Log 7 🛫

Objectives

• Deploy watsonx.ai on self-managed AWS infrastructure.

Accomplishments

AWS

• Shift from CP Deployer to OpenShift UPI deployment.
• Artifactory proxy details procured.
• Discussion of on-site logistics
• RHEL 8 AMI changed for BootNode.

RAG

• Creation of cronjob to capture logs from Python app.
• Enabled metadata insertion into chunks in vector store -> (hopefully) increases retrieval accuracy
• Return context to user (shows sources used to generate responses)
• Added mixtral model support
• Enable functionality for user to give custom rag parameters
• Migrated vector DB from FAISS to chromaDB to enable the metadata functionality
• Script written to easily test rag implementation and save results in csv
• Implemented cache logic to make sure it considers combination of parameters as well before chosing to send a cached response

In Progress

• End-to-end deployment of OCP, CP4D, and watsonx.ai (with GPU node)
• Tagging cp-deployer.sh generated resources.
• Updating solution docs with better asset linking.

Next Steps

• Setup bootnode with necessary downloads and resources.
• Creation of IAM Role request creation Cloudformation templates.
• Kick off on-site over the shoulder working sessions.
• Collating information and resources to be created via OpenShift UPI deployment.
• Setup Artifactory proxy.
• Kick off Cloud Pak for Deployment entitlement key.
• Deploy latest RAG version on AWS
• Build out actions & flow in Watsonx Assistant after properly defining personas & objectives.

Tracking (Issues)

• Require sign-off on final CloudFormation template.
• Red Hat CoreOS AMI still pending approval.