Similarity Search on PostgreSQL Using OpenAI Embeddings and Pgvector

This article discusses the use of OpenAI Embeddings models in conjunction with PostgreSQL and pgvector to power similarity search. Vector embeddings are numerical representations of data such as words, sentences, images, audio, time-series data, or even molecular structures. They help capture semantic or contextual relationships between data points. The article explores how OpenAI's Embedding Models generate vector embeddings and why these embeddings are useful for similarity search. It also explains how to utilize them to build retrieval-augmented generation (RAG) applications. Finally, the article demonstrates how to perform similarity search using an SQL query on a PostgreSQL table with embedded data.

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