Choosing Distance Metrics in pgvector
Understand cosine, L2, and inner product distance operators in pgvector and how to pick the right one for your embeddings.
Why Distance Matters
Similarity search ranks vectors by distance. The metric you choose must match how your embedding model was trained, or results will be subtly wrong.
The Three Operators
pgvector provides three distance operators:
<->Euclidean (L2) distance<=>Cosine distance<#>Negative inner product
SELECT '[1,2,3]'::vector <=> '[1,2,4]'::vector AS cosine_distance;All lessons in this course
- Setting Up pgvector Extension
- Storing Vectors in PostgreSQL
- Performing Similarity Queries
- Choosing Distance Metrics in pgvector