elasticsearch安装与使用(2)-基于term匹配的简单搜索引擎搭建

把一篇pdf论文解析后，放入es数据库中，建立倒排索引表，并实现简单搜索。

1、pdf论文解析(英文)

安装pdf解析包

pip install pdfminer.six

def extract_text_from_pdf(filename, page_numbers=None, min_line_length=1):'''从pdf文件中提取文字:param filename: pdf文件:param page_numbers: 指定页码，list:param min_line_length: 文本最小分隔长度:return:'''paragraphs = []buffer = ''full_text = ''# 提取全部文本for i, page_layout in enumerate(extract_pages(filename)):if page_numbers is not None and i not in page_numbers:continuefor element in page_layout:if isinstance(element, LTTextContainer):full_text += element.get_text() + '\n'# 按空行分隔，将文本重新组织成段落lines = full_text.split('\n')for text in lines:if len(text) >= min_line_length:buffer += (' ' + text) if not text.endswith('-') else text.strip('-')elif buffer:paragraphs.append(buffer)buffer = ''if buffer:paragraphs.append(buffer)return paragraphs

2、关键字提取

nltk安装见nltk安装与使用

import re
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwordsdef keywords(text):'''提取文本关键字(简化版):param text: 一段字符串:return:'''# 提取所有字母数字，并替换所有非字母数字的字符为空格no_symbols = re.sub(r'[^a-zA-Z0-9\s]', ' ', text)# 分词word_tokens = word_tokenize(no_symbols)# 去停用词stop_words = set(stopwords.words('english'))filtered_words = [w for w in word_tokens if not w.lower() in stop_words]# 取词根ps = PorterStemmer()key_words = [ps.stem(w) for w in filtered_words]return ' '.join(key_words)

3、创建Elasticsearch连接，注意添加证书

Elasticsearch安装见elasticsearch安装与使用(1)-使用docker安装Elasticsearch

from elasticsearch import Elasticsearch, helpers
# 创建Elasticsearch连接
es = Elasticsearch(hosts=['https://localhost:9200'],  # 服务地址与端口basic_auth=("elastic", "N-sf6R*O0Ur344otTfzc"),  # 用户名，密码ca_certs="/Users/sunwenjun/data/elastic8/http_ca.crt"  # 证书
)

4、建立倒排索引库

def add_data_to_es(index_name="test_index", text_list=["text1","text2"]):'''建立索引，并往索引里添加数据:param index_name: 定义索引名称param paragraphs: 需要检索的文本列表  :return:'''# 如果索引已存在，删除它if es.indices.exists(index=index_name):es.indices.delete(index=index_name)# 创建索引es.indices.create(index=index_name)# 灌库指令actions = []for text in text_list:action = {"_index": index_name,"_source": {"keywords": keywords(text),"text": text}}actions.append(action)# 文本灌库res = helpers.bulk(es, actions)return res# 往es里添加数据
index_name = "index_test"
add_data_to_es(index_name, paragraphs)

索引库可视化见elasticsearch安装与使用(3)-索引库可视化

5、实现搜索

def search(index_name, query, top_n=3):'''查询:param index_name::param query::param top_n::return:'''search_query = {"match":{"keywords": keywords(query)}}search_res = es.search(index=index_name, query=search_query, size=top_n)results = [hit["_source"]["text"] for hit in search_res["hits"]["hits"]]return resultsquery = "retrieval "
results = search(index_name, query, 5)
for res in results:print(res)

5、完整代码

esdemo-01

参考

无需重新学习，使用 Kibana 查询/可视化 SLS 数据