Task Overview

This guide will walk you through the process of building a Graph-Vector RAG in Python to recommend professors based on their research areas.

Introduction

We want to create a Python script that uses HelixDB to help students find professors based on their research areas and bio. For example a user could ask

• “What professors does High-Energy Physics research?”
• “What professors are working in the X University?”
• “What professors are working in the Computer Science department?”
• “What professors are working in the X University and are working in the Computer Science department?”
• “I like doing research in Large Language Models, can you recommend me some professors doing this in X University?”

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The dataset

In this example, we have a dataset on Professors with fields like:

• Name
• Department
• University
• Short biography
• Key Research Areas

We will be ingesting this data from a JSON file, an example is shown below:

{
  "name": "James",
  "department": "Computer Science",
  "university": "Uni X",
  "bio": "James is an Assistant Professor focusing on basketball analytics, computer vision, and machine learning. Projects include ShotNet and DunkGPT.",
  "key_research_areas": [
    {"area": "Computer Vision for Basketball"},
    {"area": "Predictive Modelling & Simulation"},
    {"area": "Sports Analytics with Large Language Models"},
    {"area": "Wearable Sensor Data Mining"},
    {"area": "Fairness & Ethics in Sports AI"}
  ]
}

Building a Graph

Based on this data, we can create a Vector Graph RAG with the following nodes and edges:

Nodes:

• Professor Node with properties name, bio
• Research Area Node with properties research_area
• Department Node with the property name
• University Node with the property name

Vector Nodes for Embeddings:

We will have an embedding of the professor’s research area

• Vector Node with the property research_area

Edges:

• Professor to Research Area Edge
• Professor to Department Edge
• Professor to University Edge
• Professor to Vector Node Edge