diff --git a/apps/knowledge/serializers/common.py b/apps/knowledge/serializers/common.py
index 9c80e5ac0..2f52e2b44 100644
--- a/apps/knowledge/serializers/common.py
+++ b/apps/knowledge/serializers/common.py
@@ -242,7 +242,7 @@ def create_knowledge_index(knowledge_id=None, document_id=None):
         if len(result) == 0:
             return
         dims = result[0]['dims'] 
-        sql = f"""CREATE INDEX "embedding_hnsw_idx_{k_id}" ON embedding USING hnsw ((embedding::vector({dims})) vector_l2_ops) WHERE knowledge_id = '{k_id}'"""
+        sql = f"""CREATE INDEX "embedding_hnsw_idx_{k_id}" ON embedding USING hnsw ((embedding::vector({dims})) vector_cosine_ops) WHERE knowledge_id = '{k_id}'"""
         update_execute(sql, [])
         maxkb_logger.info(f'Created index for knowledge ID: {k_id}')
 
diff --git a/apps/knowledge/sql/blend_search.sql b/apps/knowledge/sql/blend_search.sql
index afb1f0040..c70e66464 100644
--- a/apps/knowledge/sql/blend_search.sql
+++ b/apps/knowledge/sql/blend_search.sql
@@ -5,15 +5,17 @@ SELECT
 FROM
 	(
 	SELECT DISTINCT ON
-		( "paragraph_id" ) ( similarity ),* ,
-		similarity AS comprehensive_score
+		( "paragraph_id" ) ( 1 - distince + ts_similarity ) as similarity, *,
+		(1 - distince + ts_similarity) AS comprehensive_score
 	FROM
 		(
 		SELECT
 			*,
-			(( 1 - ( embedding.embedding <=>  %s ) )+ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS similarity
+			(embedding.embedding::vector(%s) <=>  %s) as distince,
+			(ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS ts_similarity
 		FROM
 			embedding ${embedding_query}
+		    ORDER BY distince
 		) TEMP
 	ORDER BY
 		paragraph_id,
diff --git a/apps/knowledge/sql/embedding_search.sql b/apps/knowledge/sql/embedding_search.sql
index ce3d4a580..1b5689959 100644
--- a/apps/knowledge/sql/embedding_search.sql
+++ b/apps/knowledge/sql/embedding_search.sql
@@ -5,12 +5,12 @@ SELECT
 FROM
 	(
 	SELECT DISTINCT ON
-		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+		("paragraph_id") ( 1 - distince ),* ,(1 - distince) AS comprehensive_score
 	FROM
-		( SELECT *, ( 1 - ( embedding.embedding <=>  %s ) ) AS similarity FROM embedding ${embedding_query}) TEMP
+		( SELECT *, ( embedding.embedding::vector(%s) <=>  %s ) AS distince FROM embedding ${embedding_query} ORDER BY distince) TEMP
 	ORDER BY
 		paragraph_id,
-		similarity DESC
+		distince
 	) DISTINCT_TEMP
 WHERE comprehensive_score>%s
 ORDER BY comprehensive_score DESC
diff --git a/apps/knowledge/vector/pg_vector.py b/apps/knowledge/vector/pg_vector.py
index d8235d2c4..f787cd83f 100644
--- a/apps/knowledge/vector/pg_vector.py
+++ b/apps/knowledge/vector/pg_vector.py
@@ -172,8 +172,13 @@ class EmbeddingSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'embedding_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(query_embedding), *exec_params, similarity, top_number])
+        embedding_model = select_list(exec_sql, [
+            len(query_embedding),
+            json.dumps(query_embedding),
+            *exec_params,
+            similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):
@@ -193,8 +198,12 @@ class KeywordsSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'keywords_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [to_query(query_text), *exec_params, similarity, top_number])
+        embedding_model = select_list(exec_sql, [
+            to_query(query_text),
+            *exec_params,
+            similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):
@@ -214,9 +223,13 @@ class BlendSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'blend_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(query_embedding), to_query(query_text), *exec_params, similarity,
-                                       top_number])
+        embedding_model = select_list(exec_sql, [
+            len(query_embedding),
+            json.dumps(query_embedding),
+            to_query(query_text),
+            *exec_params, similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):