Automating End-to-End RAG Workflow Deployment with Knowledge Bases for Amazon Bedrock
Retrieval Augmented Generation (RAG) is an advanced approach to developing question answering systems that leverage retrieval and foundation models (FMs). RAG models retrieve relevant information from a large text corpus and then generate answers based on this retrieved information.
Creating an end-to-end RAG solution involves multiple components, including a knowledge base, a retrieval system, and a generation system. However, setting up these components can be complex, especially with large-scale data and models.
This blog post showcases how you can automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation. This streamlined process enables organizations to quickly establish a robust RAG system effortlessly.
Solution Overview
This solution offers an automated deployment of a RAG workflow using Knowledge Bases for Amazon Bedrock. By utilizing AWS CloudFormation, necessary resources are set up, including:
- An AWS IAM role
- An Amazon OpenSearch Serverless collection and index
- A knowledge base with its associated data source
The RAG workflow allows you to integrate document data from an Amazon S3 bucket with the powerful natural language processing capabilities of FMs in Amazon Bedrock. This solution simplifies the setup process, enabling you to quickly deploy and query your data using the selected FM.
Prerequisites
To implement the solution, you should have:
- An active AWS account and familiarity with FMs, Amazon Bedrock, and OpenSearch Serverless
- An S3 bucket with documents stored in supported formats
- The Amazon Titan Embeddings G1-Text model enabled in Amazon Bedrock
Set up the Solution
Follow these steps to set up the solution:
- Clone the GitHub repository containing the solution files:
git clone https://github.com/aws-samples/amazon-bedrock-samples.git- Navigate to the solution directory:
cd knowledge-bases/features-examples/04-infrastructure/e2e-rag-deployment-using-bedrock-kb-cfn
About the Authors