bvandeusenandClaude Opus 4.8
807f478cac
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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
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
"""search — semantic search across the user's notes and tasks.
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Mirrors the existing fable-mcp contract so Claude's prior usage pattern keeps
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working. Differences from fable-mcp:
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- calls services.embeddings.semantic_search_notes directly instead of HTTP
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- user_id comes from mcp.current_user_id() rather than a global API key
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"""
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from __future__ import annotations
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import time
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from scribe.mcp._context import current_user_id
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from scribe.services.embeddings import DEFAULT_SIMILARITY_THRESHOLD, semantic_search_notes
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from scribe.services.retrieval_telemetry import record_retrieval
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async def search(
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q: str,
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content_type: str = "all",
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limit: int = 10,
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project_id: int = 0,
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) -> dict:
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"""Semantic search over the user's existing notes and tasks — Scribe's recall.
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Reach for this BEFORE answering a question about the user's work or starting
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a task: the user's second-brain almost always already holds related prior
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art. Check for an existing ticket before opening a new one (search with
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content_type='task'), and for prior notes/decisions before re-deriving them.
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Treating Scribe as the first place to look — not a place to only write — is
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the difference between it being a trustworthy record and a write-only log.
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Args:
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q: search query string.
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content_type: 'all' (default), 'note' (notes only), or 'task' (tasks only).
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limit: maximum number of results (1-50).
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project_id: Scope results to one project. PASS THE ACTIVE PROJECT'S ID
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whenever a project is in scope (the one you entered with
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enter_project) — otherwise this searches across ALL projects and
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bleeds unrelated work into the result set. 0 = search everything
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(use only when you genuinely want a cross-project sweep).
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Returns:
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{"results": [{"id", "title", "body", "is_task", "tags", "similarity"}],
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"total": int}
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"""
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uid = current_user_id()
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limit = max(1, min(limit, 50))
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is_task = {"note": False, "task": True}.get(content_type) # None => any
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t0 = time.perf_counter()
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raw = await semantic_search_notes(
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uid, q, limit=limit, is_task=is_task,
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project_id=project_id or None,
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)
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record_retrieval(
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user_id=uid, source="mcp_search", query=q,
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threshold=DEFAULT_SIMILARITY_THRESHOLD, limit=limit,
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project_id=project_id or None, is_task=is_task, results=raw,
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duration_ms=(time.perf_counter() - t0) * 1000.0,
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)
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return {
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"results": [
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{
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"id": note.id,
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"title": note.title,
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"body": (note.body or "")[:240],
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"is_task": bool(note.is_task),
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"tags": list(note.tags or []),
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"similarity": float(score),
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}
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for score, note in raw
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],
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"total": len(raw),
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}
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def register(mcp) -> None:
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mcp.tool(name="search")(search)
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