What is Vector Indexing?
It is the process of converting large data sets into a digital map from which a computer can find meaningful similarities in seconds.
Overview
Vector indexing is converting data into mathematical vectors and then placing these vectors into a quickly queryable structure. This process allows you to find the one closest to the subject you are looking for among thousands of documents. Once the data is indexed, it works like a search engine, quickly returning content with similar meaning.
How it works
First, the data is converted into numerical coordinates with embedding models. These coordinates are then saved in a vector database, with those close to each other stopping in the same region.
Where it is used
It is used in RAG systems, large-scale search engines and recommendation systems.
Commonly confused with
Can be confused with Vector Database; Vector indexing is the method of organizing and mapping within this database.
Frequently asked questions
Why do we use this instead of classic search?
Classical search matches words, vector indexing matches meaning.
Is there a speed difference?
Yes, since the data is indexed, results can be obtained within milliseconds from millions of records.
Related terms
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →