harden: NaN-safe sort + dimension-mismatch guard in VectorIndex

src/vector.rs

@@ -13,11 +13,17 @@ impl VectorIndex {
 
     /// (entry_index, cosine_similarity) descending.
     pub fn top_k(&self, query: &[f32], k: usize) -> Vec<(usize, f32)> {
+        // dimension mismatch → no results (caller bug / corrupt cache); never silently mispair
+        let dim = self.vectors.first().map(|v| v.len()).unwrap_or(0);
+        if !self.vectors.is_empty() && query.len() != dim {
+            return Vec::new();
+        }
+
         let qn = dot(query, query).sqrt().max(1e-8);
         let mut scored: Vec<(usize, f32)> = self.vectors.iter().enumerate()
             .map(|(i, v)| (i, dot(v, query) / (self.norms[i] * qn)))
             .collect();
-        scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+        scored.sort_by(|a, b| b.1.total_cmp(&a.1));
         scored.truncate(k);
         scored
     }

tests/vector_tests.rs

@@ -12,3 +12,14 @@ fn cosine_topk_ranks_nearest_first() {
     assert_eq!(hits[1].0, 2);
     assert!(hits[0].1 > hits[1].1);
 }
+
+#[test]
+fn nan_score_does_not_panic_and_dim_mismatch_returns_empty() {
+    // NaN in a stored vector must not panic the sort.
+    let vi = VectorIndex::from_vectors(vec![vec![1.0, 0.0], vec![f32::NAN, 1.0]]);
+    let hits = vi.top_k(&[1.0, 0.0], 2);          // must not panic
+    assert!(hits.iter().any(|&(i, _)| i == 0));   // the valid vector still found
+    // Dimension mismatch → empty, never silently mispaired.
+    let vi2 = VectorIndex::from_vectors(vec![vec![1.0, 0.0, 0.0]]);
+    assert!(vi2.top_k(&[1.0, 0.0], 1).is_empty());
+}