{"id":2521,"library":"google-cloud-recommendations-ai","title":"Google Cloud Recommendations AI Client Library","description":"The Google Cloud Recommendations AI API client library enables developers to integrate recommendation capabilities into their applications. It provides functionalities for managing product catalogs, ingesting user events, and generating personalized recommendations. The current version is `0.13.0`, and new releases typically occur frequently as part of the broader Google Cloud Python client libraries monorepo.","status":"active","version":"0.13.0","language":"en","source_language":"en","source_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-recommendations-ai","tags":["google-cloud","recommendations","ai","machine-learning","client-library","gcp"],"install":[{"cmd":"pip install google-cloud-recommendations-ai","lang":"bash","label":"Install stable version"}],"dependencies":[{"reason":"Core library for Google Cloud API clients, handling authentication, retries, and API calls.","package":"google-api-core","optional":false},{"reason":"Provides authentication credentials for accessing Google Cloud services.","package":"google-auth","optional":false},{"reason":"Provides Pythonic wrappers around Protocol Buffer messages.","package":"proto-plus","optional":false}],"imports":[{"note":"Clients are typically nested under their API version module (e.g., `_v1beta1`).","wrong":"from google.cloud import RecommendationsAiClient","symbol":"RecommendationsAiClient","correct":"from google.cloud.recommendations_ai_v1beta1 import RecommendationsAiClient"},{"note":"Specific service clients like CatalogServiceClient are available for granular control over resources.","symbol":"CatalogServiceClient","correct":"from google.cloud.recommendations_ai_v1beta1 import CatalogServiceClient"}],"quickstart":{"code":"import os\nfrom google.cloud.recommendations_ai_v1beta1 import CatalogServiceClient\n\nproject_id = os.environ.get('GCP_PROJECT_ID', 'your-gcp-project-id')\nlocation_id = os.environ.get('GCP_LOCATION_ID', 'global') # Recommendations AI often uses 'global'\n\ndef list_first_five_catalog_items():\n    \"\"\"Lists the first five catalog items in the default catalog.\"\"\"\n    client = CatalogServiceClient()\n    # The parent for listing catalog items is a catalog resource.\n    # 'default_catalog' is the common default for Recommendations AI.\n    parent = f\"projects/{project_id}/locations/{location_id}/catalogs/default_catalog\"\n\n    request = {\n        \"parent\": parent,\n        \"page_size\": 5\n    }\n\n    print(f\"Listing catalog items for parent: {parent}\")\n    try:\n        page_iterator = client.list_catalog_items(request=request)\n\n        found_items = False\n        for item in page_iterator:\n            print(f\"Catalog Item ID: {item.id}, Title: {item.title}\")\n            found_items = True\n        \n        if not found_items:\n            print(\"No catalog items found. Ensure your catalog is populated.\")\n\n    except Exception as e:\n        print(f\"An error occurred: {e}\")\n        print(\"Make sure 'GCP_PROJECT_ID' and 'GCP_LOCATION_ID' environment variables are set or replaced.\")\n        print(\"Also, ensure the Recommendations AI API is enabled and your service account has permissions.\")\n\nif __name__ == '__main__':\n    list_first_five_catalog_items()","lang":"python","description":"This quickstart demonstrates how to instantiate the `CatalogServiceClient` and list existing catalog items. Replace 'your-gcp-project-id' and 'global' with your actual Google Cloud project ID and the relevant location, or set them via environment variables. This requires the Recommendations AI API to be enabled and your service account to have appropriate permissions (e.g., `Recommendations AI Admin`)."},"warnings":[{"fix":"Consult the official documentation for the specific API method you intend to use to determine which client object provides it. For basic catalog/event management, use `CatalogServiceClient` or `UserEventServiceClient`.","message":"Recommendations AI offers several clients (`RecommendationsAiClient`, `CatalogServiceClient`, `UserEventServiceClient`, `PredictionServiceClient`). Use the appropriate client for your specific operation (e.g., `CatalogServiceClient` for managing items, `PredictionServiceClient` for recommendations). `RecommendationsAiClient` often acts as an orchestrator but direct access to specific service clients is common.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Ensure your development and deployment environments use Python 3.11 or newer to maintain compatibility with future updates. Regularly check the `requires_python` field on PyPI for the latest supported versions.","message":"Google Cloud Python client libraries are progressively dropping support for older Python versions. While version `0.13.0` currently supports Python 3.9, future minor or major releases will likely drop support for Python 3.9 (and potentially 3.10) in alignment with Python's EOL schedule, potentially causing compatibility issues for older environments.","severity":"breaking","affected_versions":"Future releases (likely >0.13.0 or 1.x.x)"},{"fix":"Always construct resource names carefully following the pattern specified in the API documentation for each method. Use f-strings for clarity, e.g., `parent = f\"projects/{project_id}/locations/{location_id}/catalogs/default_catalog\"`.","message":"Google Cloud APIs use strict resource name formats (e.g., `projects/{project}/locations/{location}/catalogs/{catalog}`). Incorrectly formatted resource names are a common source of `InvalidArgument` or `NotFound` errors.","severity":"gotcha","affected_versions":"All versions"},{"fix":"If working with `asyncio`, ensure you import and use the `Async*Client` variants (e.g., `from google.cloud.recommendations_ai_v1beta1.services.catalog_service.async_client import AsyncCatalogServiceClient`).","message":"The library provides both synchronous and asynchronous client versions (e.g., `CatalogServiceClient` and `AsyncCatalogServiceClient`). Using the incorrect client in an asynchronous context (e.g., calling synchronous methods with `await`) will lead to `TypeError` or unexpected behavior.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Verify that your Recommendations AI instance has the necessary data (catalog items, user events) and potentially trained models if you are using prediction functionality. Consult the Recommendations AI setup guide for ingestion steps.","message":"Many `list` or `get` operations for Recommendations AI will return empty results or `NotFound` errors if the underlying catalog, user events, or models have not been properly populated and ingested through the API or console.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-10T00:00:00.000Z","next_check":"2026-07-09T00:00:00.000Z"}