The Problem
Setting up a vector RAG pipeline requires embedding a corpus, running an embedding model, maintaining a vector store, handling index updates, and tuning similarity thresholds. For queries about current events or public knowledge, this is overkill.
The Scavio Solution
Replace the vector retrieval step with a live search API call. The query goes directly to the search API, which returns relevant snippets from the web. Inject those snippets into the LLM prompt as context.
Before
RAG pipeline: embed 50,000 documents, store in Pinecone, embed each query at runtime, retrieve top-k chunks, inject into prompt. Embedding costs $5/million tokens. Index goes stale for current events. Cold start is slow.
After
Search RAG: call Scavio with the user's query, get 5 relevant snippets in 1-2 seconds, inject into prompt. No embedding, no vector store, no index maintenance. Always fresh data. Cost: $0.005 per query.
Who It Is For
Developers building Q&A systems, chatbots, or research tools over public or current-events knowledge who want to avoid vector database complexity.
Key Benefits
- No embedding costs or vector store infrastructure
- Always-fresh data — no stale index problem
- Works for any topic without pre-indexing
- 1 API call replaces the entire retrieval pipeline
Python Example
import requests
import anthropic
SCAVIO_KEY = "your-scavio-api-key"
def search_rag_answer(question: str) -> str:
# Retrieval: search instead of vector similarity
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json={"query": question, "num_results": 5},
headers={"x-api-key": SCAVIO_KEY}, timeout=15
)
results = r.json().get("organic_results", [])
context = "\n\n".join(
f"[{i+1}] {res['title']}\n{res.get('snippet','')}\n{res['link']}"
for i, res in enumerate(results)
)
# Generation
prompt = f"Answer using ONLY these search results. Cite source numbers.\n\n{context}\n\nQuestion: {question}"
client = anthropic.Anthropic()
msg = client.messages.create(model="claude-sonnet-4-6", max_tokens=512,
messages=[{"role": "user", "content": prompt}])
return msg.content[0].text
print(search_rag_answer("What are the best vector databases in 2026?"))JavaScript Example
const SCAVIO_KEY = 'your-scavio-api-key';
async function searchRag(question) {
const res = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': SCAVIO_KEY },
body: JSON.stringify({ query: question, num_results: 5 })
});
const data = await res.json();
const context = (data.organic_results ?? [])
.map((r, i) => `[${i+1}] ${r.title}\n${r.snippet ?? ''}\n${r.link}`).join('\n\n');
return context; // Pass to your LLM
}Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
YouTube
Video search with transcripts and metadata