1 files changed, 56 insertions, 0 deletions

diff --git a/examples/redis-unstable/modules/vector-sets/tests/large_scale.py b/examples/redis-unstable/modules/vector-sets/tests/large_scale.py
new file mode 100644
index 0000000..eac5dca
--- /dev/null
+++ b/examples/redis-unstable/modules/vector-sets/tests/large_scale.py
@@ -0,0 +1,56 @@
+from test import TestCase, fill_redis_with_vectors, generate_random_vector
+import random
+
+class LargeScale(TestCase):
+    def getname(self):
+        return "Large Scale Comparison"
+
+    def estimated_runtime(self):
+        return 10
+
+    def test(self):
+        dim = 300
+        count = 20000
+        k = 50
+
+        # Fill Redis and get reference data for comparison
+        random.seed(42)  # Make test deterministic
+        data = fill_redis_with_vectors(self.redis, self.test_key, count, dim)
+
+        # Generate query vector
+        query_vec = generate_random_vector(dim)
+
+        # Get results from Redis with good exploration factor
+        redis_raw = self.redis.execute_command('VSIM', self.test_key, 'VALUES', dim, 
+                                             *[str(x) for x in query_vec],
+                                             'COUNT', k, 'WITHSCORES', 'EF', 500)
+
+        # Convert Redis results to dict
+        redis_results = {}
+        for i in range(0, len(redis_raw), 2):
+            key = redis_raw[i].decode()
+            score = float(redis_raw[i+1])
+            redis_results[key] = score
+
+        # Get results from linear scan
+        linear_results = data.find_k_nearest(query_vec, k)
+        linear_items = {name: score for name, score in linear_results}
+
+        # Compare overlap
+        redis_set = set(redis_results.keys())
+        linear_set = set(linear_items.keys())
+        overlap = len(redis_set & linear_set)
+
+        # If test fails, print comparison for debugging
+        if overlap < k * 0.7:
+            data.print_comparison({'items': redis_results, 'query_vector': query_vec}, k)
+
+        assert overlap >= k * 0.7, \
+            f"Expected at least 70% overlap in top {k} results, got {overlap/k*100:.1f}%"
+
+        # Verify scores for common items
+        for item in redis_set & linear_set:
+            redis_score = redis_results[item]
+            linear_score = linear_items[item]
+            assert abs(redis_score - linear_score) < 0.01, \
+                f"Score mismatch for {item}: Redis={redis_score:.3f} Linear={linear_score:.3f}"