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Weaviate is an open-source vector search engine and knowledge-graph database designed for semantic search, similarity retrieval and retrieval-augmented generation. It stores vectors, metadata and graph relations, providing scalable vector indexes, built-in ML connectors, and REST/GraphQL APIs for production semantic applications. Teams use it to store embedd…
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Weaviate is an open-source vector database that stores objects with vector representations and searches them semantically.
Weaviate was started in 2019 by Bob van Luijt and his company SeMI Technologies as an open-source vector search engine. The approach responded to the need to find unstructured content by meaning rather than only by exact keywords.
An object sits next to a numeric vector describing its meaning. A query is vectorized as well; Weaviate finds the nearest vectors and can refine results with metadata and filters.
A numeric representation of an object's semantic properties.
A model-generated vector representation of text or an object.
Combining vector similarity with conventional text search.
Weaviate supports semantic search and retrieval-augmented generation; quality and cost depend heavily on the embedding model, indexing, and data maintenance.
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