This post is co-written with Harrison Chase, Erick Friis and Linda Ye from LangChain.
Generative AI is set to revolutionize user experiences over the next few years. A crucial step in that journey involves bringing in AI assistants that intelligently use tools to help customers navigate the digital landscape. In this post, we demonstrate how to deploy a contextual AI assistant. Built using Amazon Bedrock Knowledge Bases, Amazon Lex, and Amazon Connect, with WhatsApp as the channel, our solution provides users with a familiar and convenient interface.
Amazon Bedrock Knowledge Bases gives foundation models (FMs) and agents contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. It also offers a powerful solution for organizations seeking to enhance their generative AI–powered applications. This feature simplifies the integration of domain-specific knowledge into conversational AI through native compatibility with Amazon Lex and Amazon Connect. By automating document ingestion, chunking, and embedding, it eliminates the need to manually set up complex vector databases or custom retrieval systems, significantly reducing development complexity and time.
The result is improved accuracy in FM responses, with reduced hallucinations due to grounding in verified data. Cost efficiency is achieved through minimized development resources and lower operational costs compared to maintaining custom knowledge management systems. The solution’s scalability quickly accommodates growing data volumes and user queries thanks to AWS serverless offerings. It also uses the robust security infrastructure of AWS to maintain data privacy and regulatory compliance. With the ability to continuously update and add to the knowledge base, AI applications stay current with the latest information. By choosing Amazon Bedrock Knowledge Bases, organizations can focus on creating value-added AI applications while AWS handles the intricacies of knowledge management and retrieval, enabling faster deployment of more accurate and capable AI solutions with less effort.
Prerequisites
To implement this solution, you need the following:
An AWS account with permissions to create resources in Amazon Bedrock, Amazon Lex, Amazon Connect, and AWS Lambda.
Model access enabling Anthropic’s Claude 3 Haiku model on Amazon Bedrock. Follow the steps at Access Amazon Bedrock foundation models.
A WhatsApp business account to integrate with Amazon Connect.
Product documentation, knowledge articles, or other relevant data to ingest into the knowledge base in a compatible format such as PDF or text.
Solution overview
This solution uses several key AWS AI services to build and deploy the AI assistant:
Amazon Bedrock – Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI
Amazon Bedrock Knowledge Bases – Gives the AI assistant contextual information from a company’s private data sources
Amazon OpenSearch Service – Works as vector store that is natively supported by Amazon Bedrock Knowledge Bases
Amazon Lex – Enables building the conversational interface for the AI assistant, including defining intents and slots
Amazon Connect – Powers the integration with WhatsApp to make the AI assistant available to users on the popular messaging application
AWS Lambda – Runs the code to integrate the services and implement the LangChain agent that forms the core logic of the AI assistant
Amazon API Gateway – Receives the incoming requests triggered from WhatsApp and routes the request to AWS Lambda for further processing
Amazon DynamoDB – Stores the messages received and generated to enable conversation memory
Amazon SNS – Handles the routing of the outgoing response from Amazon Connect
LangChain – Provides a powerful abstraction layer for building the LangChain agent that helps your FMs perform context-aware reasoning
LangSmith – Uploads agent traces to LangSmith for added observability, including debugging, monitoring, and testing and evaluation capabilities
The following diagram illustrates the architecture.
Flow description
Numbers in red on the right side of the diagram illustrate the data ingestion process:
Upload files to Amazon Simple Storage Service (Amazon S3) Data Source
New files trigger Lambda Function
Lambda Function invokes sync operation of the knowledge base data source
Amazon Bedrock Knowledge Bases fetches the data from Amazon S3, chunks it, and generates the embeddings through the FM of your selection
Amazon Bedrock Knowledge Bases stores the embeddings in Amazon OpenSearch Service
Numbers on the left side of the diagram illustrate the messaging process:
User initiates communication by sending a message through WhatsApp to the webhook hosted on .
Amazon API Gateway routes the incoming message to the inbound message handler, executed on AWS Lambda.
The inbound message handler records the user’s contact details in Amazon DynamoDB.
For first-time users, the inbound message handler establishes a new session in Amazon Connect and logs it in DynamoDB. For returning users, it resumes their existing Amazon Connect session.
Amazon Connect forwards the user’s message to Amazon Lex for natural language processing.
Amazon Lex triggers the LangChain AI assistant, implemented as a Lambda function.
The LangChain AI assistant retrieves the conversation history from DynamoDB.
Using Amazon Bedrock Knowledge Bases, the LangChain AI assistant fetches relevant contextual information.
The LangChain AI assistant compiles a prompt, incorporating context data and the user’s query, and submits it to a FM running on Amazon Bedrock.
Amazon Bedrock processes the input and returns the model’s response to the LangChain AI assistant.
The LangChain AI assistant relays the model’s response back to Amazon Lex.
Amazon Lex transmits the model’s response to Amazon Connect.
Amazon Connect publishes the model’s response to Amazon Simple Notification Service (Amazon SNS).
Amazon SNS triggers the outbound message handler Lambda function.
The outbound message handler retrieves the relevant chat contact information from Amazon DynamoDB.
The outbound message handler dispatches the response to the user through Meta’s WhatsApp API.
Deploying this AI assistant involves three main steps:
Create the knowledge base using Amazon Bedrock Knowledge Bases and ingest relevant product documentation, FAQs, knowledge articles, and other useful data that the AI assistant can use to answer user questions. The data should cover the key use cases and topics the AI assistant will support.
Create a LangChain agent that powers the AI assistant’s logic. The agent is implemented in a Lambda function and uses the knowledge base as its primary tool to look up information. Deploying the agent with other resources is automated through the provided AWS CloudFormation template. See the list of resources in the next section.
Create the Amazon Connect instance and configure the WhatsApp integration. This allows users to chat with the AI assistant using WhatsApp, providing a familiar interface and enabling rich interactions such as images and buttons. WhatsApp’s popularity improves the accessibility of the AI assistant.
Solution deployment
We’ve provided pre-built AWS CloudFormation templates that deploy everything you need in your AWS account.
Sign in to the AWS console if you aren’t already.
Choose the following Launch Stack button to open the CloudFormation console and create a new stack.
Enter the following parameters:
StackName: Name your Stack, for example, WhatsAppAIStack
LangchainAPIKey: The API key generated through LangChain
Region
Deploy button
Template URL – use to upgrade existing stack to a new release
AWS CDK stack to customize as needed
N. Virginia (us-east-1)
YML
GitHub
Check the box to acknowledge that you are creating AWS Identity and Access Management (IAM) resources and choose Create Stack.
Wait for the stack creation to be complete in approximately 10 minutes, which will create the following:
LangChain agent
Amazon Lex bot
Amazon Bedrock Knowledge Base
The vector store (Amazon OpenSearch Serverless)
Lambdas (for data ingestion and providers)
Data source (Amazon S3)
DynamoDB
Parameter Store for the LangChain API key
IAM roles and permissions
Upload files to the data source (Amazon S3) created for WhatsApp. As soon as you upload a file, the data source will synchronize automatically.
To test the agent, on the Amazon Lex console, select the most recently created assistant. Choose English, choose Test, and send it a message.
Create the Amazon Connect instance and integrate WhatsApp
Configure Amazon Connect to integrate with your WhatsApp business account and enable the WhatsApp channel for the AI assistant:
Navigate to Amazon Connect in the AWS console. If you haven’t already, create an instance. Copy your Instance ARN under Distribution settings. You will need this information later to link your WhatsApp business account.
Choose your instance, then in the navigation panel, choose Flows. Scroll down and select Amazon Lex. Select your bot and choose Add Amazon Lex Bot.
In the navigation panel, choose Overview. Under Access Information, choose Log in for emergency access.
On the Amazon Connect console, under Routing in the navigation panel, choose Flows. Choose Create flow. Drag a Get customer input block onto the flow. Select the block. Select Text-to-speech or chat text and add an intro message such as, “Hello, how can I help you today?†Scroll down and choose Amazon Lex, then select the Amazon Lex bot you created in step 2.
After you save the block, add another block called “Disconnect.†Drag the Entry arrow to the Get customer input and the Get customer input arrow to Disconnect. Choose Publish.
After it’s published, choose Show additional flow information at the bottom of the navigation panel. Copy the flow’s Amazon Resource Name (ARN), which you will need to deploy the WhatsApp integration. The following screenshot shows the Amazon Connect console with the flow.
Deploy the WhatsApp integration as detailed in Provide WhatsApp messaging as a channel with Amazon Connect.
Testing the solution
Interact with the AI assistant through WhatsApp, as shown in the following video:
Clean up
To avoid incurring ongoing costs, delete the resources after you are done:
Delete the CloudFormation stacks.
Delete the Amazon Connect instance.
Conclusion
This post showed you how to create an intelligent conversational AI assistant by integrating Amazon Bedrock, Amazon Lex, and Amazon Connect and deploying it on WhatsApp.
The solution ingests relevant data into a knowledge base on Amazon Bedrock Knowledge Bases, implements a LangChain agent that uses the knowledge base to answer questions, and makes the agent available to users through WhatsApp. This provides an accessible, intelligent AI assistant that can guide users through your company’s products and services.
Possible next steps include customizing the AI assistant for your specific use case, expanding the knowledge base, and analyzing conversation logs using LangSmith to identify issues, improve errors, and break down performance bottlenecks in your FM call sequence.
About the Authors
Kenton Blacutt is an AI Consultant within the GenAI Innovation Center. He works hands-on with customers helping them solve real-world business problems with cutting edge AWS technologies, especially Amazon Q and Bedrock. In his free time, he likes to travel, experiment with new AI techniques, and run an occasional marathon.
Lifeth Ãlvarez is a Cloud Application Architect at Amazon. She enjoys working closely with others, embracing teamwork and autonomous learning. She likes to develop creative and innovative solutions, applying special emphasis on details. She enjoys spending time with family and friends, reading, playing volleyball, and teaching others.
Mani Khanuja is a Tech Lead – Generative AI Specialist, author of the book Applied Machine Learning and High Performance Computing on AWS, and a member of the Board of Directors for Women in Manufacturing Education Foundation Board. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI. She speaks at internal and external conferences such as AWS re:Invent, Women in Manufacturing West, YouTube webinars, and GHC 23. In her free time, she likes to go for long runs along the beach.
Linda Ye leads product marketing at LangChain. Previously, she worked at Sentry, Splunk, and Harness, driving product and business value for technical audiences, and studied economics at Sanford. In her free time, Linda enjoys writing half-baked novels, playing tennis, and reading.
Erick Friis, Founding Engineer at LangChain, currently spends most of his time on the open source side of the company. He’s an ex-founder with a passion for language-based applications. He spends his free time outdoors on skis or training for triathlons.
Harrison Chase is the CEO and cofounder of LangChain, an open source framework and toolkit that helps developers build context-aware reasoning applications. Prior to starting LangChain, he led the ML team at Robus Intelligence, led the entity linking team at Kensho, and studied statistics and computer science at Harvard.
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