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Using Vectorize to build an unreasonably good search engine in 160 lines of code

Blog post from PartyKit

Post Details
Company
Date Published
Author
Matt Webb
Word Count
1,886
Company Posts That Month
2
Language
English
Hacker News Points
-
Post removed?
No
Summary

The text discusses the implementation of semantic search using AI and embedding models. It explains how to convert any string of text into a vector and store it in a vector database, where nearby vectors mean approximately the same thing. The author demonstrates building a search engine for their side project website Braggoscope, which uses an unofficial directory of BBC Radio 4's show In Our Time. They walk through setting up a vector database, embedding model, and creating a minimal PartyKit server to manage indexing and querying. The text also touches upon the use of vector databases in retrieval-augmented generation (RAG) for AI chatbots and copilot experiences.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
Vector Search 40 1,728 228 84 +63%
RAG 4 1,418 170 60 +93%
Real-time 2 2,723 657 208 +15%
AI Coding Assistant 1 186 60 33 +15%
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