diff --git a/core/vector/distance.rs b/core/vector/distance.rs
new file mode 100644
index 000000000..d6f39e39b
--- /dev/null
+++ b/core/vector/distance.rs
@@ -0,0 +1,31 @@
+use super::vector_types::Vector;
+use crate::Result;
+
+pub(crate) mod euclidean;
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+#[non_exhaustive]
+pub enum DistanceType {
+    /// Euclidean distance. This is a very common distance metric that
+    /// accounts for both magnitude and direction when determining the distance
+    /// between vectors. Euclidean distance has a range of [0, ∞).
+    Euclidean,
+
+    // TODO(asukamilet): Refactor the current `vector_types.rs` to integrate
+    #[allow(dead_code)]
+    /// Cosine distance. This is a measure of similarity between two vectors
+    Cosine,
+}
+
+pub trait DistanceCalculator {
+    #[allow(unused)]
+    fn distance_type() -> DistanceType;
+
+    fn calculate(v1: &[Vector], v2: &[Vector]) -> Result<f64>;
+
+    #[allow(unused)]
+    fn batch_calculate<'a>(
+        v1: &'a [Vector],
+        v2: &'a [Vector],
+    ) -> Box<dyn Iterator<Item = Result<f64>> + 'a>;
+}
diff --git a/core/vector/distance/euclidean.rs b/core/vector/distance/euclidean.rs
new file mode 100644
index 000000000..cfe036a0d
--- /dev/null
+++ b/core/vector/distance/euclidean.rs
@@ -0,0 +1,210 @@
+use super::{DistanceCalculator, DistanceType};
+use crate::vector::vector_types::{Vector, VectorType};
+use crate::{LimboError, Result};
+
+#[derive(Debug, Clone)]
+pub struct Euclidean;
+
+impl DistanceCalculator for Euclidean {
+    fn distance_type() -> DistanceType {
+        DistanceType::Euclidean
+    }
+
+    fn calculate(v1: &[Vector], v2: &[Vector]) -> Result<f64> {
+        let mut sum = 0.0;
+        // We only pass two vectors, so it will only iterate once, it's ok to use `sum`
+        for (v1, v2) in v1.iter().zip(v2.iter()) {
+            if v1.dims != v2.dims {
+                return Err(LimboError::ConversionError(
+                    "Vectors must have the same dimensions".to_string(),
+                ));
+            }
+
+            match (&v1.vector_type, &v2.vector_type) {
+                (VectorType::Float32, VectorType::Float32) => {
+                    sum += euclidean_distance_f32(v1.as_f32_slice(), v2.as_f32_slice());
+                }
+                (VectorType::Float64, VectorType::Float64) => {
+                    sum += euclidean_distance_f64(v1.as_f64_slice(), v2.as_f64_slice());
+                }
+                _ => {
+                    return Err(LimboError::ConversionError(
+                        "Vectors must be of the same type (either Float32 or Float64)".to_string(),
+                    ));
+                }
+            }
+        }
+        Ok(sum)
+    }
+
+    // TODO(asukamilet): Implement batch calculation for Euclidean distance
+    fn batch_calculate<'a>(
+        _v1: &'a [Vector],
+        _v2: &'a [Vector],
+    ) -> Box<dyn Iterator<Item = Result<f64>> + 'a> {
+        unimplemented!();
+    }
+}
+
+fn euclidean_distance_f32(v1: &[f32], v2: &[f32]) -> f64 {
+    let sum = v1
+        .iter()
+        .zip(v2.iter())
+        .map(|(a, b)| (a - b).powi(2))
+        .sum::<f32>() as f64;
+    sum.sqrt()
+}
+
+fn euclidean_distance_f64(v1: &[f64], v2: &[f64]) -> f64 {
+    let sum = v1
+        .iter()
+        .zip(v2.iter())
+        .map(|(a, b)| (a - b).powi(2))
+        .sum::<f64>();
+    sum.sqrt()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use quickcheck::{Arbitrary, Gen};
+
+    // Helper to generate arbitrary vectors of specific type and dimensions
+    #[derive(Debug, Clone)]
+    struct ArbitraryVector<const DIMS: usize> {
+        vector_type: VectorType,
+        data: Vec<u8>,
+    }
+
+    /// How to create an arbitrary vector of DIMS dims.
+    impl<const DIMS: usize> ArbitraryVector<DIMS> {
+        fn generate_f32_data(g: &mut Gen) -> Vec<f32> {
+            (0..DIMS)
+                .map(|_| {
+                    loop {
+                        let f = f32::arbitrary(g);
+                        // f32::arbitrary() can generate "problem values" like NaN, infinity, and very small values
+                        // Skip these values
+                        if f.is_finite() && f.abs() >= 1e-6 {
+                            // Scale to [-1, 1] range
+                            return f % 2.0 - 1.0;
+                        }
+                    }
+                })
+                .collect()
+        }
+
+        fn generate_f64_data(g: &mut Gen) -> Vec<f64> {
+            (0..DIMS)
+                .map(|_| {
+                    loop {
+                        let f = f64::arbitrary(g);
+                        // f64::arbitrary() can generate "problem values" like NaN, infinity, and very small values
+                        // Skip these values
+                        if f.is_finite() && f.abs() >= 1e-6 {
+                            // Scale to [-1, 1] range
+                            return f % 2.0 - 1.0;
+                        }
+                    }
+                })
+                .collect()
+        }
+
+        fn generate_f32_vector(g: &mut Gen) -> ArbitraryVector<DIMS> {
+            let vector_type = VectorType::Float32;
+            let data = Self::generate_f32_data(g);
+            let data_bytes: Vec<u8> = data.iter().flat_map(|f| f.to_le_bytes()).collect();
+            ArbitraryVector {
+                vector_type,
+                data: data_bytes,
+            }
+        }
+
+        fn generate_f64_vector(g: &mut Gen) -> ArbitraryVector<DIMS> {
+            let vector_type = VectorType::Float64;
+            let data = Self::generate_f64_data(g);
+            let data_bytes: Vec<u8> = data.iter().flat_map(|f| f.to_le_bytes()).collect();
+            ArbitraryVector {
+                vector_type,
+                data: data_bytes,
+            }
+        }
+    }
+
+    /// Convert an ArbitraryVector to a Vector.
+    impl<const DIMS: usize> From<ArbitraryVector<DIMS>> for Vector {
+        fn from(v: ArbitraryVector<DIMS>) -> Self {
+            Vector {
+                vector_type: v.vector_type,
+                dims: DIMS,
+                data: v.data,
+            }
+        }
+    }
+
+    #[test]
+    fn test_type_check() {
+        let v1: Vector = ArbitraryVector::<1536>::generate_f32_vector(&mut Gen::new(0)).into();
+        let v2: Vector = ArbitraryVector::<1536>::generate_f32_vector(&mut Gen::new(0)).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_ok());
+
+        let v1: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut Gen::new(0)).into();
+        let v2: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut Gen::new(0)).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_ok());
+
+        let v1: Vector = ArbitraryVector::<1536>::generate_f32_vector(&mut Gen::new(0)).into();
+        let v2: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut Gen::new(0)).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_err());
+    }
+
+    #[test]
+    fn test_dims_check() {
+        let mut gen = Gen::new(0);
+
+        let v1: Vector = ArbitraryVector::<1536>::generate_f32_vector(&mut gen).into();
+        let v2: Vector = ArbitraryVector::<1024>::generate_f32_vector(&mut gen).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_err());
+
+        let v1: Vector = ArbitraryVector::<1024>::generate_f32_vector(&mut gen).into();
+        let v2: Vector = ArbitraryVector::<1024>::generate_f32_vector(&mut gen).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_ok());
+
+        let v: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut gen).into();
+        let v2: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut gen).into();
+        assert!(Euclidean::calculate(&[v], &[v2]).is_ok());
+
+        let v1: Vector = ArbitraryVector::<1536>::generate_f64_vector(&mut gen).into();
+        let v2: Vector = ArbitraryVector::<1024>::generate_f64_vector(&mut gen).into();
+        assert!(Euclidean::calculate(&[v1], &[v2]).is_err());
+    }
+
+    #[test]
+    fn test_euclidean_distance_f32() {
+        let vectors = vec![
+            (0..8).map(|x| x as f32).collect::<Vec<f32>>(),
+            (1..9).map(|x| x as f32).collect::<Vec<f32>>(),
+            (2..10).map(|x| x as f32).collect::<Vec<f32>>(),
+            (3..11).map(|x| x as f32).collect::<Vec<f32>>(),
+        ];
+        let query = (2..10).map(|x| x as f32).collect::<Vec<f32>>();
+
+        let expected: Vec<f64> = vec![
+            32.0_f64.sqrt(),
+            8.0_f64.sqrt(),
+            0.0_f64.sqrt(),
+            8.0_f64.sqrt(),
+        ];
+        let results = vectors
+            .iter()
+            .map(|v| euclidean_distance_f32(&query, v))
+            .collect::<Vec<f64>>();
+        assert_eq!(results, expected);
+    }
+
+    #[test]
+    fn test_odd_len() {
+        let v = (0..5).map(|x| x as f32).collect::<Vec<f32>>();
+        let query = (2..7).map(|x| x as f32).collect::<Vec<f32>>();
+        assert_eq!(euclidean_distance_f32(&v, &query), 20.0_f64.sqrt());
+    }
+}
diff --git a/core/vector/mod.rs b/core/vector/mod.rs
index d68bbe90c..207af44f0 100644
--- a/core/vector/mod.rs
+++ b/core/vector/mod.rs
@@ -3,6 +3,7 @@ use crate::vdbe::Register;
 use crate::LimboError;
 use crate::Result;
 
+pub mod distance;
 pub mod vector_types;
 use vector_types::*;