Google 搜索结果中的“People Also Ask”（PAA）部分显示了用户对某个主题提出的后续问题。这些数据对于内容策略非常有价值——确定您应该在页面上回答哪些相关问题、构建常见问题解答部分以及了解用户意图。当 Google 显示此框时，Scadio API 会在 SERP 响应中包含 people_also_ask 数组。本教程演示如何提取任何关键字的 PAA 数据并将其用于内容差距分析。
前置条件
- Python 3.8 或更高版本
- 请求已安装库
- Scavio API 密钥
- 对 Python 列表和字典的基本了解
操作指南
步骤 1: 使用 PAA 数据获取 SERP
使用您的关键字发布到 Scavio 端点。当 Google 显示 PAA 框时，响应将包含 people_also_ask 数组。
Python
data = search_google("how does retrieval augmented generation work")
paa = data.get("people_also_ask", [])
print(f"Found {len(paa)} PAA questions")步骤 2: 打印问题和答案
每个 PAA 项目都包含一个问题、答案片段和源链接。迭代并打印它们。
Python
for item in paa:
print("Q:", item["question"])
print("A:", item.get("snippet", "No answer available"))
print("Source:", item.get("link", ""))
print()步骤 3: 提取内容差距分析的问题
建立一个仅包含问题的列表。这些代表您应该考虑在内容中讨论的主题。
Python
questions = [item["question"] for item in paa]
print("Content gaps to address:")
for q in questions:
print(f" - {q}")步骤 4: 导出为 JSON 以供编辑审核
将 PAA 数据保存为 JSON，以便内容团队可以对其进行审核或将其输入到 LLM 中以生成常见问题解答。
Python
import json
with open("paa_data.json", "w") as f:
json.dump({"keyword": keyword, "questions": paa}, f, indent=2)
print("Saved PAA data to paa_data.json")Python 示例
Python
import os
import json
import requests
API_KEY = os.environ.get("SCAVIO_API_KEY", "your_scavio_api_key")
ENDPOINT = "https://api.scavio.dev/api/v1/search"
def get_paa(keyword: str) -> list[dict]:
r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY},
json={"query": keyword, "country_code": "us"})
r.raise_for_status()
return r.json().get("people_also_ask", [])
def analyze_content_gaps(keywords: list[str]) -> dict:
all_questions = {}
for kw in keywords:
questions = [item["question"] for item in get_paa(kw)]
all_questions[kw] = questions
return all_questions
if __name__ == "__main__":
keywords = ["vector database", "embedding models 2026"]
gaps = analyze_content_gaps(keywords)
print(json.dumps(gaps, indent=2))JavaScript 示例
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY || "your_scavio_api_key";
const ENDPOINT = "https://api.scavio.dev/api/v1/search";
async function getPAA(keyword) {
const res = await fetch(ENDPOINT, {
method: "POST",
headers: { "x-api-key": API_KEY, "Content-Type": "application/json" },
body: JSON.stringify({ query: keyword, country_code: "us" })
});
const data = await res.json();
return data.people_also_ask || [];
}
async function main() {
const keywords = ["vector database", "embedding models 2026"];
for (const kw of keywords) {
const paa = await getPAA(kw);
console.log(`\n${kw}:`);
paa.forEach(item => console.log(` Q: ${item.question}`));
}
}
main().catch(console.error);预期输出
JSON
{
"people_also_ask": [
{
"question": "What is the difference between RAG and fine-tuning?",
"snippet": "RAG retrieves external documents at inference time, while fine-tuning...",
"link": "https://example.com/rag-vs-finetuning"
},
{
"question": "How do vector databases work?",
"snippet": "Vector databases store high-dimensional embeddings and retrieve...",
"link": "https://example.com/vector-databases"
}
]
}