What Is a Vector Database?
Vector databases store and retrieve information using mathematical representations, enabling semantic search for AI systems.
Cassandra Research editorial team
Research methodology: Check the cited research, model documentation and evaluation conditions before applying an AI technique to professional work.
Plain-English Explanation
A vector database is a specialised database designed to store and search information based on meaning rather than exact keywords. It converts text, images, or other data into numerical representations (vectors) that capture semantic meaning, allowing you to find information that is conceptually related even if it uses different words.
How It Works
Vector databases store high-dimensional embeddings generated by AI models. When a query is made, the query is also converted to an embedding, and the database performs similarity search to find the closest matching vectors. Common algorithms include HNSW and IVF for approximate nearest neighbour search.
Why It Matters for Professional AI
Vector databases are foundational to RAG systems and AI-powered knowledge retrieval. For legal, tax, and medical AI, vector databases enable semantic search across legislation, case law, rulings, and clinical literature — finding relevant information based on meaning rather than exact keyword matches.
Frequently Asked Questions
How is a vector database different from a traditional database?
Traditional databases search by exact matches on structured fields. Vector databases search by semantic similarity — finding information that is conceptually related based on meaning rather than exact keywords.
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