Continuous Retrieval Augmentation Generation (RAG) with the HPE MLOPs Platform

Author: andrew.mendez@hpe.com

This is a proof of concept showing how developers can create a Retrieval Augmentation Generation (RAG) system using Pachyderm and Determined AI. This is a unique RAG system sitting on top of an MLOPs platform, allowing developers to continuously update and deploy a RAG application as more data is ingested. We also provide an example of how developers can automatically trigger finetuning an LLM on a instruction tuning dataset.

We use the following stack:

• ChromaDB for the vector database
• Chainlit for the User Interface
• Mistral 7B Instruct for the large language model
• Determined for finetuning the Mistral Model
• Pachyderm to manage dataset versioning and pipeline orchestration.

Pre-requisite

• This Demo requires running with an A100 80GB GPU.
• This Demo assumes you have pachyderm and determined installed on top of kubernetes. A guide will be provided soon to show how to install pachyderm and kubernetes.

How to Run

• Run Deploy RAG with PDK.pynb to deploy a RAG system using a pretrained LLM
• Run Finetune and Deploy RAG with PDK.ipynb to both finetune an LLM and deploy a finetuned model.