feat(search): retrieval telemetry — log every semantic retrieval

Add retrieval_logs (migration 0068) + services/retrieval_telemetry with a
fire-and-forget record_retrieval(), wired into the MCP search tool
(source=mcp_search) and the REST search route (source=rest_search). Captures
query, effective params, and the per-result score distribution so KB-injection
thresholds can be tuned from data rather than guessed.

Scribe: project 2, milestone 93, task 1032.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Xz4j1H7pjYSjKsEpgcNH5E

src/scribe/models/retrieval_log.py

@@ -0,0 +1,70 @@
+from datetime import datetime, timezone
+
+from sqlalchemy import Boolean, DateTime, Float, Index, Integer, Text
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from scribe.models import Base
+
+
+class RetrievalLog(Base):
+    """One row per semantic-retrieval call, for KB-injection tuning.
+
+    Captures what a query asked for, what came back, and the score
+    distribution of the results — the empirical basis for tuning the
+    similarity threshold and top-k per surface. `result_ids` holds the ranked
+    hits (id + score + rank) so a later pass can correlate "what we surfaced"
+    against "what the agent then fetched/referenced".
+
+    Deliberately FK-free on user_id (mirrors AppLog): telemetry should outlive
+    the row it describes, and a deleted user shouldn't cascade away history.
+    """
+
+    __tablename__ = "retrieval_logs"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
+    )
+    user_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    # Retrieval surface: 'mcp_search' | 'rest_search' | 'auto_inject' | ...
+    source: Mapped[str] = mapped_column(Text, nullable=False)
+    query: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # Effective parameters actually used for this call.
+    threshold: Mapped[float | None] = mapped_column(Float, nullable=True)
+    limit_n: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    project_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    # The content-type filter as passed to semantic_search_notes: True=tasks,
+    # False=notes, NULL=any.
+    is_task: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
+    result_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    top_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    min_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    # [{"id": int, "score": float, "rank": int}, ...], highest-first.
+    result_ids: Mapped[list] = mapped_column(JSONB, nullable=False, default=list)
+    duration_ms: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    __table_args__ = (
+        Index("ix_retrieval_logs_created_at", "created_at"),
+        Index("ix_retrieval_logs_user_id", "user_id"),
+        Index("ix_retrieval_logs_source", "source"),
+        Index("ix_retrieval_logs_source_created_at", "source", created_at.desc()),
+    )
+
+    def to_dict(self) -> dict:
+        return {
+            "id": self.id,
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+            "user_id": self.user_id,
+            "source": self.source,
+            "query": self.query,
+            "threshold": self.threshold,
+            "limit_n": self.limit_n,
+            "project_id": self.project_id,
+            "is_task": self.is_task,
+            "result_count": self.result_count,
+            "top_score": self.top_score,
+            "min_score": self.min_score,
+            "result_ids": self.result_ids,
+            "duration_ms": self.duration_ms,
+        }