Semantic Search with OpenAI and PostgreSQL in 10 Minutes

This article discusses the use of large language models, specifically retrieval-augmented generation (RAG), in industries such as chatbots and automotive experiences. It highlights the importance of context in LLMs and introduces semantic search, a strategy for finding relevant results by focusing on word associations and meanings. The article showcases how to set up and perform a semantic search using pgai, pgvector, and OpenAI in just 10 minutes. This involves installing required libraries, initializing the OpenAI client, setting up a database, creating a vectorizer, and defining a function for performing semantic searches. The tutorial demonstrates how to visualize the database and test the function with custom queries.

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