仅用不到150行代码，我开发出了一个搜索引擎

<doc>
    <title>Wikipedia: London Beer Flood</title>
    <url>https://en.wikipedia.org/wiki/London_Beer_Flood</url>
    <abstract>The London Beer Flood was an accident at Meux & Co's Horse Shoe Brewery, London, on 17 October 1814. It took place when one of the  wooden vats of fermenting porter burst.</abstract>
    ...
</doc>

from dataclasses import dataclass
@dataclass
class Abstract:
    """Wikipedia abstract"""
    ID: int
    title: str
    abstract: str
    url: str
    @property
    def fulltext(self):
        return ' '.join([self.title, self.abstract])

import gzip
from lxml import etree
from search.documents import Abstract
def load_documents():
    # open a filehandle to the gzipped Wikipedia dump
    with gzip.open('data/enwiki.latest-abstract.xml.gz', 'rb') as f:
        doc_id = 1
        # iterparse will yield the entire `doc` element once it finds the
        # closing `</doc>` tag
        for _, element in etree.iterparse(f, events=('end',), tag='doc'):
            title = element.findtext('./title')
            url = element.findtext('./url')
            abstract = element.findtext('./abstract')
            yield Abstract(ID=doc_id, title=title, url=url, abstract=abstract)
            doc_id += 1
            # the `element.clear()` call will explicitly free up the memory
            # used to store the element
            element.clear()

{
    ...
    "london": [5245250, 2623812, 133455, 3672401, ...],
    "beer": [1921376, 4411744, 684389, 2019685, ...],
    "flood": [3772355, 2895814, 3461065, 5132238, ...],
    ...
}

import Stemmer
STEMMER = Stemmer.Stemmer('english')
def tokenize(text):
    return text.split()
def lowercase_filter(text):
    return [token.lower() for token in tokens]
def stem_filter(tokens):
    return STEMMER.stemWords(tokens)

import re
import string
PUNCTUATION = re.compile('[%s]' % re.escape(string.punctuation))
def punctuation_filter(tokens):
    return [PUNCTUATION.sub('', token) for token in tokens]

# top 25 most common words in English and "wikipedia":
# https://en.wikipedia.org/wiki/Most_common_words_in_English
STOPWORDS = set(['the', 'be', 'to', 'of', 'and', 'a', 'in', 'that', 'have',
                 'I', 'it', 'for', 'not', 'on', 'with', 'he', 'as', 'you',
                 'do', 'at', 'this', 'but', 'his', 'by', 'from', 'wikipedia'])
def stopword_filter(tokens):
    return [token for token in tokens if token not in STOPWORDS]

def analyze(text):
    tokens = tokenize(text)
    tokens = lowercase_filter(tokens)
    tokens = punctuation_filter(tokens)
    tokens = stopword_filter(tokens)
    tokens = stem_filter(tokens)
    return [token for token in tokens if token]

class Index:
    def __init__(self):
        self.index = {}
        self.documents = {}
    def index_document(self, document):
        if document.ID not in self.documents:
            self.documents[document.ID] = document
        for token in analyze(document.fulltext):
            if token not in self.index:
                self.index[token] = set()
            self.index[token].add(document.ID)

def _results(self, analyzed_query):
    return [self.index.get(token, set()) for token in analyzed_query]
def search(self, query):
    """
    Boolean search; this will return documents that contain all words from the
    query, but not rank them (sets are fast, but unordered).
    """
    analyzed_query = analyze(query)
    results = self._results(analyzed_query)
    documents = [self.documents[doc_id] for doc_id in set.intersection(*results)]
    return documents
In [1]: index.search('London Beer Flood')
search took 0.16307830810546875 milliseconds
Out[1]:
[Abstract(ID=1501027, title='Wikipedia: Horse Shoe Brewery', abstract='The Horse Shoe Brewery was an English brewery in the City of Westminster that was established in 1764 and became a major producer of porter, from 1809 as Henry Meux & Co. It was the site of the London Beer Flood in 1814, which killed eight people after a porter vat burst.', url='https://en.wikipedia.org/wiki/Horse_Shoe_Brewery'),
 Abstract(ID=1828015, title='Wikipedia: London Beer Flood', abstract="The London Beer Flood was an accident at Meux & Co's Horse Shoe Brewery, London, on 17 October 1814. It took place when one of the  wooden vats of fermenting porter burst.", url='https://en.wikipedia.org/wiki/London_Beer_Flood')]

def search(self, query, search_type='AND'):
    """
    Still boolean search; this will return documents that contain either all words
    from the query or just one of them, depending on the search_type specified.
    We are still not ranking the results (sets are fast, but unordered).
    """
    if search_type not in ('AND', 'OR'):
        return []
    analyzed_query = analyze(query)
    results = self._results(analyzed_query)
    if search_type == 'AND':
        # all tokens must be in the document
        documents = [self.documents[doc_id] for doc_id in set.intersection(*results)]
    if search_type == 'OR':
        # only one token has to be in the document
        documents = [self.documents[doc_id] for doc_id in set.union(*results)]
    return documents
In [2]: index.search('London Beer Flood', search_type='OR')
search took 0.02816295623779297 seconds
Out[2]:
[Abstract(ID=5505026, title='Wikipedia: Addie Pryor', abstract='| birth_place    = London, England', url='https://en.wikipedia.org/wiki/Addie_Pryor'),
 Abstract(ID=1572868, title='Wikipedia: Tim Steward', abstract='|birth_place         = London, United Kingdom', url='https://en.wikipedia.org/wiki/Tim_Steward'),
 Abstract(ID=5111814, title='Wikipedia: 1877 Birthday Honours', abstract='The 1877 Birthday Honours were appointments by Queen Victoria to various orders and honours to reward and highlight good works by citizens of the British Empire. The appointments were made to celebrate the official birthday of the Queen, and were published in The London Gazette on 30 May and 2 June 1877.', url='https://en.wikipedia.org/wiki/1877_Birthday_Honours'),
 ...
In [3]: len(index.search('London Beer Flood', search_type='OR'))
search took 0.029065370559692383 seconds
Out[3]: 49627

# in documents.py
from collections import Counter
from .analysis import analyze
@dataclass
class Abstract:
    # snip
    def analyze(self):
        # Counter will create a dictionary counting the unique values in an array:
        # {'london': 12, 'beer': 3, ...}
        self.term_frequencies = Counter(analyze(self.fulltext))
    def term_frequency(self, term):
        return self.term_frequencies.get(term, 0)

# in index.py we add `document.analyze()
def index_document(self, document):
    if document.ID not in self.documents:
        self.documents[document.ID] = document
        document.analyze()

def search(self, query, search_type='AND', rank=True):
    # snip
    if rank:
        return self.rank(analyzed_query, documents)
    return documents
def rank(self, analyzed_query, documents):
    results = []
    if not documents:
        return results
    for document in documents:
        score = sum([document.term_frequency(token) for token in analyzed_query])
        results.append((document, score))
    return sorted(results, key=lambda doc: doc[1], reverse=True)

# index.py
import math
def document_frequency(self, token):
    return len(self.index.get(token, set()))
def inverse_document_frequency(self, token):
    # Manning, Hinrich and Schütze use log10, so we do too, even though it
    # doesn't really matter which log we use anyway
    # https://nlp.stanford.edu/IR-book/html/htmledition/inverse-document-frequency-1.html
    return math.log10(len(self.documents) / self.document_frequency(token))
def rank(self, analyzed_query, documents):
    results = []
    if not documents:
        return results
    for document in documents:
        score = 0.0
        for token in analyzed_query:
            tf = document.term_frequency(token)
            idf = self.inverse_document_frequency(token)
            score += tf * idf
        results.append((document, score))
    return sorted(results, key=lambda doc: doc[1], reverse=True)