Browse Code
Our mission is to revolutionize the way users discover AI products by providing the most accurate, comprehensive, lightning-fast, and intelligent search experience.
Developers can effortlessly integrate their own data on top of this framework, enabling them to swiftly build specialized vertical search engines or internal document search systems for their organizations.
Under the hood, AskAITools employs a hybrid search engine architecture, seamlessly combining keyword search (full-text search) and semantic search (vector search/embedding search) capabilities. By leveraging statistical data and weighted fusion techniques, it achieves a balance between relevance and popularity.
Project Architecture and Tech Stack
- Front-end: Next.js
- Deployment: Vercel
- Styling: Tailwind CSS
- Database: Supabase
- Keyword Search: PostgreSQL Full-Text Search Engine
- Semantic Search: Pgvector Vector Database
- Semantic Vector Generation: OpenAI text-embedding-3 model
Features
- Combines keyword and semantic search for more comprehensive results than traditional methods.
- Balances relevance and monthly visit data in ranking, ensuring both relevance and application quality.
- Displays metrics like monthly visits, visit duration, and interaction rate for each result to aid user decisions.
