async def handle_recognized( self, recognition: NluIntentParsed ) -> typing.AsyncIterable[typing.Union[typing.Tuple[NluIntent, TopicArgs], ]]: """Intent successfully recognized.""" try: if not recognition.session_id: _LOGGER.warning("Missing session id on intent message.") return site_session = self.all_sessions.get(recognition.session_id) if site_session is None: _LOGGER.warning( "No session for id %s. Dropping recognition.", recognition.session_id, ) return original_input = site_session.text_captured.text _LOGGER.debug("Recognized %s", recognition) # intent yield (NluIntent( input=recognition.input, id=recognition.id, site_id=recognition.site_id, session_id=recognition.session_id, intent=Intent( intent_name=recognition.intent.intent_name, confidence_score=recognition.intent.confidence_score, ), slots=recognition.slots, asr_tokens=[ NluIntent.make_asr_tokens(recognition.input.split()) ], raw_input=original_input, custom_data=site_session.custom_data, ), { "intent_name": recognition.intent.intent_name }) except Exception: _LOGGER.exception("handle_recognized")
async def handle_query( self, query: NluQuery ) -> typing.AsyncIterable[typing.Union[NluIntentParsed, typing.Tuple[ NluIntent, TopicArgs], NluIntentNotRecognized, NluError, ]]: """Do intent recognition.""" original_input = query.input try: if not self.intent_graph and self.graph_path and self.graph_path.is_file( ): # Load graph from file _LOGGER.debug("Loading %s", self.graph_path) with open(self.graph_path, mode="rb") as graph_file: self.intent_graph = rhasspynlu.gzip_pickle_to_graph( graph_file) if self.intent_graph: def intent_filter(intent_name: str) -> bool: """Filter out intents.""" if query.intent_filter: return intent_name in query.intent_filter return True # Replace digits with words if self.replace_numbers: # Have to assume whitespace tokenization words = rhasspynlu.replace_numbers(query.input.split(), self.language) query.input = " ".join(words) input_text = query.input # Fix casing for output event if self.word_transform: input_text = self.word_transform(input_text) if self.failure_token and (self.failure_token in query.input.split()): # Failure token was found in input recognitions = [] else: # Pass in raw query input so raw values will be correct recognitions = recognize( query.input, self.intent_graph, intent_filter=intent_filter, word_transform=self.word_transform, fuzzy=self.fuzzy, extra_converters=self.extra_converters, ) else: _LOGGER.error("No intent graph loaded") recognitions = [] if NluHermesMqtt.is_success(recognitions): # Use first recognition only. recognition = recognitions[0] assert recognition is not None assert recognition.intent is not None intent = Intent( intent_name=recognition.intent.name, confidence_score=recognition.intent.confidence, ) slots = [ Slot( entity=(e.source or e.entity), slot_name=e.entity, confidence=1.0, value=e.value_dict, raw_value=e.raw_value, range=SlotRange( start=e.start, end=e.end, raw_start=e.raw_start, raw_end=e.raw_end, ), ) for e in recognition.entities ] if query.custom_entities: # Copy user-defined entities for entity_name, entity_value in query.custom_entities.items( ): slots.append( Slot( entity=entity_name, confidence=1.0, value={"value": entity_value}, )) # intentParsed yield NluIntentParsed( input=recognition.text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=intent, slots=slots, ) # intent yield ( NluIntent( input=recognition.text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=intent, slots=slots, asr_tokens=[ NluIntent.make_asr_tokens(recognition.tokens) ], asr_confidence=query.asr_confidence, raw_input=original_input, wakeword_id=query.wakeword_id, lang=(query.lang or self.lang), custom_data=query.custom_data, ), { "intent_name": recognition.intent.name }, ) else: # Not recognized yield NluIntentNotRecognized( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, custom_data=query.custom_data, ) except Exception as e: _LOGGER.exception("handle_query") yield NluError( site_id=query.site_id, session_id=query.session_id, error=str(e), context=original_input, )
async def handle_query( self, query: NluQuery ) -> typing.AsyncIterable[typing.Union[NluIntentParsed, typing.Tuple[ NluIntent, TopicArgs], NluIntentNotRecognized, NluError, ]]: """Do intent recognition.""" original_input = query.input try: self.maybe_load_engine() assert self.engine, "Snips engine not loaded. You may need to train." input_text = query.input # Fix casing for output event if self.word_transform: input_text = self.word_transform(input_text) # Do parsing result = self.engine.parse(input_text, query.intent_filter) intent_name = result.get("intent", {}).get("intentName") if intent_name: slots = [ Slot( slot_name=s["slotName"], entity=s["entity"], value=s["value"], raw_value=s["rawValue"], range=SlotRange(start=s["range"]["start"], end=s["range"]["end"]), ) for s in result.get("slots", []) ] # intentParsed yield NluIntentParsed( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent(intent_name=intent_name, confidence_score=1.0), slots=slots, ) # intent yield ( NluIntent( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent(intent_name=intent_name, confidence_score=1.0), slots=slots, asr_tokens=[ NluIntent.make_asr_tokens(query.input.split()) ], raw_input=original_input, wakeword_id=query.wakeword_id, lang=query.lang, ), { "intent_name": intent_name }, ) else: # Not recognized yield NluIntentNotRecognized( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, ) except Exception as e: _LOGGER.exception("handle_query") yield NluError( site_id=query.site_id, session_id=query.session_id, error=str(e), context=original_input, )
async def async_test_handle_query(self): """Verify valid input leads to a query message.""" query_id = str(uuid.uuid4()) text = "set the bedroom light to red" query = NluQuery(input=text, id=query_id, site_id=self.site_id, session_id=self.session_id) results = [] async for result in self.hermes.on_message(query): results.append(result) # Check results intent = Intent(intent_name="SetLightColor", confidence_score=1.0) slots = [ Slot( entity="name", slot_name="name", value={ "kind": "Unknown", "value": "bedroom" }, raw_value="bedroom", confidence=1.0, range=SlotRange(start=8, end=15, raw_start=8, raw_end=15), ), Slot( entity="color", slot_name="color", value={ "kind": "Unknown", "value": "red" }, raw_value="red", confidence=1.0, range=SlotRange(start=25, end=28, raw_start=25, raw_end=28), ), ] self.assertEqual( results, [ NluIntentParsed( input=text, id=query_id, site_id=self.site_id, session_id=self.session_id, intent=intent, slots=slots, ), ( NluIntent( input=text, id=query_id, site_id=self.site_id, session_id=self.session_id, intent=intent, slots=slots, asr_tokens=[NluIntent.make_asr_tokens(text.split())], raw_input=text, ), { "intent_name": intent.intent_name }, ), ], )
async def handle_query( self, query: NluQuery ) -> typing.AsyncIterable[typing.Union[NluIntentParsed, typing.Tuple[ NluIntent, TopicArgs], NluIntentNotRecognized, NluError, ]]: """Do intent recognition.""" # Check intent graph try: if (not self.intent_graph and self.intent_graph_path and self.intent_graph_path.is_file()): _LOGGER.debug("Loading %s", self.intent_graph_path) with open(self.intent_graph_path, mode="rb") as graph_file: self.intent_graph = rhasspynlu.gzip_pickle_to_graph( graph_file) # Check examples if (self.intent_graph and self.examples_path and self.examples_path.is_file()): def intent_filter(intent_name: str) -> bool: """Filter out intents.""" if query.intent_filter: return intent_name in query.intent_filter return True original_text = query.input # Replace digits with words if self.replace_numbers: # Have to assume whitespace tokenization words = rhasspynlu.replace_numbers(query.input.split(), self.language) query.input = " ".join(words) input_text = query.input # Fix casing if self.word_transform: input_text = self.word_transform(input_text) recognitions: typing.List[rhasspynlu.intent.Recognition] = [] if input_text: recognitions = rhasspyfuzzywuzzy.recognize( input_text, self.intent_graph, str(self.examples_path), intent_filter=intent_filter, extra_converters=self.extra_converters, ) else: _LOGGER.error("No intent graph or examples loaded") recognitions = [] # Use first recognition only if above threshold if (recognitions and recognitions[0] and recognitions[0].intent and (recognitions[0].intent.confidence >= self.confidence_threshold)): recognition = recognitions[0] assert recognition.intent intent = Intent( intent_name=recognition.intent.name, confidence_score=recognition.intent.confidence, ) slots = [ Slot( entity=(e.source or e.entity), slot_name=e.entity, confidence=1.0, value=e.value_dict, raw_value=e.raw_value, range=SlotRange( start=e.start, end=e.end, raw_start=e.raw_start, raw_end=e.raw_end, ), ) for e in recognition.entities ] if query.custom_entities: # Copy user-defined entities for entity_name, entity_value in query.custom_entities.items( ): slots.append( Slot( entity=entity_name, confidence=1.0, value={"value": entity_value}, )) # intentParsed yield NluIntentParsed( input=recognition.text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=intent, slots=slots, ) # intent yield ( NluIntent( input=recognition.text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=intent, slots=slots, asr_tokens=[ NluIntent.make_asr_tokens(recognition.tokens) ], asr_confidence=query.asr_confidence, raw_input=original_text, wakeword_id=query.wakeword_id, lang=(query.lang or self.lang), custom_data=query.custom_data, ), { "intent_name": recognition.intent.name }, ) else: # Not recognized yield NluIntentNotRecognized( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, custom_data=query.custom_data, ) except Exception as e: _LOGGER.exception("handle_query") yield NluError( site_id=query.site_id, session_id=query.session_id, error=str(e), context=original_text, )
async def handle_query( self, query: NluQuery ) -> typing.AsyncIterable[ typing.Union[ typing.Tuple[NluIntent, TopicArgs], NluIntentParsed, NluIntentNotRecognized, NluError, ] ]: """Do intent recognition.""" try: input_text = query.input # Fix casing if self.word_transform: input_text = self.word_transform(input_text) if self.nlu_url: # Use remote server _LOGGER.debug(self.nlu_url) params = {} # Add intent filter if query.intent_filter: params["intentFilter"] = ",".join(query.intent_filter) async with self.http_session.post( self.nlu_url, data=input_text, params=params, ssl=self.ssl_context ) as response: response.raise_for_status() intent_dict = await response.json() elif self.nlu_command: # Run external command _LOGGER.debug(self.nlu_command) proc = await asyncio.create_subprocess_exec( *self.nlu_command, stdin=asyncio.subprocess.PIPE, stdout=asyncio.subprocess.PIPE, ) input_bytes = (input_text.strip() + "\n").encode() output, error = await proc.communicate(input_bytes) if error: _LOGGER.debug(error.decode()) intent_dict = json.loads(output) else: _LOGGER.warning("Not handling NLU query (no URL or command)") return intent_name = intent_dict["intent"].get("name", "") if intent_name: # Recognized tokens = query.input.split() slots = [ Slot( entity=e["entity"], slot_name=e["entity"], confidence=1, value=e.get("value_details", {"value": ["value"]}), raw_value=e.get("raw_value", e["value"]), range=SlotRange( start=e.get("start", 0), end=e.get("end", 1), raw_start=e.get("raw_start"), raw_end=e.get("raw_end"), ), ) for e in intent_dict.get("entities", []) ] yield NluIntentParsed( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent( intent_name=intent_name, confidence_score=intent_dict["intent"].get("confidence", 1.0), ), slots=slots, ) yield ( NluIntent( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent( intent_name=intent_name, confidence_score=intent_dict["intent"].get( "confidence", 1.0 ), ), slots=slots, asr_tokens=[NluIntent.make_asr_tokens(tokens)], raw_input=query.input, wakeword_id=query.wakeword_id, lang=query.lang, ), {"intent_name": intent_name}, ) else: # Not recognized yield NluIntentNotRecognized( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, ) except Exception as e: _LOGGER.exception("handle_query") yield NluError( error=repr(e), context=repr(query), site_id=query.site_id, session_id=query.session_id, )
async def handle_query( self, query: NluQuery ) -> typing.AsyncIterable[typing.Union[NluIntentParsed, typing.Tuple[ NluIntent, TopicArgs], NluIntentNotRecognized, NluError, ]]: """Do intent recognition.""" try: original_input = query.input # Replace digits with words if self.replace_numbers: # Have to assume whitespace tokenization words = rhasspynlu.replace_numbers(query.input.split(), self.number_language) query.input = " ".join(words) input_text = query.input # Fix casing for output event if self.word_transform: input_text = self.word_transform(input_text) parse_url = urljoin(self.rasa_url, "model/parse") _LOGGER.debug(parse_url) async with self.http_session.post( parse_url, json={ "text": input_text, "project": self.rasa_project }, ssl=self.ssl_context, ) as response: response.raise_for_status() intent_json = await response.json() intent = intent_json.get("intent", {}) intent_name = intent.get("name", "") if intent_name and (query.intent_filter is None or intent_name in query.intent_filter): confidence_score = float(intent.get("confidence", 0.0)) slots = [ Slot( entity=e.get("entity", ""), slot_name=e.get("entity", ""), confidence=float(e.get("confidence", 0.0)), value={ "kind": "Unknown", "value": e.get("value", "") }, raw_value=e.get("value", ""), range=SlotRange( start=int(e.get("start", 0)), end=int(e.get("end", 1)), raw_start=int(e.get("start", 0)), raw_end=int(e.get("end", 1)), ), ) for e in intent_json.get("entities", []) ] if query.custom_entities: # Copy user-defined entities for entity_name, entity_value in query.custom_entities.items( ): slots.append( Slot( entity=entity_name, confidence=1.0, value={"value": entity_value}, )) # intentParsed yield NluIntentParsed( input=input_text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent(intent_name=intent_name, confidence_score=confidence_score), slots=slots, ) # intent yield ( NluIntent( input=input_text, id=query.id, site_id=query.site_id, session_id=query.session_id, intent=Intent( intent_name=intent_name, confidence_score=confidence_score, ), slots=slots, asr_tokens=[ NluIntent.make_asr_tokens(input_text.split()) ], asr_confidence=query.asr_confidence, raw_input=original_input, lang=(query.lang or self.lang), custom_data=query.custom_data, ), { "intent_name": intent_name }, ) else: # Not recognized yield NluIntentNotRecognized( input=query.input, id=query.id, site_id=query.site_id, session_id=query.session_id, custom_data=query.custom_data, ) except Exception as e: _LOGGER.exception("nlu query") yield NluError( site_id=query.site_id, session_id=query.session_id, error=str(e), context=query.input, )