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Vector databases are specialized stores for dense vector representations (embeddings) that provide indices, approximate nearest neighbor algorithms and distance metrics for fast semantic and neighborhood search. They form the infrastructure for retrieval, recommendation and semantic search in embedding‑driven applications.
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A vector database stores numerical vector representations and efficiently finds entries that are similar in meaning or geometry.
Vector databases grew from research in information retrieval, machine learning and similarity search. Embeddings and applications such as semantic search made specialized systems popular; Facebook AI Research released FAISS, a foundational library for efficient vector search.
Content is stored as an embedding point in a vector space. A query is embedded in the same way; the database finds nearby points and returns their content. Indexing accelerates search, while model choice and data quality determine the results.
Semantically similar content is found through positions in a vector space.
Embedding, distance metric and index determine which neighbours a query receives.
Data model, updates, filters and access are operated for the application’s needs.
Vector databases provide the technical foundation for fast semantic search, recommendations and many RAG applications.
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