Add Fresh Data to Your RAG Pipeline

The Problem

RAG pipelines are usually built around a vector store that was indexed from a corpus snapshot. That works beautifully for internal docs and knowledge bases, but falls apart when a user asks about anything that happened after the last nightly sync. Teams try to patch this by running incremental crawlers, but crawlers are brittle, expensive, and still run on a delay. The result is a chatbot that confidently answers from last month's data and never tells the user it is out of date. The freshness gap becomes the single biggest trust failure in production RAG.

The Scavio Solution

Use Scavio as a live retrieval source alongside your vector store. When the router decides a question is time-sensitive, the pipeline calls Scavio instead of, or in addition to, the embedding search. Results come back as structured snippets that can be chunked, scored, and merged into the final context window. No re-indexing required. The vector store keeps serving evergreen knowledge, and Scavio handles anything that is moving. Together they give the LLM both long-term memory and short-term awareness.

Who It Is For

RAG engineers at companies shipping internal assistants, support bots, or research copilots. If your eval set includes any question with the word latest or today, you need a live retrieval source.

Key Benefits

• Pair a static vector store with live retrieval in one prompt
• No ingestion pipeline to operate for breaking-news queries
• Pre-chunked snippets fit directly into context windows
• Works alongside Pinecone, Weaviate, pgvector, or any store
• Router-friendly latency keeps end-to-end response under three seconds

Python Example

import requests

API_KEY = "your_scavio_api_key"

def live_retrieve(query: str, k: int = 5):
    r = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": "google", "query": query},
        timeout=10,
    )
    data = r.json()
    return [
        {"text": f"{o['title']}. {o['snippet']}", "source": o["link"]}
        for o in data.get("organic", [])[:k]
    ]

def rag_context(query, vector_hits):
    live = live_retrieve(query)
    return vector_hits + live

print(rag_context("latest fed rate decision", []))

JavaScript Example

const API_KEY = "your_scavio_api_key";

async function liveRetrieve(query, k = 5) {
  const r = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "x-api-key": API_KEY,
      "content-type": "application/json",
    },
    body: JSON.stringify({ platform: "google", query }),
  });
  const data = await r.json();
  return (data.organic ?? []).slice(0, k).map((o) => ({
    text: `${o.title}. ${o.snippet}`,
    source: o.link,
  }));
}

async function ragContext(query, vectorHits) {
  const live = await liveRetrieve(query);
  return [...vectorHits, ...live];
}

console.log(await ragContext("latest fed rate decision", []));

Platforms Used