amazon-bedrock-rag and rag-with-amazon-bedrock-and-opensearch
These are competitors, as both repositories provide sample implementations for building a RAG solution using Amazon Bedrock, with **A** leveraging the fully managed Knowledge Bases for Amazon Bedrock and **B** demonstrating a more hands-on approach with OpenSearch.
About amazon-bedrock-rag
aws-samples/amazon-bedrock-rag
Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock
Implements RAG with dual data sources (S3 documents and web crawling), using Amazon OpenSearch Serverless for vector storage and automatic document chunking with Titan Embeddings. Provides a complete Q&A chatbot application with multi-turn conversation support, model selection UI, and citation tracking—deployed via AWS CDK with API Gateway access controls and built-in security hardening.
About rag-with-amazon-bedrock-and-opensearch
aws-samples/rag-with-amazon-bedrock-and-opensearch
Opinionated sample on how to build and deploy a RAG application with Amazon Bedrock and OpenSearch
Implements an event-driven document ingestion pipeline where PDFs trigger Lambda functions to extract text, generate vector embeddings via OpenAI, and automatically index content in OpenSearch for semantic search. The architecture layers Streamlit frontend, LangChain orchestration, and Bedrock foundation models on ECS with Cognito authentication and ALB routing, using AWS CDK for infrastructure-as-code deployment across multiple stacks.
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