1 files changed, 0 insertions, 56 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
deleted file mode 100644
index eac5dca..0000000
--- a/examples/redis-unstable/modules/vector-sets/tests/large_scale.py
+++ /dev/null
@@ -1,56 +0,0 @@
-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}"