interactivetech/rag-pdf

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RAG demo (Chat with HPE Press Release version)

Author: Andrew Mendez
Date: 05/01/2024
Revision: 0.1

This demonstration was built to showcase Retrieval Augmented Generation (RAG) on HPE press release documents . It shows how RAG can be used to assist customers with keeping up with recent HPE news articles. The information to answer the questions is sourced from the owners manuals which are publicly available.

To replicate this demo, you will need:

• A functioning Kubernetes cluster with loadbalancers for external facing services configured
  • cluster having shared mounted folder /nvmefs1/tyler.britten
• Pachyderm/HPE MLDM 2.9.2 installed on the cluster and fully functional
• At least 1x NVIDIA A100 80GB GPUs
• Determined.AI/HPE MLDE environment for finetuning models (not included in the base code here)

NOTE: You might be able to replicate this demo with other GPUs (for example L40s) as well, but you need to consider the memory footprint of other GPUs and make adjustments accordingly.

Recorded demos of RAG demo

[ToDo]

Implementation Overview

The below chart gives a logical overview of the application flow of Mercedes Me Chat.

• Step 1: Connect to deployed MLDM application
• Step 2: Create MLDM project named rag-demo-hpe
• Step 3: Set new project as current context
• Step 4: Create repo documents to hold xml documents
• Step 5: Upload xml documents
• Step 6: Pipeline step to parse documents
• Step 7: Pipeline step to chunk documents
• Step 8: Pipeline step to embed documents using embedding model bge-large-en-v1.5
• Step 9: Pipeline step to deploy gui application
• Step 10: Interact with gui application
• Step 11: Add new documents to repo documents to improve RAG App
• Step 12: (Optional Step) Pipeline step to develop dataset for finetuning embeddings: qna pipeline
• Step 13: (Optional Step) Pipeline step to fientune embeddings
• Step 14: Delete pipelines

RAG demo includes solution components from:

• Pachyderm / HPE MLDM
• ChromaDB
• Streamlit frontend

Steps to run the demo

Step 1: Connect to deployed MLDM application

pachctl connect pachd-peer.pachyderm.svc.cluster.local:30653

Step 2: Create MLDM project named rag-demo-hpe

pachctl create project rag-demo-hpe

Step 3: Set new project as current context

pachctl config update context --project rag-demo-hpe

Pipeline will be available at the url:

http://mldm-pachyderm.us.rdlabs.hpecorp.net/lineage/pdf-rag-andrew

Step 4: Create repo documents to hold xml documents

pachctl create repo documents

Step 5: Upload xml documents

pachctl put file documents@master: -f data/antonio-neri.xml pachctl put file documents@master: -f data/aruba_wifi_7_press.xml pachctl put file documents@master: -f data/e2e_ai_platform_press_release.xml

Step 6: Pipeline step to parse documents

This pipeline takes the raw xml documents, and parses them into json format.

pachctl create pipeline -f pipelines/parsing.pipeline.json

Step 7: Pipeline step to chunk documents

This pipeline takes the parsed documents, and applies chunking to create chunked documents.

pachctl create pipeline -f pipelines/chunking.pipeline.json

Step 8: Pipeline step to embed documents using embedding model bge-large-en-v1.5

This pipeline takes the chunked documents, and creates vector embeddings using the vector embedding bge-large-en-v1.5

pachctl create pipeline -f pipelines/embedding.pipeline.json

Step 9: Pipeline step to deploy gui application

This pipeline will deploy a streamlit application for user to interact with the GUI

pachctl create pipeline -f pipelines/gui.pipeline.json

Step 10: Interact with gui application

Note: There is an issue with the houston cluster where there are not enough IP addresses for service pipeline.

run command to ssh into node:

ssh andrew@mlds-mgmt.us.rdlabs.hpecorp.net -L 8080:localhost:8080

run command to port forward gui to local computer:

kubectl port-forward -n pachyderm svc/pdf-rag-andrew-gui-v1-user 8080:80

Open web browswer and go to url localhost:8080

Ask in the UI:

Who is Antonio Neri?

Now ask, the app wont answer it correctly:

Who is Neil MacDonald?

Step 11: Add new documents to repo documents to improve RAG App

We will show the key value proposition with a data driven pipeline, add more documents, the RAG app will automatically be updated.

In the terminal, add new document:

pachctl put file documents@master: -f neil-macdonald.xml

When pipeline is done, refresh webpage and ask: Who is Neil MacDonald?

Optional Steps

Step 12: (Optional Step) Pipeline step to develop dataset for finetuning embeddings: qna pipelinee

pachctl create pipeline -f pipelines/qna.pipeline.json

Step 13: (Optional Step) Pipeline step to fientune embeddings

Note, what is hardcoded is the following in fientune/experiment/const.yaml:

name: arctic-embed-fine-tune
workspace: Tyler
project: doc_embeds

Also the bind_mounts are hardcoded. This assumes you are running on a cluster (i.e. the houston cluster) where you have a mounted shared folder called /nvmefs1

bind_mounts:
  - container_path: /nvmefs1/
    host_path: /nvmefs1/
    propagation: rprivate
    read_only: false
  - container_path: /determined_shared_fs
    host_path: /nvmefs1/determined/checkpoints
    propagation: rprivate
    read_only: false

Step to trigger fientune pipeline:

pachctl create pipeline -f pipelines/finetune.pipeline.json

Delete pipelines

pachctl delete pipeline gui pachctl delete pipeline finetune-embedding pachctl delete pipeline generate-qna pachctl delete pipeline embed-docs pachctl delete pipeline chunk-doc pachctl delete pipeline parse-docs pachctl delete repo documents