如何跟踪预算中的人工智能概述引用

import requests, os

API_KEY = os.environ['SCAVIO_API_KEY']

def check_ai_overview(query: str) -> dict:
    resp = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
        json={'query': query, 'country_code': 'us'})
    data = resp.json()
    ai_overview = data.get('ai_overview', {})
    has_overview = bool(ai_overview and ai_overview.get('text'))
    citations = ai_overview.get('citations', []) if has_overview else []
    return {
        'query': query,
        'has_ai_overview': has_overview,
        'citation_count': len(citations),
        'cited_domains': [c.get('domain', '') for c in citations]
    }

result = check_ai_overview('what is the best crm for startups')
print(f'AI Overview: {result["has_ai_overview"]}, Citations: {result["citation_count"]}')

import time
from collections import Counter

def batch_citation_check(keywords: list) -> dict:
    results = []
    domain_counts = Counter()
    for kw in keywords:
        data = check_ai_overview(kw)
        results.append(data)
        for domain in data['cited_domains']:
            domain_counts[domain] += 1
        time.sleep(0.3)
    return {
        'total_keywords': len(keywords),
        'with_ai_overview': sum(1 for r in results if r['has_ai_overview']),
        'top_cited_domains': domain_counts.most_common(10),
        'details': results
    }

keywords = [
    'what is the best crm for startups',
    'how to choose a crm 2026',
    'crm software comparison',
    'best free crm tools'
]
report = batch_citation_check(keywords)
print(f'{report["with_ai_overview"]}/{report["total_keywords"]} keywords have AI Overviews')

def citation_share(report: dict, your_domain: str) -> dict:
    total_citations = sum(count for _, count in report['top_cited_domains'])
    your_citations = sum(count for domain, count in report['top_cited_domains']
                         if your_domain in domain)
    share = (your_citations / max(total_citations, 1)) * 100
    # Find keywords where you are cited
    your_keywords = [r['query'] for r in report['details']
                     if any(your_domain in d for d in r['cited_domains'])]
    # Find keywords where you are NOT cited
    missing_keywords = [r['query'] for r in report['details']
                        if r['has_ai_overview'] and
                        not any(your_domain in d for d in r['cited_domains'])]
    return {
        'your_domain': your_domain,
        'citation_share': round(share, 1),
        'your_citations': your_citations,
        'total_citations': total_citations,
        'cited_keywords': your_keywords,
        'missing_keywords': missing_keywords
    }

share = citation_share(report, 'yoursite.com')
print(f'Citation share: {share["citation_share"]}% ({share["your_citations"]}/{share["total_citations"]})')
print(f'Missing from: {share["missing_keywords"]}')

import json
from datetime import date

def save_citation_history(report: dict, your_domain: str) -> None:
    history_file = 'citation_history.json'
    try:
        with open(history_file) as f:
            history = json.load(f)
    except FileNotFoundError:
        history = []
    share = citation_share(report, your_domain)
    history.append({
        'date': date.today().isoformat(),
        'citation_share': share['citation_share'],
        'your_citations': share['your_citations'],
        'total_citations': share['total_citations'],
        'keywords_checked': report['total_keywords'],
        'keywords_with_overview': report['with_ai_overview']
    })
    with open(history_file, 'w') as f:
        json.dump(history, f, indent=2)
    print(f'Saved citation data for {date.today()}')
    if len(history) > 1:
        prev = history[-2]['citation_share']
        curr = share['citation_share']
        delta = curr - prev
        print(f'Trend: {prev}% -> {curr}% ({delta:+.1f}%)')

def monitoring_budget(num_keywords: int, checks_per_day: int = 1) -> dict:
    daily_credits = num_keywords * checks_per_day
    monthly_credits = daily_credits * 30
    monthly_cost = monthly_credits * 0.005
    return {
        'keywords': num_keywords,
        'daily_checks': checks_per_day,
        'monthly_credits': monthly_credits,
        'monthly_cost': f'${monthly_cost:.2f}',
        'plan': '$30/7K' if monthly_credits <= 7000
            else '$100/28K' if monthly_credits <= 28000
            else '$250/85K',
        'vs_semrush': f'Semrush Position Tracking: $139.95+/mo'
    }

for n in [50, 100, 500]:
    b = monitoring_budget(n)
    print(f'{b["keywords"]} keywords: {b["monthly_cost"]}/mo ({b["plan"]} plan)')
print(f'Comparison: Semrush starts at $139.95/mo for similar tracking')