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VMRecommender

Getting started

This project aims to build a dynamic network diagram generator using a Retrieval Augmented Generation (RAG) system. By leveraging a database of VM types, we'll design a workflow that takes customer specifications as input and produces an output representing a complete network diagram. This includes the allocation of virtual machines, VLAN configuration, and the specific details of each chosen machine type. This approach promises a highly automated and flexible solution for creating tailored network diagrams based on customer needs.

Technologies used

  • LLM choice: I used TinyLlama because of its light-weight but robust in processing language.
  • Vector storing: In this project, I prefer FAISS to other because it is a vector indexing. Every information, that is embedded, is wrapped in an object. At this moment, the dataset is quite small, therefore, it would reduce the storing cost.

Usage

  1. Cloning the project and install requirements:
git clone https://github.com/MinLee0210/VMRecommender
cd VMRecommender 
pip install -r requirments.txt
  1. Ready to use
python main.py

Gallery

  • Asking the TinyAgent:

  • The generated result:

Problems

  • User's description is not clear and attach with the information from the dataset => hard for the retrieval.
  • The generated reponse is still not attached with the relevant context => poor UX.
  • The storing object is not set-up globally. It needs to be processed every request => high latency.