feat: add create_extension parameter to control vector extension cr…

…eation (#1213) * Create feature called create_extension and updated documentation. * small refinements * update docker image --------- Co-authored-by: anakin87 <[email protected]>
deepset-ai · Nov 22, 2024 · f286bdf · f286bdf
1 parent de32fa3
commit f286bdf
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Showing 4 changed files with 20 additions and 2 deletions.
diff --git a/.github/workflows/pgvector.yml b/.github/workflows/pgvector.yml
@@ -33,7 +33,7 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
     services:
       pgvector:
-        image: ankane/pgvector:latest
+        image: pgvector/pgvector:pg17
         env:
           POSTGRES_USER: postgres
           POSTGRES_PASSWORD: postgres

diff --git a/integrations/pgvector/src/haystack_integrations/document_stores/pgvector/document_store.py b/integrations/pgvector/src/haystack_integrations/document_stores/pgvector/document_store.py
@@ -78,6 +78,7 @@ def __init__(
         self,
         *,
         connection_string: Secret = Secret.from_env_var("PG_CONN_STR"),
+        create_extension: bool = True,
         schema_name: str = "public",
         table_name: str = "haystack_documents",
         language: str = "english",
@@ -102,6 +103,10 @@ def __init__(
             e.g.: `PG_CONN_STR="host=HOST port=PORT dbname=DBNAME user=USER password=PASSWORD"`
             See [PostgreSQL Documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING)
             for more details.
+        :param create_extension: Whether to create the pgvector extension if it doesn't exist.
+            Set this to `True` (default) to automatically create the extension if it is missing.
+            Creating the extension may require superuser privileges.
+            If set to `False`, ensure the extension is already installed; otherwise, an error will be raised.
         :param schema_name: The name of the schema the table is created in. The schema must already exist.
         :param table_name: The name of the table to use to store Haystack documents.
         :param language: The language to be used to parse query and document content in keyword retrieval.
@@ -138,6 +143,7 @@ def __init__(
         """
 
         self.connection_string = connection_string
+        self.create_extension = create_extension
         self.table_name = table_name
         self.schema_name = schema_name
         self.embedding_dimension = embedding_dimension
@@ -194,7 +200,8 @@ def _create_connection(self):
         conn_str = self.connection_string.resolve_value() or ""
         connection = connect(conn_str)
         connection.autocommit = True
-        connection.execute("CREATE EXTENSION IF NOT EXISTS vector")
+        if self.create_extension:
+            connection.execute("CREATE EXTENSION IF NOT EXISTS vector")
         register_vector(connection)  # Note: this must be called before creating the cursors.
 
         self._connection = connection
@@ -246,6 +253,7 @@ def to_dict(self) -> Dict[str, Any]:
         return default_to_dict(
             self,
             connection_string=self.connection_string.to_dict(),
+            create_extension=self.create_extension,
             schema_name=self.schema_name,
             table_name=self.table_name,
             embedding_dimension=self.embedding_dimension,

diff --git a/integrations/pgvector/tests/test_document_store.py b/integrations/pgvector/tests/test_document_store.py
@@ -66,6 +66,7 @@ def test_init(monkeypatch):
     monkeypatch.setenv("PG_CONN_STR", "some_connection_string")
 
     document_store = PgvectorDocumentStore(
+        create_extension=True,
         schema_name="my_schema",
         table_name="my_table",
         embedding_dimension=512,
@@ -79,6 +80,7 @@ def test_init(monkeypatch):
         keyword_index_name="my_keyword_index",
     )
 
+    assert document_store.create_extension
     assert document_store.schema_name == "my_schema"
     assert document_store.table_name == "my_table"
     assert document_store.embedding_dimension == 512
@@ -97,6 +99,7 @@ def test_to_dict(monkeypatch):
     monkeypatch.setenv("PG_CONN_STR", "some_connection_string")
 
     document_store = PgvectorDocumentStore(
+        create_extension=False,
         table_name="my_table",
         embedding_dimension=512,
         vector_function="l2_distance",
@@ -113,6 +116,7 @@ def test_to_dict(monkeypatch):
         "type": "haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore",
         "init_parameters": {
             "connection_string": {"env_vars": ["PG_CONN_STR"], "strict": True, "type": "env_var"},
+            "create_extension": False,
             "table_name": "my_table",
             "schema_name": "public",
             "embedding_dimension": 512,

diff --git a/integrations/pgvector/tests/test_retrievers.py b/integrations/pgvector/tests/test_retrievers.py
@@ -50,6 +50,7 @@ def test_to_dict(self, mock_store):
                     "type": "haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore",
                     "init_parameters": {
                         "connection_string": {"env_vars": ["PG_CONN_STR"], "strict": True, "type": "env_var"},
+                        "create_extension": True,
                         "schema_name": "public",
                         "table_name": "haystack",
                         "embedding_dimension": 768,
@@ -82,6 +83,7 @@ def test_from_dict(self, monkeypatch):
                     "type": "haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore",
                     "init_parameters": {
                         "connection_string": {"env_vars": ["PG_CONN_STR"], "strict": True, "type": "env_var"},
+                        "create_extension": False,
                         "table_name": "haystack_test_to_dict",
                         "embedding_dimension": 768,
                         "vector_function": "cosine_similarity",
@@ -106,6 +108,7 @@ def test_from_dict(self, monkeypatch):
 
         assert isinstance(document_store, PgvectorDocumentStore)
         assert isinstance(document_store.connection_string, EnvVarSecret)
+        assert not document_store.create_extension
         assert document_store.table_name == "haystack_test_to_dict"
         assert document_store.embedding_dimension == 768
         assert document_store.vector_function == "cosine_similarity"
@@ -176,6 +179,7 @@ def test_to_dict(self, mock_store):
                     "type": "haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore",
                     "init_parameters": {
                         "connection_string": {"env_vars": ["PG_CONN_STR"], "strict": True, "type": "env_var"},
+                        "create_extension": True,
                         "schema_name": "public",
                         "table_name": "haystack",
                         "embedding_dimension": 768,
@@ -207,6 +211,7 @@ def test_from_dict(self, monkeypatch):
                     "type": "haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore",
                     "init_parameters": {
                         "connection_string": {"env_vars": ["PG_CONN_STR"], "strict": True, "type": "env_var"},
+                        "create_extension": False,
                         "table_name": "haystack_test_to_dict",
                         "embedding_dimension": 768,
                         "vector_function": "cosine_similarity",
@@ -230,6 +235,7 @@ def test_from_dict(self, monkeypatch):
 
         assert isinstance(document_store, PgvectorDocumentStore)
         assert isinstance(document_store.connection_string, EnvVarSecret)
+        assert not document_store.create_extension
         assert document_store.table_name == "haystack_test_to_dict"
         assert document_store.embedding_dimension == 768
         assert document_store.vector_function == "cosine_similarity"