If you want to build something that lets ChatGPT or Claude answer questions from your own data — an AI support agent, a company knowledge base, document Q&A — you will hit one problem right away: the model does not know your data. Stuffing whole documents into the conversation is expensive and will not fit. This is where a vector database comes in.
What is a vector?
An embedding model turns a piece of text into a string of numbers (768 decimals, say). That string of numbers is the “vector”, and you can think of it as the coordinates of that text in a “semantic space”. Text that means similar things ends up at similar coordinates: “how do I return this” and “returns and exchanges policy” are very close together as vectors, even though the two sentences have no words in common.
What does a vector database do?
A vector database exists to store those coordinates and to answer “which five entries are closest to this vector” in a few milliseconds. That is semantic search — finding things by meaning rather than by keyword. A traditional database’s LIKE '%return%' cannot do this.
How a RAG knowledge base works
- Split your company documents into chunks, turn each chunk into a vector with an embedding model, and store them in Qdrant
- When a user asks a question, turn the question into a vector as well and use Qdrant to find the most relevant chunks
- Hand those chunks to ChatGPT / Claude together with the question, and the AI can answer “from your data” instead of making things up
This flow is called RAG (Retrieval-Augmented Generation), and it is the standard way to build an AI knowledge base today.
Why Qdrant?
Qdrant is an open-source vector database written in Rust (Apache 2.0 license), known for being fast and memory-efficient, and n8n, Dify, LangChain and LlamaIndex all ship integrations for it. Once you provision managed Qdrant on RoamerHost, you get a dedicated HTTPS endpoint and an API key, so you can start building RAG applications without standing up a server yourself. If you would rather start with something that already has a ready-made interface, see Dify tutorial: build your first AI chatbot.
Want someone to build it for you?
If you would rather not put all of this together yourself, or the project is big enough that you want someone to help plan it, Roamer Tech (RoamerHost’s parent company) takes on contract work for business process automation and AI agents:
