Infer vector_size/length from arguments

src/langchain_google_spanner/vector_store.py

@@ -382,7 +382,7 @@ def init_vector_store_table(
         - content_column (str): The name of the content column. Defaults to CONTENT_COLUMN_NAME.
         - embedding_column (str): The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
         - metadata_columns (Optional[List[Tuple]]): List of tuples containing metadata column information. Defaults to None.
-        - vector_size (Optional[int]): The size of the vector for KNN. Defaults to None.
+        - vector_size (Optional[int]): The size of the vector for KNN or ANN. Defaults to None. It is presumed that exactly ONLY 1 field will have the vector.
         """
 
         client = client_with_user_agent(client, USER_AGENT_VECTOR_STORE)
@@ -407,6 +407,7 @@ def init_vector_store_table(
             metadata_columns,
             primary_key,
             secondary_indexes,
+            vector_size,
         )
 
         print("ddl", "\n".join(ddl))
@@ -427,6 +428,7 @@ def _generate_sql(
         column_configs,
         primary_key,
         secondary_indexes: Optional[List[SecondaryIndex | VectorSearchIndex]] = None,
+        vector_size: int = None,
     ):
         """
         Generate SQL for creating the vector store table.
@@ -438,6 +440,7 @@ def _generate_sql(
         - content_column: The name of the content column.
         - embedding_column: The name of the embedding column.
         - column_names: List of tuples containing metadata column information.
+        - vector_size: The vector length to be used by default. It is presumed by proxy of the langchain usage patterns, that exactly ONE column will be used as the embedding.
 
         Returns:
         - str: The generated SQL.
@@ -462,6 +465,7 @@ def _generate_sql(
                 column_configs,
                 primary_key,
                 dialect,
+                vector_length=vector_size,
             )
         ]
 
@@ -497,6 +501,7 @@ def _generate_create_table_sql(
         column_configs,
         primary_key,
         dialect=DatabaseDialect.GOOGLE_STANDARD_SQL,
+        vector_length=None,
     ):
         create_table_statement = f"CREATE TABLE IF NOT EXISTS {table_name} (\n"
 
@@ -524,6 +529,11 @@ def _generate_create_table_sql(
                     embedding_column, "ARRAY<FLOAT64>", is_null=True
                 )
 
+        if not embedding_column.vector_length:
+            ok_vector_length = vector_length and vector_length > 0
+            if ok_vector_length:
+                embedding_column.vector_length = vector_length
+
         configs = [id_column, content_column, embedding_column]
 
         if column_configs is not None:
@@ -539,8 +549,13 @@ def _generate_create_table_sql(
                 # Append column name and data type
                 column_sql = f"  {column_config.name} {column_config.type}"
 
+                vector_len = vector_length
+
                 if column_config.vector_length and column_config.vector_length >= 1:
-                    column_sql += f"(vector_length=>{column_config.vector_length})"
+                    vector_len = column_config.vector_length
+
+                if vector_len and vector_len > 0:
+                    column_sql += f"(vector_length=>{vector_len})"
 
                 # Add nullable constraint if specified
                 if not column_config.is_null:

tests/integration/test_spanner_vector_store.py

@@ -619,134 +619,6 @@ def test_ann_add_data1(self, setup_database):
             metadata_json_column="metadata",
         )
 
-    def test_ann_add_data2(self, setup_database):
-        loader, embeddings = setup_database
-
-        db = SpannerVectorStore(
-            instance_id=instance_id,
-            database_id=google_database,
-            table_name=table_name,
-            id_column="row_id",
-            ignore_metadata_columns=[],
-            embedding_service=embeddings,
-            metadata_json_column="metadata",
-        )
-
-        texts = [
-            "Langchain Test Text 1",
-            "Langchain Test Text 2",
-            "Langchain Test Text 3",
-        ]
-        ids = [str(uuid.uuid4()) for _ in range(len(texts))]
-        ids_row_inserted = db.add_texts(
-            texts=texts,
-            ids=ids,
-            metadatas=[
-                {"title": "Title 1"},
-                {"title": "Title 2"},
-                {"title": "Title 3"},
-            ],
-        )
-        assert ids == ids_row_inserted
-
-    def test_delete_data(self, setup_database):
-        loader, embeddings = setup_database
-
-        db = SpannerVectorStore(
-            instance_id=instance_id,
-            database_id=google_database,
-            table_name=table_name,
-            id_column="row_id",
-            ignore_metadata_columns=[],
-            embedding_service=embeddings,
-            metadata_json_column="metadata",
-        )
-
-        docs = loader.load()
-        deleted = db.delete(documents=[docs[0], docs[1]])
-        assert deleted == True
-
-    def test_search_data1(self, setup_database):
-        # loader, embeddings = setup_database
-        # db = SpannerVectorStore(
-        #     instance_id=instance_id,
-        #     database_id=google_database,
-        #     table_name=table_name,
-        #     id_column="row_id",
-        #     ignore_metadata_columns=[],
-        #     embedding_service=embeddings,
-        #     metadata_json_column="metadata",
-        # )
-        # docs = db.similarity_search(
-        #     "Testing the langchain integration with spanner", k=2
-        # )
-        # assert len(docs) == 2
-        pass
-
-    def test_search_data2(self, setup_database):
-        # TODO: Implement me
-        # loader, embeddings = setup_database
-        # db = SpannerVectorStore(
-        #     instance_id=instance_id,
-        #     database_id=google_database,
-        #     table_name=table_name,
-        #     id_column="row_id",
-        #     ignore_metadata_columns=[],
-        #     embedding_service=embeddings,
-        #     metadata_json_column="metadata",
-        # )
-        # embeds = embeddings.embed_query(
-        #     "Testing the langchain integration with spanner"
-        # )
-        # docs = db.similarity_search_by_vector(embeds, k=3, pre_filter="1 = 1")
-        # assert len(docs) == 3
-        pass
-
-    def test_search_data3(self, setup_database):
-        # TODO: Implement me
-        # loader, embeddings = setup_database
-        # db = SpannerVectorStore(
-        #     instance_id=instance_id,
-        #     database_id=google_database,
-        #     table_name=table_name,
-        #     id_column="row_id",
-        #     ignore_metadata_columns=[],
-        #     embedding_service=embeddings,
-        #     metadata_json_column="metadata",
-        #     query_parameters=QueryParameters(
-        #         distance_strategy=DistanceStrategy.COSINE,
-        #         max_staleness=datetime.timedelta(seconds=15),
-        #     ),
-        # )
-        #
-        # docs = db.similarity_search(
-        #     "Testing the langchain integration with spanner", k=3
-        # )
-        #
-        # assert len(docs) == 3
-        pass
-
-    def test_search_data4(self, setup_database):
-        # loader, embeddings = setup_database
-        # db = SpannerVectorStore(
-        #     instance_id=instance_id,
-        #     database_id=google_database,
-        #     table_name=table_name,
-        #     id_column="row_id",
-        #     ignore_metadata_columns=[],
-        #     embedding_service=embeddings,
-        #     metadata_json_column="metadata",
-        #     query_parameters=QueryParameters(
-        #         distance_strategy=DistanceStrategy.COSINE,
-        #         max_staleness=datetime.timedelta(seconds=15),
-        #     ),
-        # )
-        # docs = db.max_marginal_relevance_search(
-        #     "Testing the langchain integration with spanner", k=3
-        # )
-        # assert len(docs) == 3
-        pass
-
 
 class TestSpannerVectorStorePGSQL:
     @pytest.fixture(scope="class")