axiom-engine-api / app /core /monitor.py
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Axiom V4.6: Production-Locked. Multilingual Neural Core Operational.
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import tiktoken
from typing import List, Optional
class ContextMonitor:
"""SOTA Token Sentry V4.6. Enterprise-grade context truncation."""
_instance: Optional["ContextMonitor"] = None
encoder: Optional[tiktoken.Encoding] = None
LIMIT: int = 100000
def __new__(cls) -> "ContextMonitor":
if cls._instance is None:
cls._instance = super(ContextMonitor, cls).__new__(cls)
return cls._instance
def _lazy_init(self) -> None:
if self.encoder is None:
print("AXIOM-CORE: Materializing Token Sentry...")
self.encoder = tiktoken.get_encoding("cl100k_base")
def count_tokens(self, text: str) -> int:
if not text: return 0
self._lazy_init()
return len(self.encoder.encode(text)) # type: ignore
def guard_context(self, context_list: List[str]) -> str:
self._lazy_init()
current_parts: List[str] = []
total_tokens = 0
for chunk in context_list:
tokens = self.count_tokens(chunk) + 4
if total_tokens + tokens > self.LIMIT:
break
current_parts.append(chunk)
total_tokens += tokens
pressure = (total_tokens / self.LIMIT) * 100
print(f"CONTEXT_PRESSURE: {pressure:.1f}% ({total_tokens} tokens)")
return "\n\n".join(current_parts)
monitor = ContextMonitor()