mypy-boto3-personalize: Type Annotations for AWS Personalize

Warnings

• breaking Starting with `mypy-boto3-builder` 8.12.0, Python 3.8 is no longer supported across all `mypy-boto3` packages, including `mypy-boto3-personalize`. Users on Python 3.8 must upgrade to Python 3.9+ or pin an older version of `mypy-boto3-personalize`.
  Fix: Upgrade your Python environment to 3.9 or newer, or pin `mypy-boto3-personalize` to an older version compatible with Python 3.8 (e.g., `<1.42.3`).
• breaking TypeDef naming conventions changed in `mypy-boto3-builder` 8.9.0. Specifically, `RequestTypeDef` for method arguments might be shortened (e.g., `CreateDistributionRequestRequestTypeDef` -> `CreateDistributionRequestTypeDef`), and `Extra` postfixes were moved (e.g., `CreateDistributionExtraRequestTypeDef` -> `CreateDistributionRequestExtraTypeDef`). This can break explicit type hints for these TypeDefs.
  Fix: Review your explicit TypeDef import and usage statements and adjust names according to the new conventions. Consult the `mypy-boto3-personalize.type_defs` module for the correct names.
• gotcha `mypy-boto3-personalize` provides only type stubs, not the actual runtime library. You *must* install `boto3` separately for your code to run, and `mypy` to perform static analysis. Not installing `boto3` will lead to runtime `ModuleNotFoundError`.
  Fix: Ensure both `boto3` and `mypy` are installed alongside `mypy-boto3-personalize` using `pip install mypy-boto3-personalize boto3 mypy`.
• gotcha While `mypy-boto3` packages are PEP 561 compliant, `mypy` might not always automatically discover stubs in complex project setups or older `mypy` versions. Ensure `mypy` is correctly configured to find the installed type packages.
  Fix: Check your `mypy` configuration (`pyproject.toml`, `mypy.ini`, or command-line arguments). Ensure `mypy` is running in an environment where `mypy-boto3-personalize` is installed. For advanced scenarios, explicitly specify `mypy` paths if needed.

Install

• pip install mypy-boto3-personalize boto3 mypy Install with core dependencies

Imports

• PersonalizeClient

  from mypy_boto3_personalize.client import PersonalizeClient

• Client

  from mypy_boto3_personalize.client import Client

  Alias for PersonalizeClient.
• CreateDatasetRequestRequestTypeDef

  from mypy_boto3_personalize.type_defs import CreateDatasetRequestRequestTypeDef

• CreateDatasetResponseTypeDef

  from mypy_boto3_personalize.type_defs import CreateDatasetResponseTypeDef

Quickstart

This quickstart demonstrates how to import and apply type hints for the AWS Personalize client using `mypy-boto3-personalize`. It shows the typical pattern of instantiating a `boto3` client and then annotating it with the provided `PersonalizeClient` type, enabling static type checking for your AWS SDK interactions.

import boto3
from mypy_boto3_personalize.client import PersonalizeClient

# mypy-boto3-personalize provides type hints for the boto3 client.
# You still use the standard boto3 client at runtime.

# Configure AWS credentials as usual (e.g., ~/.aws/credentials, environment variables)

# Instantiate a boto3 client and apply the type hint
personalize_client: PersonalizeClient = boto3.client("personalize")

print(f"Client type successfully inferred by mypy-boto3-personalize: {type(personalize_client)}")

# Example of how type hints are used for method calls (for static analysis).
# This code block is for demonstration of type-checking, not intended for live execution
# without proper setup and AWS resources.

# from mypy_boto3_personalize.type_defs import CreateDatasetRequestRequestTypeDef

# try:
#     # Type checkers like MyPy will ensure 'name', 'datasetGroupArn', etc. are correct types
#     # and that the return value matches CreateDatasetResponseTypeDef.
#     create_request: CreateDatasetRequestRequestTypeDef = {
#         "name": "MyNewDataset",
#         "datasetGroupArn": "arn:aws:personalize:us-east-1:123456789012:dataset-group/my-group",
#         "datasetType": "ITEMS",
#         "schemaArn": "arn:aws:personalize:us-east-1:123456789012:schema/my-schema",
#     }
#     response = personalize_client.create_dataset(**create_request)
#     print(f"Type-checked call example response: {response}")
# except Exception as e:
#     print(f"Example call failed (expected without real AWS setup): {e}")