Category: Vector Search

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…

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…

What is a vector database? At a high level, a vector database is a specialized system for storing, managing, and querying data as high-dimensional vectors. Unlike traditional relational databases that store structured data in…

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…