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