Categoria: Busca Vetorial

The Metrics That Matter (And How to Measure Them) When you’re testing at the scale of a few million vectors, you can get away with basic scripts. But when you cross the 1 billion…

The most common vector database use cases are semantic search, retrieval-augmented generation (RAG), recommendation systems, chatbots and virtual assistants, anomaly detection, and multimodal search. Each use case depends on a vector database’s ability to…

A maioria das equipes que desenvolve busca semântica aprende a mesma lição tarde. O que no fim das contas controla a revocação, a latência de consulta e o custo de hardware não é o modelo de incorporação ou qual banco de dados elas escolhem. É a configuração do índice vetorial, que…

Retrieval-augmented generation (RAG), semantic search, and AI agents all depend on one thing: the ability to quickly find the most relevant vectors in a large dataset. As embedding datasets grow from millions to billions…

Most AI demos feel impressive for 30 seconds and then leave one question unanswered: How would you actually build this? This one is different. In this post, we’ll build a simple but compelling multimodal…

This post is the fourth part of a multi-part series exploring composite vector indexing in Couchbase. If you missed the previous posts, be sure to catch up on Part 1, Part 2 and Part…

This post is the third part of a multi-part series exploring composite vector indexing in Couchbase. If you missed the previous posts, be sure to catch up on Part 1 and Part 2. The…

This is the second blog post of a multi-part series exploring composite vector indexing in Couchbase, check out the first post here.  The series will cover: Implementation of Composite Vector Indexes GSI uses the…

This post kicks off a multi-part series on composite vector indexing in Couchbase. We will start by building intuition, then progressively dive into internals, execution optimizations, and performance. The series will cover: Smart Grocery…