Select Alexa Skills Kit as the trigger and then paste in the Alexa Skills ID that you copied in a previous step. Then click Add.

You should now see that the Alexa Skill has been added as a trigger for this Lambda Function.

Alexa Skill trigger

The Alexa Skills Kit trigger is currently selected. Click on the attached box for the Lambda Function UpdateVertexBusinessEntity.

Scroll down, skipping over the Function Code section, to Environment Variables and click Edit. You will need to enter your AWS access key and secret access key, TigerGraph IP address (domain), TigerGraph secret, and S3 bucket name, the file name and prefix for the business_entity.json file and the tigergraph.jpg file (if you put them in a subdirectory, otherwise just use the file names). Set the following variables then click Save:

Environment Variables 

Continue scrolling down to the Basic Settings section and click Edit. Change the Timeout setting to 2 minutes and 0 seconds. Then click Save.

Continue scrolling down to the VPC Section and click Edit. Select the name of your VPC and include at least two subnets that are associated with your NAT interface as described in the VPC Configuration section above. Also add at least one security group that allows egress from your VPC on ports used for TCP, HTTP and HTTPS. Then click Save.

Scroll back up to the Function Code section:

On the right, look for the Actions drop down box. Select Upload a .zip file. Upload the zip file in /lambda/UpdateVertexBusinessEntity.zip from the git repository. Once uploaded, the Python code will be visible:

Let’s take a look at the most important parts of the code. First, we create a function that connects to S3 and returns a connection:

def get_s3_conn():
    s3_conn = boto3.resource(
        ‘s3’,
        region_name=’us-east-1',
        aws_access_key_id=MY_AWS_ACCESS_KEY,
        aws_secret_access_key=MY_AWS_SECRET_ACCESS_KEY
    )    return s3_conn

We also create a function that gets an API token from TigerGraph. We cannot GET or POST using the TigerGraph API without a token:

def get_tg_auth_token() -> str:
try:
url = "{ip}:{port}/requesttoken?secret={secret}&lifetime={lt}".format(
    ip=tigergraph_ip,
    port=tigergraph_apitoken_port,
    secret=tigergraph_secret,
    lt=tigergraph_apitoken_lifetime
)    response = requests.get(url).json()
    return response['token']
except Exception as e:
    print('Error getting authentication token from TigerGraph: {0}'.format(e))

Next, we get the contents of the business_entity.json file from S3.

try:
    s3_object = s3_conn.Object(s3_bucket, s3_data_file).get()
    payload = s3_object['Body']
except Exception as e:
    print("Error getting metadata file from S3: {a}".format(a=e))
    return False

Now that we have the data, we can create our request header and then POST it. One of the benefits of TigerGraph is that each vertex or query is automatically accessible from a built-in REST interface. No coding is required.

try:
    url = "{ip}:{port}/graph/{graph}".format(
        ip=tigergraph_ip,
        port=tigergraph_apitoken_port,
        graph=tigergraph_graph_name
    )    response = requests.request(
        "POST", 
        url, 
        headers=request_header, 
        data=payload
    )except Exception as e:
    return_msg = e
else:
    return_msg = " reloaded successfully"

Last, after the data is posted to TigerGraph, we need to return a JSON structure to our Alexa skill so that an Alexa-enabled device can speak the return message. The response must be in the following format:

json_resp = {
"version": "1.0.0",
"sessionAttributes": {
"TigerGraph": "Load data"
},"response": {
"outputSpeech": {
"type": "PlainText",
"text": return_msg,
"ssml": "" + return_msg + ""
},
"card": {
"type": "Standard",
"title": tigergraph_graph_name,
"content": return_msg,
"text": return_msg,
"image": {
"smallImageUrl": "https://{0}.s3.amazonaws.com/{1}".format(s3_bucket, s3_image_file),"largeImageUrl": "https://{0}.s3.amazonaws.com/{1}".format(s3_bucket, s3_image_file)
}
},
"reprompt": {
"outputSpeech": {
"type": "PlainText",
"text": "Plain text string to speak",
"ssml": "SSML text string to speak"
}
},
"shouldEndSession": should_end_session
}
}

Click Save in the top right corner of the Lambda function.

Register the Alexa Skill as a trigger for the Lambda Function

In the upper right-hand corner of the Lambda Editor window, click the copy icon next to the lambda ARN:

Return to the Alexa Developer Console. From the menu on the left, choose Endpoint.

Enter the ARN for the lambda function in both the default region and the North American region:

From the menu on the left, click Interaction Model. Then click Build Model. We can now test the new skill!

Test!

We can test the new skill directly in the Alexa Developer Console. Simply click on the Test link on the top menu and make sure that skill testing is enabled for Development. You can then enter the text for the new skill. Simply update vertex should be sufficient:

We can hear Alexa say that our vertex data was loaded successfully. We can also see the JSON input (to the Lambda function) and output (results from the lambda function):

Input and Output

We can also see the image we uploaded along with the data. This shows the output for an Alexa-enabled device with a screen or mobile phone app:

Alexa Screen Results

If testing passes in the Alexa Developer Console, we can then try our skill on an Alexa device. I can also see that the two rows from the business_entities.json file have been loaded into the business_entity vertex of my graph:

TigerGraph Studio query results

Conclusion

I hope you found this post informative. This method for linking an Alexa Skill with a Lambda function is a quick and simple way to load graph data, but this pattern could be equally applicable for more complex interactions. In a future post I will explore other scenarios like voice-activated (and touchless) patient check-in functionality or a graph search application that finds health care providers based on proximity to a user’s location.