Implementing a RAG Pipeline With AWS Step Functions

Retrieval Augmented Generation (RAG) based knowledge bases enable you to interrogate your documents and information using a Generative AI chat interface. To use them most effectively on an ongoing basis, you need to ingest and transform your data to make it available for use by a Generative AI model. AWS Step Functions can be used to build and run a pipeline capable of processing your data.

Learning how to implement a pipeline in AWS Step Functions will benefit those looking to use their own data with Generative AI models.

In this hands-on lab, you will implement a pipeline to ingest and process PDF documents, and observe the pipeline running.

Learning objectives

Upon completion of this beginner-level lab, you will be able to:

• Implement ingestion, transformation AWS Lambda functions
• Generate AI model embeddings of plain text data
• Create a vector index in Amazon OpenSearch
• Implement an AWS Step Functions state machine

Intended audience

• Anyone who is interested in using Generative AI in the public AWS cloud
• Cloud Architects
• Data Engineers
• DevOps Engineers
• Machine Learning Engineers
• Software Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• AWS Lambda
• Amazon OpenSearch Service
• AWS Step Functions
• The Python scripting language

The following content can be used to fulfill the prerequisites:

• Understanding AWS Lambda to Run and Scale Your Code
• Monitor Like a DevOps Pro: Build A Log Aggregation System in AWS
• AWS Step Functions
• Python for Beginners