# Creating a Multi-Model Conversational Chatbot with Amazon Web Services – Part 1
In the rapidly evolving landscape of artificial intelligence, conversational chatbots have become an integral part of customer service, e-commerce, and various other domains. These chatbots can handle a multitude of tasks, from answering frequently asked questions to providing personalized recommendations. In this two-part series, we will explore how to create a multi-model conversational chatbot using Amazon Web Services (AWS). This first part will cover the foundational aspects, including setting up AWS services and creating a basic chatbot.
## Understanding Multi-Model Chatbots
A multi-model chatbot leverages different AI models to handle various types of interactions. For instance, it might use natural language processing (NLP) for understanding text, machine learning models for making predictions, and even computer vision models for interpreting images. This approach allows the chatbot to provide more comprehensive and versatile responses.
## Why AWS?
Amazon Web Services offers a robust suite of tools and services that make it easier to build, deploy, and scale AI-driven applications. Key services include:
– **Amazon Lex**: A service for building conversational interfaces using voice and text.
– **Amazon Polly**: Converts text into lifelike speech.
– **Amazon Comprehend**: Analyzes text to extract key phrases, entities, and sentiment.
– **AWS Lambda**: Runs code in response to events and automatically manages the underlying compute resources.
– **Amazon S3**: Scalable storage for any type of data.
– **Amazon SageMaker**: A fully managed service for building, training, and deploying machine learning models.
## Prerequisites
Before we dive into the implementation, ensure you have the following:
1. An AWS account.
2. Basic knowledge of Python programming.
3. Familiarity with AWS Management Console.
## Step 1: Setting Up Amazon Lex
### Creating a Lex Bot
1. **Sign in to the AWS Management Console** and open the Amazon Lex console.
2. Click on **Create bot**.
3. Choose **Custom bot** and provide a name for your bot.
4. Set the output voice to `None` if you are only interested in text interactions.
5. Configure the session timeout and IAM role. You can create a new role or use an existing one with the necessary permissions.
6. Click **Create**.
### Defining Intents
Intents represent actions that users want to perform. For example, if you are building a customer service bot, an intent could be “CheckOrderStatus”.
1. In your newly created bot, click on **Create intent**.
2. Provide a name for the intent (e.g., `CheckOrderStatus`).
3. Add sample utterances like “Where is my order?” or “Track my order”.
4. Define slots if your intent requires additional information (e.g., Order ID).
5. Set up fulfillment by choosing how the bot should respond. You can use AWS Lambda functions for dynamic responses.
### Building and Testing the Bot
1. Click on **Build** to compile your bot.
2. Use the **Test Bot** pane to interact with your bot and ensure it understands the defined intents.
## Step 2: Integrating Amazon Polly
To add voice capabilities to your chatbot, you can integrate Amazon Polly.
### Setting Up Polly
1. Open the Amazon Polly console.
2. Enter text in the input box and choose a voice from the dropdown menu.
3. Click on **Synthesize Speech** to generate an audio file.
### Using Polly with Lex
You can use AWS Lambda to integrate Polly with Lex for text-to-speech conversion.
1. Create a new Lambda function in the AWS Lambda console.
2. Use the following Python code as a starting point:
“`python
import boto3
def lambda_handler(event, context):
polly = boto3.client(‘polly’)
response = polly.synthesize_speech(
Text=event[‘text’],
OutputFormat=’mp3′,
VoiceId=’Joanna’
)
return {
‘statusCode’: 200,
‘body’: response[‘AudioStream’].read()
}
“`
3. Update your Lex bot’s fulfillment settings to call this Lambda function.
## Step 3: Enhancing Text Understanding with Amazon Comprehend
Amazon Comprehend can be used to analyze user input for sentiment, key phrases, and entities.
### Setting Up Comprehend
1. Open the Amazon Comprehend console.
2. Create a new analysis job or use the API to analyze text.
### Using Comprehend with Lex
1. Create another Lambda function to call Amazon Comprehend.
2. Use the following Python code as a starting point:
“`python
import boto3
def lambda_handler(event, context):
comprehend = boto3.client(‘comprehend’)
text = event[‘text’]
sentiment