A vector database stores embeddings – numerical representations of content – and enables semantic similarity search: you query not for exact words but for content that is semantically similar. Vector databases are a central component of RAG systems because they quickly find the relevant documents the language model needs for a fact-based answer. Well-known products include Pinecone, Weaviate, or Qdrant.
In short
What is a vector database?
A vector database stores embeddings and enables semantic similarity search – you search for content that is semantically similar rather than for exact words. It is a central component of RAG systems because it finds the relevant documents for fact-based answers. Well-known products include Pinecone, Weaviate, or Qdrant.
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