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copyright lastupdated subcollection
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2019, 2024
2024-04-19
watson-assistant

{{site.data.keyword.attribute-definition-list}}

Building a custom client by using the API

{: #api-client}

If none of the built-in integrations meet your requirements, you can deploy your assistant by developing a custom client application that interacts with your users and communicates with the {{site.data.keyword.conversationfull}} service. {: shortdesc}

The Watson SDKs help you write code that interacts with {{site.data.keyword.conversationshort}}. For more information about the SDKs, see Watson SDKs.

Setting up the assistant

{: #api-client-setup}

The example application that we create in implements several simple functions to illustrate how a client application interacts with {{site.data.keyword.conversationshort}}. The application code collects input and sends it to an assistant, which sends responses the application shows to the user.

To try this example yourself, you first need to set up the simple example assistant that the client connects to:

  1. Download the actions JSON file{: external}.
  2. Create an assistant.
  3. In the new assistant, open the global action settings. Go to the Upload/Download tab and import the actions from the file you downloaded.

The example actions include a Greet customer action that asks the customer's name, and simple actions for making and canceling appointments.

Getting service information

{: #api-client-get-info}

To access the {{site.data.keyword.conversationshort}} REST APIs, your application needs to be able to authenticate with {{site.data.keyword.Bluemix}} and connect to the assistant in the environment where it is deployed. You need to copy the service credentials and environment ID and paste them into your application code. You also need the URL for the location of your service instance (for example, https://api.us-south.assistant.watson.cloud.ibm.com).

To find this information:

  1. Go to the Environments page and choose he environment you want to connect to.

  2. Click the Settings icon Gear icon icon to open the environment settings.

  3. Select API details to see details for the environment, including the service instance URL and environment ID. To find the API key, follow the link in the Service credentials section.

Communicating with the {{site.data.keyword.conversationshort}} service

{: #api-client-communicate}

Interacting with the {{site.data.keyword.conversationshort}} service from your client application is simple. We start with an example that connects to the service, sends a single empty message, and prints the output to the console:

// Example 1: Creates service object, sends initial message, and
// receives response.

const AssistantV2 = require('ibm-watson/assistant/v2');
const { IamAuthenticator } = require('ibm-watson/auth');

// Create Assistant service object.
const assistant = new AssistantV2({
  version: '2021-11-27',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}', // replace with API key
  }),
  url: '{url}', // replace with URL
});

const assistantId = '{environment_id}'; // replace with environment ID

// Start conversation with empty message
messageInput = {
  messageType: 'text',
  text: '',
};
sendMessage(messageInput);

// Send message to assistant.
function sendMessage(messageInput) {
  assistant
    .messageStateless({
      assistantId,
      input: messageInput,
    })
    .then(res => {
      processResult(res.result);
    })
    .catch(err => {
      console.log(err); // something went wrong
    });
}

// Process the result.
function processResult(result) {
  // Print responses from actions, if any. Supports only text responses.
  if (result.output.generic) {
    if (result.output.generic.length > 0) {
      result.output.generic.forEach( response => {
        if (response.response_type == 'text') {
          console.log(response.text);
        }
      });
    }
  }
}

{: codeblock} {: javascript}

# Example 1: Creates service object, sends initial message, and
# receives response.

from ibm_watson import AssistantV2
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

# Create Assistant service object.
authenticator = IAMAuthenticator('{apikey}') # replace with API key
assistant = AssistantV2(
    version = '2021-11-27',
    authenticator = authenticator
)
assistant.set_service_url('{url}') # replace with service instance URL
assistant_id = '{environment_id}' # replace with environment ID

# Start conversation with empty message.
result = assistant.message_stateless(
    assistant_id,
).get_result()

# Print responses from actions, if any. Supports only text responses.
if result['output']['generic']:
    for response in result['output']['generic']:
        if response['response_type'] == 'text':
            print(response['text'])

{: codeblock} {: python}

The first step is to create a service object, a sort of wrapper for the {{site.data.keyword.conversationshort}} service.

You use the service object for sending input to, and receiving output from, the service. When you create the service object, you specify the API key for authentication, and the version of the {{site.data.keyword.conversationshort}} API you are using.

In this Node.js example, the service object is an instance of AssistantV2, stored in the variable assistant. The Watson SDKs for other languages provide equivalent mechanisms for instantiating a service object. {: javascript}

In this Python example, the service object is an instance of watson_developer_cloud.AssistantV2, stored in the variable assistant. The Watson SDKs for other languages provide equivalent mechanisms for instantiating a service object. {: python}

After you create the service object, we use it to send a message to the assistant, by using the stateless message method. In this example, the message is empty; we want to trigger the Greet customer action to start the conversation, so we don't need any input text. We then print any text responses that are returned in the generic array in the returned output.

Use the node <filename.js> command to run the example application. {: javascript}

Use the python3 <filename.py> command to run the example application. {: python}

Note: Make sure you install the Watson SDK for Node.js by using npm install ibm-watson. {: javascript}

Note: Make sure you install the Watson SDK for Python by using pip install --upgrade ibm-watson or easy_install --upgrade ibm-watson. {: python}

Assuming everything works as expected, the assistant returns the output from the assistant, which the app then prints to the console:

Welcome to the watsonx Assistant example. What's your name?

{: screen}

This output tells us that we communicated with the assistant and received the greeting message that is specified by the Greet customer action. But we don't yet have a way of responding to the assistant's question.

Processing user input

{: #api-client-process-input}

To be able to process user input, we need to add a user interface to our client application. For this example, we keep things simple and use standard input and output. You can use the Node.js prompt-sync module. (You can install prompt-sync by using npm install prompt-sync.) You can use the Python 3 input function.

// Example 2: Adds user input.

const prompt = require('prompt-sync')();
const AssistantV2 = require('ibm-watson/assistant/v2');
const { IamAuthenticator } = require('ibm-watson/auth');

// Create Assistant service object.
const assistant = new AssistantV2({
  version: '2021-11-27',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}', // replace with API key
  }),
  url: '{url}', // replace with URL
});
  
const assistantId = '{environment_id}'; // replace with environment ID

// Start conversation with empty message
messageInput = {
  messageType: 'text',
  text: '',
};
sendMessage(messageInput);

// Send message to assistant.
function sendMessage(messageInput) {
  assistant
    .messageStateless({
      assistantId,
      input: messageInput,
    })
    .then(res => {
      processResult(res.result);
    })
    .catch(err => {
      console.log(err); // something went wrong
    });
}

// Process the result.
function processResult(result) {

  // Print responses from actions, if any. Supports only text responses.
  if (result.output.generic) {
    if (result.output.generic.length > 0) {
      result.output.generic.forEach( response => {
        if (response.response_type === 'text') {
          console.log(response.text);
        }  
      });
    }
  }

  // Prompt for the next round of input unless skip_user_input is true.
  let newMessageFromUser = '';
  if (result.context.global.system.skip_user_input !== true) {
    newMessageFromUser = prompt('>> ');
  }
  
  if (newMessageFromUser !== 'quit') {
    newMessageInput = {
      messageType: 'text',
      text: newMessageFromUser,
    }
    sendMessage(newMessageInput);
  }
}

{: codeblock} {: javascript}

# Example 2: Adds user input.

from ibm_watson import AssistantV2
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

# Create Assistant service object.
authenticator = IAMAuthenticator('{apikey}') # replace with API key
assistant = AssistantV2(
    version = '2021-11-27',
    authenticator = authenticator
)
assistant.set_service_url('{url}') # replace with service instance URL
assistant_id = '{environment_id}' # replace with environment ID

# Initialize with empty value to start the conversation.
message_input = {
    'message_type:': 'text',
    'text': ''
    }

context = None
    
# Main input/output loop
while message_input['text'] != 'quit':

    # Send message to assistant.
    result = assistant.message_stateless(
        assistant_id,
        input = message_input,
        context=context
    ).get_result()
    context = response['context']

    # Print responses from actions, if any. Supports only text responses.
    if result['output']['generic']:
        for response in result['output']['generic']:
            if response['response_type'] == 'text':
                print(response['text'])

    # Prompt for the next round of input unless skip_user_input is True.
    if not result['context']['global']['system'].get('skip_user_input', False):
        user_input = input('>> ')
        message_input = {
            'text': user_input
        }

{: codeblock } {: python }

This version of the application begins the same way as before: sending an empty message to the assistant to start the conversation.

The processResult() function displays the text of any responses that are received from the assistant. It then prompts for the next round of user input. {: javascript }

It then displays the text of any responses that are received from the assistant, and it prompts for the next round of user input. {: python }

The example checks for the global context variable skip_user_input and prompts for user input only if this variable is not set to trueTrue. The skip_user_input variable is set by the assistant in some situations where no user input is needed (for example, if the assistant called an external service but is still waiting for the result). It's good practice always to make this check before you prompt for user input. {: tip}

Because we need a way to end the conversation, the client app is also watching for the literal command quit to indicate that the program should exit.

But something still isn't right:

Welcome to the watsonx Assistant example. What's your name?
>> Robert
I'm afraid I don't understand. Please rephrase your question.
>> I want to make an appointment.
What day would you like to come in?
>> Thursday
I'm afraid I don't understand. Please rephrase your question.
>>

{: screen}

The assistant is starting out with the correct greeting, but it doesn't understand when you tell it your name. And if you tell it you want to make an appointment, the correct action is triggered; but again, it doesn't understand when you answer the follow-up question.

The reason is because we are using the stateless message method, which means that it is the responsibility of our client application to maintain state information for the conversation. Because we are not yet doing anything to maintain the state, the assistant sees every round of user input as the first turn of a new conversation. Because it has no memory of asking a question, it tries to interpret your answer as a new question or request.

Maintaining the state

{: #api-client-maintain-state}

State information for your conversation is maintained by using the context. The context is an object that is passed back and forth between your application and the assistant, storing information that can be preserved and updated as the conversation goes on. Because we are using the stateless message method, the assistant does not store the context, so it is the responsibility of our client application to maintain it from one turn of the conversation to the next.

The context includes a session ID for each conversation and a counter that is incremented with each turn of the conversation. The assistant updates the context and returns it with each response. But our previous version of the example did not preserve the context, so these updates were lost, and each round of input appeared to be the start of a new conversation. We can fix that by saving the context and sending it back to the assistant each time.

In addition to maintaining our place in the conversation, the context can contain action variables that store other data you want to pass back and forth between your application and the assistant. For example, you can include persistent data that you want to maintain throughout the conversation (such as a customer's name or account number), or any other data you want to track (such as the contents of a shopping cart or user preferences).

// Example 3: Preserves context to maintain state.

const prompt = require('prompt-sync')();
const AssistantV2 = require('ibm-watson/assistant/v2');
const { IamAuthenticator } = require('ibm-watson/auth');

// Create Assistant service object.
const assistant = new AssistantV2({
  version: '2021-11-27',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}', // replace with API key
  }),
  url: '{url}', // replace with URL
});

const assistantId = '{environment_id}'; // replace with environment ID

// Start conversation with empty message
messageInput = {
  messageType: 'text',
  text: '',
};
context = {};
sendMessage(messageInput);

// Send message to assistant.
function sendMessage(messageInput, context) {
  assistant
    .messageStateless({
      assistantId,
      input: messageInput,
      context: context,
    })
    .then(res => {
      processResult(res.result);
    })
    .catch(err => {
      console.log(err); // something went wrong
    });
}

// Process the result.
function processResult(result) {

  let context = result.context;

  // Print responses from actions, if any. Supports only text responses.
  if (result.output.generic) {
    if (result.output.generic.length > 0) {
      result.output.generic.forEach( response => {
        if (response.response_type === 'text') {
          console.log(response.text);
        }  
      });
    }
  }

  // Prompt for the next round of input unless skip_user_input is true.
  let newMessageFromUser = '';
  if (result.context.global.system.skip_user_input !== true) {
    newMessageFromUser = prompt('>> ');
  }

  if (newMessageFromUser !== 'quit') {
    newMessageInput = {
      messageType: 'text',
      text: newMessageFromUser,
    }
    sendMessage(newMessageInput, context);
  }
}

{: codeblock} {: javascript }

# Example 3: Preserves context to maintain state.

from ibm_watson import AssistantV2
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

# Create Assistant service object.
authenticator = IAMAuthenticator('{apikey}') # replace with API key
assistant = AssistantV2(
    version = '2021-11-27',
    authenticator = authenticator
)
assistant.set_service_url('{url}') # replace with service instance URL
assistant_id = '{environment_id}' # replace with environment ID

# Initialize with empty message to start the conversation.
message_input = {
    'message_type:': 'text',
    'text': ''
    }
context = {}

# Initialize with empty message to start the conversation.
message_input = {
    'message_type:': 'text',
    'text': ''
    }
context = {}

# Main input/output loop
while message_input['text'] != 'quit':

    # Send message to assistant.
    result = assistant.message_stateless(
        assistant_id,
        input = message_input,
        context = context
    ).get_result()

    context = result['context']

    # Print responses from actions, if any. Supports only text responses.
    if result['output']['generic']:
        for response in result['output']['generic']:
            if response['response_type'] == 'text':
                print(response['text'])

    # Prompt for the next round of input unless skip_user_input is True.
    if not result['context']['global']['system'].get('skip_user_input', False):
        user_input = input('>> ')
        message_input = {
            'text': user_input
        }

{: codeblock } {: python }

The only change from the previous example is that we are now storing the context that is received from the assistant in a variable that is called context, and we're sending it back with the next round of user input: {: javascript }

The only change from the previous example is that we are now storing the context that is received from the assistant in a variable that is called context, and we're sending it back with the next round of user input: {: python }

  assistant
    .messageStateless({
      assistantId,
      input: messageInput,
      context: context,
    })

{: codeblock} {: javascript }

response = assistant.message_stateless(
    assistant_id,
    input = message_input,
    context = context
).get_result()

{: codeblock } {: python }

This ensures that the context is maintained from one turn to the next, so the {{site.data.keyword.conversationshort}} service no longer thinks every turn is the first:

Welcome to the watsonx Assistant example. What's your name?
>> Robert
Hi, Robert! How can I help you?
>> I want to make an appointment.
What day would you like to come in?
>> Next Monday
What time works for you?
>> 10 AM
OK, Robert. You have an appointment for 10:00 AM on Sep 12. See you then!

{: screen}

Success! The application now uses the {{site.data.keyword.conversationshort}} service to understand natural-language input, and it displays the appropriate responses.

This simple example illustrates how you can build a custom client app to communicate with the assistant. A real-world application would use a more sophisticated user interface, and it might integrate with other applications such as a customer database or other business systems. It would also need to send more data to the assistant, such as a user ID to identify each unique user. But the basic principles of how the application interacts with the {{site.data.keyword.conversationshort}} service would remain the same.

Including clarifying questions

{: #api-client-clarifying-questions}

When your assistant finds that more than one action might fulfill a customer's request, it can automatically ask for clarification. For more information, see Asking clarifying questions.

To include clarifying questions in your custom client, you need to:

  • Display the clarification suggestion options that are returned from the message API
  • Call the message API in the next round with a payload that corresponds to the suggestion option that a customer chose to answer the clarifying question. If you don't implement the call, autolearning and using unrecognized requests to get action recommendations don't work correctly.

Each clarification suggestion includes:

  • A label that can be displayed to the customer
  • A value that specifies the input that is sent to the assistant if the user chooses the corresponding suggestion

To implement clarification suggestions in your application:

  1. Use the value.input object from the selected suggestion as the next round of message input, rather than building a new input object. The assistant then responds by triggering the action that is associated with the suggestion option to start.

  2. Verify that you implemented this correctly by using your custom client by using the Analyze page. Enter an input that triggers a clarification, and then click the None of the Above option. When you view the request in Conversations, check that the user request that initiated clarification is marked with Unrecognized, which indicates that your client is properly sending the clarification input to your assistant.

Using the v1 runtime API

{: #api-client-v1-api}

Using the v2 API is the recommended way to build a runtime client application that communicates with the {{site.data.keyword.conversationshort}} service. However, some older applications might still be using the v1 runtime API, which includes a similar method for sending messages to the workspace within a dialog skill. If your app uses the v1 runtime API, it communicates directly with the workspace, bypassing the skill orchestration and state-management capabilities of the assistant.

For more information about the v1 /message method and context, see the v1 API Reference{: external}.