Retrieval-Augmented Generation (RAG) architectures are gaining traction in AI applications by addressing traditional generative AI models' limitations, such as accuracy and hallucinations, by combining retrieval-based systems with generative models like Large Language Models (LLMs). This blog post discusses implementing a RAG application using LangChain as the orchestrator, Amazon Bedrock as the generative model provider, Kong AI Gateway for security and management, and Redis as the vector database. The Kong AI Gateway enhances the deployment of AI applications by integrating AI-focused plugins for data security, response quality, and observability, allowing seamless integration with existing API traffic flows. It also supports multi-LLM integrations, providing features like prompt engineering, semantic caching, and AI analytics. The architecture involves preparing data by converting documents into embeddings stored in Redis, which are then used during query time to enhance LLM prompts with relevant data. The process is orchestrated by LangChain, leveraging Kong AI Gateway to route requests to Amazon Bedrock, ensuring data is contextually enriched and secure. This approach enables the creation of scalable, contextually aware AI applications with advanced RAG capabilities, demonstrating the synergy of modular components in a complex AI ecosystem.