Google I/O 2026 changed how search results are structured with AI Mode reaching 1B+ users, Gemini 3.5 Flash, and a redesigned search box. RAG pipelines that ground responses in search data need to adapt to these changes. This tutorial updates your RAG grounding to handle new result formats, AI Overview citations, and Information Agent responses.
Prerequisites
- Python 3.8+
- requests library
- A Scavio API key from scavio.dev
- Existing RAG pipeline to update
Walkthrough
Step 1: Detect the new search result format
Identify which new post-I/O result types your queries return.
Python
import os, requests, json
from datetime import datetime
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
def detect_result_format(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}, timeout=10).json()
format_info = {
'query': query,
'has_ai_overview': bool(data.get('ai_overview', data.get('answer_box'))),
'has_featured_snippet': bool(data.get('featured_snippet')),
'has_knowledge_graph': bool(data.get('knowledge_graph')),
'has_paa': len(data.get('people_also_ask', [])) > 0,
'paa_count': len(data.get('people_also_ask', [])),
'organic_count': len(data.get('organic_results', [])),
'ai_overview_data': data.get('ai_overview', data.get('answer_box', {})),
}
return format_info, data
TEST_QUERIES = [
'what is model context protocol',
'best search api for ai agents 2026',
'how to build rag pipeline python',
]
print('Post-I/O 2026 Result Format Detection:\n')
for q in TEST_QUERIES:
fmt, _ = detect_result_format(q)
ai = 'AI' if fmt['has_ai_overview'] else '--'
fs = 'FS' if fmt['has_featured_snippet'] else '--'
kg = 'KG' if fmt['has_knowledge_graph'] else '--'
print(f' {q[:40]:40} | {ai} {fs} {kg} | PAA: {fmt["paa_count"]} | Org: {fmt["organic_count"]}')
print(f'\nCost: ${len(TEST_QUERIES) * 0.005:.3f}')Step 2: Build adaptive grounding extraction
Extract grounding data that works with both old and new result formats.
Python
def extract_grounding(query):
"""Extract grounding data adaptive to post-I/O 2026 result formats."""
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}, timeout=10).json()
grounding = {
'query': query,
'sources': [],
'direct_answer': '',
'related_questions': [],
'confidence': 'low',
}
# Priority 1: AI Overview (post-I/O, most authoritative)
ai = data.get('ai_overview', data.get('answer_box', {}))
if ai:
answer = ai.get('snippet', ai.get('answer', ai.get('description', '')))
if answer:
grounding['direct_answer'] = answer[:500]
grounding['confidence'] = 'high'
# Priority 2: Featured Snippet
featured = data.get('featured_snippet', {})
if not grounding['direct_answer'] and featured:
grounding['direct_answer'] = featured.get('snippet', '')[:500]
grounding['confidence'] = 'medium'
# Priority 3: Organic results (always available)
for r in data.get('organic_results', [])[:5]:
grounding['sources'].append({
'title': r.get('title', ''),
'url': r.get('link', ''),
'text': r.get('snippet', ''),
'domain': r.get('displayed_link', '').split('/')[0],
})
if not grounding['direct_answer'] and grounding['sources']:
grounding['direct_answer'] = grounding['sources'][0]['text']
grounding['confidence'] = 'low'
# Related questions for follow-up
grounding['related_questions'] = [q.get('question', '') for q in data.get('people_also_ask', [])[:3]]
return grounding
print('\n=== Adaptive Grounding Extraction ===')
for q in TEST_QUERIES:
g = extract_grounding(q)
print(f'\n Query: {q[:40]}')
print(f' Confidence: {g["confidence"]}')
print(f' Answer: {g["direct_answer"][:80]}...' if g['direct_answer'] else ' No direct answer')
print(f' Sources: {len(g["sources"])} | Related Qs: {len(g["related_questions"])}')Step 3: Integrate with RAG pipeline
Replace your existing search grounding with the adaptive extractor.
Python
def grounded_rag_response(question):
"""Generate a grounded response using adaptive post-I/O search data."""
print(f'\n Question: {question}')
# Step 1: Get grounding data
grounding = extract_grounding(question)
print(f' Grounding confidence: {grounding["confidence"]}')
print(f' Sources: {len(grounding["sources"])}')
# Step 2: Build context for LLM
context_parts = []
if grounding['direct_answer']:
context_parts.append(f'Direct answer: {grounding["direct_answer"]}')
for s in grounding['sources'][:3]:
context_parts.append(f'Source ({s["domain"]}): {s["text"]}')
context = '\n'.join(context_parts)
# Step 3: Format response with citations
print(f'\n Grounded Context ({len(context)} chars):')
for s in grounding['sources'][:3]:
print(f' [{s["domain"]:20}] {s["title"][:45]}')
# Step 4: Follow-up grounding
if grounding['related_questions']:
print(f'\n Available follow-ups:')
for q in grounding['related_questions']:
print(f' - {q[:55]}')
return {
'context': context,
'sources': grounding['sources'],
'confidence': grounding['confidence'],
}
print('=== Post-I/O 2026 Grounded RAG ===')
for q in ['what is MCP protocol', 'best search api for rag 2026']:
grounded_rag_response(q)
print(f'\n Grounding cost: $0.005/query')
print(f' Adapts to AI Mode, Featured Snippets, and organic results')
print(f' Works with Gemini 3.5 Flash and new search box format')Python Example
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def ground(query):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}, timeout=10).json()
ai = data.get('ai_overview', data.get('answer_box', {}))
answer = ai.get('snippet', '') if ai else data.get('organic_results', [{}])[0].get('snippet', '')
sources = [r.get('link', '') for r in data.get('organic_results', [])[:3]]
print(f'Answer: {answer[:80]}')
print(f'Sources: {len(sources)}')
ground('what is MCP protocol')JavaScript Example
JavaScript
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH,
body: JSON.stringify({ query: 'what is MCP protocol', country_code: 'us' })
}).then(r => r.json());
const ai = data.ai_overview || data.answer_box || {};
const answer = ai.snippet || (data.organic_results?.[0]?.snippet || '');
console.log(`Grounded answer: ${answer.slice(0, 80)}`);
console.log(`Sources: ${(data.organic_results || []).length}`);Expected Output
JSON
Post-I/O 2026 Result Format Detection:
what is model context protocol | AI FS -- | PAA: 4 | Org: 10
best search api for ai agents 2026 | AI -- -- | PAA: 3 | Org: 10
how to build rag pipeline python | -- FS -- | PAA: 4 | Org: 10
=== Adaptive Grounding Extraction ===
Query: what is model context protocol
Confidence: high
Answer: Model Context Protocol (MCP) is an open standard for connecting...
Sources: 5 | Related Qs: 3
=== Post-I/O 2026 Grounded RAG ===
Question: what is MCP protocol
Grounding confidence: high
Sources: 5
Grounding cost: $0.005/query
Adapts to AI Mode, Featured Snippets, and organic results