Categoría: Búsqueda vectorial
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Benchmarking Billion-Scale Vector Indexes
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…
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Casos de uso de bases de datos vectoriales: Búsqueda, RAG y aplicaciones de IA
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…
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Arquitectura de bases de datos vectoriales: qué deben evaluar los compradores de tecnología
A vector database’s architecture has four layers: embeddings, indexing, retrieval, and storage. How those layers are designed and how well they integrate with your existing data infrastructure determines whether your AI search, semantic search,…
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Los índices vectoriales no son intercambiables. La mayoría de las aplicaciones los tratan como si lo fueran.
Most teams building semantic search learn the same lesson late. What ultimately controls recall, query latency, and hardware cost isn’t the embedding model or which database they choose. It’s the vector index configuration, which…
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¿Qué es DiskANN? Explicación de la búsqueda de vectores a escala de miles de millones
La generación aumentada por recuperación (RAG), la búsqueda semántica y los agentes de IA dependen de una sola cosa: la capacidad de encontrar rápidamente los vectores más relevantes en un conjunto de datos grande. A medida que los conjuntos de datos de incrustaciones crecen de millones a miles de millones...
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Crear una aplicación para parecer una celebridad con búsqueda multimodal de vectores y Couchbase
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…
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Búsqueda ANN filtrada con índices vectoriales compuestos (Parte 4)
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…
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Filtered ANN Search With Composite Vector Indexes (Part 3)
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…
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Filtered ANN Search With Composite Vector Indexes (Part 2)
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…
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Filtered ANN Search With Composite Vector Indexes (Part 1)
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…
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Couchbase 8.0: Plataforma de datos unificada para aplicaciones de IA a hiperescala
La inteligencia artificial está transformando la forma en que construimos y ejecutamos aplicaciones modernas. Desde recomendaciones en tiempo real hasta asistentes agénticos, los equipos necesitan plataformas de datos que puedan mantenerse al día con las nuevas demandas de rendimiento y flexibilidad. De eso trata Couchbase 8.0...
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Building Smarter Agents: How Vector Search Drives Semantic Intelligence
The way we search and interact with information has shifted dramatically over the past decade. Traditional keyword-based search engines once served us well in finding documents or answers, but today’s business challenges demand much…
