Scalable RAG solutions/Agentic Workflows with Amazon Bedrock and Amazon Opensearch serverless service

Overview

Widespread AI adoption is being driven by generative AI models that can generate human-like content. However, these foundation models are trained on general data making it less effective for domain specific tasks. There lies the importance of Retrieval Augmented Generation (RAG). RAG allows augmenting prompts with relevant external data for better domain-specific outputs. With RAG, documents and queries are converted to embeddings, compared to find relevant context, and that context is appended to the original prompt before being passed to the LLM. Knowledge libraries can be updated asynchronously to provide the most relevant external data for augmenting prompts.

Amazon Opensearch Serverless(AOSS) offers vector engine to store embeddings for faster similarity searches. The vector engine provides a simple, scalable, and high-performing similarity search capability in Amazon OpenSearch Serverless that makes it easy for you to build generative artificial intelligence (AI) applications without having to manage the underlying vector database infrastructure.

[!NOTE] This repository offers a production ready easily deployable Generative AI solution with the below features:

1. Document chat
2. Multi-Agent collaboration
3. Sentiment Analysis
4. PII Redaction
5. OCR

[!IMPORTANT] The Older UI is maintained in the v0.0.1(Old-UI) branch.

Demos

Prerequisites

Architecture

Deploying the Solution to your AWS account with AWS Cloudshell

Create an Admin User to deploy this stack

#### Section1 - Create an IAM user with Administrator permissions (OPTIONAL: If you're already an Admin role, you may skip this step) 1. Search for the service IAM on the AWS Console and go the IAM Dashboard and click on “Roles“ tab under ”Access Management” and Click on “Create Role” 2. Select AWS Account and click “Next“ 3. Under permissions select Administrator access 4. Give the role a name and create the role 5. You can now assume this role and proceed to deploy the stack. Click on Switch-Role 6. Switch role 7. Proceed to cloudshell step

Deploy the RAG based Solution (Total deployment time 40 minutes)

#### Section 2 - Deploy this RAG based Solution (The below commands should be executed in the region of deployment) 1. Switch to Admin role. Search for Cloudshell service on the AWS Console and follow the steps below to clone the github repository 2. Git Clone the serverless-rag-demo repository from aws-samples ``` git clone https://github.com/aws-samples/serverless-rag-demo.git ``` 3. Go to the directory where we have the downloaded files. ``` cd serverless-rag-demo ``` 4. Fire the bash script that creates the RAG based solution. Pass the environment and region for deployment. environment can be dev,qa,sandbox. Look at Prerequisites to deploy to the correct region. ``` sh creator.sh ``` 5. Press **Enter** to proceed with deployment of the stack or **ctrl+c** to exit  6. The UI is hosted on AppRunner the link to AppRunner could be found in CloudShell once the script execution is complete, or you could also go to the AppRunner service on the AWS Console and obtain the https url. The UI is authenticated through Amazon Cognito hence the very first time you would have to sign-up and then sign-in to login to the application  7. On Amazon Bedrock page enable access to the below models