feat: wire Typesense search into backend, fix sync performance data bug

sync_typesense.py:
- Fix query string replacement: was matching 'ST_X(l.geom) as lng' but
  QUERY_BASE uses 'l.longitude as lng' — KS2/KS4 lateral joins were
  silently dropped on every sync run

backend:
- Add typesense_url/typesense_api_key settings to config.py
- Add search_schools_typesense() to data_loader.py — queries Typesense
  'schools' alias, returns URNs in relevance order with typo tolerance;
  falls back to empty list if Typesense is unavailable
- /api/schools: replace pandas str.contains with Typesense search;
  results are filtered from the DataFrame and returned in relevance order;
  graceful fallback to substring match if Typesense is down

requirements.txt: add typesense==0.21.0, numpy==1.26.4

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

backend/data_loader.py

@@ -20,6 +20,47 @@ from .models import (
 from .schemas import SCHOOL_TYPE_MAP
 
 _postcode_cache: Dict[str, Tuple[float, float]] = {}
+_typesense_client = None
+
+
+def _get_typesense_client():
+    global _typesense_client
+    if _typesense_client is not None:
+        return _typesense_client
+    url = settings.typesense_url
+    key = settings.typesense_api_key
+    if not url or not key:
+        return None
+    try:
+        import typesense
+        host = url.split("//")[-1]
+        host_part, _, port_str = host.partition(":")
+        port = int(port_str) if port_str else 8108
+        _typesense_client = typesense.Client({
+            "nodes": [{"host": host_part, "port": str(port), "protocol": "http"}],
+            "api_key": key,
+            "connection_timeout_seconds": 2,
+        })
+        return _typesense_client
+    except Exception:
+        return None
+
+
+def search_schools_typesense(query: str, limit: int = 250) -> List[int]:
+    """Search Typesense. Returns URNs in relevance order, or [] if unavailable."""
+    client = _get_typesense_client()
+    if client is None:
+        return []
+    try:
+        result = client.collections["schools"].documents.search({
+            "q": query,
+            "query_by": "school_name,local_authority,postcode",
+            "per_page": min(limit, 250),
+            "typo_tokens_threshold": 1,
+        })
+        return [int(h["document"]["urn"]) for h in result.get("hits", [])]
+    except Exception:
+        return []
 
 
 def normalize_school_type(school_type: Optional[str]) -> Optional[str]: