def store_vacancy_record(es: Elasticsearch, index_name: str, record: dict,
parent_id: str) -> str:
hash_string = ''
for k, v in record.items():
hash_string += "{}{}".format(k, v)
hash_string += parent_id
hash_object = hashlib.md5(hash_string.encode())
es.index(index=index_name,
doc_type='vacancies',
id=hash_object.hexdigest(),
body=record,
parent=parent_id)
return hash_string
class ElasticClient:
def __init__(self, index_name, index_type, ip="127.0.0.1"):
'''
@param index_name: 索引名称
@param index_type: 索引类型
'''
self.index_name = index_name
self.index_type = index_type
self.es = Elasticsearch([ip])
def create_index(self, index_name="teacher_resume", index_type="tr_type"):
#创建索引
_index_mappings = {
"mappings": {
self.index_type: {
"properties": {
"teachername": {
"type": "keyword"
},
"telephone": {
"type": "text"
},
"email": {
"type": "keyword"
},
"research_direction": {
"type": "array"
},
"personal_profile": {
"type": "text"
},
"teaching_results": {
"type": "text"
},
"research_results": {
"type": "text"
},
"lab_introduction": {
"type": "text"
},
}
}
}
}
self.es.indices.create(index=self.index_name,
body=_index_mappings,
ignore=400)
def load_index(self):
with open(os.path.join(BASE_DIR, 'static', 'files',
'test_json.json')) as f:
result = json.load(f)
for item in result:
res = self.es.index(index=self.index_name,
doc_type=self.index_type,
body=item)
print(res)
class SoSoImp(object):
'''
classdocs
'''
def __init__(self):
self.es = Elasticsearch(['192.168.2.129', '192.168.2.130'])
'''
Constructor
'''
'''
添加搜索信息
'''
def addSoso(self,Content):
title=""
if Content.title !=None:
title = Content.title
txt =""
if Content.txt != None :
txt = Content.txt
#获取情感
source=fenci.mm(title,txt)
body={"title":Content.title,"summary":Content.summary,"context":Content.txt,"site_cls":Content.site_cls,"domaintype":Content.domaintype,
"countryid":Content.countryid,"province":Content.province,"city":Content.city,"area":Content.area,"url":Content.url,"publictime":Content.pubdate,
"createtime":Content.created,"sitename":Content.site_name,"domain1":Content.domain_1,"domain2":Content.domain_2,"sentiment":source,
"subname":Content.subname}
self.es.index(index="yuqing", doc_type="yuqing_type", body=body, id=Content.rowkey)
#es.
def store_change_record(es: Elasticsearch, index_name: str, record: dict,
url: str) -> None:
es.index(index=index_name, doc_type='changes', id=url, body=record)
def store_url_record(es: Elasticsearch, index_name: str, record: dict,
record_id: str) -> None:
es.index(index=index_name, doc_type='url', body=record, id=record_id)
}
},
"mappings": {
"diary": {
"properties": {
"content": {
"term_vector": "yes",
"type": "text",
"analyzer": "morfologik"
}
}
}
}
})
list_of_files = glob.glob('../ustawy/*.txt') # create the list of file
print("loading files....")
for file_name in list_of_files:
with open(file_name, 'r') as myfile:
data = myfile.read()
es.index(index=INDEX,
doc_type=TYPE,
id=file_name,
body={
"content": data,
})
print(es.mtermvectors(index=INDEX, doc_type=TYPE))
class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(
self.indices, doc_type, body, doc_id, **kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(
self.indices, doc_type, body, doc_id, **kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(
self.indices, doc_type, doc_id, body, **kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(
self.indices, doc_type, body, **kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(
self.indices, doc_type, doc_id, body, **kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(
self.indices, doc_type, body, **kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(
self.indices, doc_type, doc_id, body, **kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(self.indices, doc_type, body, doc_id,
**kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(self.indices, doc_type, body, doc_id,
**kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(self.indices, doc_type, doc_id, body,
**kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(self.indices, doc_type, body,
**kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(self.indices, doc_type, doc_id, body,
**kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(self.indices, doc_type, body,
**kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(self.indices, doc_type, doc_id, body,
**kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(self.indices, doc_type, doc_id,
body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(self.indices, doc_type, doc_id, body,
**kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
for sentence in sentences:
### Tokenize sentence in paragraph
sentence = underthesea.word_tokenize(sentence, format="text")
### Lower case
sentence = sentence.lower()
paragraph_tokenized = paragraph_tokenized + sentence
paragraph_tokenized = paragraph_tokenized.replace("\n", "")
content_tokenized.append({
"type": "text",
"content": paragraph_tokenized
})
### Convert và đẩy dữ liệu lên elasticsearch
es_push_body = {
"Trang": news_page,
"Title": title_tokenized,
"NoiDung": content_tokenized,
"Description": des_tokenized,
"NewspaperLink": news_link,
}
es.index(index="my-index", body=es_push_body)
### Đếm tổng số bản ghi hiện tại trên ES
es_check_body = {"query": {"match_all": {}}}
result_check = es.search(index="my-index", body=es_check_body)
print(result_check["hits"]["total"]["value"])