RAG Implementation

Setting up Retrieval-Augmented Generation for the HPC Dashboard AI

RAG Implementation

Under Development:

This section is currently being developed. The complete guide will be available soon!

Coming Soon

This guide will explain how the Retrieval-Augmented Generation (RAG) system is implemented in the HPC Dashboard and how to customize it for your environment. Topics will include:

  • Understanding RAG architecture
  • Configuring document ingestion and chunking
  • Setting up vector similarity search

What is RAG?

Retrieval-Augmented Generation combines a large language model with a knowledge base of specific information. When a user asks a question, the system:

  1. Converts the question into a vector embedding
  2. Searches the knowledge base for relevant information
  3. Feeds the retrieved information to the language model
  4. Generates an informed response based on your specific documentation

This approach allows the AI assistant to provide accurate, specific answers about your cluster without requiring retraining of the core AI model.

Benefits:

RAG allows the HPC Dashboard to provide contextually relevant responses about your specific SLURM configuration, policies, and documentation without hallucinating information.

Check back soon for the complete documentation! s