Postgres and the Artificial Intelligence Landscape, artificial intelligence use has exploded, with much anticipation about its future. This talk explores many of the advances that has fueled this explosion, including multi-dimensional vectors, text embeddings, semantic/vector search, transformers, generative AI, and Retrieval-Augmented Generation (RAG). The talk includes semantic/vector search and RAG examples. It covers how the valuable data stored in databases can be used to enhance AI usage.

Hands-on workshop building a CLI chatbot with Retrieval-Augmented Generation using Couchbase Shell and Nushell.

Overview of how LLMs, embeddings, and vector databases enable semantic search; introduction to Couchbase Shell (a Nushell-based CLI) and how to build a chatbot, with setup and connection to Couchbase Capella and an OpenAI (or compatible) API key.

Search engines are at the heart of the user experience. But how do you move from a “classic” keyword-based search to a semantic search that truly understands user intent? In this session, The Fork team will share their journey toward evolving into an AI-augmented search. Building on their existing OpenSearch stack, they added a semantic layer powered by LLMs. The goal: analyze user queries, extract the key elements, and translate them into a much more relevant semantic search. You’ll discover the challenges they faced, the implementation choices, and the real-world results achieved in a high-impact user case.

Models need up-to-date facts (data) to solve tasks. But data (retrieval) needs models, too: for semantic search and for ranking top candidates. At this meetup, we will go through the data/model interplay: you will learn how to transform problems into the numeric domain using tensors, and with this, work with text, image, and videos. We’ll do live demos from e-commerce and media. Whether it’s personalizing the shopping experience in real time or finding the next song to autoplay, this session will help you think beyond LLMs—and design retrieval-first GenAI systems that deliver real-world impact.

Abstract: Models need up-to-date facts (data) to solve tasks. But data (retrieval) needs models, too: for semantic search and for ranking top candidates. At this meetup, we will go through the data/model interplay: you will learn how to transform problems into the numeric domain using tensors, and with this, work with text, image, and videos.\nWe’ll do live demos from e-commerce and media. Whether it’s personalizing the shopping experience in real time or finding the next song to autoplay, this session will help you think beyond LLMs—and design retrieval-first GenAI systems that deliver real-world impact.

Learning the different search techniques is essential for developers aiming to implement effective search functionality. In this talk we’ll break down keyword, semantic, vector and hybrid search approaches. We will explore how each method works, their advantages and disadvantages, and practical use cases. This talk is for developers created by a developer and will break down what can be overly complex concepts into practical takeaways for our everyday work. By the end of the session, you’ll have a better understanding of when and how to use each search technique to optimize your user experience.

Nous vous présenterons les enjeux de l’IA dans l’exercice de notre mission pour augmenter la connaissance sur les contenus radio et TV et améliorer leur découvrabilité. Nous l’illustrerons par les différents chantiers en cours : chapitrage automatique des podcasts, amélioration de la qualité de la transcription, recherche sémantique, etc.

Présentation d’un prototype de recherche sémantique à l’INA par Alexandra Benamar, lead data scientist NLP

Dive into the world of vector databases and Retrieval Augmented Generation (RAG) as we explore how we built a practical application and the challenges we faced. Discover how semantic search can enrich data, enabling recommendation engines, fraud detection, and more. Learn how these technologies can fit into your current applications and data, sparking new ideas for innovation.

This talk goes into details of how Intercom implemented Semantic search for Fin, their AI Agent, and how their implementation has evolved. Ketan shares practical lessons from his experience on avoiding unnecessary complexity when possible.

Vector search is typically associated with embeddings - sequences of floating-point numbers. However, semantic search isn’t limited to dense_vector. In this talk, I’ll introduce you to sparse vectors, semantic text, and more.