apache/beam · error · ImportError

Google Cloud Recommendation AI not supported for this…

Error message

Google Cloud Recommendation AI not supported for this execution environment (could not import google.cloud.recommendationengine).

What it means

Module-level guard in recommendations_ai.py: the google.cloud.recommendationengine package could not be imported, so the Recommendations AI connector is unusable in this environment. The error surfaces at module import, not at transform runtime.

Solutions

1. Install the client library: pip install google-cloud-recommendation-ai
2. Install the Beam extra that bundles GCP connectors: pip install 'apache_beam[gcp]'

When it happens

Trigger: Thrown at sdks/python/apache_beam/ml/gcp/recommendations_ai.py:42 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/6c40e18c60f1458d. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/ml/gcp/recommendations_ai.py:42

from typing import Sequence

from cachetools.func import ttl_cache
from google.api_core.retry import Retry

from apache_beam import pvalue
from apache_beam.metrics import Metrics
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform
from apache_beam.transforms.util import GroupIntoBatches
from apache_beam.utils import retry

# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
try:
  from google.cloud import recommendationengine
except ImportError:
  raise ImportError(
      'Google Cloud Recommendation AI not supported for this execution '
      'environment (could not import google.cloud.recommendationengine).')
# pylint: enable=wrong-import-order, wrong-import-position, ungrouped-imports

__all__ = [
    'CreateCatalogItem',
    'WriteUserEvent',
    'ImportCatalogItems',
    'ImportUserEvents',
    'PredictUserEvent'
]

FAILED_CATALOG_ITEMS = "failed_catalog_items"
MAX_RETRIES = 5


@ttl_cache(maxsize=128, ttl=3600)
def get_recommendation_prediction_client():

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