First Steps

The simplest PyQPL implementation could look like this

Basic PyQPL Usage

When using PyQPL your primary task will be defining a 

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 (Parsed or Manual)
3. The Query Translation into an engine specific