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6d9c72b | 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 | """Pydantic data models - typed contracts for ContextForge."""
from pydantic import BaseModel, Field
from datetime import datetime
from typing import Literal
class ContextEntry(BaseModel):
"""A registered agent context with compression support."""
agent_id: str
context: str
compressed_context: str | None = None
embedding: list[float] | None = None
token_count: int
compressed_token_count: int | None = None
created_at: datetime = Field(default_factory=datetime.now)
ttl_seconds: int = 300
def model_post_init(self, __context) -> None:
if self.embedding is None:
self.embedding = []
class ContextMatch(BaseModel):
"""A semantic match between contexts."""
agent_id: str
similarity: float
shared_prefix: str
tokens_saved: int
class CompressionDecision(BaseModel):
"""Decision made by the compression coordinator."""
strategy: Literal["apc_reuse", "compress", "compress_and_reuse", "passthrough"]
shared_prefix: str | None = None
compressed_context: str | None = None
original_tokens: int
final_tokens: int
savings_pct: float
class MetricsSnapshot(BaseModel):
"""Real-time system metrics."""
timestamp: datetime = Field(default_factory=datetime.now)
vram_used_gb: float
vram_total_gb: float
ttft_ms: float
tokens_processed: int
tokens_saved: int
dedup_rate: float
compression_ratio: float
active_agents: int
class ContextRegistration(BaseModel):
"""Request to register a new context."""
agent_id: str
context: str
class OptimizedContextRequest(BaseModel):
"""Request for optimized context."""
agent_id: str
context: str
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