Vector Database

Executive Definition: Vector Database — A specialized database designed to store and query high-dimensional vectors, enabling rapid semantic similarity search.

Vector databases store mathematical representations of data called embeddings. When queried, they retrieve results based on semantic meaning and contextual proximity rather than exact keyword matches. This capability is essential for powering retrieval-augmented generation (RAG), recommendation systems, and advanced AI search applications.

Frequently Asked Questions

How is a vector database different from a SQL database?

SQL databases match exact values and keywords. Vector databases find similar meanings—so a search for ‘automobile’ retrieves results for ‘car’ even without exact word matches.

Pinecone, Weaviate, Milvus, Chroma, and pgvector (a PostgreSQL extension) are the most widely adopted, each offering different trade-offs for scale and hosting.

Do I need a dedicated vector database?

Not always. Relational databases like PostgreSQL with the pgvector extension handle moderate workloads well. Dedicated vector databases are necessary for low-latency searches across millions of documents.

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