AI Chatbot Glossary
What Is Vector Database?
A vector database stores and searches high-dimensional vector embeddings efficiently — powering the semantic search in RAG-based chatbots.
Definition
A specialized database designed to store and search high-dimensional vector embeddings efficiently. Vector databases power the semantic search capability of RAG-based chatbots, enabling them to find the most relevant knowledge base content for any given user query.
Why Vector Database Matters for AI Chatbots
You do not interact with the vector database directly — but its quality decides whether your chatbot finds the right passage on the first try. Chatonbo uses PostgreSQL with pgvector, so your data stays in a single system and scales linearly with knowledge base size.
Related Terms
Embedding
An embedding is a numerical representation of text in a high-dimensional vector space — used to measure semantic similarity between pieces of text.
RAG (Retrieval-Augmented Generation)
RAG is a technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer — reducing hallucinations and grounding replies in real data.
Knowledge Base
A knowledge base is a structured collection of information (documents, FAQs, policies) that a chatbot uses to find accurate answers — searched in real time to ground responses.
Query Understanding
Query understanding is the AI step of interpreting what a user is asking — even when vague, misspelled, or phrased with slang — via semantic search plus language models.
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