Many ecommerce applications want to provide their users with a human-like chatbot that guides them to choose the best product as a gift for their loved ones or friends. To enhance the customer experience, the chatbot need to engage in a natural, conversational manner to understand the user’s preferences and requirements, such as the recipient’s gender, the occasion for the gift, and the desired product category. Based on the discussion with the user, the chatbot should be able to query the ecommerce product catalog, filter the results, and recommend the most suitable products.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Bedrock Agents is a feature that enables generative AI applications to run multistep tasks across company systems and data sources. In this post, we show you how to build an ecommerce product recommendation chatbot using Amazon Bedrock Agents and FMs available in Amazon Bedrock.
Solution overview
Traditional rule-based chatbots often struggle to handle the nuances and complexities of open-ended conversations, leading to frustrating experiences for users. Furthermore, manually coding all the possible conversation flows and product filtering logic is time-consuming and error-prone, especially as the product catalog grows.
To address this challenge, you need a solution that uses the latest advancements in generative AI to create a natural conversational experience. The solution should seamlessly integrate with your existing product catalog API and dynamically adapt the conversation flow based on the user’s responses, reducing the need for extensive coding.
With Amazon Bedrock Agents, you can build intelligent chatbots that can converse naturally with users, understand their preferences, and efficiently retrieve and recommend the most relevant products from the catalog. Amazon Bedrock Agents simplifies the process of building and deploying generative AI models, enabling businesses to create engaging and personalized conversational experiences without the need for extensive machine learning (ML) expertise.
For our use case, we create a recommender chatbot using Amazon Bedrock Agents that prompts users to describe who they want to buy the gift for and the relevant occasion. The agent queries the product information stored in an Amazon DynamoDB table, using an API implemented as an AWS Lambda function. The agent adapts the API inputs to filter products based on its discussion with the user, for example gender, occasion, and category. After obtaining the user’s gift preferences by asking clarifying questions, the agent responds with the most relevant products that are available in the DynamoDB table based on user preferences.
The following diagram illustrates the solution architecture.
As shown in the preceding diagram, the ecommerce application first uses the agent to drive the conversation with users and generate product recommendations. The agent uses an API backed by Lambda to get product information. Lastly, the Lambda function looks up product data from DynamoDB.
Prerequisites
You need to have an AWS account with a user or role that has at minimum the following AWS Identity and Access Management (IAM) policies and permissions:
AWS managed policies:
AmazonBedrockFullAccess
AWSMarketplaceManageSubscriptions
AWSLambda_ReadOnlyAccess
AmazonDynamoDBReadOnlyAccess
IAM actions:
iam:CreateRole
iam:CreatePolicy
iam:AttachRolePolicy
Deploy the solution resources with AWS CloudFormation
Before you create your agent, you need to set up the product database and API. We use an AWS CloudFormation template to create a DynamoDB table to store product information and a Lambda function to serve as the API for retrieving product details.
At the time of writing this post, you can use any of the following AWS Regions to deploy the solution: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Sydney), Europe (Frankfurt, Paris), Canada (Central), or South America (São Paulo). Visit Supported regions and models for Amazon Bedrock Agents for updates.
To deploy the template, choose Launch Stack:
This template creates a DynamoDB table named Products with the following attributes: product_name (partition key), category, gender, and occasion. It also defines a global secondary index (GSI) for each of these attributes to enable efficient querying.
Additionally, the template sets up a Lambda function named GetProductDetailsFunction that acts as an API for retrieving product details, This Lambda function accepts query parameters such as category, gender, and occasion. It constructs a filter expression based on the provided parameters and scans the DynamoDB table to retrieve matching products. If no parameters are provided, it retrieves all the products in the table and returns the first 100 products.
The template also creates another Lambda function called PopulateProductsTableFunction that generates sample data to store in the Products table. The CloudFormation template includes a custom resource that will run the PopulateProductsTableFunction function one time as part of the template deployment, to add 100 sample product entries in the products DynamoDB table, with various combinations of product names, descriptions, categories, genders, and occasions.
You can optionally update the sample product entries or replace it with your own product data. To do so, open the DynamoDB console, choose Explore items, and select the Products table. Choose Scan and choose Run to view and edit the current items or choose Create item to add a new item. If your data has different attributes than the sample product entries, you need to adjust the code of the Lambda function GetProductDetailsFunction, the OpenAPI schema, and the instructions for the agent that are used in the following section.
Create the agent
Now that you have the infrastructure in place, you can create the agent. The first step is to request model access.
On the Amazon Bedrock console, choose Model access in the navigation pane.
Choose Enable specific models.
Select the model you need access to (for this post, we select Claude 3 Sonnet).
Wait for the model access status to change to Access granted.
Now you can create your agent. We use a CloudFormation template to create the agent and the action group that will invoke the Lambda function.
To deploy the template, choose Launch Stack:
Now you can check the details of the agent that was created by the stack.
On the Amazon Bedrock console, choose Agents under Builder tools in the navigation pane.
Choose the agent product-recommendation-agent, then choose Edit in Agent Builder.
The Instructions for the Agent section includes a set of instructions that guides the agent in how to communicate with the user and use the API. You can adjust the instructions based on different use cases and business scenarios as well as the available APIs.
The agent’s primary goal is to engage in a conversation with the user to gather information about the recipient’s gender, the occasion for the gift, and the desired category. Based on this information, the agent will query the Lambda function to retrieve and recommend suitable products.
Your next step is to check the action group that enables the agent to invoke the Lambda function.
In the Action groups section, choose the Get-Product-Recommendations action group.
You can see the GetProductDetailsFunction Lambda function is selected in the Action group invocation section.
In the Action group schema section, you can see the OpenAPI schema, which enables the agent to understand the description, inputs, outputs, and the actions of the API that it can use during the conversation with the user.
Now you can use the Test Agent pane to have conversations with the chatbot.
Test the chatbot
The following screenshots show example conversations, with the chatbot recommending products after calling the API.
In the sample conversation, the chatbot asks relevant questions to determine the gift recipient’s gender, the occasion, and the desired category. After it has gathered enough information, it queries the API and presents a list of recommended products matching the user’s preferences.
You can see the rationale for each response by choosing Show trace. The following screenshots show how the agent decided to use different API filters based on the discussion.
You can see in the rationale field how the agent made its decision for each interaction. This trace data can help you understand the reasons behind a recommendation. Logging this information can be beneficial for future refinements of your agent’s recommendations.
Clean up
Complete the following steps to clean up your resources:
On the AWS CloudFormation console, delete the stack AgentStack.
Then delete the stack Productstableandapi.
Conclusion
This post showed you how to use Amazon Bedrock Agents to create a conversational chatbot that can assist users in finding the perfect gift. The chatbot intelligently gathers user preferences, queries a backend API to retrieve relevant product details, and presents its recommendations to the user. This approach demonstrates the power of Agents for Amazon Bedrock in building engaging and context-aware conversational experiences.
We recommend you follow best practices while using Amazon Bedrock Agents. For instance, using AWS CloudFormation to create and configure the agent allows you to minimize human error and recreate the agent across different environments and Regions. Also, automating your agent testing using a set of golden questions and their expected answers enables you to test the quality of the instructions for the agent and compare the outputs of the different models on Amazon Bedrock in relation to your use case.
Visit Amazon Bedrock Agents to learn more about features and details.
About the Author
Mahmoud Salaheldin is a Senior Solutions Architect in AWS, working with customers in the Middle East, North Africa, and Turkey, where he helps enterprises, digital-centered businesses, and independent software vendors innovate new products that can enhance their customer experience and increase their business efficiency. He is a generative AI ambassador as well as a containers community member. He lives in Dubai, United Arab Emirates, and enjoys riding motorcycles and traveling.
