poetry install
poetry run codegen
poetry build
pip install mauna_sdk
Takes an input and a list of history messages, determines if chitchat is started and generates the corresponding response
from mauna_sdk import Mauna
from mauna_sdk.api.chitchat import chitchat
from mauna_sdk.api.input.turn import Turn, Agent
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = chitchat.execute(
client,
input="This is the good weather today",
history=[
Turn(agent=Agent.USER, said="Hello"),
Turn(agent=Agent.BOT, said="Hi there")
]
)
# result == chitchat.chitchatData.ChitchatResponse(response='{\'response\': "It\'s been raining for a few days now."}')Pulls various relations out of the given text
from mauna_sdk import Mauna
from mauna_sdk.api.commonsense_reasoning import commonsenseReasoning
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = commonsenseReasoning.execute(client, text="Hello")
# result == [
# commonsenseReasoning.commonsenseReasoningData.RelationResult(
# type='xAttr',
# result=[
# 'determined',
# 'curious',
# 'brave',
# 'confident',
# 'capable',
# 'smart',
# 'dedicated',
# 'thoughtful',
# 'careless',
# 'mean'
# ]
# ),
# commonsenseReasoning.commonsenseReasoningData.RelationResult(
# type='xEffect',
# result=[
# 'gets yelled at',
# 'personx sweats from nervousness',
# 'gets called a liar',
# 'personx is arrested',
# 'gets arrested',
# 'gets tired',
# 'is praised',
# 'personx sweats',
# 'none',
# 'personx is arrested for assault'
# ]
# ),
# commonsenseReasoning.commonsenseReasoningData.RelationResult(
# type='xIntent',
# result=[
# 'to have fun',
# 'to get something done',
# 'to satisfy his hunger',
# 'to show off skills',
# 'to satisfy his cravings',
# 'to do something',
# 'to have a good time',
# 'to show off',
# 'none',
# 'to be a part of something'
# ]
# ),
# commonsenseReasoning.commonsenseReasoningData.RelationResult(
# type='xWant',
# result=[
# 'to take a break',
# 'to be successful',
# 'to do something else',
# 'to go home',
# 'to have fun',
# 'to have a good time',
# 'to show off',
# 'to rest',
# 'to show off their skills',
# 'to show off their new purchase'
# ]
# )
# ]Takes a text and returns related texts, according to relation type(s).
from mauna_sdk import Mauna
from mauna_sdk.api.conceptnet_grounding import conceptnetGrounding
from mauna_sdk.api.enum.relations import Relations
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = conceptnetGrounding.execute(client, text="max with axe", relations=[Relations.CapableOf])
# result == [
# conceptnetGrounding.conceptnetGroundingData.RelationResult(
# type='CapableOf',
# result=[
# 'chop down tree',
# 'split wood',
# 'chop wood',
# 'cut wood',
# 'break window',
# 'chop firewood',
# 'cut firewood',
# 'cut lumber',
# 'cut tree',
# 'cut you in half'
# ]
# )
# ]Takes an ACE text and an output format and produces the parsed ACE according to the format
from mauna_sdk import Mauna
from mauna_sdk.api.parse_ace import parseACE
from mauna_sdk.api.enum.a_c_e_output_type import ACEOutputType
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = parseACE.execute(client, text="John walks.", format=ACEOutputType.drs)
# result == parseACE.parseACEData.ACEResult(parsed="drs([A],[predicate(A,walk,named('John'))-1/2])\n")Takes a list of turns ({ content: string }) and parses them to produce a semantic frames-based context object.
from mauna_sdk import Mauna
from mauna_sdk.api.parse_context import parseContext
from mauna_sdk.api.input.context_object import ContextObject
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = parseContext.execute(client, turns=[ContextObject(content="Today is a good day")])
# result == [
# parseContext.parseContextData.ContextResult(
# context=parseContext.parseContextData.ContextResult.SlingDocument(
# mentions=[
# parseContext.parseContextData.ContextResult.SlingDocument.SlingMention(
# evokes=['{=#1 :DATE}'],
# phrase='Today'
# ),
# parseContext.parseContextData.ContextResult.SlingDocument.SlingMention(
# evokes=['{=#1 :/pb/predicate /pb/ARG1: {=#2 :DATE} /pb/ARG2: {=#3 :thing}}'], phrase='is'
# ),
# parseContext.parseContextData.ContextResult.SlingDocument.SlingMention(
# evokes=['{=#1 :thing}'], phrase='day'
# )
# ]
# )
# )
# ]Takes an english sentence and produces paraphrased versions of it that retain the semantic meaning of the original.
from mauna_sdk import Mauna
from mauna_sdk.api.paraphrase_sentence import paraphraseSentence
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = paraphraseSentence.execute(client, sentence="I like tomatoes", count=2)
# result == paraphraseSentence.paraphraseSentenceData.Paraphrase(
# paraphrases=[
# 'I like tomatoes.',
# 'I enjoy tomatoes.',
# 'I like to eat tomatoes.',
# 'I am a fan of tomatoes.',
# 'I enjoy eating tomatoes.',
# 'I like eating tomatoes.',
# 'I love tomatoes.',
# 'I like the taste of tomatoes.',
# 'I like fresh tomatoes.',
# 'I like to eat fruit.'
# ]
# )Takes a list of utterances as history and a list of possible alternatives that can be replied with. Returns the most likely alternative and confidence in that prediction.
from mauna_sdk import Mauna
from mauna_sdk.api.predict_next_turn import predictNextTurn
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = predictNextTurn.execute(client, history=["Hello", "How are you?"], alternatives=["I am fine", "Hello"])
# result == [
# predictNextTurn.predictNextTurnData.DialogAlternative(
# nextTurn='I am fine',
# confidence=0.6935682892799377
# ),
# predictNextTurn.predictNextTurnData.DialogAlternative(
# nextTurn='Hello',
# confidence=0.5061840415000916
# )
# ]Takes a list of intents (with slots) and a user input. Performs structured information extraction to find the correct intent and fill the corresponding slots.
from mauna_sdk import Mauna
from mauna_sdk.api.match_intent import matchIntent
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = matchIntent.execute(
client,
input="I require insurance",
intent=[
"Someone requires insurance",
"An ENTITYPERSON takes out insurance"
]
)
# result == matchIntent.matchIntentData.MatchIntentOutput(
# matches=[
# matchIntent.matchIntentData.MatchIntentOutput.PhraseMatch(
# intent='Someone requires insurance',
# confidence=1.0,
# slots=[
# matchIntent.matchIntentData.MatchIntentOutput.PhraseMatch.WordMatch(
# slot='require',
# value='require',
# match_type='direct',
# confidence=1.0
# ),
# matchIntent.matchIntentData.MatchIntentOutput.PhraseMatch.WordMatch(
# slot='insurance',
# value='insurance',
# match_type='direct',
# confidence=1.0)
# ]
# )
# ]
# )Takes a target sentence and a list of other sentences to compare with for similarity. Returns an array of pairwise similarity scores.
from mauna_sdk import Mauna
from mauna_sdk.api.measure_similarity import measureSimilarity
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = measureSimilarity.execute(
client, sentence="Today is a good day",
compareWith=["Today is an awesome day", "Today is a bad day"]
)
# result == measureSimilarity.measureSimilarityData.SentenceSimilarityScores(
# result=[
# measureSimilarity.measureSimilarityData.SentenceSimilarityScores.PairSimilarity(
# score=0.6445285081863403,
# sentencePair=['Today is a good day', 'Today is an awesome day']
# ),
# measureSimilarity.measureSimilarityData.SentenceSimilarityScores.PairSimilarity(
# score=0.3357277512550354,
# sentencePair=['Today is a good day', 'Today is a bad day']
# )
# ]
# )from mauna_sdk import Mauna
from mauna_sdk.api.resolve_coreferences import resolveCoreferences
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = resolveCoreferences.execute(client, text="Emma said that she thinks that Nelson really likes to dance.")
# result == resolveCoreferences.resolveCoreferencesData.NlpDoc(
# coref=resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension(
# detected=True,
# resolvedOutput='Emma said that Emma thinks that Nelson really likes to dance.',
# clusters=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores(
# mention='Emma',
# references=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Emma',
# score=0.9530903100967407
# )
# ]
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores(
# mention='she',
# references=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she',
# score=0.2935597896575928
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Emma',
# score=8.278848648071289
# )
# ]
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores(
# mention='she thinks that Nelson really likes to dance',
# references=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she thinks that Nelson really likes to dance',
# score=1.7855921983718872
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Emma',
# score=-1.801807165145874
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she',
# score=-1.6977876424789429
# )
# ]
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores(
# mention='Nelson',
# references=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Nelson',
# score=0.4910166263580322
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Emma',
# score=-2.120563507080078
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she',
# score=-2.0476505756378174
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she thinks that Nelson really likes to dance',
# score=-1.5593587160110474
# )
# ]
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores(
# mention='Nelson really likes to dance',
# references=[
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Nelson really likes to dance',
# score=1.6450811624526978
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Emma',
# score=-1.7996364831924438
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she',
# score=-2.02427339553833
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='she thinks that Nelson really likes to dance',
# score=-1.497442603111267
# ),
# resolveCoreferences.resolveCoreferencesData.NlpDoc.DocExtension.CorefScores.Scores(
# match='Nelson',
# score=-1.5290888547897339
# )
# ]
# )
# ]
# )
# )Takes an English text as an input and returns vector representation for passage, its sentences and entities if found.
from mauna_sdk import Mauna
from mauna_sdk.api.to_vec import toVec
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = toVec.execute(client, text="John likes to play piano")
# result == toVec.toVecData.NlpDoc(
# has_vector=True,
# vector=[
# 0.08898600190877914,
# ...
# ],
# vector_norm=3.998985419599661,
# sentences=[
# toVec.toVecData.NlpDoc.Span(
# has_vector=True,
# vector_norm=3.9989852905273438,
# vector=[
# 0.08898600190877914,
# ...
# ],
# text='John likes to play piano'
# )
# ],
# entities=[
# toVec.toVecData.NlpDoc.Span(
# has_vector=True,
# vector_norm=6.533577919006348,
# vector=[
# -0.29218998551368713,
# ...
# ],
# text='John'
# )
# ]
# )Takes plain English input and returns overall and sentence-level sentiment information. Represents positivity or negativity of the passage as a floating point value.
from mauna_sdk import Mauna
from mauna_sdk.api.get_sentiment import getSentiment
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = getSentiment.execute(client, text="The movie is awesome")
# result == getSentiment.getSentimentData.NlpDoc(
# sentiment=0.9467527270317078,
# sentences=[
# getSentiment.getSentimentData.NlpDoc.Span(
# text='The movie is awesome',
# sentiment=0.0
# )
# ]
# )Takes some plain English input and returns parsed categories, entities and sentences.
from mauna_sdk import Mauna
from mauna_sdk.api.parse_text import parseText
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = parseText.execute(client, text="Today is a good day")
# result == parseText.parseTextData.NlpDoc(
# categories=[],
# entities=[
# parseText.parseTextData.NlpDoc.Span(
# label='DATE',
# lemma='today',
# text='Today'
# ),
# parseText.parseTextData.NlpDoc.Span(
# label='DATE',
# lemma='a good day',
# text='a good day'
# )
# ],
# sentences=[
# parseText.parseTextData.NlpDoc.Span(
# label='',
# lemma='today be a good day',
# text='Today is a good day'
# )
# ]
# )Takes some text and extracts numeric references as a list of tokens with numeric annotations.
from mauna_sdk import Mauna
from mauna_sdk.api.extract_numeric_data import extractNumericData
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = extractNumericData.execute(client, text="I told you two")
# result == extractNumericData.extractNumericDataData.NlpDoc(
# tokens=[
# extractNumericData.extractNumericDataData.NlpDoc.Token(
# numeric_analysis=extractNumericData.extractNumericDataData.NlpDoc.Token.TokenExtension(
# data=None,
# has_numeric=False
# )
# ),
# extractNumericData.extractNumericDataData.NlpDoc.Token(
# numeric_analysis=extractNumericData.extractNumericDataData.NlpDoc.Token.TokenExtension(
# data=None,
# has_numeric=False
# )
# ),
# extractNumericData.extractNumericDataData.NlpDoc.Token(
# numeric_analysis=extractNumericData.extractNumericDataData.NlpDoc.Token.TokenExtension(
# data=None,
# has_numeric=False
# )
# ),
# extractNumericData.extractNumericDataData.NlpDoc.Token(
# numeric_analysis=extractNumericData.extractNumericDataData.NlpDoc.Token.TokenExtension(
# data='PEOPLE',
# has_numeric=True
# )
# )
# ]
# )Takes some plain English string as input and returns a list of its tokens annotated with linguistic information.
from mauna_sdk import Mauna
from mauna_sdk.api.parse_text_tokens import parseTextTokens
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = parseTextTokens.execute(client, text="Hello there")
# result == parseTextTokens.parseTextTokensData.NlpDoc(
# tokens=[
# parseTextTokens.parseTextTokensData.NlpDoc.Token(
# dependency='ROOT',
# entity_type='',
# is_alpha=True,
# is_currency=False,
# is_digit=False, is_oov=False,
# is_sent_start=True,
# is_stop=False,
# is_title=True,
# lemma='hello',
# like_email=False,
# like_num=False,
# like_url=False,
# part_of_speech='INTJ',
# prob=-10.583807945251465,
# tag='UH',
# text='Hello'
# ),
# parseTextTokens.parseTextTokensData.NlpDoc.Token(
# dependency='advmod',
# entity_type='',
# is_alpha=True,
# is_currency=False,
# is_digit=False,
# is_oov=False,
# is_sent_start=None,
# is_stop=True,
# is_title=False,
# lemma='there',
# like_email=False,
# like_num=False,
# like_url=False,
# part_of_speech='ADV',
# prob=-6.135282039642334,
# tag='RB',
# text='there'
# )
# ]
# )Takes ssml and corresponding styles as a css string. Returns base64 encoded audio.
from mauna_sdk import Mauna
from mauna_sdk.api.render_css import renderCSS
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = renderCSS.execute(client, ssml="<s class='test'>Hello</s>", css=".test {volume: 120%;}")
# result == renderCSS.renderCSSData.ComposeResult(
# result={'$result': {'callTextToSpeech': {'audioB64': 'UklGRoDyAgBXQVZFZm ... '}}, '$context': {}}
# )Takes base64 encoded audio as input and returns a list of possible transcripts (sorted in order of decreasing confidence).
from mauna_sdk import Mauna
from mauna_sdk.api.speech_to_text import speechToText
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = speechToText.execute(client, audio="AAAA ...")
# result == speechToText.speechToTextData.STTResult(
# transcript=[
# speechToText.speechToTextData.STTResult.TextAlternative(text="Hello.")
# ]
# )Takes text (string) as input and returns audio encoded as a base64 string.
from mauna_sdk import Mauna
from mauna_sdk.api.text_to_speech import textToSpeech
developer_id = <int> # Check your profile on the dashboard for this.
api_key = "<64 letter api key available on your mauna dashboard>"
client = Mauna(api_key, developer_id)
result = textToSpeech.execute(client, text="Hello")
# result == textToSpeech.textToSpeechData.TTSResult(audio='UklGRpJb ...')