Scavio for Hybrid RAG with Live Search Augmentation

The Problem

Pure vector RAG only retrieves from a static internal corpus and cannot answer about current events, competitor pricing, or public information. Pure search RAG cannot access private documents. Neither alone provides complete answers.

How Scavio Helps

• Answers both internal and external questions accurately
• Query classifier reduces unnecessary search API calls
• Structured search results integrate cleanly with RAG context
• Multi-platform search enriches responses with diverse sources
• Reduces hallucination for time-sensitive queries

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Customer asks support agent: 'How does our pricing compare to [competitor]?' Hybrid RAG retrieves internal pricing docs from vector DB AND queries Google for competitor's current pricing page. Agent combines both sources for an accurate, current comparison.":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for AI engineers building RAG systems, enterprise chatbot developers, teams building customer-facing AI assistants

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your hybrid rag with live search augmentation solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.