Qdrant

An open-source vector database for similarity search — the memory layer behind RAG and 'search by meaning' features.

What it is

A database that stores embeddings (numeric fingerprints of text or images) and finds the closest matches fast. It’s how you give an AI app a “find the relevant chunks” superpower.

When to use it

Retrieval-augmented generation (chat with your docs), semantic search, recommendations, anywhere you match by meaning instead of exact keywords.

When NOT to

If your data is small (a few hundred items), you don’t need a dedicated vector DB yet, in-memory search, or Postgres with pgvector, is simpler. Reach for Qdrant when scale or query speed starts to bite.

Used in

The retrieval step you’d add when extending Build an AI Chatbot into a doc-aware bot.