Initial commit: Tasteby - YouTube restaurant map service

Backend (FastAPI + Oracle ADB), Frontend (Next.js), daemon worker.
Features: channel/video/restaurant management, semantic search,
Google OAuth, user reviews.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/api/routes/search.py

@@ -0,0 +1,66 @@
+"""Search API routes — keyword + semantic search."""
+
+from fastapi import APIRouter, Query
+
+from core import restaurant, vector
+from core.db import conn
+
+router = APIRouter()
+
+
+@router.get("")
+def search_restaurants(
+    q: str = Query(..., min_length=1),
+    mode: str = Query("keyword", pattern="^(keyword|semantic|hybrid)$"),
+    limit: int = Query(20, le=100),
+):
+    """Search restaurants by keyword, semantic similarity, or hybrid."""
+    if mode == "semantic":
+        return _semantic_search(q, limit)
+    elif mode == "hybrid":
+        kw = _keyword_search(q, limit)
+        sem = _semantic_search(q, limit)
+        # merge: keyword results first, then semantic results not already in keyword
+        seen = {r["id"] for r in kw}
+        merged = list(kw)
+        for r in sem:
+            if r["id"] not in seen:
+                merged.append(r)
+                seen.add(r["id"])
+        return merged[:limit]
+    else:
+        return _keyword_search(q, limit)
+
+
+def _keyword_search(q: str, limit: int) -> list[dict]:
+    sql = """
+        SELECT id, name, address, region, latitude, longitude,
+               cuisine_type, price_range
+        FROM restaurants
+        WHERE latitude IS NOT NULL
+          AND (UPPER(name) LIKE UPPER(:q)
+               OR UPPER(address) LIKE UPPER(:q)
+               OR UPPER(region) LIKE UPPER(:q)
+               OR UPPER(cuisine_type) LIKE UPPER(:q))
+        FETCH FIRST :lim ROWS ONLY
+    """
+    pattern = f"%{q}%"
+    with conn() as c:
+        cur = c.cursor()
+        cur.execute(sql, {"q": pattern, "lim": limit})
+        cols = [d[0].lower() for d in cur.description]
+        return [dict(zip(cols, row)) for row in cur.fetchall()]
+
+
+def _semantic_search(q: str, limit: int) -> list[dict]:
+    similar = vector.search_similar(q, top_k=limit)
+    if not similar:
+        return []
+
+    rest_ids = list({s["restaurant_id"] for s in similar})
+    results = []
+    for rid in rest_ids[:limit]:
+        r = restaurant.get_by_id(rid)
+        if r and r.get("latitude"):
+            results.append(r)
+    return results