# Copyright (c) Microsoft. All rights reserved.

from typing import ClassVar

from semantic_kernel.connectors.ai.bedrock.services.model_provider.bedrock_model_provider import BedrockModelProvider
from semantic_kernel.kernel_pydantic import KernelBaseSettings
from semantic_kernel.utils.feature_stage_decorator import experimental


@experimental
class BedrockSettings(KernelBaseSettings):
    """Amazon Bedrock service settings.

    The settings are first loaded from environment variables with
    the prefix 'BEDROCK_'.
    If the environment variables are not found, the settings can
    be loaded from a .env file with the encoding 'utf-8'.
    If the settings are not found in the .env file, the settings
    are ignored; however, validation will fail alerting that the
    settings are missing.

    Optional settings for prefix 'BEDROCK_' are:
        - chat_model_id: str | None - The Amazon Bedrock chat model ID to use.
            (Env var BEDROCK_CHAT_MODEL_ID)
        - text_model_id: str | None - The Amazon Bedrock text model ID to use.
            (Env var BEDROCK_TEXT_MODEL_ID)
        - embedding_model_id: str | None - The Amazon Bedrock embedding model ID to use.
            (Env var BEDROCK_EMBEDDING_MODEL_ID)
        - model_provider: BedrockModelProvider | None - The Bedrock model provider to use.
            If not provided, the model provider will be extracted from the model ID.
            When using an Application Inference Profile where the model provider is not part
            of the model ID, this setting must be provided.
            (Env var BEDROCK_MODEL_PROVIDER)
    """

    env_prefix: ClassVar[str] = "BEDROCK_"

    chat_model_id: str | None = None
    text_model_id: str | None = None
    embedding_model_id: str | None = None
    model_provider: BedrockModelProvider | None = None