IBM watsonx Orchestrate

https://www.ibm.com/docs/en/watsonx/watson-orchestrate
Admin
IBM watsonx Orchestrate is an intelligent automation platform that enables users to build and deploy...
Tokens:427,228
Snippets:3,600
Trust Score:10
Update:2 weeks ago
Show doc for...
Tokens:
Raw
### Send initial message to Assistant builder service (Node.js and Python)

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=overview-building-custom-client-by-using-api

Creates an AssistantV2 service object, sends an empty stateless message to trigger the Greet customer action, and prints text responses from the generic array. Replace {apikey}, {url}, and {environment_id} with your credentials. Run with `node <filename.js>` (Node.js) or `python3 <filename.py>` (Python).

```javascript
// 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);
        }
      });
    }
  }
}
```

```python
# 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'])
```

--------------------------------

### Node.js: Add user input with prompt-sync

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=overview-building-custom-client-by-using-api

Node.js example using prompt-sync to add user input. It sends an empty message to start the conversation, processes text responses, and prompts for the next input unless skip_user_input is true. The loop ends when the user types 'quit'.

```javascript
// 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);
  }
}

```

--------------------------------

### Example conversation output

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=overview-building-custom-client-by-using-api

Sample output showing a conversation with the assistant, including the 'quit' command handling.

```text
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.
>>

```

--------------------------------

### Python: Add user input with input()

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=overview-building-custom-client-by-using-api

Python example using the input function to add user input. It sends an empty message to start the conversation, processes text responses, and prompts for the next input unless skip_user_input is True. The loop ends when the user types 'quit'.

```python
# 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
        }

```

--------------------------------

### View performance trends over time

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=plane-investigating-execution-issues-in-chat

Example questions to see how an agent's performance has changed over a period by including a time range. The agent returns a line chart with dates in your local timezone and the metric on the vertical axis. The summary states start and end dates, timezone, and trend direction.

```text
Show me the conversation volume trend for the HR Assistant over the last 7 days.
```

```text
How has the failure rate for the Travel Booking Agent changed this week?
```

--------------------------------

### On-premises: Call API with Python

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=api-getting-started

Python example using http.client to call the same endpoint on-premises. Requires MYHOST, PORT, MY_API_KEY, MY_USER_NAME, MY_NAMESPACE, and MY_INSTANCE_ID. It encodes the Zen API key and prints the response body.

```python
import http.client
import base64


region_code = f"https://MYHOST:PORT/"
api_key = "MY_API_KEY"
username = "MY_USER_NAME"
api_key_bytes = f"{username}:{api_key}".encode(encoding="utf-8")
zen_api_key = base64.encodebytes(api_key_bytes).decode(encoding="utf-8")
namespace = "MY_NAMESPACE"
instance_id = "MY_INSTANCE_ID"
endpoint = "/orchestrate/{namespace}/instances/{instanceid}/v1/orchestrate/digital-employees/allskills/"

conn = http.client.HTTPSConnection(f"{myhost}")

headers = {
    'Authorization': f"ZenApiKey {zen_api_key}",\
    'accept': "application/json"
}

conn.request("GET", endpoint, headers=headers)

res = conn.getresponse()
data = res.read()

print(data.decode("utf-8"))
```

--------------------------------

### Get reference to custom element

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=tutorials-tutorial-customizing-size-position-web-chat

Uses document.querySelector to get a reference to the custom element by its ID.

```javascript
const customElement = document.querySelector('#WebChatContainer');
```

--------------------------------

### Find underperforming agents quickly with Performance hotspots

Source: https://www.ibm.com/docs/en/watsonx/watson-orchestrate/base?topic=plane-investigating-execution-issues-in-chat

Example question from the Performance hotspots starter prompt to surface agents with high conversation duration or volume. Select the tile to run this question.

```text
Which agents have the highest average conversation duration or conversation volume in the last 24 hours?
```
Tokens:
Raw