Vector Distance Functions
This section provides reference information for vector distance functions in Databend. These functions are essential for measuring similarity between vectors in machine learning applications, vector search, and AI-powered analytics.
Available Vector Distance Functions
| Function | Description | Example |
|---|---|---|
| COSINE_DISTANCE | Calculates angular distance between vectors (range: 0-1) | COSINE_DISTANCE([1,2,3], [4,5,6]) |
| L1_DISTANCE | Calculates Manhattan (L1) distance between vectors | L1_DISTANCE([1,2,3], [4,5,6]) |
| L2_DISTANCE | Calculates Euclidean (straight-line) distance | L2_DISTANCE([1,2,3], [4,5,6]) |
Function Comparison
| Function | Description | Range | Best For | Use Cases |
|---|---|---|---|---|
| COSINE_DISTANCE | Angular distance between vectors | [0, 1] | When direction matters more than magnitude | • Document similarity • Semantic search • Recommendation systems • Text analysis |
| L1_DISTANCE | Calculates Manhattan (L1) distance between vectors | [0, ∞) | When direction matters more than magnitude | • Document similarity • Semantic search • Recommendation systems • Text analysis |
| L2_DISTANCE | Euclidean (straight-line) distance | [0, ∞) | When magnitude matters | • Image similarity • Geographical data • Anomaly detection • Feature-based clustering |