【2026年最新】AI検索エンジン徹底比較：Perplexity・Google AI Overview・ChatGPT Searchの実力

# Perplexity API を使った技術調査の自動化
import requests

def search_with_perplexity(query: str, focus: str = "internet") -> dict:
    """Perplexity APIで検索"""
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {PERPLEXITY_API_KEY}",
        "Content-Type": "application/json",
    }
    payload = {
        "model": "sonar-pro",
        "messages": [
            {
                "role": "system",
                "content": "技術的な質問に対して、正確で最新の情報を"
                           "コード例付きで回答してください。"
            },
            {
                "role": "user",
                "content": query,
            },
        ],
        "search_focus": focus,
        "return_citations": True,
        "return_related_questions": True,
    }

    response = requests.post(url, json=payload, headers=headers)
    result = response.json()

    return {
        "answer": result["choices"][0]["message"]["content"],
        "citations": result.get("citations", []),
        "related": result.get("related_questions", []),
    }


# ChatGPT Search APIの活用
from openai import OpenAI

def search_with_chatgpt(query: str) -> dict:
    """ChatGPT Search APIで検索"""
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": "Web検索を活用して最新の情報を提供してください。"
                           "出典のURLを必ず含めてください。"
            },
            {
                "role": "user",
                "content": query,
            },
        ],
        tools=[{"type": "web_search"}],
    )
    return {
        "answer": response.choices[0].message.content,
        "usage": response.usage.to_dict(),
    }

<!-- 構造化データの実装例 -->
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