Definition: A vector database stores data as high-dimensional numerical vectors (embeddings), enabling AI systems to find semantically similar content quickly — even when exact keywords don't match.
Traditional databases store text, numbers, and structured data — and search them using exact matches or keyword queries. If you search for "cancellation policy," a traditional database finds documents containing those exact words.
A vector database works differently. It stores every piece of content as a mathematical vector — a list of hundreds or thousands of numbers that represents the meaning of that content. When you search a vector database, you're searching by similarity of meaning, not exact word matches.
This means a user asking "How do I cancel?" and "What's the cancellation process?" and "I want to end my subscription" all retrieve the same relevant content — because all three phrases have similar semantic meaning, even though they use different words.
In a RAG-powered AI chatbot like Converso, your content is stored in a vector database. When a user asks a question, it's converted to a vector and the most semantically similar content chunks are retrieved. Those chunks are then passed to the LLM to generate an accurate, grounded answer.
Example: A user asks "How do I get my money back?" — Vector search finds your refund policy page, even though it uses the word "refund" not "money back." Traditional keyword search would miss this entirely.
Finds the most relevant content regardless of how the question is phrased — crucial for natural conversation.
Modern vector databases return results in under 100ms, making real-time chatbot responses possible.
Vector search scales efficiently even across enormous knowledge bases with millions of content chunks.
Embedding models work across languages — a question in French can retrieve content indexed in English.