https://github.com/neondatabase/yc-idea-matcher

Submit your idea and get a list of similar ideas that YCombinator has invested in in the past.
https://github.com/neondatabase/yc-idea-matcher

nextjs openai pgvector postgres postgresql redis serverless tailwindcss vercel-deployment

Last synced: about 1 month ago
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Submit your idea and get a list of similar ideas that YCombinator has invested in in the past.

• Host: GitHub
• URL: https://github.com/neondatabase/yc-idea-matcher
• Owner: neondatabase
• Created: 2023-08-03T07:49:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-15T14:55:02.000Z (6 months ago)
• Last Synced: 2024-11-06T09:12:24.616Z (about 1 month ago)
• Topics: nextjs, openai, pgvector, postgres, postgresql, redis, serverless, tailwindcss, vercel-deployment
• Language: TypeScript
• Homepage: https://neon.tech/ycmatcher
• Size: 1.75 MB
• Stars: 150
• Watchers: 9
• Forks: 17
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# YC idea matcher

![Screenshot of the app UI](ui.png)

This project allows you Submit your idea and get a list of similar ideas that YCombinator has invested in before.

The project is built using the following technologies:

- [Neon](https://neon.tech): Serverless Postgres

- [pgvector](https://github.com/pgvector/pgvector): open-source Postgres extension for vector storage and similarity search

- [Neon Serverless Driver](https://github.com/neondatabase/serverless)

- [Next.js](https://nextjs.org): Fullstack framework for React

- [Vercel](https://vercel.com): deployment platform

- [OpenAI API](https://openai.com): generating vector embeddings

- [TailwindCSS](https://tailwindcss.com): Utility-first CSS framework

- [Upstash Redis](https://upstash.com): serverless Redis for rate limiting

- [Zod](https://zod.dev): TypeScript-first schema validation

- [React Query](https://react-query.tanstack.com): data fetching and caching library

- [Vaul](https://vaul.emilkowal.ski/): Drawer component for React.

## How the app works

You will find a script called `generate-embeddings.ts` located in the root directory of this project. After running `npm run generate-embeddings`, the script does the following:

1. It creates the database schema and installs the `pgvector` extension

2. It goes through the YCombinator API 'https://api.ycombinator.com/v0.1/companies?page=1' and gets all the companies

3. For each company it generates embeddings using the long description and then stores the company data in the database.

  

> Some companies don't have a long description, so we needed to manually remove those from the database by running `delete from companies WHERE embedding = ARRAY[]::real[];`

The app itself is a Next.js app with an API route located at `/api/idea`. Whenever a user submits an idea, the following happens:

1. The idea is sent to the OpenAI API to generate an embedding

2. We then use pgvector to retrieve the top 3 most similar ideas