test: Adding integration tests for static utility to create vector em…

…beddings table.

tests/integration/test_spanner_vector_store.py

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+import pytest
+from google.cloud.spanner import Client, KeySet  # type: ignore
+
+from langchain_google_spanner.vector_store import SpannerVectorStore, TableColumn
+
+project_id = "span-cloud-testing"
+instance = "vertex-vector-search-tests"
+google_database = "langchain-test"
+pg_database =  "langchain-pg-db"
+table_name = "vectortest1"
+
+OPERATION_TIMEOUT_SECONDS = 240
+
+
+@pytest.fixture(scope="module")
+def client() -> Client:
+  return Client(project=project_id)
+
+@pytest.fixture(autouse=True)
+def cleanupGSQL(client):
+    print("\nPerforming cleanup after each test...")
+
+    database = client.instance(instance).database(google_database)
+    operation = database.update_ddl([f"DROP TABLE IF EXISTS {table_name}"])
+    operation.result(OPERATION_TIMEOUT_SECONDS)
+
+    yield
+
+    # Code to perform teardown after each test goes here
+    print("\nCleanup complete.")
+
+@pytest.fixture(autouse=True)
+def cleanupPGSQL(client):
+    print("\nPerforming cleanup after each test...")
+
+    database = client.instance(instance).database(pg_database)
+    operation = database.update_ddl([f"DROP TABLE IF EXISTS {table_name}"])
+    operation.result(OPERATION_TIMEOUT_SECONDS)
+
+    yield
+
+    # Code to perform teardown after each test goes here
+    print("\nCleanup complete.")
+
+
+
+class TestStaticUtilityGoogleSQL: 
+    @pytest.fixture(autouse=True)
+    def setup_database(self, client):
+        yield
+
+    def test_init_vector_store_table1(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= google_database,
+            table_name= table_name,
+            metadata_columns=[TableColumn(name="product_name", type="STRING(1024)", is_null=False)
+                            , TableColumn(name="title",  type="STRING(1024)")
+                            , TableColumn(name="price",  type="INT64")])
+
+    def test_init_vector_store_table2(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= google_database,
+            table_name= table_name,
+            id_column= "custom_id1",
+            content_column= "custom_content_id1",
+            embedding_column= "custom_embedding_id1",
+            metadata_columns=[TableColumn(name="product_name", type="STRING(1024)", is_null=False)
+                            , TableColumn(name="title",  type="STRING(1024)")
+                            , TableColumn(name="price",  type="INT64")])
+
+    def test_init_vector_store_table3(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= google_database,
+            table_name= table_name,
+            id_column= TableColumn(name="product_id", type="STRING(1024)", is_null=False),
+            embedding_column= TableColumn(name="custom_embedding_id1", type="ARRAY<FLOAT64>", is_null=True),
+            metadata_columns=[TableColumn(name="product_name", type="STRING(1024)", is_null=False)
+                            , TableColumn(name="title",  type="STRING(1024)")
+                            , TableColumn(name="metadata_json_column",  type="JSON")])
+
+    def test_init_vector_store_table4(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= google_database,
+            table_name= table_name,
+            id_column= TableColumn(name="product_id", type="STRING(1024)", is_null=False),
+            embedding_column= TableColumn(name="custom_embedding_id1", type="ARRAY<FLOAT64>", is_null=True),
+            metadata_columns=[TableColumn(name="product_name", type="STRING(1024)", is_null=False)
+                            , TableColumn(name="title",  type="STRING(1024)")
+                            , TableColumn(name="metadata_json_column",  type="JSON")],
+                            primary_key= "product_name, title, product_id") 
+
+
+class TestStaticUtilityPGSQL: 
+    @pytest.fixture(autouse=True)
+    def setup_database(self, client):
+        yield
+
+    def test_init_vector_store_table1(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= pg_database,
+            table_name= table_name,
+            metadata_columns=[TableColumn(name="product_name", type="TEXT", is_null=False)
+                            , TableColumn(name="title",  type="varchar(36)")
+                            , TableColumn(name="price",  type="bigint")])
+
+    def test_init_vector_store_table2(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= pg_database,
+            table_name= table_name,
+            id_column= "custom_id1",
+            content_column= "custom_content_id1",
+            embedding_column= "custom_embedding_id1",
+            metadata_columns=[TableColumn(name="product_name", type="TEXT", is_null=False)
+                            , TableColumn(name="title",  type="varchar(36)")
+                            , TableColumn(name="price",  type="bigint")])
+
+    def test_init_vector_store_table3(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= pg_database,
+            table_name= table_name,
+            id_column= TableColumn(name="product_id", type="varchar(36)", is_null=False),
+            embedding_column= TableColumn(name="custom_embedding_id1", type="float8[]", is_null=True),
+            metadata_columns=[TableColumn(name="product_name", type="TEXT", is_null=False)
+                            , TableColumn(name="title",  type="varchar(36)")
+                            , TableColumn(name="price",  type="bigint")])
+
+    def test_init_vector_store_table4(self):
+        SpannerVectorStore.init_vector_store_table(
+            instance_id= instance,
+            database_id= pg_database,
+            table_name= table_name,
+            id_column= TableColumn(name="product_id", type="varchar(36)", is_null=False),
+            embedding_column= TableColumn(name="custom_embedding_id1", type="float8[]", is_null=True),
+            metadata_columns=[TableColumn(name="product_name", type="TEXT", is_null=False)
+                            , TableColumn(name="title",  type="varchar(36)")
+                            , TableColumn(name="price",  type="bigint")],
+                primary_key= "product_name, title, product_id")
+