Vector databases and embeddings: how machines grasp meaning
Semantic search sits behind many AI features. Here is what embeddings are and why modern data work rests on them.
Classic search looks for matching words. Type "returning goods" and it will miss a document that talks about a "complaint" or "withdrawal from contract", even though it is the same topic. Semantic search solves this by comparing meaning, not characters.
What an embedding is
An embedding turns text into a list of numbers — a vector — such that texts with similar meaning have similar vectors. "Returning goods" and "complaint" end up close together in a mathematical space even though they share not a single word. This is what lets a machine capture meaning.
Where vectors are stored
To search quickly among millions of vectors, you use a vector database. Often the pgvector extension on top of PostgreSQL — which the team already knows and runs — is enough. For speed, approximate indexes (such as HNSW) find the most similar records in a fraction of a second.
- Semantic search across documents and products.
- Recommendations based on similarity.
- The foundation of RAG — retrieving context for a language model.
Why we mention it
Embeddings are not technology for its own sake. They are the "plumbing" behind many practical features: better e-shop search, intelligent support, connecting scattered data. When we understand how a machine grasps meaning, we can also judge realistically what to expect from it — and where we still need clear rules.
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