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.
Stars
99
Forks
17
Language
Python
License
MIT-0
Category
Last pushed
Oct 07, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/aws-samples/rag-with-amazon-bedrock-and-pgvector"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
aws-samples/generative-ai-use-cases
Application implementation with business use cases for safely utilizing generative AI in...
aws-samples/serverless-rag-demo
Amazon Bedrock Foundation models with Amazon Opensearch Serverless as a Vector DB
aws-samples/amazon-bedrock-rag
Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock
IBM/granite-workshop
Source code for the IBM Granite AI Model Workshop
terraform-ibm-modules/stack-ibm-retrieval-augmented-generation
A deployable architecture that automates the deployment of a sample gen AI Pattern on IBM Cloud,...