How to Build a RAG System on AWS Using Terraform, Bedrock, S3, and OpenSearch

A technical guide published at the AWS Builder Center demonstrates how to implement a Retrieval-Augmented Generation (RAG) system entirely on AWS using Terraform as the infrastructure-as-code tool. The setup uses Amazon S3 as the document source, Amazon Bedrock Knowledge Bases to manage the ingestion pipeline, and Amazon OpenSearch Serverless as the vector database for storing and searching embeddings. During ingestion, Bedrock splits uploaded documents into chunks, converts them into vector embeddings using Amazon Titan Text Embeddings v2, and stores the results in OpenSearch Serverless with KNN similarity search powered by the HNSW algorithm and FAISS engine. At query time, a user's question is converted into an embedding, matched against stored vectors via cosine similarity search, and the retrieved context is passed to a large language model to generate a grounded answer with source citations. A Streamlit-based UI ties the workflow together, allowing users to upload documents, trigger knowledge base syncs, and submit natural-language queries interactively.