Adding Offline support To SUSI Linux

Till now, SUSI smart speaker was working only as an online model like the other speakers in the market. For the first time, we have introduced a feature which allows the speaker to work offline. We deployed the server on the hardware itself and also provide the option of an online server as a fallback.


The Offline Support was implemented in the following steps


Step 1: Deploying SUSI Server Locally


Firstly , configure a bash script to allow automatic deployment of the server along with the initialization of the susi_linux script.


echo “Deploying local server”
if  [ ! -e “susi-server” ]
   git clone

if [ -e “susi_server” ]
   cd susi_server
   git submodule update –recursive –remote
   git submodule update –init –recursive
   ./gradlew build


The above builds the server and deploys it on ‘localhost:4000’.


Then, add the following test on SUSI Linux wrapper to check if the local server is up and running. Using the local server not adds an offline support but also increases the efficiency by around 30%.

def check_local_server():
   test_params = {
       ‘q’: ‘Hello’,
       ‘timezoneOffset’: int(time.timezone / 60)
       chat_url = ‘http://localhost:4000/susi/chat.json’
       if (requests.get(chat_url, test_params)):
           print(‘connected to local server’)
           global api_endpoint
           api_endpoint = ‘http://localhost:4000’
   except requests.exceptions.ConnectionError:
       print(‘local server is down’)



As shown above, this is a test checking for the local server. If the local server is down, the online server is chosen as a fallback


Step 2: Adding an Offline STT Service

Now, that we are able to process a query offline. We must have a way in which, we can recognize the user’s voice commands without using the internet. For that, we use the service of PocketSphinx. But first, we check if the internet is available or not


def internet_on():
           urllib2.urlopen(‘’, timeout=1)  # nosec #pylint-disable type: ignore
           return True  # pylint-enable
       except urllib2.URLError as err:
           return False


If the internet connection is available, we use the online STT service which is Google STT ( default) and switch over to PocketSphinx in case the internet connection is not available.


Step 3: Adding the Offline TTS service

Finally, we’ll need an offline TTS service which will help us turn SUSI’s response to voice commands. We’ll be using a service called flite TTS as our offline TTS.


elif payload == ‘ConnectionError’:
            self.notify_renderer(‘error’, ‘connection’)                                  self.notify_renderer(‘error’, ‘connection’)
            config[‘default_tts’] = ‘flite’
            os.system(‘play extras/connect-error.wav’)              


We check if there is a ConnectionError, and then we switch to flite TTS after play an error query


Final Output:

We now get a Smart Speaker which is functional without any internet connection.





Fossasia, susi, gsoc, gsoc’18, offline_tts , offline_stt ,flite , pocketsphinx

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Displaying Skills Feedback on SUSI.AI Android App

SUSI.AI has a feedback system where the user can post feedback for a skill using Android, iOS, and web clients. In skill details screen, the feedback posted by different users is displayed. This blog shows how the feedback from different users can be displayed in the skill details screen under feedback section.

Three of the items from the feedback list are displayed in the skill details screen. To see the entire list of feedback, the user can tap the ‘See All Reviews’ option at the bottom of the list.

The API endpoint that has been used to get skill feedback from the server is

The following query params are attached to the above URL to get the specific feedback list :

  • Model
  • Group
  • Language
  • Skill Name

The list received is an array of `Feedback` objects, which hold three values :

  • Feedback String (feedback) – Feedback string posted by a user
  • Email (email) – Email address of the user who posted the feedback
  • Time Stamp – Time of posting feedback

To display feedback, use the RecyclerView. There can be three possible cases:

  • Case – 1: Size of the feedback list is greater than three
    In this case, set the size of the list to three explicitly in the FeedbackAdapter so that only three view holders are inflated. Inflate the fourth view holder with “See All Reviews” text view and make it clickable if the size of the received feedback list is greater than three.
    Also, when the user taps “See All Reviews”, launch an explicit intent to open the Feedback Activity. Set the AllReviewsAdapter for this activity. The size of the list will not be altered here because this activity must show all feedback.
  • Case – 2: Size of the feedback list is less than or equal to three
    In this case simply display the feedback list in the SkillDetailsFragment and there is no need to launch any intent here. Also, “See All Reviews” will not be displayed here.

    Case – 3: Size of the feedback list is zero
    In this case simply display a message that says no feedback has been submitted yet.Here is an example of how a “See All Reviews” screen looks like :


First of all, define an XML layout for a feedback item and then create a data class for storing the query params.

data class FetchFeedbackQuery(
       val model: String,
       val group: String,
       val language: String,
       val skill: String

Now, make the GET request using Retrofit from the model (M in MVP).

override fun fetchFeedback(query: FetchFeedbackQuery, listener: ISkillDetailsModel.OnFetchFeedbackFinishedListener) {

   fetchFeedbackResponseCall = ClientBuilder.fetchFeedbackCall(query)

   fetchFeedbackResponseCall.enqueue(object : Callback<GetSkillFeedbackResponse> {
       override fun onResponse(call: Call<GetSkillFeedbackResponse>, response: Response<GetSkillFeedbackResponse>) {

       override fun onFailure(call: Call<GetSkillFeedbackResponse>, t: Throwable) {

override fun cancelFetchFeedback() {

The feedback list received in the JSON response can now be used to display the user reviews with the help of custom adapters, keeping in mind the three cases already discussed above.


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Display skills sorted in different orders in SUSI.AI Android App

Skills in SUSI.AI were displayed in a random order earlier as per the response received from the server. To provide more flexibility to the users, the skills can be sorted by various orders like top-rated, lexicographical, recently updated and so on. This blog shows how to get sorted skills from the server using the getSkillList.json API.

API Information

For requesting a list of SUSI skills, the endpoint used is /cms/getSkillList.json.
This will give you the sorted skills as per the applied filter. Some of the filters include top rated skills, recently updated skills, newly created skills, etc.

Base URL :

Parameters to be passed :

  • group – This is the group to which a skill belongs to.
  • language – The language in which the skill is needed.
  • applyFilter – This parameter tells if the filtering needs to be enabled.
  • filter_type – This is the order in which the skills need to be sorted and is applicable if applyFilter is true.
  • filter_name – This tells whether the order of sorting needs to be ascending or descending and is applicable if applyFilter is true.

Currently, there are following filters available :

  • Top Rated : The skills will be sorted based on the skills ratings by users.

filter_type :  rating
filter_name : ascending or descending (based on the requirement).


  • Lexicographical : The skills will be sorted in alphabetical order.

filter_type :  lexicographical
filter_name : ascending (to show skills in the order A-Z) or (descending to show skills in the order Z-A).


  • Newly Created : The skills will be displayed based on the date of creation.

filter_type :  creation_date
filter_name : ascending to show newly created skills first and descending to show the oldest created skills first.


  • Recently Updated : The skills will be sorted based on the date when they were last updated.

filter_type :  modified_date
filter_name : ascending or descending as per requirement.


  • Feedback Count : The skills will be sorted as per the feedback count.

filter_type :  feedback
filter_name : ascending to show skills with the most number of feedbacks first and descending to show skills with the least number of feedbacks first.


  • This Week Usage : The skills will be sorted as per the usage analytics of the week.

filter_type :  usage
duration : 7
filter_name : descending to show the most used skill first and vice-versa.


  • This Week Usage : The skills will be sorted as per the usage analytics for the last 30 days.

filter_type :  usage
duration : 30
filter_name : descending to show the most used skill first and vice-versa.


Note: In all the above cases, the ‘applyFilter’ param will be passed with the value ‘true’ otherwise the skills will not be sorted.

Here is an example of a URL for displaying the top rated skills:


To make a request to the getSkillList.json API, make a GET request as follows :

Call<ListSkillsResponse> fetchListSkills(@QueryMap Map<String, String> query);


Here the query map contains all the aforementioned params.

Now, make the GET request using Retrofit from the model :

private lateinit var authResponseCallSkills: Call<ListSkillsResponse>

override fun fetchSkills(group: String, language: String, listener: IGroupWiseSkillsModel.OnFetchSkillsFinishedListener) {
   val queryObject = SkillsListQuery(group, language, "true", "descending", "rating")
   authResponseCallSkills = ClientBuilder.fetchListSkillsCall(queryObject)

   authResponseCallSkills.enqueue(object : Callback<ListSkillsResponse> {
       override fun onResponse(call: Call<ListSkillsResponse>, response: Response<ListSkillsResponse>) {
           listener.onSkillFetchSuccess(response, group)

       override fun onFailure(call: Call<ListSkillsResponse>, t: Throwable) {

override fun cancelFetch() {
   try {
   } catch (e: Exception) {


The skills in the filteredData array, received in the JSON response, shall be sorted in the order based on the filter_type and filter_name params that you passed. Now, this array can be used to display skills on the skills listing page.


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Displaying Avatar of Users using Gravatar on SUSI.AI Android App

This blog post shows how the avatar image of the user is displayed in the feedback section using the Gravatar service on SUSI.AI Android app. A Gravatar is a Globally Recognized Avatar. Your Gravatar is an image that follows you from site to site appearing beside your name when you do things like comment or post on a blog. So, it was decided to integrate the service to SUSI.AI, so that it helps to  identify the user via the avatar image too.


  • The aim is to use the user’s email address to get his/her avatar. For this purpose, Gravatar exposes a publicly available avatar of the user, which can be accessed via following steps :
    • Creating the Hash of the email
    • Sending the image request to Gravatar
  • For the purpose of creating the MD5 hash of the email, use the MessageDigest class. The function takes an algorithm such as SHA-1, MD5, etc. as input and returns a MessageDigest object that implements the specified digest algorithm.

Perform a final update on the digest using the specified array of bytes, which then completes the digest computation. That is, this method first calls update(input), passing the input array to the update method, then calls digest().

fun toMd5Hash(email: String?): String? {
   try {
       val md5 = MessageDigest.getInstance("MD5")
       val md5HashBytes: Array<Byte> = md5.digest(email?.toByteArray()).toTypedArray()
       return byteArrayToString(md5HashBytes)
   } catch (e: Exception) {
       return null


  • Convert the byte array to String, which is the requires MD5 hash of the email string.

fun byteArrayToString(array: Array<Byte>): String {
   val result = StringBuilder(array.size * 2)
   for (byte: Byte in array) {
       val toAppend: String = String.format("%x", byte).replace(" ", "0")
   return result.toString()


  • Now, a URL is generated using the hash. This is the URL that will be used to fetch the avatar.
  • The URL format is, where HASH is the hash of the email of the user. In case, the hash is invalid, Gravatar returns a placeholder avatar.
  • Also, append ‘.jpg’ to the URL to maintain image format consistency in the app.
  • Finally, load this URL using Picasso and set it in the appropriate view holder.

fun setAvatar(context: Context, email: String?, imageView: ImageView) {
   val imageUrl: String = GRAVATAR_URL + toMd5Hash(email) + ".jpg"


You can now see the avatar image of the users in the feedback section. 😀



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Use objects to pass multiple query parameters when making a request using Retrofit

There are multiple instances where there is a need to make an API call to the SUSI.AI server to send or fetch data. The Android client uses Retrofit to make this process easier and more convenient.
While making a GET or POST request, there are often multiple query parameters that need to sent along with the base url. Here is an example how this is done:

Call<GetSkillFeedbackResponse> fetchFeedback(
                          @Query("model") String model,
                          @Query("group") String group,
                          @Query("language") String language,
                          @Query("skill") String skill);


It can be seen that the list of params can be very long indeed. A long list of params would lead to more risks of incorrect key value pairs and typos.

This blog would talk about replacing such multiple params with objects. The entire process would be explained with the help of an example of the API call being made to the getSkillFeedback.json API.

Step – 1 : Replace multiple params with a query map.

Call<GetSkillFeedbackResponse> fetchFeedback(@QueryMap Map<String, String> query);


Step – 2 : Make a data class to hold query param values.

data class FetchFeedbackQuery(
       val model: String,
       val group: String,
       val language: String,
       val skill: String


Step – 3 : Instead of passing all different strings for different query params, pass an object of the data class. Hence, add the following code to the ISkillDetailsModel.kt interface.


fun fetchFeedback(query: FetchFeedbackQuery, listener: OnFetchFeedbackFinishedListener)


Step – 4 : Add a function in the singleton file ( to get SUSI client. This method should return a call.


public static Call<GetSkillFeedbackResponse> fetchFeedbackCall(FetchFeedbackQuery queryObject){
   Map<String, String> queryMap = new HashMap<String, String>();
   queryMap.put("model", queryObject.getModel());
   queryMap.put("group", queryObject.getGroup());
   queryMap.put("language", queryObject.getLanguage());
   queryMap.put("skill", queryObject.getSkill());
   //Similarly add other params that might be needed
   return susiService.fetchFeedback(queryMap);


Step – 5 : Send a request to the getSkillFeedback.json API by passing an object of FetchFeedbackQuery data class to the fetchFeedbackCall method of the file which in turn would return a call to the aforementioned API.


override fun fetchFeedback(query: FetchFeedbackQuery, listener:  
                                   ISkillDetailsModel.OnFetchFeedbackFinishedListener) {

   fetchFeedbackResponseCall = ClientBuilder.fetchFeedbackCall(query)


No other major changes are needed except that instead of passing individual strings for each query param as params to different methods and creating maps at different places like in a view, create an object of FetchFeedbackQuery class and use it to pass data throughout the project. This ensures type safety. Also, data classes reduce the code length significantly and hence are more convenient to use in practice.


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Displaying Avatar Image of Users using Gravatar on SUSI.AI

This blog discusses how the avatar of the user has been shown at different places in the UI like the app bar, feedback comments, etc using the Gravatar service on SUSI.AI. A Gravatar is a Globally Recognized Avatar. Your Gravatar is an image that follows you from site to site appearing beside your name when you do things like comment or post on a blog. So, the Gravatar service has been integrated in SUSI.AI, so that it helps identify the user via the avatar too.

Going through the implementation

  • The aim is to get an avatar of the user from the email id. For that purpose, Gravatar exposes a publicly available avatar of the user, which can be accessed via the following steps :
    • Creating the Hash of the email
    • Sending the image request
  • For creating the MD5 hash of the email, use the npm library md5. The function takes a string as input and returns the hash of the string.
  • Now, a URL is generated using this hash.
  • The URL format is, where ‘HASH’ is the hash of the email of the user. In case, the hash is invalid, Gravatar returns a default avatar image.
  • Also, append ‘.jpg’ to the URL to maintain image format consistency on the website. When, the generated URL is used in an <img> tag, it behaves like an image and an avatar is returned when the URL is requested by the browser.
  • It has been displayed on various instances in the UI like app bar , feedback comments section, etc. The implementation in the feedback section has been discussed below.
  • The CircleImage component has been used for displaying the avatar, which takes name as a required property and src as the link of the image, if present. Following function returns props to the CircleImage component.

import md5 from 'md5';
import { urls } from './';

// urls.GRAVATAR_URL = ‘’;

let getAvatarProps = emailId => {
  const emailHash = md5(emailId);
  const GRAVATAR_IMAGE_URL = `${urls.GRAVATAR_URL}/${emailHash}.jpg`;
  const avatarProps = {
    name: emailId.toUpperCase(),
  return avatarProps;

export default getAvatarProps;


  • Then pass the returned props on the CircleImage component and set it as the leftAvatar property of the feedback comments ListItem. Following is the snippet –

  leftAvatar={<CircleImage {...avatarProps} size="40" />}
        0,'@') + 1,
      <div className="feedback-timestamp">


  • This displays the avatar of the user on the UI. The UI changes have been shown below :


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Scan and detect available networks in SUSI.AI Android app

We all have experienced Apple HomPad, Google Home, Alexa etc or read about smart speakers which offer interactive actions over voice commands. Smart speaker uses hotword for activation. They utilize WiFi, bluetooth and other wireless protocols.

SUSI.AI is also coming with an Open Source smart speaker using  as simple as a RaspberryPi for the speaker which can perform various actions like playing music etc over voice commands. To use SUSI Smart Speaker, you have to connect it to the SUSI iOS or Android App. You can manage your connected devices in SUSI Android,SUSI iOS and Web clients. Here we will see initial setups for connecting SUSI Smart Speaker with an Android device.

Unlike iOS, Android allows any app to change the WiFi settings of the phone if the specific permissions are added in the app and on the run time while using the app. To connect to a particular WiFi in android given that permissions are enabled a few lines of code is sufficient enough to connect to the new WiFi network, but in our case before connecting to the WiFi we must know that the SUSI.AI hotspot is available around or not.

To know if the SUSI hotspot is available or not we have to scan for the available WiFi networks present and then choose the SUSI WiFi hotspot.

The app simply detects the available WiFi networks and displays a set of them that have the name SUSI.AI. To detect the available WiFi networks we have used a BroadCastReceiver that listens for the action :


The broadcast receiver listens to this action and fetches the list of networks available. The broadcast receiver is registered after the objects of WifiReceiver class and WifiManager class are declared. Once the broadcast receiver is registered the object of WifiManager is used to start the scanning process of the available Wifi networks.

Here is the object of the WifiManager and WifiReceiver declared in the onCreate() method of the DeviceActivity.kt file :

mainWifi = application.getSystemService(Context.WIFI_SERVICE) as WifiManager
receiverWifi = WifiReceiver()

The broadcast receiver class that scans the available networks is as follows :

inner class WifiReceiver : BroadcastReceiver() {
  override fun onReceive(p0: Context?, p1: Intent?) {
      Timber.d(“Inside the app”)
      if (p1 != null) {
          if (p1.action.equals(WifiManager.SCAN_RESULTS_AVAILABLE_ACTION)) {
              var wifiList: List<ScanResult> = ArrayList<ScanResult>()
              wifiList = mainWifi.getScanResults()

In the above code all the available networks on scanning are sent to the presenter for further processing.

In the presenter class in the method inflateList() the available WiFi networks list is scanned on the basis of their SSID and all the networks with name are “SUSI.AI” are selected and then displayed as a list to the user.

The inflateList() function is as follows :

override fun inflateList(list: List<ScanResult>) {
  Timber.d(“size “ + list.size)
  connections = ArrayList<String>()
  for (i in list.indices) {
      if (list[i].SSID.equals(utilModel.getString(R.string.device_name)))

  if (connections.size > 0) {
  } else {
      deviceView?.onDeviceConnectionError(utilModel.getString(R.string.no_device_found), utilModel.getString(R.string.setup_tut))

Now after the wifi list is scanned the sorted and selected list is sent to the Recycler adapter that will display the list to available SUSI networks to the user.



  1. Android Manipulation of wifi using WifiManager :
  2. Scan and list all Wifi in Android :
  3. Kotlin android broadcast receivers :


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Feature to Report a Skill as Inappropriate

There are hundreds of skills on SUSI Skill CMS. News skills are created daily. Often some skills are made only for testing purpose. Also, some skills are published even though they are not completely developed. Further users may also create some skills that are not suitable for all age groups. To avoid this a skill reporting feature has been added on the CMS.

Server side implementation

Create a JSONTray object in that stores the reported skill data. These reports are stored in reportedSkill.json.

Then create an API to report a skill as inappropriate. It runs at /cms/reportSkill.json endpoint and accepts the following parameters :

  • Model
  • Group
  • Language
  • Skill name
  • Feedback

A user should be logged in to report a skill as inappropriate, so the minimum user role is set to user.

public ServiceResponse serviceImpl(Query call, HttpServletResponse response, Authorization authorization, final JsonObjectWithDefault permissions) throws APIException {
	String model_name = call.get("model", "general");
	File model = new File(DAO.model_watch_dir, model_name);
	String group_name = call.get("group", "Knowledge");
	File group = new File(model, group_name);
	String language_name = call.get("language", "en");
	File language = new File(group, language_name);
	String skill_name = call.get("skill", null);
	File skill = SusiSkill.getSkillFileInLanguage(language, skill_name, false);
	String skill_feedback = call.get("feedback", null);

Next search for the reported skill in reportedSkill.json through DAO object. If it is found then add a new report object to it else create a new skill object containing the report and store it in the reportedSkill.json.

JSONObject reportObject = new JSONObject();
Timestamp timestamp = new Timestamp(System.currentTimeMillis());
if (authorization.getIdentity().isEmail()) reportObject.put("email", idvalue);
if (authorization.getIdentity().isUuid()) reportObject.put("uuid", idvalue);
reportObject.put("feedback", skill_feedback);
reportObject.put("timestamp", timestamp.toString());
skillName.put("reports", reports);

Also, increment the counter of the total number of reports on the skill. This helps in getting better an overview of the skill and in future may also help in taking automatic actions on the reported skills.

Finally, add the API to



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Limit the Chatbots to Run on Specified Domains

SUSI botbuilder enables users to make their own private skill and deploy a chatbot widget in their websites. Users can copy paste a javascript code into their website’s source code to activate the bot. But what if someone copies that code from your website and put it in their own website? You won’t want the chat bot to work for such users in some cases. Thus we have a feature through which the bot creator can restrict the usage of the chatbot to only certain domains. The chat bot will not work from other domains.

Understanding the APIs Used

In working of the chatbot, there are mainly two APIs used from the server which play a mainstream role. The first API is the cms/getSkillMetaData.json API. It is used to get the design and configurations of the chatbot. The second API is the susi/chat.json API. It is used to get responses from the server applying the private skill. By restricting the chatbot usage we try to restrict the usage of these two APIs. Also, on the client side we display the chatbot only if the server sends a valid response indicating that the chatbot is legitimate. However, this can be circumvented if the person modifies the javascript of the chatbot. Hence, we need to secure the above two APIs. We check the domain from where the request is coming by checking the referer field in the request’s header.

Securing the APIs

In each of the above two APIs, we check if the bot owner has checked “allow bot only on own site”. If no, then the APIs can be accessed from any domain we need not check. If selected yes, then we need to check if the current site’s domain is allowed in the allowed sites list. For this, we extract the current domain from the request’s referer field. The allowed sites list is fetched from the configure object of that skill.

public static boolean allowDomainForChatbot(JSONObject configureObject, String referer) {
    Boolean allowed_site = true;
    if (configureObject.getBoolean("allow_bot_only_on_own_sites") && configureObject.has("allowed_sites") && configureObject.getString("allowed_sites").length() > 0) {
        allowed_site = false;
        if (referer != null && referer.length() > 0) {
            String[] sites = configureObject.getString("allowed_sites").split(",");
            for (int i = 0; i < sites.length; i++) {
                String site = sites[i].trim();
                int referer_index = referer.indexOf("://");
                String host = referer;
                if (referer.indexOf('/',referer_index+3) > -1) {
                    host = referer.substring(0,referer.indexOf('/',referer_index+3));
                if (host.equalsIgnoreCase(site)) {
                    allowed_site = true;
    return allowed_site; 



Not allowed from other domains:
(For getSkillMetaData.json API)

Allowed on approved domains:
(For getSkillMetaData.json API)



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Applying Private Skill as Web Bots in SUSI Chat

Along with public skills, we now have private skills as web bots! Users can create their own private skills which can be used only by them and in the chatbots deployed by them. The SUSI Server accepts parameters to identify a valid private skill and applies that private skill for a particular chat. It then executes the query and sends the response to the client. This blog explains how private skill is applied in the SUSI Chat.

Understanding the API

The API to receive response from SUSI is /susi/chat.json. For applying only the public skills, we can send a request like /susi/chat.json?q=hello. Here only one parameter “q” is involved in which we send the query. However, for a private skill, the following parameters are involved:

  • privateskill – when the client sends this parameter, it indicates to use a private skill.
  • userid – the userid of the user who has created the private skill
  • group – the group name of the private skill
  • language – the language of the private skill
  • skill – the skill name of the private skill

Thus, the four parameters userid, group, language, skill serves to uniquely identify a private skill.

Fetching the private skill

After the client sends the appropriate parameters to apply a private skill, the server must actually apply the private skill. This is done in a similar manner how persona and dream skills are applied. The First step is the fetch the private skill from the susi_private_skill_data folder.

// read the private skill
File private_skill_dir = new File(DAO.private_skill_watch_dir,userId);
File group_file = new File(private_skill_dir, group_name);
File language_file = new File(group_file, language);
skillfile = SusiSkill.getSkillFileInLanguage(language_file, skill_name, false);
String text = new String(Files.readAllBytes(skillfile.toPath()), StandardCharsets.UTF_8);

Applying the private skill

After we have fetched the private skill, the next step is to apply it. To do this, we create a SusiMind object and add it to the general minds variable. Thus the skill will be applied to the particular chat with the highest priority, since it will be the first skill to be added to the minds variable. Later, the public skills can be added to the minds variable, thus their priority will be lower than the private skill.

String text = new String(Files.readAllBytes(skillfile.toPath()), StandardCharsets.UTF_8);
// fill an empty mind with the private skill
SusiMind awakeMind = new SusiMind(DAO.susi_chatlog_dir, DAO.susi_skilllog_dir); // we need the memory directory here to get a share on the memory of previous dialoges, otherwise we cannot test call-back questions
JSONObject rules = SusiSkill.readLoTSkill(new BufferedReader(new InputStreamReader(new ByteArrayInputStream(text.getBytes(StandardCharsets.UTF_8)), StandardCharsets.UTF_8)), SusiLanguage.unknown, dream);
awakeMind.learn(rules, skillfile);
SusiSkill.ID skillid = new SusiSkill.ID(skillfile);
SusiSkill activeskill = awakeMind.getSkillMetadata().get(skillid);
// we are awake!



Example API: http://localhost:4000/susi/chat.json?q=hi&privateskill=1&userid=17a70987d09c33e6f56fe05dca6e3d27&group=Knowledge&language=en&skill=knowprides

The skill exists in the correct location:

The skill file content is:

Thus, on sending the query “tell me” with the other parameters, we get the correct reply i.e “yes sure” from the server:




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