{"id":1068885,"date":"2024-12-31T16:47:10","date_gmt":"2024-12-31T08:47:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1068885.html"},"modified":"2024-12-31T16:47:12","modified_gmt":"2024-12-31T08:47:12","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%81%9a%e6%9c%ac%e5%9c%b0%e6%90%9c%e7%b4%a2%e5%bc%95%e6%93%8e","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1068885.html","title":{"rendered":"\u5982\u4f55\u7528Python\u505a\u672c\u5730\u641c\u7d22\u5f15\u64ce"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u505a\u672c\u5730\u641c\u7d22\u5f15\u64ce\"<\/p>\n

\u4f7f\u7528Python\u6784\u5efa\u672c\u5730\u641c\u7d22\u5f15\u64ce\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406\u3001\u5efa\u7acb\u7d22\u5f15\u3001\u67e5\u8be2\u5904\u7406\u3001\u6392\u540d\u7b97\u6cd5\u3001\u5c55\u793a\u7ed3\u679c\u3002<\/strong> \u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e76\u6df1\u5165\u63a2\u8ba8\u5982\u4f55\u5b9e\u73b0\u6bcf\u4e00\u4e2a\u6b65\u9aa4\uff0c\u5e2e\u52a9\u4f60\u6784\u5efa\u4e00\u4e2a\u529f\u80fd\u9f50\u5168\u7684\u672c\u5730\u641c\u7d22\u5f15\u64ce\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u6570\u636e\u6536\u96c6\u548c\u9884\u5904\u7406\u662f\u6784\u5efa\u672c\u5730\u641c\u7d22\u5f15\u64ce\u7684\u7b2c\u4e00\u6b65\u3002\u6570\u636e\u53ef\u4ee5\u6765\u81ea\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\u3001\u6570\u636e\u5e93\u3001\u7f51\u7ad9\u722c\u866b\u7b49\u3002\u65e0\u8bba\u6570\u636e\u6765\u6e90\u5982\u4f55\uff0c\u9884\u5904\u7406\u90fd\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u4e00\u6b65\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u53ef\u7528\u6027\u3002<\/p>\n<\/p>\n

\u6570\u636e\u6536\u96c6<\/h4>\n<\/p>\n
    \n
  1. \n

    \u6587\u4ef6\u7cfb\u7edf<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528Python\u7684os<\/code>\u6a21\u5757\u904d\u5386\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\uff0c\u6536\u96c6\u6587\u672c\u6587\u4ef6\u6216\u5176\u4ed6\u683c\u5f0f\u7684\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    import os<\/p>\n
    def collect_files(directory):<\/p>\n
        file_paths = []<\/p>\n
        for root, dirs, files in os.walk(directory):<\/p>\n
            for file in files:<\/p>\n
                if file.endswith(".txt"):  # \u5047\u8bbe\u6211\u4eec\u53ea\u5904\u7406\u6587\u672c\u6587\u4ef6<\/p>\n
                    file_paths.append(os.path.join(root, file))<\/p>\n
        return file_paths<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u6570\u636e\u5e93<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528SQLAlchemy\u7b49ORM\u5de5\u5177\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
    from sqlalchemy import create_engine<\/p>\n
    from sqlalchemy.orm import sessionmaker<\/p>\n
    def collect_data_from_db(connection_string, query):<\/p>\n
        engine = create_engine(connection_string)<\/p>\n
        Session = sessionmaker(bind=engine)<\/p>\n
        session = Session()<\/p>\n
        result = session.execute(query)<\/p>\n
        data = result.fetchall()<\/p>\n
        session.close()<\/p>\n
        return data<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  3. \n
    \u7f51\u9875\u722c\u866b<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528Scrapy\u6216BeautifulSoup\u7b49\u5de5\u5177\u4ece\u7f51\u7ad9\u6536\u96c6\u6570\u636e\u3002<\/p>\n<\/p>\n
    import requests<\/p>\n
    from bs4 import BeautifulSoup<\/p>\n
    def collect_data_from_web(url):<\/p>\n
        response = requests.get(url)<\/p>\n
        soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
        texts = soup.find_all('p')  # \u5047\u8bbe\u6211\u4eec\u53ea\u63d0\u53d6\u6bb5\u843d\u6587\u672c<\/p>\n
        return [text.get_text() for text in texts]<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n
      \n
    1. \n
      \u6e05\u6d17\u6570\u636e<\/strong>\uff1a\u53bb\u9664HTML\u6807\u7b7e\u3001\u7279\u6b8a\u5b57\u7b26\u7b49\u3002<\/p>\n<\/p>\n
      import re<\/p>\n
      def clean_text(text):<\/p>\n
          text = re.sub(r'<[^>]+>', '', text)  # \u53bb\u9664HTML\u6807\u7b7e<\/p>\n
          text = re.sub(r'\\W+', ' ', text)  # \u53bb\u9664\u7279\u6b8a\u5b57\u7b26<\/p>\n
          return text.lower()  # \u8f6c\u4e3a\u5c0f\u5199<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u5206\u8bcd\u4e0e\u8bcd\u5e72\u63d0\u53d6<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528NLTK\u6216spaCy\u7b49\u5de5\u5177\u8fdb\u884c\u5206\u8bcd\u548c\u8bcd\u5e72\u63d0\u53d6\u3002<\/p>\n<\/p>\n
      import nltk<\/p>\n
      from nltk.stem import PorterStemmer<\/p>\n
      from nltk.tokenize import word_tokenize<\/p>\n
      nltk.download('punkt')<\/p>\n
      stemmer = PorterStemmer()<\/p>\n
      def preprocess_text(text):<\/p>\n
          words = word_tokenize(text)<\/p>\n
          return [stemmer.stem(word) for word in words]<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e8c\u3001\u5efa\u7acb\u7d22\u5f15<\/h3>\n<\/p>\n
      \u5efa\u7acb\u7d22\u5f15\u662f\u641c\u7d22\u5f15\u64ce\u7684\u6838\u5fc3\u90e8\u5206\u3002\u7d22\u5f15\u4f7f\u5f97\u641c\u7d22\u64cd\u4f5c\u53ef\u4ee5\u5728\u5408\u7406\u7684\u65f6\u95f4\u5185\u5b8c\u6210\u3002\u901a\u5e38\u4f7f\u7528\u5012\u6392\u7d22\u5f15\u6765\u63d0\u9ad8\u641c\u7d22\u6548\u7387\u3002<\/p>\n<\/p>\n
      \u5012\u6392\u7d22\u5f15<\/h4>\n<\/p>\n
      \u5012\u6392\u7d22\u5f15\u662f\u4e00\u79cd\u5c06\u6587\u6863\u4e2d\u7684\u8bcd\u6620\u5c04\u5230\u5305\u542b\u8be5\u8bcd\u7684\u6587\u6863\u5217\u8868\u7684\u6570\u636e\u7ed3\u6784\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684collections<\/code>\u6a21\u5757\u5b9e\u73b0\u5012\u6392\u7d22\u5f15\u3002<\/p>\n<\/p>\n
      from collections import defaultdict<\/p>\n
      def build_inverted_index(docs):<\/p>\n
          inverted_index = defaultdict(list)<\/p>\n
          for doc_id, text in docs.items():<\/p>\n
              for word in preprocess_text(text):<\/p>\n
                  inverted_index[word].append(doc_id)<\/p>\n
          return inverted_index<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e09\u3001\u67e5\u8be2\u5904\u7406<\/h3>\n<\/p>\n
      \u67e5\u8be2\u5904\u7406\u662f\u7528\u6237\u8f93\u5165\u67e5\u8be2\u540e\uff0c\u641c\u7d22\u5f15\u64ce\u5982\u4f55\u89e3\u91ca\u5e76\u5904\u7406\u8fd9\u4e9b\u67e5\u8be2\u7684\u8fc7\u7a0b\u3002\u901a\u5e38\u5305\u62ec\u67e5\u8be2\u89e3\u6790\u3001\u67e5\u8be2\u6269\u5c55\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n
      \u67e5\u8be2\u89e3\u6790<\/h4>\n<\/p>\n
      \u5c06\u7528\u6237\u8f93\u5165\u7684\u67e5\u8be2\u89e3\u6790\u4e3a\u4e00\u7ec4\u8bcd\u6216\u77ed\u8bed\uff0c\u5e76\u8fdb\u884c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
      def parse_query(query):<\/p>\n
          return preprocess_text(clean_text(query))<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u67e5\u8be2\u6269\u5c55<\/h4>\n<\/p>\n
      \u53ef\u4ee5\u4f7f\u7528\u540c\u4e49\u8bcd\u6269\u5c55\u67e5\u8be2\uff0c\u589e\u52a0\u641c\u7d22\u7ed3\u679c\u7684\u8986\u76d6\u9762\u3002\u53ef\u4ee5\u4f7f\u7528NLTK\u7684WordNet\u5e93\u8fdb\u884c\u540c\u4e49\u8bcd\u6269\u5c55\u3002<\/p>\n<\/p>\n
      from nltk.corpus import wordnet<\/p>\n
      def expand_query(query):<\/p>\n
          expanded_query = set(query)<\/p>\n
          for word in query:<\/p>\n
              for syn in wordnet.synsets(word):<\/p>\n
                  for lemma in syn.lemmas():<\/p>\n
                      expanded_query.add(lemma.name())<\/p>\n
          return list(expanded_query)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u6392\u540d\u7b97\u6cd5<\/h3>\n<\/p>\n
      \u6392\u540d\u7b97\u6cd5\u51b3\u5b9a\u4e86\u641c\u7d22\u7ed3\u679c\u7684\u6392\u5e8f\u3002\u5e38\u7528\u7684\u6392\u540d\u7b97\u6cd5\u5305\u62ecTF-IDF\u3001BM25\u7b49\u3002<\/p>\n<\/p>\n
      TF-IDF<\/h4>\n<\/p>\n
      TF-IDF\uff08Term Frequency-Inverse Document Frequency\uff09\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6587\u672c\u76f8\u5173\u6027\u5ea6\u91cf\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
      import math<\/p>\n
      def compute_tf_idf(docs, inverted_index):<\/p>\n
          tf_idf = defaultdict(lambda: defaultdict(float))<\/p>\n
          doc_count = len(docs)<\/p>\n
          for term, doc_ids in inverted_index.items():<\/p>\n
              idf = math.log(doc_count \/ len(doc_ids))<\/p>\n
              for doc_id in doc_ids:<\/p>\n
                  tf = docs[doc_id].count(term) \/ len(docs[doc_id])<\/p>\n
                  tf_idf[doc_id][term] = tf * idf<\/p>\n
          return tf_idf<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u6392\u540d\u6587\u6863<\/h4>\n<\/p>\n
      \u6839\u636e\u67e5\u8be2\u8bcd\u7684TF-IDF\u503c\u5bf9\u6587\u6863\u8fdb\u884c\u6392\u5e8f\u3002<\/p>\n<\/p>\n
      def rank_documents(query, tf_idf):<\/p>\n
          scores = defaultdict(float)<\/p>\n
          for term in query:<\/p>\n
              for doc_id, tf_idf_score in tf_idf.items():<\/p>\n
                  scores[doc_id] += tf_idf_score[term]<\/p>\n
          return sorted(scores.items(), key=lambda item: item[1], reverse=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e94\u3001\u5c55\u793a\u7ed3\u679c<\/h3>\n<\/p>\n
      \u6700\u540e\u4e00\u6b65\u662f\u5c06\u641c\u7d22\u7ed3\u679c\u5c55\u793a\u7ed9\u7528\u6237\u3002\u53ef\u4ee5\u4f7f\u7528Flask\u7b49\u6846\u67b6\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684Web\u754c\u9762\u3002<\/p>\n<\/p>\n
      \u4f7f\u7528Flask\u5c55\u793a\u641c\u7d22\u7ed3\u679c<\/h4>\n<\/p>\n
        \n
      1. \n
        \u5b89\u88c5Flask<\/strong>\uff1a<\/p>\n<\/p>\n
        pip install flask<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u521b\u5efaFlask\u5e94\u7528<\/strong>\uff1a<\/p>\n<\/p>\n
        from flask import Flask, request, render_template<\/p>\n
        app = Flask(__name__)<\/p>\n
        @app.route('\/')<\/p>\n
        def index():<\/p>\n
            return render_template('index.html')<\/p>\n
        @app.route('\/search', methods=['POST'])<\/p>\n
        def search():<\/p>\n
            query = request.form['query']<\/p>\n
            parsed_query = parse_query(query)<\/p>\n
            expanded_query = expand_query(parsed_query)<\/p>\n
            ranked_docs = rank_documents(expanded_query, tf_idf)<\/p>\n
            results = [docs[doc_id] for doc_id, score in ranked_docs]<\/p>\n
            return render_template('results.html', query=query, results=results)<\/p>\n
        if __name__ == '__mAI<\/a>n__':<\/p>\n
            app.run(debug=True)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u521b\u5efaHTML\u6a21\u677f<\/strong>\uff1a<\/p>\n<\/p>\n
          \n
        • templates\/index.html<\/code><\/li>\n<\/ul>\n
          <!doctype html><\/p>\n
          <html><\/p>\n
          <head><\/p>\n
              <title>Search Engine<\/title><\/p>\n
          <\/head><\/p>\n
          <body><\/p>\n
              <form action="\/search" method="post"><\/p>\n
                  <input type="text" name="query"><\/p>\n
                  <input type="submit" value="Search"><\/p>\n
              <\/form><\/p>\n
          <\/body><\/p>\n
          <\/html><\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          • templates\/results.html<\/code><\/li>\n<\/ul>\n
            <!doctype html><\/p>\n
            <html><\/p>\n
            <head><\/p>\n
                <title>Search Results<\/title><\/p>\n
            <\/head><\/p>\n
            <body><\/p>\n
                <h1>Search Results for "{{ query }}"<\/h1><\/p>\n
                <ul><\/p>\n
                {% for result in results %}<\/p>\n
                    <li>{{ result }}<\/li><\/p>\n
                {% endfor %}<\/p>\n
                <\/ul><\/p>\n
            <\/body><\/p>\n
            <\/html><\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u4f18\u5316\u4e0e\u6269\u5c55<\/h3>\n<\/p>\n
            \u6784\u5efa\u4e00\u4e2a\u57fa\u672c\u7684\u672c\u5730\u641c\u7d22\u5f15\u64ce\u53ea\u662f\u5f00\u59cb\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u8fdb\u4e00\u6b65\u4f18\u5316\u548c\u6269\u5c55\uff1a<\/p>\n<\/p>\n
            \u4f18\u5316\u7d22\u5f15\u6784\u5efa<\/h4>\n<\/p>\n
              \n
            • \u5e76\u884c\u5904\u7406<\/strong>\uff1a\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u52a0\u901f\u7d22\u5f15\u6784\u5efa\u3002<\/li>\n
            • \u538b\u7f29\u7d22\u5f15<\/strong>\uff1a\u4f7f\u7528\u538b\u7f29\u7b97\u6cd5\u51cf\u5c11\u7d22\u5f15\u5927\u5c0f\u3002<\/li>\n<\/ul>\n
              \u6539\u8fdb\u67e5\u8be2\u5904\u7406<\/h4>\n<\/p>\n
                \n
              • \u62fc\u5199\u7ea0\u9519<\/strong>\uff1a\u4f7f\u7528Levenshtein\u8ddd\u79bb\u7b49\u7b97\u6cd5\u8fdb\u884c\u62fc\u5199\u7ea0\u9519\u3002<\/li>\n
              • \u81ea\u7136\u8bed\u8a00\u5904\u7406<\/strong>\uff1a\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u66f4\u590d\u6742\u7684\u67e5\u8be2\u7406\u89e3\u3002<\/li>\n<\/ul>\n
                \u589e\u5f3a\u6392\u540d\u7b97\u6cd5<\/h4>\n<\/p>\n
                  \n
                • \u4f7f\u7528BM25<\/strong>\uff1aBM25\u662f\u4e00\u79cd\u6539\u8fdb\u7684TF-IDF\u7b97\u6cd5\uff0c\u901a\u5e38\u8868\u73b0\u66f4\u597d\u3002<\/li>\n
                • \u7ed3\u5408\u7528\u6237\u884c\u4e3a<\/strong>\uff1a\u4f7f\u7528\u70b9\u51fb\u7387\u3001\u505c\u7559\u65f6\u95f4\u7b49\u7528\u6237\u884c\u4e3a\u6570\u636e\u6539\u8fdb\u6392\u540d\u3002<\/li>\n<\/ul>\n
                  \u6269\u5c55\u641c\u7d22\u529f\u80fd<\/h4>\n<\/p>\n
                    \n
                  • \u652f\u6301\u591a\u79cd\u6587\u4ef6\u683c\u5f0f<\/strong>\uff1a\u5904\u7406PDF\u3001Word\u7b49\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\u3002<\/li>\n
                  • \u591a\u8bed\u8a00\u652f\u6301<\/strong>\uff1a\u652f\u6301\u591a\u79cd\u8bed\u8a00\u7684\u641c\u7d22\u3002<\/li>\n
                  • \u589e\u91cf\u7d22\u5f15<\/strong>\uff1a\u652f\u6301\u5b9e\u65f6\u6570\u636e\u66f4\u65b0\u3002<\/li>\n<\/ul>\n
                    \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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