"""AWS Bedrock provider — Claude via the Converse API and cross-Region inference profiles.

Authentication uses the standard boto3 credential chain (environment variables,
shared config/SSO, or an attached IAM role) — there is no single API-key env var like
the other providers. The region is read from ``BEDROCK_REGION`` (matching the
terraform-infra convention), falling back to ``AWS_REGION`` / ``AWS_DEFAULT_REGION``,
then ``us-east-1``.

The default model IDs are cross-Region inference profiles (the ``us.`` prefix), which
route across US regions for availability. The exact version-dated profile ID must match
what is enabled in your account and the Bedrock IAM policy (see this provider's notes in
TASK.md §11) — override per run with ``--model`` / ``EVAL_MODEL`` / ``[skill_eval.models]``.
"""

from environs import Env
from langchain_aws import ChatBedrockConverse
from langchain_core.language_models import BaseChatModel

from skill_eval_runner.providers import Provider, register

DEFAULT_REGION = "us-east-1"

_env = Env()
_env.read_env()


def make_model(model_name: str) -> BaseChatModel:
    """Build a ``ChatBedrockConverse`` model using the boto3 credential chain."""
    region = (
        _env.str("BEDROCK_REGION", None)
        or _env.str("AWS_REGION", None)
        or _env.str("AWS_DEFAULT_REGION", None)
        or DEFAULT_REGION
    )
    model: BaseChatModel = ChatBedrockConverse(
        model=model_name,
        region_name=region,
        max_tokens=8192,
    )
    return model


register(
    Provider(
        name="bedrock",
        default_model="us.anthropic.claude-sonnet-4-6",
        small_model="us.anthropic.claude-haiku-4-5",
        large_model="us.anthropic.claude-sonnet-4-6",
        make_model=make_model,
    )
)