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82"""Model tier definitions for multi-agent document processing.
This module defines the model tiers used to balance cost and capability
across different agent types in the remediation pipeline, plus the
backend-specific model ID maps consumed by ``model_factory``.
Model Tiers:
- REASONING: Claude Sonnet 4.5 - For analysis, auto-correction, complex reasoning
- EFFICIENT: Claude Haiku 4.5 - For transcription, simple tasks, bulk processing
Pricing (per 1M tokens):
- Sonnet 4.5: ~$3.00 input, ~$15.00 output (Anthropic direct and Bedrock are comparable)
- Haiku 4.5: ~$1.00 input, ~$5.00 output
"""
from enum import Enum
class ModelTier(str, Enum):
"""Model tier for cost/capability tradeoff.
Attributes:
REASONING: High-capability tier (Sonnet) for analysis and complex reasoning
EFFICIENT: Cost-effective tier (Haiku) for transcription and bulk work
"""
REASONING = "reasoning" # Sonnet - analysis, auto-correction
EFFICIENT = "efficient" # Haiku - transcription, simple tasks
# Bedrock inference profile IDs for each tier.
# Claude 4.5 models on Bedrock require inference profiles (us. prefix), not on-demand IDs.
BEDROCK_TIER_MAP: dict[ModelTier, str] = {
ModelTier.REASONING: "us.anthropic.claude-sonnet-4-5-20250929-v1:0",
ModelTier.EFFICIENT: "us.anthropic.claude-haiku-4-5-20251001-v1:0",
}
# Direct Anthropic API model IDs for each tier. Pinned to the same dates as the
# Bedrock profiles above to keep output parity between the two backends.
ANTHROPIC_TIER_MAP: dict[ModelTier, str] = {
ModelTier.REASONING: "claude-sonnet-4-5-20250929",
ModelTier.EFFICIENT: "claude-haiku-4-5-20251001",
}
# Backwards-compatibility alias. Historically this module exported a single
# ``MODEL_TIER_MAP`` that was implicitly the Bedrock map. The alias preserves
# that import surface for any remaining callers; new code should import
# ``BEDROCK_TIER_MAP`` or ``ANTHROPIC_TIER_MAP`` directly, or preferably use
# ``model_factory.get_model_for_tier`` which handles backend selection.
MODEL_TIER_MAP: dict[ModelTier, str] = BEDROCK_TIER_MAP
def get_model_id(tier: ModelTier) -> str:
"""Get the Bedrock inference profile ID for a given tier.
Retained for backwards compatibility. New call sites should use
``model_factory.get_model_for_tier`` instead, which returns a fully
constructed PydanticAI ``Model`` for the selected backend.
Args:
tier: The model tier to get the ID for
Returns:
The AWS Bedrock inference profile ID string
Example:
>>> get_model_id(ModelTier.REASONING)
'us.anthropic.claude-sonnet-4-5-20250929-v1:0'
"""
return BEDROCK_TIER_MAP[tier]
__all__ = [
"ModelTier",
"BEDROCK_TIER_MAP",
"ANTHROPIC_TIER_MAP",
"MODEL_TIER_MAP",
"get_model_id",
]