Preprocess maximises RAG performances

Product Hunt+106API

Magic the Gathering RAG App42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Magic the Gathering RAG App

Hacker News1

I made a RAG agent for the leyman24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a RAG agent for the leyman

Hacker News5

Code-Chunk for RAG48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Code-Chunk for RAG

Hacker News1

Cotter – Add Cotter's magic link to your Gatsby app under 5 minutes37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cotter – Add Cotter's magic link to your Gatsby app under 5 minutes

Hacker News3

Add gestures to a web app in 2 minutes45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Add gestures to a web app in 2 minutes

Hacker News40

Alter HN Firefox add-on44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Alter HN Firefox add-on

Hacker News1

Browse all products Analyze your launch