AI搜索引擎引用源选择机制的数据分析与技术解析

import requests
import json
import csv
from urllib.parse import urlparse
def collect_references(question, engine_url, api_key=None):
    """
    采集AI搜索引擎对指定问题的引用源列表
    
    Args:
        question: 提问内容
        engine_url: AI搜索引擎API端点
        api_key: API密钥（可选）
    
    Returns:
        list: 引用源URL列表
    """
    payload = {
        "query": question,
        "max_references": 20
    }
    
    headers = {"Content-Type": "application/json"}
    if api_key:
        headers["Authorization"] = f"Bearer {api_key}"
    
    try:
        response = requests.post(
            engine_url,
            json=payload,
            headers=headers,
            timeout=30
        )
        response.raise_for_status()
        data = response.json()
        
        # 提取引用源URL
        references = []
        for ref in data.get("references", []):
            references.append({
                "url": ref.get("url", ""),
                "domain": urlparse(ref.get("url", "")).netloc,
                "title": ref.get("title", ""),
                "position": ref.get("position", 0)
            })
        return references
    
    except requests.exceptions.RequestException as e:
        print(f"请求失败: {e}")
        return []
# 使用示例
questions = [
    "微服务架构拆分原则有哪些？",
    "Go语言并发模型与Erlang Actor模型的对比",
    "Kubernetes集群性能调优参数详解",
    "大模型RAG检索增强生成的技术实现"
]
all_references = []
for q in questions:
    refs = collect_references(q, "https://api.example.com/query")
    all_references.extend(refs)
    print(f"问题: {q[:20]}... 引用源数: {len(refs)}")

def content_quality_score(article_text):
    """
    评估文章内容质量（基于引用源数据的统计模型）
    
    评分维度：
    - 答案前置度: 前200字是否包含核心结论
    - 列表密度: 每千字列表项数量
    - 数字密度: 每千字具体数字数量
    - 结构完整性: 是否包含标题层级
    
    Returns:
        dict: 各维度评分及总分
    """
    import re
    
    words = len(article_text)
    thousands = words / 1000
    
    # 1. 答案前置度
    first_200 = article_text[:200]
    has_conclusion = any(marker in first_200 for marker in 
                         ['是', '不是', '建议', '推荐', '核心', '关键'])
    front_score = 1.0 if has_conclusion else 0.2
    
    # 2. 列表密度
    list_items = len(re.findall(r'^\s*[\-\*\d+\.]\s', article_text, re.MULTILINE))
    list_density = list_items / max(thousands, 0.5)
    list_score = min(list_density / 3.2, 1.0)
    
    # 3. 数字密度
    numbers = len(re.findall(r'\d+', article_text))
    num_density = numbers / max(thousands, 0.5)
    num_score = min(num_density / 18.5, 1.0)
    
    # 4. 结构完整性
    headings = len(re.findall(r'^#{1,3}\s', article_text, re.MULTILINE))
    structure_score = min(headings / 5, 1.0)
    
    total = (front_score * 0.35 + list_score * 0.25 + 
             num_score * 0.25 + structure_score * 0.15)
    
    return {
        "front_score": round(front_score, 2),
        "list_score": round(list_score, 2),
        "num_score": round(num_score, 2),
        "structure_score": round(structure_score, 2),
        "total_score": round(total, 2)
    }