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/__init__.py

@@ -63,6 +63,22 @@ from .exceptions import (
     TokenCountError,
 )
 
+# Prioritization
+from .prioritization import (
+    ContextRanker,
+    RankingResult,
+)
+
+# Scoring
+from .scoring import (
+    BaseScorer,
+    CompositeScorer,
+    PriorityScorer,
+    RecencyScorer,
+    RelevanceScorer,
+    ScoredContext,
+)
+
 # Types
 from .types import (
     AssembledContext,
@@ -101,6 +117,16 @@ __all__ = [
     "InvalidContextError",
     "ScoringError",
     "TokenCountError",
+    # Prioritization
+    "ContextRanker",
+    "RankingResult",
+    # Scoring
+    "BaseScorer",
+    "CompositeScorer",
+    "PriorityScorer",
+    "RecencyScorer",
+    "RelevanceScorer",
+    "ScoredContext",
     # Types - Base
     "AssembledContext",
     "BaseContext",