feat(context): implement context scoring and ranking (Phase 3)

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>

backend/app/services/context/prioritization/__init__.py

@@ -3,3 +3,10 @@ Context Prioritization Module.
 
 Provides context ranking and selection.
 """
+
+from .ranker import ContextRanker, RankingResult
+
+__all__ = [
+    "ContextRanker",
+    "RankingResult",
+]