Build a Serverless AI Chatbot with Amazon Bedrock

Architecture

Our chatbot will use:

User
↓
API Gateway
↓
AWS Lambda
↓
Amazon Bedrock
↓
AI Response
↓
DynamoDB

AWS Services

- Amazon Bedrock — provides access to foundation models.
- AWS Lambda — processes the chatbot requests without managing servers.
- Amazon API Gateway — provides an HTTP API for the chatbot.
- Amazon DynamoDB — stores conversation history.
- AWS IAM — controls access between the services.

How It Works

When a user sends a message, API Gateway sends the request to Lambda.

Lambda sends the user's message to Amazon Bedrock:

response = bedrock.converse(
modelId=MODEL_ID,
messages=[
{
"role": "user",
"content": [
{"text": user_message}
]
}
]
)

Amazon Bedrock returns the AI-generated response.

Lambda can then store the conversation in DynamoDB and return the response to the user.

Example Request

{
"conversationId": "user-123",
"message": "What is AWS Lambda?"
}

Example response:

{
"conversationId": "user-123",
"response": "AWS Lambda is a serverless compute service that runs code without requiring you to manage servers."
}

Why Serverless?

This architecture is useful because we don't need to manage servers.

AWS handles the underlying infrastructure while we focus on the application.

It can also be extended with:

- Authentication
- Conversation history
- Document Q&A
- CloudWatch monitoring
- Infrastructure as Code
- Custom web or mobile interfaces

Conclusion

Amazon Bedrock makes it easier to add generative AI capabilities to AWS applications, while Lambda, API Gateway, and DynamoDB provide a simple serverless foundation.

This project is a good starting point for building more advanced AI applications on AWS.

Next step: Add authentication and conversation history to turn this simple chatbot into a more complete application.