Ku

KuralGPT. Search ancient Tamil scripture with ChatGPT

Hacker News

KuralGPT. Search ancient Tamil scripture with ChatGPT

Hello HN, Senthil here. I run a venture studio called Sprint1< https://sprint1.vc >Last weekend we launched KuralGPT < https://kuralgpt.app/ >. It is an effort to improve the search experience of Thirukural. Thirukural aka Kural < https://en.wikipedia.org/wiki/Kural > is an ancient Tamil text passed down from generation to generation for at least two millennia. This text has been continually in print since the 1800s. This is really astonishing for a largely secular text to survive for centuries before printing presses were invented. It is a collection of 1330 couplets divided into 3 parts, Virtue, Wealth and Love. Each part has a collection of chapters, each chapter has 10 couplets under a specific theme. I try to read one to two couplets a day and I read most of them. As a kid, I used to be fascinated by a few folks who memorized all the text and could give one or two examples for any given scenario. Couple of times I tried to create a search engine that could find relevant Kurals for a given query. Most of my past efforts relied around keyword matching. During last Christmas break, I started this project to tackle this problem again. I think it largely works. Please take it for a spin and provide your feedback. For those who grew up in west, this phrase might be interesting starting point. - Can you find kurals that are similar to the bible phrase "turn the other cheek"? How is it done? Each couplet had a few Tamil explanations and one English translation with one line English explanation. My first attempt was to create an embedding with Tamil and English explanations. Search was not that great. Then I took each couplet and wrote a prompt with original text, explanations and asked ChatGPT to create a page long summary. With this summary, I created embeddings and stored it along with some meta data. When you search something, it creates an embedding for the query and compares the querie's embedding with each couplet's embedding and returns the top 10 matching couplets. Tech Stack: - ChatGPT - NextJs - Supabase with pgvector. What is next?(Would love your feedback) - Improve the search. Currently all results are purely based on couplet level. I would like to expand this. If you ask about a prticular chapter or part, it doesn't do really well. It just simply returns the top 10 matching couplets. I would like to make it more like Perplexity.ai and provide links to chapters. - I have an idea for a game that lets you pick 7 words for a given couplet and see how close your selection to others. - Tamil has a large body of ancient texts<https://en.wikipedia.org/wiki/Sangam_literature> that are very intresting to me. I would like to implement search on these.

Share card

Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
94%94% 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: perplexity, chatgpt · Missing: mac, agents, macos
74%74% 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
57%57% 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
37%37% 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
35%35% 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

Ch
ChatGPT – The Memoir25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ChatGPT – The Memoir

Hacker News2
Ch
ChatGPT in Emacs43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ChatGPT in Emacs

Hacker News23
St
StackOverflow for ChatGPT43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

StackOverflow for ChatGPT

Hacker News2
Th
This Is How ChatGPT Will Be Monetized27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

This Is How ChatGPT Will Be Monetized

Hacker News9
Ch
ChatGPT Wrote a “Memoir”23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ChatGPT Wrote a “Memoir”

Hacker News2
Ch
ChatGPT Pitches OnlyBots Concept52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ChatGPT Pitches OnlyBots Concept

Hacker News2
Ti
Tic Tac Toe Against ChatGPT27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tic Tac Toe Against ChatGPT

Hacker News1
Al
Alongside ChatGPT and DALL-E Emacs shells50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Alongside ChatGPT and DALL-E Emacs shells

Hacker News1
Fi
Filtir – Fixing ChatGPT Hallucinations42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Filtir – Fixing ChatGPT Hallucinations

Hacker News2
Hy
Hypnotizing ChatGPT40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hypnotizing ChatGPT

Hacker News2