Building a Production-Ready RAG Chatbot with AWS Bedrock, LangChain, and Terraform

Introduction In the era of generative AI, chatbots have evolved from simple rule-based systems to intelligent assistants capable of understanding context, retrieving relevant information, and providing accurate responses. This project showcases a production-grade implementation of a dual-mode chatbot system that combines the power of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) capabilities. The system addresses a common challenge in enterprise AI applications: how to provide both general conversational AI and domain-specific knowledge retrieval in a single, unified platform. By leveraging AWS Bedrock's foundation models, LangChain's orchestration framework, and OpenSearch's vector database, we've built a solution that is not only intelligent but also scalable, maintainable, and production-ready. What sets this project apart is its automatic categorization feature—users don't need to manually select document categories. The LLM intelligently analyzes each quer