How to Use PostgreSQL for Retrieval-Augmented Generation (RAG)

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Squid Game is back—and this time, the knives are out. In the thrilling Season 3 premiere, Player 456 is spiraling and a brutal round of hide-and-seek forces players to kill or be killed. Hosts Phil Yu and Kiera Please break down Gi-hun’s descent into vengeance, Guard 011’s daring betrayal of the Game, and the shocking moment players are forced to choose between murdering their friends… or dying. Then, Carlos Juico and Gavin Ruta from the Jumpers Jump podcast join us to unpack their wild theories for the season. Plus, Phil and Kiera face off in a high-stakes round of “Hot Sweet Potato.” SPOILER ALERT! Make sure you watch Squid Game Season 3 Episode 1 before listening on. Play one last time. IG - @SquidGameNetflix X (f.k.a. Twitter) - @SquidGame Check out more from Phil Yu @angryasianman , Kiera Please @kieraplease and the Jumpers Jump podcast Listen to more from Netflix Podcasts . Squid Game: The Official Podcast is produced by Netflix and The Mash-Up Americans.…

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How to Use PostgreSQL for Retrieval-Augmented Generation (RAG)

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Retrieval-augmented generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) by retrieving relevant documents from a database to generate more context-aware responses. PostgreSQL, an advanced open-source relational database, is a great choice for implementing RAG because it supports full-text search, vector similarity search (via extensions like pgvector), and scalability. This article will guide you through using PostgreSQL for RAG and integrating it with LLMs to create AI-powered applications.

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