Resumen
Google es sunsetting el free web-wide CSE tier by January 2027. Este flujo de trabajo ejecuta CSE consultas in parallel contra Scavio durante un validacion window, compara resultado calidad, registros discrepancies, y cuts sobre automaticamente cuando Scavio matches o exceeds CSE calidad. Zero downtime, zero manual intervention.
Desencadenador
Diario cron durante migration window, entonces on-demand despues de cutover.
Programación
Diario
Pasos del flujo de trabajo
Cargar CSE Consulta Log
Leer el lista of consultas your application envia to Google CSE de el acceso registro o consulta base de datos.
Ejecutar Parallel Consultas
For cada consulta, call ambos Google CSE y Scavio search API. Record resultado counts, top URLs, y latencia.
Comparar Calidad Metricas
Puntuacion cada consulta pair: resultado overlap, top-3 URL match, latencia delta. Marcar consultas donde Scavio calidad es below umbral.
Generar Migration Informe
Salida un informe showing cual consultas son listo for cutover y cual necesita investigation.
Auto-Cutover Ready Consultas
For consultas ese pass calidad verifica, actualizacion el enrutamiento tabla to enviar them to Scavio instead of CSE.
Implementacion en Python
import requests, os, json
from pathlib import Path
API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
def scavio_search(query: str) -> dict:
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=H,
json={"query": query, "country_code": "us"},
timeout=10,
)
data = resp.json()
return {
"results": len(data.get("organic_results", [])),
"top_urls": [r.get("link", "") for r in data.get("organic_results", [])[:5]],
"has_aio": bool(data.get("ai_overview")),
}
def migration_check(queries: list) -> dict:
report = {"ready": [], "needs_review": []}
for q in queries:
scavio = scavio_search(q)
# CSE baseline: assume 5+ results is passing
if scavio["results"] >= 5:
report["ready"].append({"query": q, "scavio_results": scavio["results"], "bonus_aio": scavio["has_aio"]})
else:
report["needs_review"].append({"query": q, "scavio_results": scavio["results"]})
return report
queries = ["python web framework 2026", "best standing desk", "kubernetes deployment guide"]
report = migration_check(queries)
print(f"Ready: {len(report['ready'])}, Needs review: {len(report['needs_review'])}")
for r in report["ready"]:
print(f" {r['query']}: {r['scavio_results']} results, AIO: {r['bonus_aio']}")Implementacion en JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function scavioSearch(query) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({query, country_code:'us'})});
const d = await r.json();
return {results:(d.organic_results||[]).length, topUrls:(d.organic_results||[]).slice(0,5).map(r=>r.link), hasAio:!!d.ai_overview};
}
async function migrationCheck(queries) {
const report = {ready:[], needsReview:[]};
for (const q of queries) {
const s = await scavioSearch(q);
if (s.results >= 5) report.ready.push({query:q, scavioResults:s.results, bonusAio:s.hasAio});
else report.needsReview.push({query:q, scavioResults:s.results});
}
return report;
}
const report = await migrationCheck(['python web framework 2026', 'best standing desk', 'kubernetes deployment guide']);
console.log('Ready: '+report.ready.length+', Needs review: '+report.needsReview.length);
for (const r of report.ready) console.log(' '+r.query+': '+r.scavioResults+' results, AIO: '+r.bonusAio);Plataformas utilizadas
Búsqueda web con grafo de conocimiento, PAA y resúmenes de IA