Google I/O 2026 后如何接地 RAG 管道

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}')

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"])}')

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')