Added support for DOT_PRODUCT distance for BigQueryVectorStore. (#555)

langchain-ai · Oct 15, 2024 · 9e90ad2 · 9e90ad2
1 parent b25bd98
commit 9e90ad2
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py
@@ -50,11 +50,11 @@ class BigQueryVectorStore(BaseBigQueryVectorStore):
         credentials: Optional Google Cloud credentials object.
         embedding_dimension: Dimension of the embedding vectors (inferred if not
             provided).
-        distance_type (Literal["COSINE", "EUCLIDEAN"]): The distance metric used for
-            similarity search. Defaults to "EUCLIDEAN".
+        distance_type (Literal["COSINE", "EUCLIDEAN", "DOT_PRODUCT"]): The distance
+            metric used for similarity search. Defaults to "EUCLIDEAN".
     """
 
-    distance_type: Literal["COSINE", "EUCLIDEAN"] = "EUCLIDEAN"
+    distance_type: Literal["COSINE", "EUCLIDEAN", "DOT_PRODUCT"] = "EUCLIDEAN"
     _creating_index: bool = False
     _have_index: bool = False
     _last_index_check: datetime = datetime.min