CacheNLPResponse

Overview

The idea behind this is to create a local cache for utterances such that we need not to hit the NLP Server Everytime and to identify things that are difficult to understand from AI.

Field in cacheNLPResponse:

  {
      "_id" : ObjectId("61f274dc66d4461fdd645309",
      "updatedAt" : ISODate("2021-08-27T14:25:28.143+05:30"),
      "createdAt" : ISODate("2021-08-27T14:25:28.143+05:30"),
      "__v" : 0,
      "text" : "book test ride",
      "analysisResponse" : {
          "initialNlpAnalysisSnapshot" : {
            "entitySnapshot" : [ ],
            "intentSnapshot" : {
                "score" : 0.9593519568443298,
                "intent" : "book_test_ride"
            },
            "name" : "oriNlp"
        },
        "statusCode" : 200,
        "intentName" : "book_test_ride"
      }
  }

How to add document in cacheNLPResponse

Go into the collection named cacheNLPResponse in DB and click on Add document.

Enter the text and the intent for which you want to map it to.

    db.getCollection("cacheNLPResponse").insert({
        "updatedAt": ISODate("2021-12-13T12:15:51.065+05:30"),
        "createdAt": ISODate("2021-12-13T12:15:51.065+05:30"),
        "__v": 0,
        "text": "recharge",
        "analysisResponse": {
            "initialNlpAnalysisSnapshot": {
                "entitySnapshot": [],
                "intentSnapshot": {
                    "score": 0.9593519568443298,
                    "intent": "recharge"
                },
                "name": "oriNlp"
            },
            "statusCode": 200,
            "intentName": "recharge"
        }
    })

The key “text” contains the predefined text, here we write the text for which we want to trigger the intent.
Ex: for "recharge" intent we can put text as “recharge”.
The key “intent” inside intentSnapsot and the key “intentName” is where we put our intent’s name which we want to trigger.

Ex: “recharge” can be put up here.
The key “score” inside “intentSnapshot” represents the probability with which the match happened, it is mainly like a virtual representation of how a match happen with AI.

Ex: in case of AI, a text matches to an intent with some probabilty so we put this no here as to depict the probabilty of the match for out text to intent.

Use case

To create a local cache in order to avoid hitting the NLP Server for every text.
To map complex text, to an intent which is hard to understand by NLP.

Working

Head to branch product-testFinal1

File name - ../ori-communication-service/BrainService/BrainService.js

User message analysing function [function name - analyzeUserMessage]

It analyses the message entered by the user

async function analyzeUserMessage(socket, serviceName, userData, session_doc, isHeartbeat = false) {
try {
   return new Promise(async (resolve, reject) => {
       .
       .
       if (process.env.cacheNLPResponse === 'true'){
           const canchedNLPDoc = cacheNLPResponses.find(o => (o.text === userText));
           if (canchedNLPDoc && canchedNLPDoc.analysisResponse) {
               console.log("used cachedNLPDoc")
               if (canchedNLPDoc.analysisResponse.initialNlpAnalysisSnapshot) {
               canchedNLPDoc.analysisResponse.initialNlpAnalysisSnapshot.name = NLP_SERVICE_NAME;
               }
               session_doc = await update_entities_in_db_without_check(session_doc, {
               cacheNLPResultSnapshot : canchedNLPDoc.analysisResponse
               });
               return resolve(_.cloneDeep(canchedNLPDoc.analysisResponse));
           }
       }
   }        
   .
   .
   .
}

When a user enters a text, analyzeUserMessage function tries to find a exact match where the text is same to as user entered in the cacheNLPResponse collection. And if it finds a match then the intent mentioned inside the analysisResponse is triggered.
cacheNLP match is only checked if the field named "cacheNLPResponse" in .env file is true.

Result

Entry in Database
Result when user wrote the same text
Here we see that the intent got triggered from the cacheNLPResponse.

Drawback

The drawback in this is that it only works when the string is exactly same as the text in cacheNLPResponse, one extra space can also cause it to dismatch.

Overview​

How to add document in cacheNLPResponse​

Use case​

Working​

Result​