- Docs can be found here.
- Typesafe queries. Written in typescript.
- Bindings are included in the package.
- Bindings for reasonml/bucklescript/rescript and flow.js coming soon.
npm install @mauna/sdk
This package ships with a CLI-based playground for quickly trying out queries and APIs.
You can call it directly if installed globally. You need to set MAUNA_DEVELOPER_ID and MAUNA_API_KEY environment variables for authentication.
npm i -g @mauna/sdk
# Or if using yarn:
# yarn global add @mauna/sdk
# Set auth variables; see the Developers section on the dashboard for this
export MAUNA_DEVELOPER_ID=<developer_id>
export MAUNA_API_KEY="<developer_api_key>"
mauna-playground
const { Mauna } = require("@mauna/sdk");
// If using esm:
// import Mauna from "@mauna/sdk/esm";
// Check the Developers section on the dashboard for this.
const developerId = 999;
const apiKey = "<64 letter api key from your mauna dashboard>";
const client = new Mauna: ({ developerId, apiKey });
// Start async block
(async () => {
await client.initialize();
// See API list for more info
const result = await client.api.chitchat({ input, history });
console.log(result);
// Do something with the result
})().then(
console.log,
console.error
);Takes a list of turns ({ content: string }) and parses them to produce a semantic frames-based context object.
api.parseContext: (turns: [{ content: string }]) => {
context {
mentions [
{
evokes,
phrase
}
]
}
}Takes an english sentence and produces paraphrased versions of it that retain the semantic meaning of the original.
api.paraphraseSentence: (sentence: string, count: Int = 3) => {
paraphrases
}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.
api.predictNextTurn: (history: [string], alternatives: [string]) => {
nextTurn,
confidence
}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.
api.matchIntent: (
input: string,
intent: [string],
threshold: Float = 0.7
) => {
matches [
{
intent,
confidence,
slots: [
{
slot,
value,
match_type,
confidence
}
]
}
]
}Takes a target sentence and a list of other sentences to compare with for similarity. Returns an array of pairwise similarity scores.
api.measureSimilarity: (sentence: string, compareWith: [string]) => {
result {
score,
sentencePair
}
}api.resolveCoreferences: (text: string) => {
coref: {
detected,
resolvedOutput, // Rewritten input with all the coreferences resolved
clusters: [
{
mention, // token(s) detected as a mention of an entity
references: [
{
match,
score
}
]
}
]
}
}Takes an English text as an input and returns vector representation for passage, its sentences and entities if found.
api.toVec: (text: string) => {
has_vector,
vector,
vector_norm,
sentences: {
has_vector,
vector_norm,
vector,
text
}
entities: {
text,
has_vector,
vector_norm,
vector
}
}Takes plain English input and returns overall and sentence-level sentiment information. Represents positivity or negativity of the passage as a floating point value.
api.getSentiment: (text: string) => {
sentiment,
sentences: {
text,
sentiment,
}
}Takes some plain English input and returns parsed categories, entities and sentences.
api.parseText: (text: string) => {
categories: {
label,
score
},
entities: {
label,
lemma,
text
},
sentences: {
text,
label,
lemma
}
}Takes some text and extracts numeric references as a list of tokens with numeric annotations.
api.extractNumericData: (text: string) => {
tokens: [
{
numeric_analysis: {
data, // numeric data
has_numeric // does this token have numeric info?
}
}
]
}Takes some plain English string as input and returns a list of its tokens annotated with linguistic information.
api.parseTextTokens: (text: string) => {
tokens: [
{
dependency, // Type of dependency: PNP, VB ...
entity_type, // Type of entity: PERSON ...
is_alpha,
is_currency,
is_digit,
is_oov, // is out of vocabulary
is_sent_start,
is_stop,
is_title,
lemma,
like_email,
like_num,
like_url,
part_of_speech, // verb, noun ...
prob,
tag,
text
}
]
}Takes ssml and corresponding styles as a css string. Returns base64 encoded audio.
api.renderCSS: (ssml: string, css: string) => {
result // base64 encoded audio
}Takes base64 encoded audio as input and returns a list of possible transcripts (sorted in order of decreasing confidence).
api.speechToText: (audio: string) => {
transcript: [
{
text
}
]
}Takes text (string) as input and returns audio encoded as a base64 string.
api.textToSpeech: (text: string) => {
audio // base64 encoded audio
}- The
esm/directory is marked as a nodejs-native ES module usingesm/package.json
- Edit files in
src/directory - Run
npm run build - Add test cases in
tests/directory. File names need to start withtest_. - Make sure to set env vars:
export MAUNA_DEVELOPER_ID=XX MAUNA_API_KEY=XXX - Run
npm test
- If build successful, before committing results, run
npm run version bump. npm publish --access public
Docs are built using typedoc and published on github pages.
- Run
npm run docs - Commit all changes,
- Then
git checkout gh-pagesandgit merge <original-branch> git push origin gh-pages
- Install GNU parallel command
- Run
NUM=100 npm run load-test
