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Building a RAG-Based Chatbot with Amazon Bedrock, React, and Lambda (PART 2) Table of Contents A. Introduction B. Background C. Backend Chatbot API D. Frontend Chatbot Application E. Deploy React Application to S3 F. Resulting Application G. Conclusion A. Introduction In the previous article , we built a RAG (Retrieval-Augmented Generation) agent using Amazon Bedrock . The agent answers customer queries about training courses by combining static information from a CSV file (stored in the Bedrock Knowledge Base) and real-time course status from DynamoDB . In this second part, we will: Build a backend REST API using Python Lambda to invoke the Bedrock RAG agent. Develop a frontend chatbot application using React and the React Chatbotify component. Host the React application as a static website on S3 . Integrate the frontend and backend using API Gateway . Provide full code...

Building a RAG-Based Training Center Assistant with AWS Bedrock and Pinecone Table of Contents Introduction Background Architecture Overview Step-by-Step Implementation A. Create Pinecone API Key B. Create Pinecone Index C. Store Pinecone API Key in AWS Secrets Manager D. Create Bedrock Knowledge Base E. Create Lambda Function for the Agent to access DynamoDB table F. Create S3 Bucket and Upload OpenAPI Schema G. Create Bedrock Agent H. Create Resource based Policy for Lambda function Conclusion Introduction In this article, I will guide you through building a Retrieval-Augmented Generation (RAG) application using AWS Bedrock , Pinecone , and Amazon Titan Text G1 - Premier Model. We will create a Training Center Assistant Chatbot capable of answering customer queries about training course...