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.

Vector search is a fundamental technology accompanying modern AI solutions such as agents. In this session, we’ll dive into how vector search works and how it solves real-world use cases for AWS customers. We’ll also discuss the challenges of implementing vector search at scale, and the mechanisms that OpenSearch provides to handle them.

KAI est une solution de datacleaning de données non structurées, basé notamment sur la recherche vectorielle d'Elasticsearch et son mode BBQ, récemment lancé. Stéphane NGO, CEO, k-ai expliquera pourquoi il est passé de la solution Cloud Hosted d'Elastic à sa version Serverless et quels sont les observations qu'il peut en tirer.

Hands-on workshop on building search solutions using Couchbase FTS and Vector Search with Capella.

Overview of Couchbase features and initial setup for exploring Full-Text Search and Vector Search capabilities.

Learn how to combine graph and vector search to build smarter GenAI features using SurrealDB and LangChain, including how to analyse symptoms and suggest scheduling appointments based on semantic similarity and graph relationships.

A session showing how to build smarter AI-powered apps by combining SurrealDB's graph and vector capabilities with LangChain. We'll walk through a complete example: a chatbot that analyses symptoms and recommends appointment scheduling based on semantic similarity and structured graph relationships. Learn how to set up SurrealDB as both a graph and vector store in a single system, use LangChain to query structured knowledge alongside embeddings, chain together document ingestion, graph construction, and AI-driven Q&A, and deploy an architecture that scales from prototype to production.

Dans cette session, nous allons voir comment fonctionne OpenSearch pour le vector search et quelle stratégie utiliser pour optimiser et gagner en performance et réduire la latence.

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.

Unlock the power of generative AI and vector search to transform vague queries into precise results. Discover practical Python examples and see how advanced search revolutionizes user interaction and business outcomes.

This session will explore the structured approach we used for experimentation to enhance the user search experience at Digital Galaxus.

See a live demonstration of how vector search works with on-device AI models, even without an internet connection.

Hands-on session to build an AI-powered chatbot using vector search.

Hands-on session building an AI-powered enterprise chatbot using vector search.

Very few advancements in technology have been so disruptive like Open AI's Chat GPT: thanks to its capability of handling unstructured data, and the ease of customization of its behaviour, we can now bring our applications to a level never seen before.

This talk presents a look at Azure OpenAI from the point of view of an application developer and show how we can leverage all its horsepower in our ASP.NET Core and Blazor solution to provide functionalities which were simply unthinkable just a few months ago.

During this talk we'll demonstrate some practical examples of how to do that: as a first step, we'll familiarise with the GPT's deployment model and completion API, and then shift our usage model from a simple chat to something closer to a programmable AI model. We'll show how, simply engineering the requests, we can bend its behaviour to accomplish a whole sort of different tasks, and how using functions will allow us to integrate it with the rest of services our application exposes.

As a last step, we'll then be tackling integration with our data. On one end, we'll learn how to use embeddings and vector search over our datasets, and what are the benefits of it. Then, we'll combine GPT models with Azure Synapse to perform data analysis over big data files.

Vector and similarity search is increasingly critical in 2023, but most libraries struggle to fully utilize modern hardware due to issues rooted in their code architecture. Many rely on object-oriented programming, which reduces memory-efficiency and data-locality. Additionally, dependence on compilers for low-level optimizations fails to properly emit key AVX-512 and SVE Assembly instructions for x86 and Arm. My talk will dissect these and other pitfalls, and demonstrate how USearch innovates in areas like architecture and SIMD utilization to overcome them.

Are you building or do you support an e-commerce website? If so, then this session is for you! Worldwide digital sales in 2021 eclipsed five trillion dollars (USD). Most consumers will leave a web page or a mobile app if it takes longer than a few seconds to load. Businesses that want to compete, need a high performing e-commerce website. In this session, we will cover how to architect a simple and high-performing product recommendation service using Vector Search and generative AI. Join us and learn how to leverage the following tech to your advantage: Java Spring Boot - OAuth - Distributed databases (Cassandra/AstraDB) - Vector Search - Cloud Native Streaming (Pulsar)

Gen AI, LLMs, AI assistants and intelligent agents are powering next-generation customer experiences. But there is no AI without data. This session covers data platforms, governance, and cutting-edge vector search to enable enterprise AI.

Gen AI, LLMs, AI assistants and intelligent agents are powering next-generation customer experiences. But there is no AI without data. This session covers data platforms, governance, and cutting-edge vector search to enable enterprise AI.