Vector Database
A database optimized for storing and searching high-dimensional vector embeddings.
A Vector Database stores and searches high-dimensional vector embeddings — numerical representations of text, images, or other data that capture semantic meaning. Vector databases power semantic search, RAG systems, recommendation engines, and similarity-based retrieval. Examples: Pinecone, Weaviate, Qdrant, Chroma, pgvector (Postgres extension).
A company stores embeddings of every paragraph in their documentation in Pinecone. When a customer asks a question, the system finds the most semantically relevant paragraphs and provides them to an LLM to generate a response.
Related terms
A technique that combines LLMs with retrieval of external information to ground responses in facts.
A neural network trained on vast text data to understand and generate human language.
A numerical representation of text, images, or other data that captures semantic meaning.
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