Update sparse+dense hybrid search example (#2005)

use IP as the BGE-M3 dense embeddings relevance example Signed-off-by: Buqian Zheng <[email protected]>
milvus-io · Apr 9, 2024 · d5de5ff · d5de5ff
1 parent 722d2b5
commit d5de5ff
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/examples/hello_hybrid_sparse_dense.py b/examples/hello_hybrid_sparse_dense.py
@@ -86,7 +86,7 @@ def random_embedding(texts):
 # into memory for efficient search.
 sparse_index = {"index_type": "SPARSE_INVERTED_INDEX", "metric_type": "IP"}
 col.create_index("sparse_vector", sparse_index)
-dense_index = {"index_type": "FLAT", "metric_type": "L2"}
+dense_index = {"index_type": "FLAT", "metric_type": "IP"}
 col.create_index("dense_vector", dense_index)
 col.load()
 
@@ -102,7 +102,7 @@ def random_embedding(texts):
 sparse_search_params = {"metric_type": "IP"}
 sparse_req = AnnSearchRequest(query_embeddings["sparse"],
                               "sparse_vector", sparse_search_params, limit=k)
-dense_search_params = {"metric_type": "L2"}
+dense_search_params = {"metric_type": "IP"}
 dense_req = AnnSearchRequest(query_embeddings["dense"],
                              "dense_vector", dense_search_params, limit=k)