Los LLM alucinan constantemente con información de marca. Inventan características del producto, citan precios incorrectos y confunden a los competidores. Basar la investigación de marca con datos de búsqueda en vivo soluciona este problema al brindarle al LLM datos verificados con los que trabajar. Este tutorial crea un canal de investigación de marca que busca en Google información de la empresa, Amazon para datos de productos, Reddit para el sentimiento de la comunidad y YouTube para presencia de contenido, todo a través de la API de Scavio a $0,005 por búsqueda.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Una clave API de LLM (OpenAI, Anthropic u Ollama local)
Guia paso a paso
Paso 1: Construir el canal de recopilación de datos de marca
Busque en múltiples plataformas para recopilar datos de marcas verificadas. Cada plataforma ofrece una perspectiva diferente: Google para información oficial, Amazon para productos, Reddit para reputación.
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
URL = 'https://api.scavio.dev/api/v1/search'
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def research_brand(brand: str) -> dict:
data = {'brand': brand, 'sources': {}}
# Google: official info, knowledge graph
resp = requests.post(URL, headers=H,
json={'query': f'{brand} company', 'country_code': 'us', 'num_results': 5})
result = resp.json()
data['sources']['google'] = {
'knowledge_graph': result.get('knowledge_graph', {}),
'top_results': [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link']} for r in result.get('organic_results', [])[:3]],
}
time.sleep(0.3)
# Amazon: product presence
resp = requests.post(URL, headers=H,
json={'query': f'site:amazon.com {brand}', 'country_code': 'us', 'num_results': 5})
data['sources']['amazon'] = [{'title': r['title'], 'url': r['link']}
for r in resp.json().get('organic_results', [])]
time.sleep(0.3)
# Reddit: community sentiment
resp = requests.post(URL, headers=H,
json={'query': f'site:reddit.com {brand} review', 'country_code': 'us', 'num_results': 5})
data['sources']['reddit'] = [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link']} for r in resp.json().get('organic_results', [])]
return data
brand_data = research_brand('Notion')
kg = brand_data['sources']['google']['knowledge_graph']
print(f'Brand: {brand_data["brand"]}')
print(f'Description: {kg.get("description", "N/A")}')
print(f'Amazon products: {len(brand_data["sources"]["amazon"])}')
print(f'Reddit discussions: {len(brand_data["sources"]["reddit"])}')Paso 2: Formatear los datos de la marca como contexto LLM
Estructurar los datos recopilados en un formato que el LLM pueda utilizar de forma eficaz. Incluya las URL de origen para que el LLM pueda citar sus fuentes.
Python
def format_brand_context(data: dict) -> str:
lines = [f'Verified brand research data for: {data["brand"]}', '']
# Knowledge graph data
kg = data['sources']['google'].get('knowledge_graph', {})
if kg:
lines.append('OFFICIAL INFO (Google Knowledge Graph):')
if kg.get('description'):
lines.append(f' Description: {kg["description"]}')
if kg.get('website'):
lines.append(f' Website: {kg["website"]}')
if kg.get('founded'):
lines.append(f' Founded: {kg["founded"]}')
lines.append('')
# Google search results
lines.append('WEB PRESENCE (Google):')
for i, r in enumerate(data['sources']['google']['top_results'], 1):
lines.append(f' [{i}] {r["title"]}')
lines.append(f' {r["snippet"][:150]}')
lines.append('')
# Amazon products
amazon = data['sources']['amazon']
if amazon:
lines.append(f'PRODUCT PRESENCE (Amazon, {len(amazon)} listings):')
for r in amazon[:3]:
lines.append(f' - {r["title"][:80]}')
lines.append('')
# Reddit sentiment
reddit = data['sources']['reddit']
if reddit:
lines.append(f'COMMUNITY SENTIMENT (Reddit, {len(reddit)} discussions):')
for r in reddit[:3]:
lines.append(f' - {r["title"][:80]}')
if r['snippet']:
lines.append(f' "{r["snippet"][:120]}"')
return '\n'.join(lines)
context = format_brand_context(brand_data)
print(context)Paso 3: Pregúntele al LLM con contexto fundamentado
Envíe los datos de la marca verificada al LLM con instrucciones para utilizar únicamente las fuentes proporcionadas. Esto elimina las alucinaciones sobre hechos de marca.
Python
def grounded_brand_analysis(brand: str, question: str) -> dict:
data = research_brand(brand)
context = format_brand_context(data)
messages = [
{'role': 'system', 'content': (
'You are a brand research analyst. Answer ONLY based on the verified '
'data provided below. Do NOT make up facts, pricing, features, or '
'statistics. If the data does not contain the answer, say so. '
'Cite sources as [Google], [Amazon], or [Reddit].'
)},
{'role': 'user', 'content': f'{context}\n\nQuestion: {question}'}
]
llm_url = os.environ.get('LLM_URL', 'http://localhost:11434/v1/chat/completions')
resp = requests.post(llm_url, json={
'model': 'llama3', 'messages': messages, 'max_tokens': 512
})
answer = resp.json()['choices'][0]['message']['content']
return {
'brand': brand,
'question': question,
'answer': answer,
'sources_used': 3, # google + amazon + reddit
'cost': 0.015, # 3 searches
}
result = grounded_brand_analysis('Notion', 'What is Notion and what products do they offer?')
print(f'Q: {result["question"]}')
print(f'A: {result["answer"]}')
print(f'\nSources: {result["sources_used"]} platforms, Cost: ${result["cost"]}')Paso 4: Investigación por lotes de varias marcas para compararlas
Investigue varias marcas y genere un informe comparativo. El LLM puede comparar marcas con precisión porque cada hecho se basa en datos en vivo.
Python
def compare_brands(brands: list[str], question: str) -> str:
all_context = []
for brand in brands:
data = research_brand(brand)
context = format_brand_context(data)
all_context.append(context)
time.sleep(0.5)
combined = '\n\n---\n\n'.join(all_context)
messages = [
{'role': 'system', 'content': (
'You are a brand research analyst. Compare the brands using ONLY '
'the verified data provided. Do not invent facts or features. '
'Cite sources. Be specific about what each brand offers.'
)},
{'role': 'user', 'content': f'{combined}\n\nCompare these brands: {question}'}
]
llm_url = os.environ.get('LLM_URL', 'http://localhost:11434/v1/chat/completions')
resp = requests.post(llm_url, json={
'model': 'llama3', 'messages': messages, 'max_tokens': 1024
})
answer = resp.json()['choices'][0]['message']['content']
cost = len(brands) * 3 * 0.005 # 3 searches per brand
print(f'Compared {len(brands)} brands using {len(brands) * 3} searches')
print(f'Cost: ${cost:.3f}')
return answer
comparison = compare_brands(['Notion', 'Obsidian', 'Coda'],
'Which is best for team collaboration and why?')
print(comparison)Ejemplo en Python
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def research_brand(brand):
results = {}
for prefix in ['', 'site:reddit.com ', 'site:amazon.com ']:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'{prefix}{brand}', 'country_code': 'us', 'num_results': 5})
platform = 'reddit' if 'reddit' in prefix else 'amazon' if 'amazon' in prefix else 'google'
results[platform] = resp.json().get('organic_results', [])
time.sleep(0.2)
kg = results.get('google', [{}])[0] if results.get('google') else {}
print(f'{brand}: {len(results["google"])} google, {len(results["reddit"])} reddit, {len(results["amazon"])} amazon')
return results
research_brand('Notion')Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function researchBrand(brand) {
const results = {};
for (const [platform, prefix] of [['google',''], ['reddit','site:reddit.com '], ['amazon','site:amazon.com ']]) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: `${prefix}${brand}`, country_code: 'us', num_results: 5 })
});
results[platform] = (await resp.json()).organic_results || [];
}
console.log(`${brand}: google=${results.google.length}, reddit=${results.reddit.length}, amazon=${results.amazon.length}`);
return results;
}
researchBrand('Notion');Salida esperada
JSON
Brand: Notion
Description: American productivity and note-taking web application
Amazon products: 4
Reddit discussions: 5
Q: What is Notion and what products do they offer?
A: Based on the verified data, Notion is an American productivity and
note-taking web application [Google]. Their Amazon presence includes
Notion-related productivity guides and templates [Amazon]. Reddit
discussions show strong community adoption for team wikis and project
management [Reddit].
Sources: 3 platforms, Cost: $0.015