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/scoring/relevance.py

@@ -0,0 +1,188 @@
+"""
+Relevance Scorer for Context Management.
+
+Scores context based on semantic similarity to the query.
+Uses Knowledge Base embeddings when available.
+"""
+
+import logging
+import re
+from typing import TYPE_CHECKING, Any
+
+from .base import BaseScorer
+from ..types import BaseContext, ContextType, KnowledgeContext
+
+if TYPE_CHECKING:
+    from app.services.mcp.client_manager import MCPClientManager
+
+logger = logging.getLogger(__name__)
+
+
+class RelevanceScorer(BaseScorer):
+    """
+    Scores context based on relevance to query.
+
+    Uses multiple strategies:
+    1. Pre-computed scores (from RAG results)
+    2. MCP-based semantic similarity (via Knowledge Base)
+    3. Keyword matching fallback
+    """
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        weight: float = 1.0,
+        keyword_fallback_weight: float = 0.5,
+    ) -> None:
+        """
+        Initialize relevance scorer.
+
+        Args:
+            mcp_manager: MCP manager for Knowledge Base calls
+            weight: Scorer weight for composite scoring
+            keyword_fallback_weight: Max score for keyword-based fallback
+        """
+        super().__init__(weight)
+        self._mcp = mcp_manager
+        self._keyword_fallback_weight = keyword_fallback_weight
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """Set MCP manager for semantic scoring."""
+        self._mcp = mcp_manager
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score context relevance to query.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional parameters
+
+        Returns:
+            Relevance score between 0.0 and 1.0
+        """
+        # 1. Check for pre-computed relevance score
+        if isinstance(context, KnowledgeContext) and context.relevance_score is not None:
+            return self.normalize_score(context.relevance_score)
+
+        # 2. Check metadata for score
+        if "relevance_score" in context.metadata:
+            return self.normalize_score(context.metadata["relevance_score"])
+
+        if "score" in context.metadata:
+            return self.normalize_score(context.metadata["score"])
+
+        # 3. Try MCP-based semantic similarity
+        if self._mcp is not None:
+            try:
+                score = await self._compute_semantic_similarity(context, query)
+                if score is not None:
+                    return score
+            except Exception as e:
+                logger.debug(f"Semantic scoring failed, using fallback: {e}")
+
+        # 4. Fall back to keyword matching
+        return self._compute_keyword_score(context, query)
+
+    async def _compute_semantic_similarity(
+        self,
+        context: BaseContext,
+        query: str,
+    ) -> float | None:
+        """
+        Compute semantic similarity using Knowledge Base embeddings.
+
+        Args:
+            context: Context to score
+            query: Query to compare
+
+        Returns:
+            Similarity score or None if unavailable
+        """
+        try:
+            # Use Knowledge Base's search capability to compute similarity
+            result = await self._mcp.call_tool(
+                server="knowledge-base",
+                tool="compute_similarity",
+                args={
+                    "text1": query,
+                    "text2": context.content[:2000],  # Limit content length
+                },
+            )
+
+            if result.success and result.data:
+                similarity = result.data.get("similarity")
+                if similarity is not None:
+                    return self.normalize_score(float(similarity))
+
+        except Exception as e:
+            logger.debug(f"Semantic similarity computation failed: {e}")
+
+        return None
+
+    def _compute_keyword_score(
+        self,
+        context: BaseContext,
+        query: str,
+    ) -> float:
+        """
+        Compute relevance score based on keyword matching.
+
+        Simple but fast fallback when semantic search is unavailable.
+
+        Args:
+            context: Context to score
+            query: Query to match
+
+        Returns:
+            Keyword-based relevance score
+        """
+        if not query or not context.content:
+            return 0.0
+
+        # Extract keywords from query
+        query_lower = query.lower()
+        content_lower = context.content.lower()
+
+        # Simple word tokenization
+        query_words = set(re.findall(r"\b\w{3,}\b", query_lower))
+        content_words = set(re.findall(r"\b\w{3,}\b", content_lower))
+
+        if not query_words:
+            return 0.0
+
+        # Calculate overlap
+        common_words = query_words & content_words
+        overlap_ratio = len(common_words) / len(query_words)
+
+        # Apply fallback weight ceiling
+        return self.normalize_score(overlap_ratio * self._keyword_fallback_weight)
+
+    async def score_batch(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        **kwargs: Any,
+    ) -> list[float]:
+        """
+        Score multiple contexts.
+
+        Args:
+            contexts: Contexts to score
+            query: Query to score against
+            **kwargs: Additional parameters
+
+        Returns:
+            List of scores (same order as input)
+        """
+        scores = []
+        for context in contexts:
+            score = await self.score(context, query, **kwargs)
+            scores.append(score)
+        return scores