Frequently Asked Questions (FAQ)
This page contains answers to some of the most frequently asked questions about Aerospike Vector Search (AVS).
What is a vector (embedding?)
A vector embedding is a numerical representation of data, such as words or images, that captures essential features and relationships in a high-dimensional space. This allows algorithms to process, compare, and analyze the data more effectively for tasks like classification, clustering, and similarity search. See our guide to generating vector embeddings for details about how to create vector embeddings.
What does a vector database do?
A vector database provides two basic functions: storage and search. Storage is straightforward and a specialized database is not required (you could store your data in files, for example). Since you can calculate the distance between two vectors, a vector database provides a unique way to perform a proximity search across vectors that are loaded in the vector space.
What do KNN and ANN stand for?
KNN stands for K-nearest neighbor, referring to a collection of search algorithms used to determine the proximity between vectors. ANN stands for approximate nearest neighbor, which is a type of KNN search that prioritizes speed over quality.
What is HNSW?
HNSW stands for Hierarchical Navigable Small World, which is the algorithm used by AVS to perform an ANN search.
What is RAG?
RAG stands for Retrieval Augmented Generation, which is a technique for using a knowledge repository to provide context to generative language models. RAG is a common application pattern used with vector databases.
What version of Aerospike is required for AVS?
AVS has been tested and developed using Aerospike Database (ASDB) version 7.x. While AVS does not rely on any specific features in version 7.x, earlier versions have not been tested and are not supported.
Can I add AVS to data already in Aerospike?
Yes. You need to add a vector to your data to add AVS search functionality to Aerospike. You must configure a metadata namespace and a namespace for storing your index. See Configure Aerospike Database for AVS for more information.
How are vectors stored in Aerospike?
Vectors are stored in Aerospike as an additional bin on the record. This bin is encoded with details about the dimensions of your vector and should not be edited directly. See Vector data in Aerospike for more information.
Why do my vectors not look like vector data?
Vector data is encoded with details about the dimensions of your vector and should not be edited directly. See more details about the vector data model.
How should I configure Aerospike for AVS?
You need to consider your goals when scaling Aerospike for AVS, but there are a few general recommendations. First, there is no need to use strong consistency in the namespace you are using for search. Second, you can configure Hybrid Memory Architecture (HMA) to optimize for cost with minimal impact on performance. See Planning a deployment for more information about configuring storage.
Can I use Aerospike tools like asbackup, asadm, and others?
Yes, Aerospike administrators can use Aerospike tools to configure an Aerospike cluster, perform basic monitoring, and take backups of data and indexes.