Multiple Languages Filter in SUSI.AI Skills CMS and Server

There are numerous users of SUSI.AI globally. Most of the users use skills in English languages while some prefer their native languages. Also,there are some users who want SUSI skills of multiple languages. So the process of fetching skills from multiple languages has been explained in this blog.

Server side implementation

The language parameter in ListSkillService.java is modified to accept a string that contains the required languages separated by a comma. Then this parameter is split by comma symbol which returns an array of the required languages.

String language_list = call.get("language", "en");
String[] language_names = language_list.split(",");

Then simple loop over this array language by language and keep adding the the skills’ metadata, in that language into the response object.

for (String language_name : language_names) {
	// fetch the skills in this language.
}

CMS side implementation

Convert the state variable languageValue, in BrowseSkill.js, from strings to an array so that multiple languages can be kept in it.

languageValue: ['en']

Change the language dropdown menu to allow selection of multiple values and attach an onChange listener to it. Its value is the same as that of state variable languageValue and its content is filled by calling a function languageMenuItems().

<SelectField
    multiple={true}
    hintText="Languages"
    value={languageValue}
    onChange={this.handleLanguageChange}
  >
    {this.languageMenuItems(languageValue)}
</SelectField>

The languageMenuItems() function gets the list of checked languages as a parameter. The whole list of languages are stored in a global variable called languages. So this function loops over the list of all the languages and check / uncheck them based on the values passed in the argument. It build a menu item for each language and put the ISO6391 native name of that language into the menu item.

languageMenuItems(values) {
  return languages.map(name => (
    <MenuItem
      insetChildren={true}
      checked={values && values.indexOf(name) > -1}
      value={name}
      primaryText={
        ISO6391.getNativeName(name)
          ? ISO6391.getNativeName(name)
          : 'Universal'
      }
    />
  ));
}

While the language change handler gets the values of the selected languages in the form of an array, from the drop down menu. It simply assigns this value to the state variable languageValue and calls the loadCards() function to load the skills based on the new filter.

this.setState({ languageValue: values }, function() {
    this.loadCards();
  });

 References

Testing Endpoints on Local Server

All servlets in SUSI.AI have a BaseUserRole defined. It represents the access level you need to access the endpoint corresponding to that servlet. The lowermost BaseUserRole a SUSI.AI servlet can have is ANONYMOUS, which means that anyone can access the endpoint corresponding to these endpoints. But if the BaseUserRole is higher than that, then you need an access token to access the endpoint. This blog post explains how you can get access token to access the endpoints on a local server.

What are endpoints in an API?

An endpoint in API is one end of a communication channel. When an API interacts with another system, the touchpoints of this communication are considered endpoints. For APIs, an endpoint can include a URL of a server or service. Each endpoint is the location from which APIs can access the resources they need to carry out their function.

APIs work using ‘requests’ and ‘responses.’ When an API requests information from a web application or web server, it will receive a response. The place that APIs send requests and where the resource lives, is called an endpoint.

For example, the endpoint for https://api.susi.ai/cms/getSkillRating.json?queryParameters would be /cms/getSkillRating.json.

Servlets and Endpoints in SUSI.AI

All servlets in our SUSI project define an endpoint and also define a BaseUserRole, that is, the amount of privileges required to access the information on those endpoints. If the BaseUserRole defined is ANONYMOUS, then anyone can access the endpoint directly. But if the BaseUserRole is anything higher than that, then we would need an access token to access that.

How to get Access Token?

If you’re trying to access the endpoints with BaseUserRole higher than ANONYMOUS on the actual hosted server, then you can simply login to https://chat.susi.ai and get the access token from the Network tab of the Developers Tool. We can then use that token and pass that as a query parameter along with the other parameters of that particular endpoint. For example,

http://localhost:4000/aaa/listUserSettings.json?access_token=6O7cqoMbzlClxPwg1is31Tz5pjVwo3

 

But, the problem arises when you are trying to access such endpoints on local server. The local User data is completely different from the server User data. Hence, we need to generate an access token in localhost itself.

To generate access token for local server, we need to follow these steps :

  1. First, we need to hit the /aaa/signup.json endpoint with a new account credentials which we want to register for the localhost session. This is done as shown in below example:

http://localhost:4000/aaa/signup.json?signup=anyEmail&password=anyPassword

 

  1. Then, we need to hit the /aaa/login.json endpoint with the same credentials you registered in the previous step. This is done as shown in below example:

http://localhost:4000/aaa/login.json?login=yourEmail&type=access-token&password=yourPassword

 

If you’ve entered the registered credentials correctly, then the output of the /aaa/login.json endpoint would be a JSON as shown below:

{
  "accepted": true,
  "valid_seconds": 604800,
  "access_token": "7JPi7zNwemg1YYnr4d9JIdZMaIWizV",
  "message": "You are logged in as anyemail",
  "session": {"identity": {
    "type": "host",
    "name": "127.0.0.1_4e75edbb",
    "anonymous": true
  }}
}

 

As it can be seen from the above JSON response, we get the access token which we needed. Hence, copy this access token and store it somewhere because you can now use this access token to access the endpoints with BaseUserRole as User for this localhost session.

Note that you’ll have to follow all the above steps again if you start a fresh localhost session.

Resources

How api.susi.ai responds to a query and send response

A direct way to access raw data for a query is https://api.susi.ai. It is an API of susi server which sends responses to a query by a user in form of a JSON (JavaScript Object Notation) object (more on JSON here). This JSON object is the raw form of any response to a query which is a bunch of key (attribute) value pair. This data is then send from the server to various APIs like chat.susi.ai, susi bots, android and ios apps of SUSI.AI.

Whenever a user in using an API, for example chat.susi.ai, the user send a query to the API. This query is then sent by the API as a request to the susi server. The server then process the request and sends the answer to the query in form of json data as a response to the request. This request is then used by the API to display the answer after applying stylings to the json data. Continue reading How api.susi.ai responds to a query and send response

Populating Database for different Event Types and Event Topics on Open Event Server

The Open Event Server enables organizers to manage events from concerts to conferences and meetups. It offers features for events with several tracks and venues. It uses the JSON 1.0 Specification and build on top of Flask Rest Json API (for building Rest APIs) and Marshmallow (for Schema). In this blog, we will talk about how to add API for accessing and updating the Speaker Image Size on Open Event Server. The focus is on its API creation.

In this blog, we will talk about how to populate database for different event types and event topics in the Open Event Server.

Populating the Database

Using populate_db,py for populating the database.

Now, let’s try to understand this now.

  1. First of all, we will write two functions create_event_topics and create_event_types in populate_db.py .
  2. In these function we will make a list of all the event topics and event types which we want to populate in database.
  3. We will loop through these lists to create their objects if not present.
  4. Last step is to call these functions in populate and populate_without_print functions in populate_db.py itself.

Resources

Skill Ratings Over Time

The SUSI SKill CMS provides an option to rate and review a skill. These feedbacks help the skill creators to improve the skills. Also, the ratings and reviews can be updated by the reviewer. But the CMS only provides the current rating of a skill. What if a user or a developer wants to see how that skill has performed over time? Are there any improvements in the skill or not?

For that, we need the skill ratings over time !

Server side implementation

Create a ratingsOverTime.json file to store the monthly average rating of the skills and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the timestamp for every month, the average ratings on a skill in that month and the total number of ratings in that month.

public static JsonTray ratingsOverTime;

Path ratingsOverTime_per = susi_skill_rating_dir.resolve("ratingsOverTime.json");
Path ratingsOverTime_vol = susi_skill_rating_dir.resolve("ratingsOverTime_session.json");
ratingsOverTime = new JsonTray(ratingsOverTime_per.toFile(), ratingsOverTime_vol.toFile(), 1000000);
OS.protectPath(ratingsOverTime_per);
OS.protectPath(ratingsOverTime_vol);

Now whenever a user rates a skill, the data in ratingsOverTime.json needs to be updated. For this fetch the overall rating data of the current month. Multiply the average rating with the total number of ratings (count) of that month.

sum = average_rating X number_of_ratings

Then add the rating given by the current user to this sum and divide by count + 1 to again get the new average rating. Also increment the total number of ratings by 1.

new_sum = sum + rating_by_user

new_avg = new_sum/(count+1)

number_of_ratings =  number_of_ratings + 1

float totalRating = skillRating * ratingCount;
float newAvgRating = (totalRating + skill_stars)/(ratingCount + 1);
ratingObject.put("rating", newAvgRating);
ratingObject.put("count", ratingCount + 1);

Now we have got the ratings over time stored in ratingsOverTime.json file. An API to access this data is also required. So create an API GetRatingOverTime.java returns the ratings over time of a particular skill. The API has the following attributes :

Endpoint : /cms/getRatingsOverTime.json

Minimum user role : anonymous

Parameters : model, group, language and skill

JSONArray skillRatings = languageName.getJSONArray(skill_name);
result.put("skill_name", skill_name);
result.put("ratings_over_time", skillRatings);
return new ServiceResponse(result);

It fetches the data corresponding to the skill from ratingsOverTime.json and returns it to the CMS.

Add this API to SusiServer.java

//Skill ratings over time
GetRatingsOverTime.class

References

Creating Feedback Logs for Analysis

The thumbs up and thumbs down feedback on the clients is meant for the improvement of the skills in SUSI.AI. So we need to scope the feedback system to a particular interaction rather than skill as a whole. The feedback logs can be used for various kinds of analysis and machine learning.

Server side implementation

Components of Feedback Log:

  • User ID – For identification of a feedback given by a particular user. For consistency in data, the user should not be able to change the feedback over the same interaction.
  • Interaction:
    • User query
    • SUSI Reply
  • Client location – The response of a skill may not be interesting for the users of a particular country. That means the skill should give localised results.
  • Skill path – The path on the server where the skill is stored.

Create a feedbackLogs.json file to store the logs of feedback given by the user and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the above mentioned components.

public static JsonTray feedbackLogs; 

Path feedbackLogs_per = susi_skill_rating_dir.resolve("feedbackLogs.json");
Path feedbackLogs_vol = susi_skill_rating_dir.resolve("feedbackLogs_session.json");
feedbackLogs = new JsonTray(feedbackLogs_per.toFile(), feedbackLogs_vol.toFile(), 1000000);
OS.protectPath(feedbackLogs_per);
OS.protectPath(feedbackLogs_vol);

Create FeedbackLogService.java file that acts as an API to create the feedback logs. The API accepts the feedback data from the client and stores it into the json file using DAO object. The user should be logged in to give feedback on an interaction. So keep the minimum user role as USER to access the API.

JSONObject feedbackLogObject = new JSONObject();
feedbackLogObject.put("timestamp", timestamp);
feedbackLogObject.put("uuid", idvalue);
feedbackLogObject.put("feedback", skill_rate);
feedbackLogObject.put("user_query", user_query);
feedbackLogObject.put("susi_reply", susi_reply);
feedbackLogObject.put("country_name", country_name);
feedbackLogObject.put("country_code", country_code);
feedbackLogObject.put("skill_path", skill_path);

The API is accessible at /cms/feedbackLog.json endpoint.

Send feedback log from Web Client

The feedback API should be called only if the user is logged in. When the user presses the feedback buttons fetch the required data for log (access token, user query, susi response, country and user feedback) and POST them on the feedbackLog.json API.

let rateEndPoint =   BASE_URL + '/cms/feedbackLog.json?model=' + skill.model + '&group=' + skill.group + '&language=' + skill.language + '&skill=' + skill.skill + '&rating=' + rating + '&access_token=' + accessToken + '&user_query=' + interaction.userQuery + '&susi_reply=' + interaction.susiReply + '&country_name=' + country.countryName + '&country_code=' + country.countryCode ;

$.ajax({
  url: rateEndPoint,
  success: function(response) {
      console.log('Skill rated successfully');
  }
})

References

Adding “All” in Skill Categories

The SUSI SKill CMS has various filters to explore the skills of interest.  For example skill category and skill language. The skills are stored in the susi_skill_data Github repo in the following structure:

susi_skill_data/models/<model_name>/<group_name>/<language_name>/<skill_name>.txt

NOTE: group and category are same terms and can be used interchangeably

So when a category filter is applied the skills from the corresponding directory are returned.

susi_skill_data/models/<model_name>/<group_name>/<language_name>/<skill_name>.txt

But there’s no directory called “All”,  so how to get skills of all groups? For this, we need to loop through all the directories present in the model.

Server side implementation

Create a helper function that returns a list of all the folders present in a directory. The function accepts the parent directory name and an empty list. First, fetch all the items (files and folders) present in that directory and store them in an array. Then apply a filter over the array to check if the element is a directory and doesn’t start with a dot(.) i.e., it’s not hidden. Add the filtered array to the list.

private void listFoldersForFolder(final File folder, ArrayList<String> fileList) {
    File[] filesInFolder = folder.listFiles();
    if (filesInFolder != null) {
        Arrays.stream(filesInFolder)
                .filter(fileEntry -> fileEntry.isDirectory() && !fileEntry.getName().startsWith("."))
                .forEach(fileEntry -> fileList.add(fileEntry.getName() + ""));
    }
}

Fetch the group name form the request and add a check if the CMS is asking for all skill. Otherwise, return the skills of a particular group only.

String group_name = call.get("group", "Knowledge");
if (group_name.equals("All")) {
  // Return the list of all skills
} else {
  // Return the list of a skills in a particular group only
}

To fetch the list of all skills, call the listFoldersForFolders() function with the model name and an empty list as arguments. The function adds all the directories, present in that model directory, to folderList.

File allGroup = new File(String.valueOf(model));
ArrayList<String> folderList = new ArrayList<String>();
listFoldersForFolder(allGroup, folderList);

Then loop over all the groups present in the list to get all the skills present in that group. This process is the same as the existing process of getting skills of a particular category. Just keep adding the skill list to a global array.

CMS side implementation

The list of categories is first fetched from the API and then added to the dropdown menu. Since the API doesn’t return “All” in it, so we need to push it to the list manually.

groups.push(<MenuItem
                value="All"
                key="All"
                primaryText="All" />);

References

Adding System Image for Event Categories

The Open Event Server is using the JSON 1.0 Specification and build on top of Flask Rest Json API (for building Rest APIs) and Marshmallow (for Schema). In this blog, we will talk about how to add feature of System Image for Event Categories on Open Event Server. The focus is on Model updation, Schema updation and migrating the Database.

Model Updation

For adding System Image, we’ll update our Model EventTopic.

In this feature, we are providing rights to the Admin to add a system image for each Event Category so that if no image is given by a organizer of event on event creation then it will use the system image of that Event Category as event image by default.

Here we are adding a Column named system_image_url which is of type String. This value cannot be nullable and having a default value.

Migrating the Database

For the migrating the Database we will use simple commands.

This command runs migrations. If it cause problems naming Multiple Migration Head, then you need to run

This problem is caused when two developers push a migration file without merging two heads to achieve one head.

The above command will give us ids of two migration heads.

This command is merging two migration heads.

This command is upgrading the migrations.

Finally, we migrate the Database using above command.

Schema Updation

For the system image, we’ll update the Schema EventTopicSchema as follows

In this feature, to provide system image for each Event Category we’ll add a field named system_image_url in the Schema.

Here we are adding a field named system_image_url which is of marshmallow field type URL. This value cannot be none.

Validating the Event Image and using System Image by default

In this step, we’ll check if a event image is provided by organizer. If that is not provided then we’ll use system image of Event Category as Event Image.

Here, we will first take the event topic of event as added by the organizer. Then we will fetch the the database row in Event Topic model which has id == event_topic_id . Then we will return the system image url of that event topic to the event image.

So we saw how we could provide a default image for any event.

Resources

Adding Event Roles concerning a User on Open Event Server

The Open Event Server enables organizers to manage events from concerts to conferences and meetups. It offers features for events with several tracks and venues. Event managers can create invitation forms for speakers and build schedules in a drag and drop interface. The event information is stored in a database. The system provides API endpoints to fetch the data, and to modify and update it. The Open Event Server is based on JSON 1.0 Specification and hence build on top of Flask Rest Json API (for building Rest APIs) and Marshmallow (for Schema).

In this blog, we will talk about how to add different events role concerning a user on Open Event Server. The focus is on its model and Schema updation.

Model Updation

For the User Table, we’ll update our User Model as follows:

Now, let’s try to understand these hybrid properties.

In this feature, we are providing Admin the rights to see whether a user is acting as a organizer, co-organizer, track_organizer, moderator, attendee and registrar of any of the event or not. Here, _is_role method is used to check whether an user plays a event role like organizer, co-organizer, track_organizer, moderator, attendee and registrar or not. This is done by querying the record from UserEventsRole model. If the record is present then the returned value is True otherwise False.

Schema Updation

For the User Model, we’ll update our Schema as follows

Now, let’s try to understand this Schema.

Since all the properties will return either True or false so these all properties are set to Boolean in Schema. Here dump_only means, we will return this property in the Schema.

So, we saw how User Model and Schema is updated to show events role concerning a user on Open Event Server.

Resources