1d ago · 11 min read · This is Part 4 of a series building a production-ready semantic search API with Java, Spring Boot, and pgvector. Part 1 covered the architecture. Part 2 defined the schema. Part 3 handled the embeddi
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Mar 20 · 11 min read · Most semantic search tutorials treat embeddings as a single line of code — call the API, get a vector, store it. In practice, this is the part of the system where the most subtle bugs live. Not the ki
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Mar 13 · 14 min read · Why the database layer matters In a semantic search system, the database schema isn’t just storage. It defines how embeddings are stored, indexed, and queried. Many tutorials treat the database as a d
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Mar 13 · 16 min read · I was building a venue discovery feature. The idea was simple: a user describes what they are looking for in natural language, and the app surfaces the best matches from a catalogue of thousands of pl
Join discussionMar 5 · 11 min read · The problem with Keyword Search Keyword search breaks more often than most engineers realize. A few months ago, I was building an internal document management tool. Users could upload policy documents
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