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f209a8f 6799cfe f209a8f 6799cfe f209a8f 6799cfe f209a8f 6799cfe f209a8f 6799cfe f209a8f 6799cfe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Optional
@dataclass(frozen=True)
class ModelProfile:
family: str
context_window: int
output_reserve_tokens: int
compact_buffer_tokens: int
recent_history_budget_tokens: int
compact_summary_max_tokens: int
compact_trigger_tokens_override: Optional[int] = None
@property
def compact_trigger_tokens(self) -> int:
if self.compact_trigger_tokens_override is not None:
if self.compact_trigger_tokens_override <= 0:
raise ValueError("compact_trigger_tokens must be positive.")
return self.compact_trigger_tokens_override
trigger_tokens = self.context_window - self.output_reserve_tokens - self.compact_buffer_tokens
if trigger_tokens <= 0:
raise ValueError(
"max_input_tokens must be larger than max_output_tokens + compact_summary_max_tokens."
)
return trigger_tokens
def _model_family(model_name: str) -> str:
normalized = str(model_name or "").strip().casefold()
if "gemini" in normalized:
return "gemini"
if "claude" in normalized:
return "claude"
if "deepseek" in normalized:
return "deepseek"
if "qwen" in normalized:
return "qwen"
if "glm" in normalized:
return "glm"
if "gpt" in normalized or "o1" in normalized or "o3" in normalized or "o4" in normalized:
return "gpt"
return "generic"
def resolve_model_profile(
model_name: str,
*,
configured_max_input_tokens: int,
configured_max_output_tokens: int,
configured_recent_history_budget_tokens: int,
configured_compact_summary_max_tokens: int,
compact_trigger_tokens: Any = None,
) -> ModelProfile:
context_window = int(configured_max_input_tokens)
if context_window <= 0:
raise ValueError("max_input_tokens must be positive.")
output_reserve_tokens = int(configured_max_output_tokens)
if output_reserve_tokens <= 0:
raise ValueError("max_output_tokens must be positive.")
if output_reserve_tokens >= context_window:
raise ValueError("max_output_tokens must be smaller than max_input_tokens.")
recent_history_budget_tokens = int(configured_recent_history_budget_tokens)
if recent_history_budget_tokens <= 0:
raise ValueError("recent_history_budget_tokens must be positive.")
compact_summary_max_tokens = int(configured_compact_summary_max_tokens)
if compact_summary_max_tokens <= 0:
raise ValueError("compact_summary_max_tokens must be positive.")
if output_reserve_tokens + compact_summary_max_tokens >= context_window:
raise ValueError(
"max_output_tokens + compact_summary_max_tokens must be smaller than max_input_tokens."
)
compact_buffer_tokens = compact_summary_max_tokens
compact_trigger_override = parse_compact_trigger_tokens(compact_trigger_tokens, context_window=context_window)
if compact_trigger_override is not None:
if compact_trigger_override <= 0:
raise ValueError("compact_trigger_tokens must be positive.")
if compact_trigger_override >= context_window - output_reserve_tokens:
raise ValueError("compact_trigger_tokens must be smaller than max_input_tokens - max_output_tokens.")
family = _model_family(model_name)
return ModelProfile(
family=family,
context_window=context_window,
output_reserve_tokens=output_reserve_tokens,
compact_buffer_tokens=compact_buffer_tokens,
recent_history_budget_tokens=recent_history_budget_tokens,
compact_summary_max_tokens=compact_summary_max_tokens,
compact_trigger_tokens_override=compact_trigger_override,
)
def parse_compact_trigger_tokens(value: Any, *, context_window: int) -> Optional[int]:
if value is None:
return None
if isinstance(value, bool):
raise ValueError("compact trigger tokens must not be a boolean.")
if isinstance(value, int):
parsed = value
else:
text = str(value).strip().casefold()
if not text:
return None
multiplier = 1
if text.endswith("k"):
multiplier = 1024
text = text[:-1].strip()
elif text.endswith("m"):
multiplier = 1024 * 1024
text = text[:-1].strip()
text = text.replace("_", "").replace(",", "")
parsed = int(text) * multiplier
return parsed
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