How Vector Storage Works Under the Hood
Vector storage works by keeping embeddings, metadata, object identifiers, and search index structures in layouts that match how…
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Structured articles covering everything from core concepts to production operations.
In-Memory vs Disk-Based Vector Search
In-memory vector search keeps the active vector index, and often the vectors themselves, in RAM so graph traversal…
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Vector Index Structures: An Overview
Vector index structures help AI databases find similar embeddings without comparing every stored vector one by one. The…
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How AI Databases Are Built
AI databases are built around one core job: storing data in a form that can be searched by…
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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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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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