Felipe Cardoso
0d2005ddcb
Add comprehensive scoring system with three strategies: - RelevanceScorer: Semantic similarity with keyword fallback - RecencyScorer: Exponential decay with type-specific half-lives - PriorityScorer: Priority-based scoring with type bonuses Implement CompositeScorer combining all strategies with configurable weights (default: 50% relevance, 30% recency, 20% priority). Add ContextRanker for budget-aware context selection with: - Greedy selection algorithm respecting token budgets - CRITICAL priority contexts always included - Diversity reranking to prevent source dominance - Comprehensive selection statistics 68 tests covering all scoring and ranking functionality. Part of #61 - Context Management Engine 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
100 lines
2.1 KiB
Python
100 lines
2.1 KiB
Python
"""
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Base Scorer Protocol and Types.
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Defines the interface for context scoring implementations.
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"""
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
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from ..types import BaseContext
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if TYPE_CHECKING:
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from app.services.mcp.client_manager import MCPClientManager
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@runtime_checkable
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class ScorerProtocol(Protocol):
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"""Protocol for context scorers."""
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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 a context item.
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Args:
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context: Context to score
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query: Query to score against
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**kwargs: Additional scoring parameters
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Returns:
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Score between 0.0 and 1.0
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"""
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...
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class BaseScorer(ABC):
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"""
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Abstract base class for context scorers.
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Provides common functionality and interface for
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different scoring strategies.
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"""
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def __init__(self, weight: float = 1.0) -> None:
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"""
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Initialize scorer.
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Args:
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weight: Weight for this scorer in composite scoring
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"""
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self._weight = weight
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@property
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def weight(self) -> float:
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"""Get scorer weight."""
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return self._weight
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@weight.setter
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def weight(self, value: float) -> None:
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"""Set scorer weight."""
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if not 0.0 <= value <= 1.0:
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raise ValueError("Weight must be between 0.0 and 1.0")
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self._weight = value
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@abstractmethod
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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 a context item.
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Args:
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context: Context to score
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query: Query to score against
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**kwargs: Additional scoring parameters
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Returns:
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Score between 0.0 and 1.0
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"""
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...
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def normalize_score(self, score: float) -> float:
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"""
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Normalize score to [0.0, 1.0] range.
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Args:
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score: Raw score
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Returns:
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Normalized score
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"""
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return max(0.0, min(1.0, score))
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