Using Vectorize to build an unreasonably good search engine in 160 lines of code
Blog post from PartyKit
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.
| 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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