From 10c51c8da0f5d0b946a231eaa88a9f077b4af51a Mon Sep 17 00:00:00 2001
From: Nikita Sivukhin <sivukhin@turso.tech>
Date: Thu, 9 Oct 2025 22:14:38 +0400
Subject: [PATCH] add test for convert operation

---
 core/vector/operations/convert.rs | 67 ++++++++++++++++++++++++++++++-
 1 file changed, 65 insertions(+), 2 deletions(-)

diff --git a/core/vector/operations/convert.rs b/core/vector/operations/convert.rs
index 175f7aa0d..9b8261418 100644
--- a/core/vector/operations/convert.rs
+++ b/core/vector/operations/convert.rs
@@ -21,8 +21,8 @@ pub fn vector_convert(v: Vector, target_type: VectorType) -> Result<Vector> {
         }
         (VectorType::Float64Dense, VectorType::Float32Dense) => {
             let mut data = Vec::with_capacity(v.dims * 4);
-            for &x in v.as_f32_slice() {
-                data.extend_from_slice(&f64::to_le_bytes(x as f64));
+            for &x in v.as_f64_slice() {
+                data.extend_from_slice(&f32::to_le_bytes(x as f32));
             }
             Ok(Vector {
                 vector_type: target_type,
@@ -91,3 +91,66 @@ pub fn vector_convert(v: Vector, target_type: VectorType) -> Result<Vector> {
         }
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use crate::vector::{
+        operations::convert::vector_convert,
+        vector_types::{Vector, VectorType},
+    };
+
+    fn concat<const N: usize>(data: &[[u8; N]]) -> Vec<u8> {
+        data.iter().flatten().cloned().collect::<Vec<u8>>()
+    }
+
+    fn assert_vectors(v1: &Vector, v2: &Vector) {
+        assert_eq!(v1.vector_type, v2.vector_type);
+        assert_eq!(v1.dims, v2.dims);
+        assert_eq!(v1.data, v2.data);
+    }
+
+    #[test]
+    pub fn test_vector_convert() {
+        let vf32 = Vector {
+            vector_type: VectorType::Float32Dense,
+            dims: 3,
+            data: concat(&[
+                1.0f32.to_le_bytes(),
+                0.0f32.to_le_bytes(),
+                2.0f32.to_le_bytes(),
+            ]),
+        };
+        let vf64 = Vector {
+            vector_type: VectorType::Float64Dense,
+            dims: 3,
+            data: concat(&[
+                1.0f64.to_le_bytes(),
+                0.0f64.to_le_bytes(),
+                2.0f64.to_le_bytes(),
+            ]),
+        };
+        let vf32_sparse = Vector {
+            vector_type: VectorType::Float32Sparse,
+            dims: 3,
+            data: concat(&[
+                1.0f32.to_le_bytes(),
+                2.0f32.to_le_bytes(),
+                0u32.to_le_bytes(),
+                2u32.to_le_bytes(),
+            ]),
+        };
+
+        let vectors = [vf32, vf64, vf32_sparse];
+        for v1 in &vectors {
+            for v2 in &vectors {
+                println!("{:?} -> {:?}", v1.vector_type, v2.vector_type);
+                let v_copy = Vector {
+                    vector_type: v1.vector_type,
+                    dims: v1.dims,
+                    data: v1.data.clone(),
+                };
+                assert_vectors(&vector_convert(v_copy, v2.vector_type).unwrap(), &v2);
+            }
+        }
+    }
+}