dnf怎么做辅助网站长春最专业的seo公司
1. 抓取用到的工具
crawl4ai 0.6.3 异步抓取，代理用 squid ，数据存储用elasticsearch
ai 分析层用本地ollama的deepseek-r1:8b
通过这些工具来构建自动化简报系统的完整技术方案
2. 系统架构设计
3. 核心代码
import os
import json
from datetime import datetime
from elasticsearch import AsyncElasticsearch
from crawl4ai import AsyncWebCrawler
from crawl4ai.models import CrawlerResponse
import ollama# 配置环境变量
os.environ["SQUID_PROXY"] = "http://user:pass@localhost:3128"
os.environ["ES_HOSTS"] = "http://localhost:9200"class KnowledgePipeline:def __init__(self):self.es = AsyncElasticsearch(hosts=[os.getenv("ES_HOSTS")])self.crawler = AsyncWebCrawler(proxy=os.getenv("SQUID_PROXY"),render_js=True, # 启用浏览器渲染max_concurrency=10,timeout=30)# 知识库分类配置self.categories = {"economic": {"urls": ["https://www.imf.org","https://data.worldbank.org"],"selector": "#main-content" # 定制CSS选择器},"technology": {"urls": ["https://arxiv.org","https://ieeexplore.ieee.org"],"screenshot": True}}async def _analyze_content(self, text: str) -> dict:"""使用本地Ollama模型分析内容"""response = ollama.generate(model='deepseek-r1:8b',prompt=f"Generate a summary and 5 keywords for this text:\n{text[:3000]}",options={"temperature": 0.7,"max_tokens": 300})return {"summary": response['choices'][0]['text'],"keywords": list(set(response['text'].split()[:5]))}async def _store_to_es(self, data: dict):"""异步存储到Elasticsearch"""doc = {"title": data["title"],"content": data["content"],"summary": data["summary"],"keywords": data["keywords"],"timestamp": datetime.utcnow(),"category": data["category"],"url": data["url"]}await self.es.index(index="daily_briefing",document=doc)async def crawl_category(self, category: str, config: dict):"""执行分类爬取任务"""results = []for url in config["urls"]:response: CrawlerResponse = await self.crawler.crawl(url=url,css_selector=config.get("selector", "body"),screenshot=config.get("screenshot", False))if response.status_code == 200:analysis = await self._analyze_content(response.content)data = {"category": category,"url": url,"title": response.metadata.get("og:title", url),"content": response.content,"screenshot": response.screenshot,**analysis}await self._store_to_es(data)results.append(data)return resultsasync def generate_daily_briefing(self):"""生成每日简报"""# 从ES获取当天数据query = {"query": {"range": {"timestamp": {"gte": "now-1d/d","lt": "now/d"}}},"size": 100}resp = await self.es.search(index="daily_briefing",body=query)# 生成HTML简报template = """<html><body><h1>每日知识简报 {{ date }}</h1>{% for category in data %}<div class="category"><h2>{{ category.name }}</h2>{% for item in category.items %}<div class="item"><h3><a href="{{ item.url }}">{{ item.title }}</a></h3><p>{{ item.summary }}</p><div class="keywords">{% for kw in item.keywords %}<span class="tag">{{ kw }}</span>{% endfor %}</div>{% if item.screenshot %}<img src="data:image/png;base64,{{ item.screenshot }}" width="300">{% endif %}</div>{% endfor %}</div>{% endfor %}</body></html>"""return await self._render_template(template, {"date": datetime.now().strftime("%Y-%m-%d"),"data": self._group_by_category(resp['hits']['hits'])})async def run(self):"""执行完整流程"""# 执行爬取任务tasks = [self.crawl_category(cat, config)for cat, config in self.categories.items()]await asyncio.gather(*tasks)# 生成并保存简报html = await self.generate_daily_briefing()with open(f"briefing_{datetime.today().strftime('%Y%m%d')}.html", "w") as f:f.write(html)# 运行示例
if __name__ == "__main__":pipeline = KnowledgePipeline()asyncio.run(pipeline.run())
4. 简报详情页动态加载
# 用户点击后的实时渲染（网页2、网页7）
@app.route('/detail')
def load_detail():url = request.args.get('url')# 检查缓存cached = es.search(index="daily_news", query={"term": {"url_hash": hash(url)}})if not cached:# 动态渲染详情页result = await crawler.arun(url=url,js_code=["document.querySelector('.paywall').remove()"], # 绕过付费墙css_selector=".main-content")es.index(...)return render_template('detail.html', content=cached['raw_html'])
5. 配套部署架构
6.系统优化建议
- 代理配置优化：
# squid.conf 关键配置
acl knowledge_crawlers src 192.168.1.0/24
delay_pools 1
delay_class 1 1
delay_parameters 1 5000/5000 # 限速5MB/s
- Elasticsearch映射模板：
{"mappings": {"properties": {"content": {"type": "text", "analyzer": "ik_max_word"},"summary": {"type": "text", "fields": {"keyword": {"type": "keyword"}}},"keywords": {"type": "keyword"},"category": {"type": "keyword"}}}
}
- Ollama API扩展：
# 启动API服务
ollama serve --host 0.0.0.0:11434 --api-key YOUR_SECRET_KEY# 自定义模型prompt模板
curl http://localhost:11434/api/prompts -d '{"name": "briefing_summary","template": "作为知识库分析师，请用中文总结内容要点，包含：\n1. 核心观点\n2. 数据指标\n3. 行业影响\n原文：{{.Content}}"
}'
- 内容处理流水线：
7. 扩展建议
- 添加知识图谱构建：
from kgforge import KnowledgeGraph
kg = KnowledgeGraph()
kg.build_from_articles(data)
kg.visualize()
- 用户定制化功能：
- 关键词订阅
- 兴趣领域偏好
- 推送渠道选择（邮件/钉钉/Slack）
8. 监控方案（非必须）
# 使用Prometheus监控指标
- Elasticsearch指标：elasticsearch_exporter
- Squid流量监控：squid_exporter
- Ollama性能指标：ollama自带/metrics端点# 关键告警规则
groups:
- name: crawlerrules:- alert: CrawlerErrorRateexpr: rate(crawl_errors_total[5m]) > 0.1- alert: ESHighLatencyexpr: elasticsearch_indexing_latency_seconds > 2
9. 效果展示示例
每日简报界面：
## 2025-05-17 财经要闻速递1. **央行降准0.5个百分点** - 预计释放长期资金约1.2万亿元- 分析认为将利好中小微企业融资2. **AI芯片巨头发布新产品** - 算力提升3倍，能效比达20TOPS/W- 概念股集体上涨（点击查看详情）[查看完整报告](http://briefing.site/detail?date=2025-05-17)
方案特点：
- 完全基于异步IO架构，适合高频抓取
- 使用本地LLM保障数据隐私
- 通过Squid实现统一代理管理
- Elasticsearch提供灵活检索能力
- 每日自动生成可存档的HTML报告
部署前请确保：
- Squid已配置正确ACL规则
- Elasticsearch索引模板已创建
- Ollama模型已下载完成（deepseek-r1:8b）
- 各服务网络策略已开放必要端口