mirage-api
v1.20.0
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Mirage API Node.
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node-mirage-api
The Mirage API NodeJS wrapper. Access AI inference services.
Copyright 2023 Crisp IM SAS. See LICENSE for copying information.
- 📝 Implements: API Reference (V1) at revision: 06/12/2024
- 😘 Maintainer: @valeriansaliou
Usage
Install the library:
npm install mirage-api --save
Then, import it:
var Mirage = require("mirage-api").Mirage;
Construct a new authenticated Mirage client with your user_id
and secret_key
tokens.
var client = new Mirage("ui_xxxxxx", "sk_xxxxxx");
Then, consume the client eg. to transcribe a audio file containing speech to text:
client.Task.TranscribeSpeech({
locale : {
to : "en"
},
media : {
type : "audio/webm",
url : "https://files.mirage-ai.com/dash/terminal/samples/transcribe-speech/hey-there.weba"
}
})
.then(function(data) {
console.info("Transcribed audio:", data);
})
.catch(function(error) {
console.error("Failed transcribing audio:", error);
});
Authentication
To authenticate against the API, get your tokens (user_id
and secret_key
).
Then, pass those tokens once when you instanciate the Mirage client as following:
// Make sure to replace 'user_id' and 'secret_key' with your tokens
var client = new Mirage("user_id", "secret_key");
Resource Methods
This library implements all methods the Mirage API provides. See the API docs for a reference of available methods, as well as how returned data is formatted.
Task API
➡️ Transcribe Speech
Method:
client.Task.TranscribeSpeech(data, { trace?, stream? })
Reference: Transcribe Speech
Request:
client.Task.TranscribeSpeech(
{
"locale": {
"to": "en"
},
"media": {
"type": "audio/webm",
"url": "https://files.mirage-ai.com/dash/terminal/samples/transcribe-speech/hey-there.weba"
}
},
{
stream : false
}
);
- Response (data):
{
"reason": "processed",
"data": {
"locale": "en",
"parts": [
{
"start": 5.0,
"end": 9.0,
"text": " I'm just speaking some seconds to see if the translation is correct"
}
]
}
}
- Response (stream):
event: system
data: [START]
event: locale
data: "en"
event: part
data: {"start": 5.0, "end": 9.0, "text": " I'm just speaking some seconds to see if the translation is correct"}
event: system
data: [DONE]
➡️ Answer Prompt
Method:
client.Task.AnswerPrompt(data, { trace? })
Reference: Answer Prompt
Request:
client.Task.AnswerPrompt({
"prompt": "Generate an article about Alpacas"
});
- Response:
{
"reason": "processed",
"data": {
"answer": "The alpaca (Lama pacos) is a species of South American camelid mammal. It is similar to, and often confused with, the llama. However, alpacas are often noticeably smaller than llamas. The two animals are closely related and can successfully crossbreed. Both species are believed to have been domesticated from their wild relatives, the vicuña and guanaco. There are two breeds of alpaca: the Suri alpaca and the Huacaya alpaca."
}
}
➡️ Answer Question
Method:
client.Task.AnswerQuestion(data, { trace?, stream? })
Reference: Answer Question
Request:
client.Task.AnswerQuestion(
{
"question": "Should I pay more for that?",
"answer": {
"start": "Sure,"
},
"context": {
"primary_id": "cf4ccdb5-df44-4668-a9e7-3ab31bebf89b",
"conversation": {
"messages": [
{
"from": "customer",
"text": "Hey there!"
},
{
"from": "agent",
"text": "Hi. How can I help?"
},
{
"from": "customer",
"text": "I want to add more sub-domains to my website."
}
]
}
}
},
{
stream : false
}
);
- Response (data):
{
"reason": "processed",
"data": {
"answer": "You can add the Crisp chatbox to your website by following this guide: https://help.crisp.chat/en/article/how-to-add-crisp-chatbox-to-your-website-dkrg1d/ :)",
"sources": []
}
}
- Response (stream):
event: system
data: [START]
event: model
data: "medium"
event: answer
{"index": 0, "chunk": "You can add the Crisp chatbox to"}
event: answer
{"index": 1, "chunk": " your website by following this guide:"}
event: answer
{"index": 2, "chunk": " https://help.crisp.chat/en/article/how-to-add-crisp-chatbox-to-your-website-dkrg1d/"}
event: answer
{"index": 3, "chunk": " :)"}
event: answer
{"index": 4, "chunk": ""}
event: system
data: [DONE]
➡️ Summarize Paragraphs
Method:
client.Task.SummarizeParagraphs(data, { trace? })
Reference: Summarize Paragraphs
Request:
client.Task.SummarizeParagraphs({
"paragraphs": [
{
"text": "GPT-4 is getting worse over time, not better."
},
{
"text": "Many people have reported noticing a significant degradation in the quality of the model responses, but so far, it was all anecdotal."
}
]
});
- Response:
{
"reason": "processed",
"data": {
"summary": "GPT-4 is getting worse over time, not better. We have a new version of GPT-4 that is not improving, but it is regressing."
}
}
➡️ Summarize Conversation
Method:
client.Task.SummarizeConversation(data, { trace? })
Reference: Summarize Conversation
Request:
client.Task.SummarizeConversation({
"transcript": [
{
"name": "Valerian",
"text": "Hello! I have a question about the Crisp chatbot, I am trying to setup a week-end auto-responder, how can I do that?"
},
{
"name": "Baptiste",
"text": "Hi. Baptiste here. I can provide you an example bot scenario that does just that if you'd like?"
}
]
});
- Response:
{
"reason": "processed",
"data": {
"summary": "Valerian wants to set up a week-end auto-responder on Crisp chatbot. Baptiste can give him an example."
}
}
➡️ Categorize Conversations
Method:
client.Task.CategorizeConversations(data, { trace? })
Reference: Categorize Conversations
Request:
client.Task.CategorizeConversations({
"conversations": [
{
"transcript": [
{
"from": "customer",
"text": "Hello! I have a question about the Crisp chatbot, I am trying to setup a week-end auto-responder, how can I do that?"
},
{
"from": "agent",
"text": "Hi. Baptiste here. I can provide you an example bot scenario that does just that if you'd like?"
}
]
}
]
});
- Response:
{
"reason": "processed",
"data": {
"categories": [
"Chatbot Configuration Issue"
]
}
}
➡️ Rank Question
Method:
client.Task.RankQuestion(data, { trace? })
Reference: Rank Question
Request:
client.Task.RankQuestion({
"question": "Hi! I am having issues setting up DNS records for my Crisp helpdesk. Can you help?",
"context": {
"source": "helpdesk",
"primary_id": "cf4ccdb5-df44-4668-a9e7-3ab31bebf89b"
}
});
- Response:
{
"reason": "processed",
"data": {
"results": [
{
"id": "15fd3f24-56c8-435e-af8e-c47d4cd6115c",
"score": 9,
"grouped_text": "Setup your Helpdesk domain name\ntutorials for most providers",
"items": [
{
"source": "helpdesk",
"primary_id": "51a32e4c-1cb5-47c9-bcc0-3e06f0dce90a",
"secondary_id": "15fd3f24-56c8-435e-af8e-c47d4cd6115c",
"text": "Setup your Helpdesk domain name\ntutorials for most providers",
"timestamp": 1682002198552,
"metadata": {
"title": "Setup your Helpdesk domain name"
}
}
]
}
]
}
}
➡️ Translate Text
Method:
client.Task.TranslateText(data, { trace? })
Reference: Translate Text
Request:
client.Task.TranslateText({
"locale": {
"from": "fr",
"to": "en"
},
"type": "html",
"text": "Bonjour, comment puis-je vous aider <span translate=\"no\">Mr Saliou</span> ?"
});
- Response:
{
"reason": "processed",
"data": {
"translation": "Hi, how can I help you Mr Saliou?"
}
}
➡️ Fraud Spamicity
Method:
client.Task.FraudSpamicity(data, { trace? })
Reference: Fraud Spamicity
Request:
client.Task.FraudSpamicity({
"name": "Crisp",
"domain": "crisp.chat",
"email_domain": "mail.crisp.chat"
});
- Response:
{
"reason": "processed",
"data": {
"fraud": false,
"score": 0.13
}
}
➡️ Spam Classify
Method:
client.Task.SpamClassify(data, { trace? })
Reference: Spam Classify
Request:
client.Task.SpamClassify({
"sender": {
"name": "John Doe",
"email": "[email protected]"
},
"transcript": [
{
"from": "customer",
"origin": "chat",
"text": "Hello, I would like to discuss your services"
}
]
});
- Response:
{
"reason": "processed",
"data": {
"fraud": false,
"score": 0.13,
"logprob": -0.01
}
}
Data API
➡️ Context Ingest
Method:
client.Data.ContextIngest(data, { trace? })
Reference: Ingest Context Data
Request:
client.Data.ContextIngest({
"items": [
{
"operation": "index",
"primary_id": "pri_cf44dd72-4ba9-4754-8fb3-83c4261243c4",
"secondary_id": "sec_6693a4a2-e33f-4cce-ba90-b7b5b0922c46",
"tertiary_id": "ter_de2bd6e7-74e1-440d-9a23-01964cd4b7da",
"text": "Text to index here...",
"source": "chat",
"timestamp": 1682002198552,
"metadata": {
"custom_key": "custom_value",
"another_key": "another_value"
}
}
]
});
- Response:
{
"reason": "processed",
"data": {
"imported": true
}
}