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