No todas las API de búsqueda producen resultados RAG iguales. La longitud del fragmento, la actualidad de los resultados y la puntuación de relevancia impactan directamente la calidad de su respuesta de LLM. Este tutorial crea un marco de referencia que prueba las API de búsqueda en todas las dimensiones importantes para RAG: cobertura de fragmentos, actualidad de los resultados, relevancia del título y costo por resultado útil. Pruebe Scavio ($0,005/crédito, 6 plataformas), Tavily ($30/mes por 10K), SerpAPI ($25/mes por 1K) y otros usando los mismos criterios de evaluación.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Opcional: claves API para los proveedores que desea comparar
Guia paso a paso
Paso 1: Definir el conjunto de pruebas comparativas
Cree un conjunto de consultas diversas que prueben diferentes escenarios de RAG: búsquedas de hechos, preguntas comparativas, consultas técnicas y eventos actuales.
Python
import os, time, requests
from dataclasses import dataclass, field
@dataclass
class BenchmarkQuery:
query: str
category: str
expected_terms: list # Terms we expect in good results
TEST_SUITE = [
BenchmarkQuery('Python 3.15 release date', 'factual',
['python', '3.15', 'release', '2026']),
BenchmarkQuery('FastAPI vs Django performance 2026', 'comparison',
['fastapi', 'django', 'performance', 'benchmark']),
BenchmarkQuery('how to deploy to Cloudflare Workers', 'technical',
['cloudflare', 'workers', 'deploy', 'wrangler']),
BenchmarkQuery('best noise cancelling headphones 2026', 'product',
['noise', 'cancelling', 'headphones', 'best']),
BenchmarkQuery('React Server Components production patterns', 'technical',
['react', 'server', 'components', 'rsc']),
]
print(f'Benchmark suite: {len(TEST_SUITE)} queries')
for q in TEST_SUITE:
print(f' [{q.category}] {q.query}')Paso 2: Construir las métricas de evaluación
Mida cuatro dimensiones: cobertura de fragmentos (cuánto texto por resultado), relevancia del término (términos esperados encontrados), actualidad (2026 menciones) y recuento de resultados.
Python
@dataclass
class EvalResult:
query: str
provider: str
result_count: int
avg_snippet_length: float
term_coverage: float # 0-1 how many expected terms found
freshness_score: float # 0-1 mentions of current year
latency_ms: float
cost_per_query: float
def evaluate_results(query: BenchmarkQuery, results: list, provider: str,
latency_ms: float, cost: float) -> EvalResult:
if not results:
return EvalResult(query.query, provider, 0, 0, 0, 0, latency_ms, cost)
# Snippet coverage
snippets = [r.get('snippet', '') for r in results]
avg_len = sum(len(s) for s in snippets) / len(snippets)
# Term relevance
all_text = ' '.join(f"{r.get('title','')} {r.get('snippet','')}" for r in results).lower()
terms_found = sum(1 for t in query.expected_terms if t.lower() in all_text)
term_coverage = terms_found / len(query.expected_terms) if query.expected_terms else 0
# Freshness
fresh_count = sum(1 for r in results if '2026' in f"{r.get('title','')} {r.get('snippet','')}")
freshness = fresh_count / len(results)
return EvalResult(
query=query.query, provider=provider,
result_count=len(results), avg_snippet_length=avg_len,
term_coverage=term_coverage, freshness_score=freshness,
latency_ms=latency_ms, cost_per_query=cost
)
print('Evaluation metrics defined')Paso 3: Ejecute el benchmark Scavio
Ejecute todas las consultas de prueba en la API de Scavio y recopile métricas de evaluación. Cada consulta cuesta $0.005.
Python
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def benchmark_scavio(test_suite: list[BenchmarkQuery]) -> list[EvalResult]:
results = []
for bq in test_suite:
start = time.time()
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': bq.query, 'country_code': 'us', 'num_results': 10})
latency = (time.time() - start) * 1000
organic = resp.json().get('organic_results', [])
search_results = [{'title': r['title'], 'snippet': r.get('snippet', ''),
'link': r['link']} for r in organic]
eval_result = evaluate_results(bq, search_results, 'scavio', latency, 0.005)
results.append(eval_result)
time.sleep(0.3)
return results
scavio_results = benchmark_scavio(TEST_SUITE)
for er in scavio_results:
print(f'[{er.provider}] {er.query[:40]}')
print(f' Results: {er.result_count}, Snippets: {er.avg_snippet_length:.0f} chars')
print(f' Relevance: {er.term_coverage:.0%}, Fresh: {er.freshness_score:.0%}')
print(f' Latency: {er.latency_ms:.0f}ms, Cost: ${er.cost_per_query}')Paso 4: Generar el informe comparativo
Agregue los resultados de todas las consultas y proveedores en un informe resumido. Clasifique a los proveedores según una puntuación compuesta ponderada según métricas relevantes para RAG.
Python
def benchmark_report(all_results: dict[str, list[EvalResult]]):
print('Search API Benchmark for RAG Quality')
print('=' * 55)
summaries = {}
for provider, results in all_results.items():
n = len(results)
summaries[provider] = {
'avg_results': sum(r.result_count for r in results) / n,
'avg_snippet': sum(r.avg_snippet_length for r in results) / n,
'avg_relevance': sum(r.term_coverage for r in results) / n,
'avg_freshness': sum(r.freshness_score for r in results) / n,
'avg_latency': sum(r.latency_ms for r in results) / n,
'cost_per_query': results[0].cost_per_query,
}
# Composite score: relevance 40%, snippets 25%, freshness 20%, cost 15%
for provider, s in summaries.items():
snippet_score = min(s['avg_snippet'] / 200, 1) # Normalize to 0-1
cost_score = 1 - min(s['cost_per_query'] / 0.05, 1) # Lower is better
composite = (s['avg_relevance'] * 0.4 + snippet_score * 0.25 +
s['avg_freshness'] * 0.2 + cost_score * 0.15)
s['composite'] = composite
# Sort by composite score
ranked = sorted(summaries.items(), key=lambda x: x[1]['composite'], reverse=True)
for rank, (provider, s) in enumerate(ranked, 1):
print(f'\n#{rank} {provider.upper()}')
print(f' Relevance: {s["avg_relevance"]:.0%} Snippets: {s["avg_snippet"]:.0f} chars')
print(f' Freshness: {s["avg_freshness"]:.0%} Latency: {s["avg_latency"]:.0f}ms')
print(f' Cost: ${s["cost_per_query"]}/query Composite: {s["composite"]:.2f}')
benchmark_report({'scavio': scavio_results})Ejemplo en Python
Python
import os, time, requests
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def benchmark(queries):
for q in queries:
start = time.time()
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': q, 'country_code': 'us', 'num_results': 10})
latency = (time.time() - start) * 1000
results = resp.json().get('organic_results', [])
snippets = [r.get('snippet', '') for r in results]
avg_len = sum(len(s) for s in snippets) / len(snippets) if snippets else 0
print(f'{q[:40]:40s} | {len(results):2d} results | {avg_len:5.0f} chars | {latency:4.0f}ms')
time.sleep(0.3)
benchmark(['Python 3.15 release date', 'FastAPI vs Django 2026',
'best headphones 2026', 'deploy cloudflare workers'])Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function benchmark(queries) {
for (const q of queries) {
const start = Date.now();
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: q, country_code: 'us', num_results: 10 })
});
const latency = Date.now() - start;
const results = (await resp.json()).organic_results || [];
const avgSnippet = results.reduce((s, r) => s + (r.snippet || '').length, 0) / (results.length || 1);
console.log(`${q.slice(0,40).padEnd(40)} | ${results.length} results | ${avgSnippet.toFixed(0)} chars | ${latency}ms`);
}
}
benchmark(['Python 3.15 release', 'FastAPI vs Django', 'best headphones 2026']);Salida esperada
JSON
Search API Benchmark for RAG Quality
=======================================================
#1 SCAVIO
Relevance: 85% Snippets: 156 chars
Freshness: 60% Latency: 340ms
Cost: $0.005/query Composite: 0.78
Python 3.15 release date | 10 results | 145 chars | 320ms
FastAPI vs Django 2026 | 10 results | 162 chars | 290ms
best headphones 2026 | 10 results | 158 chars | 310ms