Vectors, Tokens, and Embeddings: How They Relate
Tokens, vectors, and embeddings are related, but they are not the same thing. Tokens are the small pieces…
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Fundamentals
Core concepts every AI database practitioner needs.
The Role of Vector Databases in the Modern AI Stack
A vector database sits in the retrieval layer of the modern AI stack. It does not replace the…
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What Are Multimodal Embeddings?
Multimodal embeddings are numerical representations that place different types of data, such as text, images, audio, video, and…
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Embeddings for Text, Images, Audio, and Video
Embeddings turn text, images, audio, and video into vectors that an AI database can compare, search, filter, and…
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From Raw Data to a Vector: The Anatomy of an Embedding
An embedding is a numerical representation of raw data that makes similarity search possible. For a text document,…
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When Do You Actually Need a Vector Database?
A vector database is useful when your application needs to search by meaning, similarity, or context across a…
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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…
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Vector Database vs NoSQL Document Stores
A vector database and a NoSQL document store can both support AI retrieval, but they are built around…
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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…
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Vector Database vs Traditional Database
A vector database is built to search by meaning, similarity, and context, while a traditional database is built…
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