No todos los resultados de búsqueda son igualmente confiables. Un dominio .gov que cita datos primarios es más confiable que una publicación de blog con una granja de contenido. Este tutorial crea un canal de puntuación de confianza que evalúa cada resultado de búsqueda en función de la autoridad de la fuente, la actualidad del contenido y la coherencia de las referencias cruzadas. Las puntuaciones ayudan a los agentes de IA a priorizar fuentes confiables y señalar las cuestionables. Costo: $0.005 por búsqueda, más consultas de verificación opcionales.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
Guia paso a paso
Paso 1: Definir niveles de autoridad de origen
Clasifique dominios en niveles de autoridad según su TLD y su reputación conocida. Esto proporciona una señal de confianza básica.
Python
AUTHORITY_TIERS = {
'tier1': {
'domains': {'gov', 'edu', 'mil'},
'known_sites': {'reuters.com', 'apnews.com', 'nature.com', 'science.org',
'arxiv.org', 'nih.gov', 'cdc.gov', 'who.int'},
'score': 90
},
'tier2': {
'domains': set(),
'known_sites': {'nytimes.com', 'bbc.com', 'washingtonpost.com',
'github.com', 'stackoverflow.com', 'docs.python.org',
'developer.mozilla.org', 'microsoft.com'},
'score': 75
},
'tier3': {
'domains': {'org', 'io'},
'known_sites': {'medium.com', 'dev.to', 'hackernoon.com', 'reddit.com'},
'score': 50
},
}
def get_authority_score(url: str) -> int:
domain = url.split('/')[2] if '/' in url else ''
tld = domain.split('.')[-1]
for tier_name, tier in AUTHORITY_TIERS.items():
if domain in tier['known_sites'] or tld in tier['domains']:
return tier['score']
return 30 # unknown domain baseline
test_urls = ['https://nih.gov/study', 'https://github.com/repo',
'https://randomsite.xyz/blog']
for url in test_urls:
print(f' {url}: authority={get_authority_score(url)}')Paso 2: Agregar puntuación de frescura
Califique los resultados según la fecha de publicación o actualización del contenido. Extraiga fechas de fragmentos y URL.
Python
import re
from datetime import datetime
def get_freshness_score(snippet: str, url: str) -> int:
"""Score freshness from 0-100 based on detected dates."""
text = snippet + ' ' + url
# Look for year patterns
years = re.findall(r'20(2[4-9])', text)
if years:
latest_year = max(int('20' + y) for y in years)
current_year = 2026
age = current_year - latest_year
if age == 0:
return 100 # current year
elif age == 1:
return 70
elif age == 2:
return 40
else:
return 10
# Look for month-year patterns
months = re.findall(r'(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\w*\s+202[4-9]', text)
if months:
return 80 # has a recent date reference
return 20 # no date information found
test_snippets = [
('Updated May 2026 - Best CRM tools', 'https://site.com/crm-2026'),
('A comprehensive guide from 2024', 'https://site.com/old-guide'),
('Learn Python programming basics', 'https://site.com/python'),
]
for snippet, url in test_snippets:
print(f' freshness={get_freshness_score(snippet, url):3d}: {snippet[:50]}')Paso 3: Construir el canal compuesto de puntuación de confianza
Combine autoridad, actualidad y coherencia de referencias cruzadas en una puntuación de confianza única para cada resultado de búsqueda.
Python
import requests, os
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def trust_score_results(query: str) -> list:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'num_results': 10})
results = resp.json().get('organic_results', [])
scored = []
# Collect all snippets for cross-reference
all_snippets = [r.get('snippet', '').lower() for r in results]
for i, r in enumerate(results):
authority = get_authority_score(r['link'])
freshness = get_freshness_score(r.get('snippet', ''), r['link'])
# Cross-reference: do other results mention similar facts?
my_keywords = set(re.findall(r'\b\w{5,}\b', r.get('snippet', '').lower()))
cross_ref = 0
for j, other in enumerate(all_snippets):
if i != j:
other_words = set(re.findall(r'\b\w{5,}\b', other))
overlap = len(my_keywords & other_words)
if overlap > 3:
cross_ref += 1
consistency = min(cross_ref * 20, 100)
# Weighted composite
trust = round(authority * 0.4 + freshness * 0.3 + consistency * 0.3)
scored.append({
'title': r['title'][:50], 'url': r['link'],
'trust_score': trust, 'authority': authority,
'freshness': freshness, 'consistency': consistency
})
scored.sort(key=lambda x: -x['trust_score'])
return scored
results = trust_score_results('best CRM software 2026')
print(f'{"Score":>5} {"Auth":>5} {"Fresh":>5} {"Cross":>5} Title')
print('-' * 70)
for r in results[:5]:
print(f'{r["trust_score"]:>5} {r["authority"]:>5} {r["freshness"]:>5} '
f'{r["consistency"]:>5} {r["title"]}')Ejemplo en Python
Python
import requests, os, re
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
KNOWN = {'gov': 90, 'edu': 90, 'github.com': 75, 'stackoverflow.com': 75}
def trust_score(query):
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'num_results': 10})
for r in resp.json().get('organic_results', []):
domain = r['link'].split('/')[2] if '/' in r['link'] else ''
tld = domain.split('.')[-1]
auth = KNOWN.get(domain, KNOWN.get(tld, 30))
fresh = 100 if '2026' in r.get('snippet', '') else 40
score = int(auth * 0.5 + fresh * 0.5)
print(f'[{score:3d}] {r["title"][:50]}')
trust_score('python best practices 2026')Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
const KNOWN = { gov: 90, edu: 90, 'github.com': 75, 'stackoverflow.com': 75 };
async function trustScore(query) {
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, country_code: 'us', num_results: 10 })
});
for (const r of (await resp.json()).organic_results || []) {
const domain = new URL(r.link).hostname;
const tld = domain.split('.').pop();
const auth = KNOWN[domain] || KNOWN[tld] || 30;
const fresh = (r.snippet || '').includes('2026') ? 100 : 40;
console.log(`[${Math.round(auth*0.5+fresh*0.5)}] ${r.title.slice(0, 50)}`);
}
}
trustScore('python best practices 2026');Salida esperada
JSON
Score Auth Fresh Cross Title
----------------------------------------------------------------------
82 90 100 40 NIH Guidelines on Data Analysis 2026
75 75 100 60 GitHub - python-best-practices: Updated May
68 75 70 60 Stack Overflow: Python 3.14 New Features
52 30 100 40 Best Python Practices 2026 - TechBlog
38 30 40 40 Python Tips and Tricks - randomsite.com