Vector Database vs Graph Database: Implicit and Explicit Relationships in GraphRAG
A vector database and a graph database both help AI systems retrieve useful information, but they do it…
Read moreKnowledge Base
Learn
Structured articles covering everything from core concepts to production operations.
Vector Database vs NoSQL Document Stores
A vector database and a NoSQL document store can both support AI retrieval, but they are built around…
Read more
Vector Database vs Relational Database: Where SQL and Vector Search Each Fit
A relational database and a vector database solve different retrieval problems. SQL databases excel when the application needs…
Read more
Vector Database vs Traditional Database
A vector database is built to search by meaning, similarity, and context, while a traditional database is built…
Read more
Understanding Vector Dimensionality
Vector dimensionality is the number of numeric values in an embedding. A 384-dimensional embedding has 384 numbers, a…
Read more
What Is Similarity Search?
Similarity search is the process of finding data items that are closest in meaning, pattern, or behavior to…
Read more
How to Choose the Right Distance Metric for Vector Search
The right distance metric is usually the one your embedding model was trained or documented to use. If…
Read more
Dense vs Sparse Vectors: Definitions, Tradeoffs, and Hybrid Retrieval
Dense vectors and sparse vectors are two different ways to represent text, documents, queries, and other data for…
Read more
The Curse of Dimensionality in AI Databases
The curse of dimensionality describes what happens when data is represented with many features, dimensions, or embedding coordinates:…
Read more
Understanding High-Dimensional Space
High-dimensional space is the mathematical setting where AI systems place embeddings: long lists of numbers that represent text,…
Read more