Source code for mycroft.skills.common_query_skill

# Copyright 2018 Mycroft AI Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import time

from enum import IntEnum
from abc import ABC, abstractmethod
from .mycroft_skill import MycroftSkill

from mycroft.configuration import Configuration
from mycroft.util.file_utils import resolve_resource_file

class CQSMatchLevel(IntEnum):
    EXACT = 1  # Skill could find a specific answer for the question
    CATEGORY = 2  # Skill could find an answer from a category in the query
    GENERAL = 3  # The query could be processed as a general quer

# Copy of CQSMatchLevel to use if the skill returns visual media
CQSVisualMatchLevel = IntEnum('CQSVisualMatchLevel',
                              [ for e in CQSMatchLevel])

def is_CQSVisualMatchLevel(match_level):
    return isinstance(match_level, type(CQSVisualMatchLevel.EXACT))

"""these are for the confidence calculation"""
# how much each topic word is worth
# when found in the answer

# elevate relevance above all else

# we like longer articles but only so much

# higher number - less bias for word length

[docs]class CommonQuerySkill(MycroftSkill, ABC): """Question answering skills should be based on this class. The skill author needs to implement `CQS_match_query_phrase` returning an answer and can optionally implement `CQS_action` to perform additional actions if the skill's answer is selected. This class works in conjunction with skill-query which collects answers from several skills presenting the best one available. """ def __init__(self, name=None, bus=None): super().__init__(name, bus) noise_words_filepath = "text/%s/noise_words.list" % (self.lang,) noise_words_filename = resolve_resource_file(noise_words_filepath) self.translated_noise_words = [] try: with open(noise_words_filename) as f: self.translated_noise_words = self.translated_noise_words = self.translated_noise_words.split() except FileNotFoundError: self.log.warning("Missing noise_words.list file in res/text/lang") # these should probably be configurable self.level_confidence = { CQSMatchLevel.EXACT: 0.9, CQSMatchLevel.CATEGORY: 0.6, CQSMatchLevel.GENERAL: 0.5 }
[docs] def bind(self, bus): """Overrides the default bind method of MycroftSkill. This registers messagebus handlers for the skill during startup but is nothing the skill author needs to consider. """ if bus: super().bind(bus) self.add_event('question:query', self.__handle_question_query) self.add_event('question:action', self.__handle_query_action)
def __handle_question_query(self, message): search_phrase =["phrase"] # First, notify the requestor that we are attempting to handle # (this extends a timeout while this skill looks for a match) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": True})) # Now invoke the CQS handler to let the skill perform its search result = self.CQS_match_query_phrase(search_phrase) if result: match = result[0] level = result[1] answer = result[2] callback = result[3] if len(result) > 3 else None confidence = self.__calc_confidence( match, search_phrase, level, answer) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "answer": answer, "callback_data": callback, "conf": confidence})) else: # Signal we are done (can't handle it) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": False}))
[docs] def remove_noise(self, phrase): """remove noise to produce essence of question""" phrase = ' ' + phrase + ' ' for word in self.translated_noise_words: mtch = ' ' + word + ' ' if phrase.find(mtch) > -1: phrase = phrase.replace(mtch, " ") phrase = ' '.join(phrase.split()) return phrase.strip()
def __calc_confidence(self, match, phrase, level, answer): # Assume the more of the words that get consumed, the better the match consumed_pct = len(match.split()) / len(phrase.split()) if consumed_pct > 1.0: consumed_pct = 1.0 consumed_pct /= 10 # bonus for more sentences num_sentences = float(float(len(answer.split("."))) / float(10)) # Add bonus if match has visuals and the device supports them. bonus = 0.0 if is_CQSVisualMatchLevel(level) and self.gui.connected: bonus = 0.1 # extract topic topic = self.remove_noise(match) # calculate relevance answer = answer.lower() matches = 0 for word in topic.split(' '): if answer.find(word) > -1: matches += TOPIC_MATCH_RELEVANCE answer_size = len(answer.split(" ")) answer_size = min(MAX_ANSWER_LEN_FOR_CONFIDENCE, answer_size) relevance = 0.0 if answer_size > 0: relevance = float(float(matches) / float(answer_size)) relevance = relevance * RELEVANCE_MULTIPLIER # extra credit for more words up to a point wc_mod = float(float(answer_size) / float(WORD_COUNT_DIVISOR)) * 2 confidence = self.level_confidence[level] + \ consumed_pct + bonus + num_sentences + relevance + wc_mod return confidence def __handle_query_action(self, message): """Message handler for question:action. Extracts phrase and data from message forward this to the skills CQS_action method. """ if["skill_id"] != self.skill_id: # Not for this skill! return phrase =["phrase"] data ="callback_data") # Invoke derived class to provide playback data self.CQS_action(phrase, data)
[docs] @abstractmethod def CQS_match_query_phrase(self, phrase): """Analyze phrase to see if it is a play-able phrase with this skill. Needs to be implemented by the skill. Args: phrase (str): User phrase, "What is an aardwark" Returns: (match, CQSMatchLevel[, callback_data]) or None: Tuple containing a string with the appropriate matching phrase, the PlayMatch type, and optionally data to return in the callback if the match is selected. """ # Derived classes must implement this, e.g. return None
[docs] def CQS_action(self, phrase, data): """Take additional action IF the skill is selected. The speech is handled by the common query but if the chosen skill wants to display media, set a context or prepare for sending information info over e-mail this can be implemented here. Args: phrase (str): User phrase uttered after "Play", e.g. "some music" data (dict): Callback data specified in match_query_phrase() """ # Derived classes may implement this if they use additional media # or wish to set context after being called. pass