Vector Database

A vector database is a data store that holds content not as keywords but as sequences of numbers (vectors) that capture its meaning. That way it finds texts that are similar in content, even when they use completely different words. It is the technical backbone of RAG and semantic search.

What is a vector database?

A classic database searches for exact terms: enter “delivery time” and you only find entries with that exact word. A vector database works differently. It stores each piece of content as an embedding, that is, as a sequence of numbers that captures its meaning. When searching, it doesn't compare words but meanings, and so it also finds matches that are phrased differently, for example “When is my parcel coming?” to a text about shipping times.

How semantic search works

As content is read in, each document is converted into embeddings and stored in the vector database. When someone asks a question, that question is translated into an embedding too. The database then searches for the vectors closest to the question vector and returns the passages most relevant in content. That is exactly what makes up the retrieval step in a RAG system.

Why this matters for AI knowledge bases

Without semantic search, a customer would have to phrase the question exactly as it appears in the documentation. With a vector database, the AI finds the right passage even with colloquial language, typos, or synonyms. That is what makes an AI chatbot genuinely usable, and it lowers the risk of an AI hallucination, because the answer rests on real source passages.

Vector search at SendSeven

SendSeven is a unified messaging platform with a built-in AI assistant. Your knowledge base is prepared for semantic search, so the AI finds the matching content for every enquiry instead of relying on exact keywords. You maintain your content; SendSeven handles the technology behind it. The knowledge base guide shows how to fill your knowledge base. In line with GDPR, Made in Germany. Try it free for 14 days.