Category: Vector Search
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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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Vector Database Use Cases: Search, RAG, and AI Apps
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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Vector Database Architecture: What Tech Buyers Should Evaluate
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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Vector Indexes Are Not Interchangeable. Most Applications Treat Them Like They Are.
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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What Is DiskANN? Billion-Scale Vector Search Explained
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…
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Build a Celebrity Look-Alike App With Multimodal Vector Search and 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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Filtered ANN Search With Composite Vector Indexes (Part 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: Unified Data Platform for Hyperscale AI Applications
AI is reshaping how we build and run modern applications. From real-time recommendations to agentic assistants, teams need data platforms that can keep up with new performance and flexibility demands. That’s what 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…
