bvandeusen
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#2463, the remaining findings (finding 3 landed with f11a547).
## The reuse slot logs (source: reuse_slot)
A real semantic query competing for an auto-inject menu slot, and the ledger
was asymmetric: the scored hit it DISPLACED was in retrieval_logs, the query
that displaced it was not — so the slot could never be evaluated against what
it replaced. #1038 and #2085 are gated on this ledger being complete.
## Browse search logs (source: browse_search)
The human's main search surface, and it logged nothing — so retrieval_logs
claimed the web's search was /api/search. Measured while fixing: /api/search
has ZERO frontend consumers; the web UI searches through /api/knowledge
exclusively. The table wasn't just under-describing the UI, it was describing
a surface the UI never touches.
The task's check — does folding human queries into the corpus skew the
precision signal thresholds are tuned against? — is answered by the source
column: distinct values (browse_search, reuse_slot beside the existing four)
mean tuning includes or excludes human traffic deliberately rather than by
accident. Same resolution as the mcp_/rest_ split in note_usage_events.
## /api/search takes project_id (and logs it)
The route logged project_id=None unconditionally while the MCP tool insists
the agent pass the active project. Now optional, default global: with no
frontend consumer, this route serves API callers, and an API caller states
its scope explicitly — the default-scope UI decision the task flagged is moot
until a UI actually consumes the route, which is recorded rather than guessed.
Refs #2463
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import time
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from quart import Blueprint, jsonify, request
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from scribe.auth import login_required, get_current_user_id
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from scribe.services.access import owner_names_for
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from scribe.services.embeddings import (
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INTERACTIVE_SEARCH_THRESHOLD as _REST_SEARCH_THRESHOLD,
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)
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from scribe.services.embeddings import semantic_search_notes
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from scribe.services.retrieval_telemetry import record_retrieval
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# The interactive floor lives in embeddings.py now, shared with Browse search
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# and the list views' semantic `q` — one number, one rationale (#2463).
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search_bp = Blueprint("search", __name__, url_prefix="/api/search")
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def _content_type_to_is_task(content_type: str) -> bool | None:
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"""Map content_type query param to semantic_search_notes is_task arg."""
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if content_type == "note":
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return False
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if content_type == "task":
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return True
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return None # "all" or unknown → no filter
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@search_bp.route("", methods=["GET"])
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@login_required
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async def search_route():
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uid = get_current_user_id()
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q = (request.args.get("q") or "").strip()
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if not q:
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return jsonify({"error": "q is required"}), 400
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content_type = request.args.get("content_type", "all")
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limit = min(request.args.get("limit", 10, type=int), 50)
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is_task = _content_type_to_is_task(content_type)
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# Same association filters the MCP tool takes (#33). Optional, default
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# global: this route has NO frontend consumer today (measured 2026-08-08 —
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# the web UI searches through /api/knowledge), so it serves API callers,
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# and an API caller states its scope explicitly.
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system_id = request.args.get("system_id", type=int)
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project_id = request.args.get("project_id", type=int)
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t0 = time.perf_counter()
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results = await semantic_search_notes(
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uid, q, limit=limit, is_task=is_task, threshold=_REST_SEARCH_THRESHOLD,
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project_id=project_id, system_id=system_id,
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# The user typed this, so it reaches everything they may read.
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scope="read",
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)
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record_retrieval(
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user_id=uid, source="rest_search", query=q,
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threshold=_REST_SEARCH_THRESHOLD, limit=limit,
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project_id=project_id, is_task=is_task, results=results,
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duration_ms=(time.perf_counter() - t0) * 1000.0,
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)
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owners = await owner_names_for(
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{int(note.user_id) for _s, note in results if note.user_id != uid}
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)
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return jsonify({
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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 "",
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"is_task": note.is_task,
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"tags": note.tags or [],
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"similarity": score,
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**(
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{"shared": True, "owner": owners.get(int(note.user_id))}
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if note.user_id != uid else {}
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),
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}
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for score, note in results # semantic_search_notes returns list[tuple[float, Note]]
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],
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"total": len(results),
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})
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