A vector database is a type of database designed to store and search data based on meaning and similarity rather than exact matches. It stores content as numerical representations called embeddings, which capture the conceptual meaning of text, images or other content in a form a computer can compare mathematically. This makes vector databases a key building block for AI applications such as retrieval-augmented generation, where finding conceptually relevant content matters more than exact keyword matching.
How a vector database works
Content such as documents or product descriptions is converted into embeddings, numerical vectors that represent their meaning. These vectors are stored in the vector database, which can then rapidly find the most similar vectors to a given query, even if the query uses different words than the original content. This similarity search capability underpins RAG systems, where relevant source documents need to be retrieved quickly based on a user's natural language question.
Vector databases in practice
- A development team builds a RAG-based internal search tool using a vector database, allowing staff to find relevant policy documents using natural language questions rather than exact keyword searches.
- A customer support tool uses a vector database to match incoming customer questions against a knowledge base, finding relevant articles even when the customer phrases their question very differently from the article title.
- A recommendation engine uses a vector database to find products similar to ones a customer has previously purchased, based on conceptual similarity rather than shared category tags alone.
- A business building a custom AI application on Azure OpenAI uses a vector database as the retrieval layer for grounding AI responses in company-specific documents.
How Advantage approaches vector databases and RAG
For most UK SMEs, standard Microsoft Copilot capabilities meet day-to-day AI needs without requiring a custom vector database. Where a genuinely bespoke retrieval-based AI solution is justified, Advantage works with development partners to scope and build it appropriately.
Frequently Asked Questions
How is a vector database different from a traditional database?
A traditional database is optimised for exact matches, such as finding a customer record by an exact ID or name. A vector database is optimised for similarity search, finding content that is conceptually similar to a query even if it does not share exact keywords, which is essential for AI applications working with meaning rather than exact text.
Do I need a vector database to use Microsoft Copilot?
Standard Microsoft Copilot experiences within Microsoft 365 are grounded in Microsoft Graph data and do not require a business to set up its own vector database. Vector databases become relevant when building custom AI applications, such as a bespoke retrieval-augmented generation solution over a specific document set.
What are embeddings and how do they relate to vector databases?
An embedding is a numerical representation of text, an image or other content that captures its meaning in a way a computer can compare mathematically. Vector databases store and search these embeddings efficiently, which is what enables fast similarity search across large amounts of content.
