Overview
Vector databases store numerical representations of data—called embeddings—and use them to perform fast similarity search across unstructured content such as text, images, audio, and more.
How they store embeddings and enable similarity search for unstructured data
Vector databases store numerical representations of data—called embeddings—and use them to perform fast similarity search across unstructured content such as text, images, audio, and more.
Dense vector representations capturing meaning and relationships in unstructured data.
Specialized data structures enabling fast similarity search, such as IVF, HNSW, and PQ.
Distance functions (cosine, Euclidean, dot-product) used to measure vector closeness.
Data (text, images, audio) is converted into embeddings using an AI model.
Embeddings are stored in a vector index optimized for similarity search.
A query is transformed into a vector and compared against indexed vectors.
The database returns the closest vectors, representing the most relevant results.
Some do, but most store embeddings and optionally link back to the original content.
They use approximate nearest neighbor (ANN) algorithms for speed and scalability.
Large language models, image encoders, audio models, and domain-specific embedding models.
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