This code sample demonstrates using RAG (Retrieval Augmented Generation) with Amazon Bedrock's Titan embeddings and a large language model (LLM). It creates text embeddings stored in Amazon OpenSearch with vector engine support. This enables prompt engineering for more accurate LLM responses.

This sample does the following

• Creates embeddings using Titan embeddings LLM
• Saves embeddings to Amazon OpenSearch
• Using LangChain, input Query from you (user) is queried for retrieval from vectorized data from Amazon OpenSearch
• Retrieval from OpenSearch matching the question is fed to Amazon Titan Text LLM as context
• Amazon Titan Text augments the response based on the User Query

1. This was tested on Python 3.11.4

2. It is advise to work on a clean environment, use virtualenv or any other virtual environment manager.

   pip install virtualenv
   python -m virtualenv venv
   source ./venv/bin/activate

3. Run ./download-beta-sdk.sh to download the beta SDK for using Amazon Bedrock

4. Install requirements pip install -r requirements.txt

5. Install terraform to create the OpenSearch cluster

   brew tap hashicorp/tap
   brew install hashicorp/tap/terraform

First, deploy an OpenSearch cluster with Terraform:

cd ./terraform
terraform init
terraform apply -auto-approve

Using Sagemaker jupyter notebook rag1.ipynb and then rag2.ipynb

cd ./terraform 
terraform destroy # Confirm with yes