Weaviate Vector Search

Weaviate is an open-source vector database designed for semantic search, similarity matching, and retrieval-augmented generation (RAG). It stores high-dimensional vector embeddings (from text, images, or other data) and enables fast similarity searches. Weaviate supports hybrid search (combining vector and keyword search), multi-tenancy, and integration with embedding models and language models. The core use case is enabling AI applications to quickly find relevant data by semantic meaning, not just keyword matches. This powers search, recommendation engines, and large language model context retrieval (RAG).