RAG basado en búsqueda reemplaza un almacén de vectores con una llamada API de búsqueda en vivo. En lugar de incrustar documentos y realizar búsquedas de similitudes, consulta la web (o una plataforma) en busca de contenido relevante e inyecta los resultados directamente en el contexto del LLM.
Requisitos previos
- Python 3.9+
- Clave API de Scavio
- SDK antrópico
Guia paso a paso
Paso 1: Comprender la diferencia de patrón
RAG tradicional: incrustar documentos -> almacenar vectores -> búsqueda de similitudes -> inyectar. Buscar RAG: consultar API -> obtener fragmentos -> inyectar. Sin paso de incrustación, sin base de datos vectorial.
Python
# Traditional vector RAG (what you're replacing)
# 1. Embed your corpus (expensive, slow)
# 2. Store in Pinecone/Chroma/Weaviate
# 3. Embed the query
# 4. Cosine similarity search
# 5. Retrieve top-k chunks
# 6. Inject into prompt
# Search RAG (this tutorial)
# 1. Convert question to search query
# 2. Call search API (1 API call, 1 credit)
# 3. Extract top snippets
# 4. Inject into prompt
# Done. Fresh data. No embedding costs.Paso 2: Construya la función de recuperación
El recuperador responde una pregunta, busca contenido relevante y devuelve fragmentos formateados.
Python
import requests
SCAVIO_KEY = "your-scavio-api-key"
def retrieve(question: str, num_results: int = 5, platform: str = None) -> list[dict]:
payload = {"query": question, "num_results": num_results}
if platform:
payload["platform"] = platform
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json=payload,
headers={"x-api-key": SCAVIO_KEY},
timeout=15
)
r.raise_for_status()
results = r.json().get("organic_results", [])
return [{"title": res["title"], "snippet": res.get("snippet", ""), "url": res["link"]} for res in results]Paso 3: Formatear los documentos recuperados como contexto
Convierta los resultados de la búsqueda en un bloque de contexto al que el LLM pueda hacer referencia.
Python
def format_context(docs: list[dict]) -> str:
lines = []
for i, doc in enumerate(docs, 1):
lines.append(f"[{i}] {doc['title']}\nURL: {doc['url']}\n{doc['snippet']}")
return "\n---\n".join(lines)Paso 4: Generar respuesta con Anthropic Claude
Inyecte el contexto recuperado en el mensaje y obtenga una respuesta fundamentada.
Python
import anthropic
ANTHROPIC_KEY = "your-anthropic-key"
def rag_answer(question: str, platform: str = None) -> dict:
docs = retrieve(question, num_results=5, platform=platform)
context = format_context(docs)
prompt = f"""Use the following search results to answer the question. Cite sources by number.
{context}
Question: {question}
Answer:"""
client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)
msg = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
return {"answer": msg.content[0].text, "sources": [d["url"] for d in docs]}
result = rag_answer("What are the latest AI models released in 2026?")
print(result["answer"])
print("\nSources:", result["sources"])Ejemplo en Python
Python
import requests
import anthropic
SCAVIO_KEY = "your-scavio-api-key"
ANTHROPIC_KEY = "your-anthropic-key"
def retrieve(question: str, n: int = 5, platform: str = None) -> list[dict]:
payload = {"query": question, "num_results": n}
if platform:
payload["platform"] = platform
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json=payload,
headers={"x-api-key": SCAVIO_KEY},
timeout=15
)
r.raise_for_status()
return [{"title": d["title"], "snippet": d.get("snippet",""), "url": d["link"]}
for d in r.json().get("organic_results", [])]
def format_context(docs: list) -> str:
return "\n---\n".join(f"[{i}] {d['title']}\n{d['snippet']}\n{d['url']}" for i, d in enumerate(docs, 1))
def rag_answer(question: str, platform: str = None) -> dict:
docs = retrieve(question, n=5, platform=platform)
context = format_context(docs)
prompt = f"Use these search results to answer. Cite source numbers.\n\n{context}\n\nQuestion: {question}\nAnswer:"
client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)
msg = client.messages.create(model="claude-sonnet-4-6", max_tokens=1024,
messages=[{"role": "user", "content": prompt}])
return {"answer": msg.content[0].text, "sources": [d["url"] for d in docs]}
if __name__ == "__main__":
questions = [
"What are the most popular vector databases in 2026?",
"Latest AI coding assistants compared"
]
for q in questions:
result = rag_answer(q)
print(f"Q: {q}")
print(f"A: {result['answer'][:300]}...\n")Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = 'your-scavio-api-key';
const ANTHROPIC_KEY = 'your-anthropic-key';
async function retrieve(question, n = 5, platform = null) {
const payload = { query: question, num_results: n };
if (platform) payload.platform = platform;
const res = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': SCAVIO_KEY },
body: JSON.stringify(payload)
});
const data = await res.json();
return (data.organic_results ?? []).map(d => ({ title: d.title, snippet: d.snippet ?? '', url: d.link }));
}
function formatContext(docs) {
return docs.map((d, i) => `[${i+1}] ${d.title}\n${d.snippet}\n${d.url}`).join('\n---\n');
}
async function ragAnswer(question, platform = null) {
const docs = await retrieve(question, 5, platform);
const context = formatContext(docs);
const prompt = `Use these search results to answer. Cite source numbers.\n\n${context}\n\nQuestion: ${question}\nAnswer:`;
const res = await fetch('https://api.anthropic.com/v1/messages', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': ANTHROPIC_KEY, 'anthropic-version': '2023-06-01' },
body: JSON.stringify({ model: 'claude-sonnet-4-6', max_tokens: 1024, messages: [{ role: 'user', content: prompt }] })
});
const msg = await res.json();
return { answer: msg.content[0].text, sources: docs.map(d => d.url) };
}
const result = await ragAnswer('What are the most popular vector databases in 2026?');
console.log(result.answer);Salida esperada
JSON
Based on the search results, the most popular vector databases in 2026 include:
1. Pinecone - serverless, widely used in production [1]
2. Weaviate - open source with hybrid search [2]
3. Qdrant - performance-focused, Rust-based [3]
4. Chroma - popular for local development [4]
5. pgvector - PostgreSQL extension for teams already using Postgres [5]
Sources: https://pinecone.io, https://weaviate.io, ...