Python基于中文分词的简单搜索引擎实现 Whoosh

# -*- coding: utf-8 -*-
"""
Created on Tue Nov 13 22:53:33 2018

@author: Lenovo
"""
from whoosh.fields import Schema,TEXT,ID
from jieba.analyse import ChineseAnalyzer
schema = Schema(title=TEXT,path=ID,content=TEXT(stored=True,analyzer=ChineseAnalyzer()))

import os
from whoosh.index import create_in
if not os.path.exists('index'):
    os.mkdir('index')
idx = create_in("index",schema)

writer = idx.writer()
writer.add_document(title="哈哈哈哈哈，嘻嘻",
                    path="99",
                    content="少时诵诗书大撒所三生三世十里桃花")

writer.commit()

from whoosh.qparser import QueryParser
with idx.searcher() as searcher:
    parser = QueryParser("content",schema=idx.schema)
    q = parser.parse('我是来自中国科学技术大学')
    results = searcher.search(q)
    print(results)

接下来看一看ChineseAnalyzer的源码：

# encoding=utf-8
from __future__ import unicode_literals
from whoosh.analysis import RegexAnalyzer, LowercaseFilter, StopFilter, StemFilter
from whoosh.analysis import Tokenizer, Token
from whoosh.lang.porter import stem

import jieba
import re

STOP_WORDS = frozenset(('a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'can',
                        'for', 'from', 'have', 'if', 'in', 'is', 'it', 'may',
                        'not', 'of', 'on', 'or', 'tbd', 'that', 'the', 'this',
                        'to', 'us', 'we', 'when', 'will', 'with', 'yet',
                        'you', 'your', '的', '了', '和','我'))

accepted_chars = re.compile(r"[\u4E00-\u9FD5]+")


class ChineseTokenizer(Tokenizer):

    def __call__(self, text, **kargs):
        words = jieba.tokenize(text, mode="search")
        token = Token()
        for (w, start_pos, stop_pos) in words:
            if not accepted_chars.match(w) and len(w) <= 1:
                continue
            token.original = token.text = w
#            print(token)
            token.pos = start_pos
            token.startchar = start_pos
            token.endchar = stop_pos
            yield token


def ChineseAnalyzer(stoplist=STOP_WORDS, minsize=1, stemfn=stem, cachesize=50000):
    return (ChineseTokenizer() | LowercaseFilter() |
            StopFilter(stoplist=stoplist, minsize=minsize) |
            StemFilter(stemfn=stemfn, ignore=None, cachesize=cachesize))

看到我们可以在STOP_WORDS里加入自己的停用词，但是如何指定Dictionary好像还不具备这个功能，我们来对源码进行改造

通过层层追踪，我们发现在Tokenizer中可以通过初始化指定词典

# encoding=utf-8
from __future__ import unicode_literals
from whoosh.analysis import RegexAnalyzer, LowercaseFilter, StopFilter, StemFilter
from whoosh.analysis import Tokenizer, Token
from whoosh.lang.porter import stem

import jieba
import re
tk = jieba.Tokenizer(dictionary=None)
STOP_WORDS = frozenset(('a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'can',
                        'for', 'from', 'have', 'if', 'in', 'is', 'it', 'may',
                        'not', 'of', 'on', 'or', 'tbd', 'that', 'the', 'this',
                        'to', 'us', 'we', 'when', 'will', 'with', 'yet',
                        'you', 'your', '的', '了', '和','我'))

accepted_chars = re.compile(r"[\u4E00-\u9FD5]+")


class ChineseTokenizer(Tokenizer):

    def __call__(self, text, **kargs):
        words = tk.tokenize(text, mode="search")
        token = Token()
        for (w, start_pos, stop_pos) in words:
            if not accepted_chars.match(w) and len(w) <= 1:
                continue
            token.original = token.text = w
#            print(token)
            token.pos = start_pos
            token.startchar = start_pos
            token.endchar = stop_pos
            yield token


def ChineseAnalyzer(stoplist=STOP_WORDS, minsize=1, stemfn=stem, cachesize=50000):
    return (ChineseTokenizer() | LowercaseFilter() |
            StopFilter(stoplist=stoplist, minsize=minsize) |
            StemFilter(stemfn=stemfn, ignore=None, cachesize=cachesize))

把初始化Tokenizer的操作作为全局变量，这样可以做到常驻内存，避免重复加载词典耗时

Python基于中文分词的简单搜索引擎实现 Whoosh

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