From Embeddings to Milvus
A model turns meaning into coordinates; Milvus finds the nearest meanings across millions or billions of those coordinates.
Raw content
Questions, documents, images, and other business data.
Embedding
One model encodes each item as a fixed-length float vector.
ANN search
Approximate nearest neighbors trade a little accuracy for much more speed.
Business result
Top-K documents go back to the app or language model.
Different words, similar meaning
“How do I get my money back?” and “refund procedure” share meaning without sharing many tokens. An embedding captures statistical semantics—not verified truth.
Brute force does not scale
Comparing every vector exactly becomes expensive. ANN narrows the candidate set, so evaluate recall and latency together.
| Metric | More similar means | Useful intuition |
|---|---|---|
| L2 | Smaller distance | Absolute distance in space |
| IP | Larger score | Vector magnitude affects the score |
| COSINE | Larger similarity | Direction matters; common for text |
Store
Vectors alongside IDs, source, category, time, and other scalar fields.
Find
Top-K vector search plus scalar filters.
Manage
Collections, indexes, loading, and data lifecycle. Milvus does not create embeddings or write the final answer.