Felipe Cardoso
6c7b72f130
Phase 8 of Context Management Engine - Final Cleanup: - Sort __all__ exports alphabetically - Sort imports per isort conventions - Fix minor linting issues Final test results: - 311 context management tests passing - 2507 total backend tests passing - 85% code coverage Context Management Engine is complete with all 8 phases: 1. Foundation: Types, Config, Exceptions 2. Token Budget Management 3. Context Scoring & Ranking 4. Context Assembly Pipeline 5. Model Adapters (Claude, OpenAI) 6. Caching Layer (Redis + in-memory) 7. Main Engine & Integration 8. Testing & Documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
"""
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Recency Scorer for Context Management.
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Scores context based on how recent it is.
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More recent content gets higher scores.
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"""
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import math
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from datetime import UTC, datetime
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from typing import Any
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from ..types import BaseContext, ContextType
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from .base import BaseScorer
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class RecencyScorer(BaseScorer):
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"""
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Scores context based on recency.
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Uses exponential decay to score content based on age.
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More recent content scores higher.
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"""
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def __init__(
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self,
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weight: float = 1.0,
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half_life_hours: float = 24.0,
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type_half_lives: dict[ContextType, float] | None = None,
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) -> None:
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"""
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Initialize recency scorer.
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Args:
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weight: Scorer weight for composite scoring
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half_life_hours: Default hours until score decays to 0.5
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type_half_lives: Optional context-type-specific half lives
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"""
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super().__init__(weight)
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self._half_life_hours = half_life_hours
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self._type_half_lives = type_half_lives or {}
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# Set sensible defaults for context types
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if ContextType.CONVERSATION not in self._type_half_lives:
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self._type_half_lives[ContextType.CONVERSATION] = 1.0 # 1 hour
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if ContextType.TOOL not in self._type_half_lives:
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self._type_half_lives[ContextType.TOOL] = 0.5 # 30 minutes
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if ContextType.KNOWLEDGE not in self._type_half_lives:
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self._type_half_lives[ContextType.KNOWLEDGE] = 168.0 # 1 week
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if ContextType.SYSTEM not in self._type_half_lives:
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self._type_half_lives[ContextType.SYSTEM] = 720.0 # 30 days
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if ContextType.TASK not in self._type_half_lives:
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self._type_half_lives[ContextType.TASK] = 24.0 # 1 day
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async def score(
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self,
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context: BaseContext,
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query: str,
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**kwargs: Any,
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) -> float:
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"""
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Score context based on recency.
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Args:
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context: Context to score
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query: Query (not used for recency, kept for interface)
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**kwargs: Additional parameters
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- reference_time: Time to measure recency from (default: now)
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Returns:
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Recency score between 0.0 and 1.0
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"""
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reference_time = kwargs.get("reference_time")
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if reference_time is None:
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reference_time = datetime.now(UTC)
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elif reference_time.tzinfo is None:
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reference_time = reference_time.replace(tzinfo=UTC)
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# Ensure context timestamp is timezone-aware
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context_time = context.timestamp
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if context_time.tzinfo is None:
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context_time = context_time.replace(tzinfo=UTC)
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# Calculate age in hours
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age = reference_time - context_time
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age_hours = max(0, age.total_seconds() / 3600)
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# Get half-life for this context type
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context_type = context.get_type()
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half_life = self._type_half_lives.get(context_type, self._half_life_hours)
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# Exponential decay
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decay_factor = math.exp(-math.log(2) * age_hours / half_life)
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return self.normalize_score(decay_factor)
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def get_half_life(self, context_type: ContextType) -> float:
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"""
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Get half-life for a context type.
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Args:
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context_type: Context type to get half-life for
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Returns:
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Half-life in hours
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"""
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return self._type_half_lives.get(context_type, self._half_life_hours)
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def set_half_life(self, context_type: ContextType, hours: float) -> None:
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"""
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Set half-life for a context type.
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Args:
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context_type: Context type to set half-life for
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hours: Half-life in hours
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"""
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if hours <= 0:
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raise ValueError("Half-life must be positive")
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self._type_half_lives[context_type] = hours
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async def score_batch(
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self,
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contexts: list[BaseContext],
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query: str,
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**kwargs: Any,
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) -> list[float]:
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"""
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Score multiple contexts.
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Args:
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contexts: Contexts to score
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query: Query (not used)
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**kwargs: Additional parameters
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Returns:
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List of scores (same order as input)
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"""
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scores = []
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for context in contexts:
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score = await self.score(context, query, **kwargs)
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scores.append(score)
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return scores
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