microsoft/semantic-kernel · error · MemoryConnectorInitializationError

Failed to create Postgres settings.

Error message

Failed to create Postgres settings.

What it means

Raised in PostgresMemoryStore.__init__ when constructing PostgresSettings raises a pydantic ValidationError. The error is wrapped as MemoryConnectorInitializationError('Failed to create Postgres settings.') with the original ValidationError chained. PostgresSettings resolves the connection_string from the arg or .env fallback; failure most often means a missing/invalid connection string or pool size bounds violation.

Source

Thrown at python/semantic_kernel/connectors/memory_stores/postgres/postgres_memory_store.py:71

        Args:
            connection_string: The connection string to the Postgres database.
            default_dimensionality: The default dimensionality of the embeddings.
            min_pool: The minimum number of connections in the connection pool.
            max_pool: The maximum number of connections in the connection pool.
            schema: The schema to use. (default: {"public"})
            env_file_path: Use the environment settings file as a fallback
                to environment variables. (Optional)
            env_file_encoding: The encoding of the environment settings file.
        """
        try:
            postgres_settings = PostgresSettings(
                connection_string=connection_string,
                env_file_path=env_file_path,
                env_file_encoding=env_file_encoding,
            )
        except ValidationError as ex:
            raise MemoryConnectorInitializationError("Failed to create Postgres settings.", ex) from ex

        min_pool = min_pool or postgres_settings.min_pool
        max_pool = max_pool or postgres_settings.max_pool

        self._check_dimensionality(default_dimensionality)

        self._default_dimensionality = default_dimensionality
        self._connection_pool = ConnectionPool(
            min_size=min_pool, max_size=max_pool, open=True, kwargs=postgres_settings.get_connection_args()
        )
        self._schema = schema
        atexit.register(self._connection_pool.close)

    async def create_collection(
        self,
        collection_name: str,
        dimension_num: int | None = None,
    ) -> None:

View on GitHub (pinned to c028a0c7dc)

Solutions

1. Set POSTGRES_CONNECTION_STRING (or pass connection_string) with a valid postgres:// URI including host, user, password, dbname.
2. Inspect the chained ValidationError to see the exact failing field.
3. Verify env_file_path points to a readable .env with the connection string.
4. Migrate to PostgresStore + Collection.

Example fix

// before
store = PostgresMemoryStore(connection_string=None, default_dimensionality=1536)

// after
import os
conn = os.environ["POSTGRES_CONNECTION_STRING"]
store = PostgresMemoryStore(connection_string=conn, default_dimensionality=1536)

import os
conn = connection_string or os.environ.get("POSTGRES_CONNECTION_STRING")
if not conn:
    raise RuntimeError("POSTGRES_CONNECTION_STRING is required")
store = PostgresMemoryStore(connection_string=conn, default_dimensionality=1536)

def has_postgres_credentials() -> bool:
    return bool(os.environ.get("POSTGRES_CONNECTION_STRING"))

from semantic_kernel.exceptions.memory_connector_exceptions import MemoryConnectorInitializationError
try:
    store = PostgresMemoryStore(connection_string=conn, default_dimensionality=1536)
except MemoryConnectorInitializationError as e:
    raise SystemExit(f"Postgres settings invalid: {e.__cause__}") from e

When it happens

Trigger: Constructing PostgresMemoryStore without a connection_string and with no POSTGRES_CONNECTION_STRING env var/.env; providing a malformed connection string; min_pool/max_pool env values out of allowed range.

Common situations: Deployment missing the POSTGRES_CONNECTION_STRING secret; .env not loaded in prod; connection string lacks required pgvector/SSL params; pool size env vars misconfigured.

Related errors

• Failed to create Pinecone settings.
• Dimensionality of {default_dimensionality} exceeds the maxim
• Collection '{collection_name}' does not exist
• Upsert failed
• Dimensionality of {dimension_num} exceeds the maximum allowe

AI-assisted analysis of microsoft/semantic-kernel@c028a0c7dc (2026-08-13). Data as JSON: /api/errors/f1630692df0798ce. Report an issue: GitHub.