First Steps

The simplest PyQPL implementation could look like this

from pyqpl.parser import QPLParser
from pyqpl.qpl import QPLOptions
from pyqpl.translator import ElasticsearchTranslator

options = QPLOptions(fields='content')
parser = QPLParser(options=options)
translator = ElasticsearchTranslator()

qpl_query = parser.parse_query('This is PyQPL')

engine_query = translator.to_engine_query(qpl_query)

print(qpl_query)
print(engine_query)

Copy that to a file main.py.

Execute the file, and the output of qpl_query, will be de text representation of the operand, returned by the parse_query function.

or(terms(This,is,PyQPL))

While the engine_query, will be the engine specific query, return by the translator (an Elasticsearch translator in this case), after processing the qpl_query, ready to be sent to the search engine, or be modified later on

{
  'multi_match': {
    'query': 'This is PyQPL',
    'fields': [
      'content'
    ]
  }
}

Although the parse_query step can be replace with the manual creation of the query, the process will be the same

from pyqpl.qpl import QPL, QPLOptions
from pyqpl.translator import ElasticsearchTranslator

options = QPLOptions(fields='content')
qpl = QPL(options=options)
translator = ElasticsearchTranslator()

qpl_query = qpl.or_(operands=qpl.terms(terms=['This', 'is', 'PyQPL']))

engine_query = translator.to_engine_query(qpl_query)

print(qpl_query)
print(engine_query)

Being the process:

1. The definition of the QPLOptions
2. The creation of a QPL query, regardless of the method used for it 
3. The translation of said query into an engine specific

QPLOptions will store the configuration on how we want our query to behave, the grammar to user and custom operator or synonym injection necessary.