/plushcap/analysis/partykit/posts-using-vectorize-to-build-search

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

What's this blog post about?

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.

Company
PartyKit

Date published
Jan. 9, 2024

Author(s)
Matt Webb

Word count
1886

Language
English

Hacker News points
None found.


By Matt Makai. 2021-2024.