bump

search_me_doris_test.py

@@ -0,0 +1,19 @@
+from doris_vector_search import DorisVectorClient, AuthOptions
+
+auth = AuthOptions(
+    host="192.168.1.242",
+    query_port=9030,
+    user="thebears",
+    password="marybear",
+)
+
+client = DorisVectorClient(database="nuggets", auth_options=auth)
+
+tbl = client.open_table("video_embeddings")
+
+query = [0.1] * 1152  # Example 128-dimensional vector
+
+# SELECT id FROM sift_1M ORDER BY l2_distance_approximate(embedding, query) LIMIT 10;
+result = tbl.search(query, metric_type="inner_product").limit(10).select(["id"]).to_pandas()
+
+print(result)