The author discusses the transformative experience of using vector embeddings for search, particularly highlighting their simplicity and efficiency. The article explains how vector embeddings encode human knowledge into arrays of numbers, making complex tasks more manageable for product engineers. The discussion covers the use of pgvector, a Postgres extension, for storing and querying embeddings seamlessly within SQL logic. The author also provides insights into implementing a scoring algorithm to enhance search results based on user interactions. The overall focus is on making embedding features accessible and user-friendly for app developers, with practical tips and implementation suggestions.
https://bawolf.substack.com/p/embeddings-are-a-good-starting-point