Introduction
In the ever – evolving landscape of technology, serverless architecture has emerged as a game – changer in app development. It removes the burden of server management and allows for effortless scalability. Meanwhile, AI – powered chatbots, especially when connected to Knowledge Bases, offer personalized and real – time responses, greatly enhancing the user experience. Amazon Bedrock, an AWS platform, stands out for its ability to create knowledge – driven chatbots using advanced language models, bringing about a revolution in customer support. This article will take you through the process of creating a serverless chatbot application using Amazon Bedrock’s Knowledge Base, highlighting its simplicity and the positive impact on customer engagement.
Table of Contents
- Setting Up the Data Source
- Creating Amazon Bedrock Knowledge Base
- Creating an AWS Lambda Function
- Creating REST API
Setting Up the Data Source
Creating an Amazon S3 bucket is a fundamental step in many AWS projects. It serves as a secure and scalable storage solution for all kinds of data. Here is a detailed guide on creating an S3 bucket through the AWS Management Console, along with some best practices for setting permissions to safeguard your stored data. First, locate the “Services” menu at the top of the console. Click on it and find “S3” under the “Storage” category or use the search bar. Then, click on “S3” to open the dashboard. Click on the “Create bucket” button, give it a unique name, select your preferred AWS Region, and keep other options at their default settings for simplicity, and click “Create bucket”. Once created, open the bucket, click the “Upload” button, and you can either drag – and – drop files or select “Add files” to choose from your computer and then click “Upload” to finish. Remember, by default, all uploaded files inherit the bucket’s permissions, which block all public access unless configured otherwise.
Creating Amazon Bedrock Knowledge Base
Creating an Amazon Bedrock Knowledge Base requires an important understanding: it is only available in certain regions currently. The first step is to create an IAM (Identity and Access Management) user. Note that creating a knowledge base is restricted to root users. To create an IAM user, navigate to the IAM console in the AWS Management Console, select ‘Users’ from the dashboard menu, click on ‘Add User’, and specify a username. After creating the user, select it from the list and click on ‘Manage Console Access’, then click ‘Apply’ to generate a CSV file with credentials. Download this file and use the ‘Console – sign – in – URL’ to access the AWS Management Console. To create the Knowledge Base, go to the relevant section in the AWS Management Console and follow the prompts, keeping track of configurations and settings. We will keep most options as default, start by providing the S3 URI of the created bucket, select embeddings, and configure the vector store, choosing Amazon’s default ones. After creating the Knowledge Base, the next step is to create a Lambda function.
Creating an AWS Lambda Function
Navigate to the AWS Lambda console in the AWS Management Console and click on ‘Create function’. Choose the appropriate runtime environment; for this article, we select Python (although in some other scenarios it might be Node). After creating the function with default settings, adjust the timeout duration to account for longer execution times. In the configuration section, navigate to the ‘Role name’, select it, and add the ‘AmazonBedrockFullAccess’ policy to grant permissions. Then, write the RetrieveAndGenerate API to access data from the Knowledge Base in the Lambda function. Here is a sample code snippet:
import json
#1. import boto3
import boto3
#2 create client connection with bedrock
client_bedrock_knowledgebase = boto3.client(‘bedrock – agent – runtime’)
def lambda_handler(event, context):
#3 Store the user prompt
print(event[‘prompt’])
user_prompt = event[‘prompt’]
# 4. Use retrieve and generate API
client_knowledgebase = client_bedrock_knowledgebase.retrieve_and_generate(
input={
‘text’: user_prompt
},
retrieveAndGenerateConfiguration={
‘type’: ‘KNOWLEDGE_BASE’,
‘knowledgeBaseConfiguration’: {
‘knowledgeBaseId’: ‘Your – ID’,
‘modelArn’: ‘arn:aws:bedrock:Your – Region::foundation – model/anthropic.claude – instant – v1’
}
})
#print(client_knowledgebase)
#print(client_knowledgebase[‘output’][‘text’])
#print(client_knowledgebase[‘citations’][0][‘generatedResponsePart’][‘textResponsePart’])
response_kbase_final = client_knowledgebase[‘output’][‘text’]
return {
‘statusCode’: 200,
‘body’: response_kbase_final
}
You can refer to the official documentation for more details. After writing the code, test the Lambda function by creating a test scenario and then click on “Deploy” to deploy it.
Creating REST API
Navigate to Amazon API Gateway in the AWS Management Console and click on “Create API”. Since we already have resources, we will click on Create Method and choose the Lambda function we created. Then, configure the URL query string parameters, specify ‘prompt’ as the parameter name, and click on ‘Create Method’. After that, edit the Integration request, specify the format for the GET request in the mapping template section. Once the configuration is complete, deploy the REST API by selecting “Deploy API”, choose “New Stage” and assign a name to the stage. You can then see the results based on the input parameters.
Conclusion
Through this exploration of creating a serverless chatbot using Amazon Bedrock and AWS technologies, we have covered all the essential steps. From setting up data storage with an S3 bucket to creating a Knowledge Base and deploying a Lambda function and REST API, we have laid a solid foundation for building intelligent chatbots. These chatbots can offer accurate and context – aware information, revolutionizing customer service and enhancing the user experience. The integration of Amazon Bedrock with AWS services opens up new opportunities for the future of AI – powered communication.
