amazon-bedrock-rag and rag-with-amazon-bedrock-and-pgvector
These are complementary approaches to RAG on AWS: the first uses the managed Knowledge Bases service for simplified vector storage and retrieval, while the second provides a self-managed alternative using PostgreSQL with pgvector for organizations requiring custom infrastructure control.
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-pgvector
aws-samples/rag-with-amazon-bedrock-and-pgvector
Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)
Implements an event-driven PDF ingestion pipeline using S3 Event Notifications and Lambda functions to extract text, generate OpenAI embeddings, and populate PGVector for semantic search. The full stack uses AWS CDK for infrastructure-as-code, deploys a Streamlit frontend via ECS with Application Load Balancer routing, and secures access through Cognito authentication while integrating LangChain for RAG orchestration across Bedrock foundation models.
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