Building a Semantic Search Engine with PartyKit's Vector Database in 160 Lines of Code
By
ColinWright
5mo ago· 10 min readen
100/100
Golden Brown
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Pulled from the oven just right. Trustworthy, fact-dense, deeply satisfying.
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Summary
The article explains how to build a highly effective search engine using PartyKit's new vector database and embedding model capabilities. It demonstrates this through a case study of Braggoscope, a directory of BBC Radio 4's In Our Time episodes, showing how semantic search can understand natural language queries like 'the biggest planet' to return relevant results about Jupiter. The guide focuses on implementing vector search with just 160 lines of code, highlighting how AI-powered embeddings make search both powerful and accessible for developers.
Key quotes
· 4 pulledThe tl;dr is that search got really good suddenly and really easy to build because of AI.
I can search for 'Jupiter' and the episode about Jupiter comes back back. But check it out! I can also search for 'the biggest planet' and the same episode is at the top.
PartyKit now includes a vector database and access to an embedding model. Here's a guide on how to use them to build a search engine.
There are over 1,000 episodes on all kinds of topics, like the Greek Myths or the Evolution of Teeth or Romeo & Juliet.
PartyKit now includes a vector database and access to an embedding model. Here’s a guide on how to use them to build a search engine.