Build a RAG data ingestion pipeline for large-scale ML workloads

This article discusses how to build a Retrieval Augmented Generation (RAG) data ingestion pipeline for large-scale machine learning workloads using Amazon OpenSearch Service and Amazon RDS for PostgreSQL with the pgvector extension as vector datastores.

Specifically, the article covers:

• Overview of OpenSearch Service and Amazon RDS with pgvector for vector similarity search
• Solution architecture using a Ray cluster for parallelizing data ingestion and creating vector embeddings
• Dataset used (OSCAR corpus and SQuAD questions) and preprocessing steps
• Infrastructure setup details for OpenSearch Service and Amazon RDS
• Step-by-step instructions to deploy the solution using AWS CDK and run the data ingestion pipelines
• Setting up the Ray dashboard to monitor cluster metrics
• Conclusion on the extensibility of the solution to integrate other vector datastores