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/app.py

@@ -26,6 +26,7 @@ from .data_loader import (
     load_school_data,
     geocode_single_postcode,
     get_supplementary_data,
+    search_schools_typesense,
 )
 from .data_loader import get_data_info as get_db_info
 from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
@@ -314,15 +315,19 @@ async def get_schools(
 
     # Apply filters
     if search:
-        search_lower = search.lower()
-        mask = (
-            schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
-        )
-        if "address" in schools_df.columns:
-            mask = mask | schools_df["address"].str.lower().str.contains(
-                search_lower, na=False
-            )
-        schools_df = schools_df[mask]
+        ts_urns = search_schools_typesense(search)
+        if ts_urns:
+            urn_order = {urn: i for i, urn in enumerate(ts_urns)}
+            schools_df = schools_df[schools_df["urn"].isin(set(ts_urns))].copy()
+            schools_df["_ts_rank"] = schools_df["urn"].map(urn_order)
+            schools_df = schools_df.sort_values("_ts_rank").drop(columns=["_ts_rank"])
+        else:
+            # Fallback: Typesense unavailable, use substring match
+            search_lower = search.lower()
+            mask = schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
+            if "address" in schools_df.columns:
+                mask = mask | schools_df["address"].str.lower().str.contains(search_lower, na=False)
+            schools_df = schools_df[mask]
 
     if local_authority:
         schools_df = schools_df[