lukasandClaude Opus 4.8
a5ec0d21cb
Three-pane webmail client. - Backend: FastAPI + async SQLAlchemy + arq worker (Postgres/Redis), IMAP/SMTP sync, threading, search, drafts/outbox with undo, attachments, 2FA. - Frontend: React 18 + Vite + TypeScript + TanStack Query + Tailwind; three-pane UI with rich-text composer, light/dark themes, resizable panels. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
"""Recipient autocomplete from known_recipients (SPEC §9.1, §4).
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Prefix match on email or name, ranked by ``times_used`` decayed by recency. The
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composer merges these with plugin capability providers client-side (M3).
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"""
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from __future__ import annotations
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from typing import Annotated
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from fastapi import APIRouter, Depends, Query
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from pydantic import BaseModel
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.db import get_session
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from app.identity.deps import CurrentUser
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from app.models import KnownRecipient
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router = APIRouter(prefix="/recipients", tags=["compose"])
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DbSession = Annotated[AsyncSession, Depends(get_session)]
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class Suggestion(BaseModel):
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name: str
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email: str
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@router.get("/suggest", response_model=list[Suggestion])
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async def suggest(
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user: CurrentUser, db: DbSession, q: str = Query("", max_length=200)
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) -> list[Suggestion]:
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term = q.strip()
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if not term:
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return []
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like = f"{term}%"
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# Recency-decayed score: times_used / (1 + age_in_days). Ranks frequent and
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# recent correspondents ahead of stale ones (SPEC §9.1).
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age_days = func.extract("epoch", func.now() - KnownRecipient.last_used_at) / 86400.0
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score = KnownRecipient.times_used / (1.0 + age_days)
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stmt = (
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select(KnownRecipient.name, KnownRecipient.email)
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.where(
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KnownRecipient.user_id == user.id,
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KnownRecipient.email.ilike(like) | KnownRecipient.name.ilike(like),
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)
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.order_by(score.desc())
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.limit(10)
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)
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rows = (await db.execute(stmt)).all()
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return [Suggestion(name=row.name, email=row.email) for row in rows]
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