Skip to main content
This section will cover the Python code to connect to HelixDB and create nodes and edges, create embeddings for the professors, search by embeddings, and filtering.

​
Step 1: Setting up the environment

python -m venv venv
source venv/bin/activate
pip install helix-py sentence-transformers

​
Step 2: Imports and Setup for HelixDB

import helix
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
db = helix.Client(local=True, port=6969, verbose=True)
Here we start creating all the nodes that we will link a professors to.
You can also make a query in queries.hx to get the ID of the research area if you have the name, but we will store it in Python for simplicity.

​
Sample Data

professor_data = {
  "name": "James",
  "bio": "James is an Assistant Professor focusing on basketball analytics, computer vision, and machine learning. Projects include ShotNet and DunkGPT.",
  "department": "Computer Science",
  "university": "Uni X",
  "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"}
  ]
}

​
Create Professor Node

james_node =db.query("create_professor", {"name": professor_data["name"], "bio": professor_data["bio"]})
// store the ID of the professor node
james_node_id = james_node[0]['professor']["id"]

​
Create Department Node

computer_science_node = db.query("create_department", {"name": professor_data["department"]})
// store the ID of the department node
computer_science_node_id = computer_science_node[0]['department']["id"]

​
Create University Node

university_node = db.query("create_university", {"name": professor_data["university"]})
// store the ID of the university node
university_node_id = university_node[0]['university']["id"]

​
Create Research Area Nodes

research_area_node_ids = {}
for area in professor_data["key_research_areas"]:
    research_area_node = db.query("create_research_area", {"name": area["area"]})
    pprint(research_area_node)
    research_area_node_id = research_area_node[0]['research_area']["id"]
    research_area_node_ids[area["area"]] = research_area_node_id

db.query("link_professor_to_department", {"professor_id": james_node_id, "department_id": computer_science_node_id})

db.query("link_professor_to_university", {"professor_id": james_node_id, "university_id": university_node_id})

for area in professor_data["key_research_areas"]:
    db.query("link_professor_to_research_area", {"professor_id": james_node_id, "research_area_id": research_area_node_ids[area["area"]]})

​
Step 5: Create Embeddings for the professor

​
Create Research Area Embedding

for area in professor_data["key_research_areas"]:
    embedding = model.encode(area["area"]).astype(float).tolist()
    db.query("create_research_area_embedding", {"professor_id": james_node_id, "research_area": area["area"], "vector": embedding})

​
Step 6: Lets answer some of the questions we asked in the beginning

  1. “What professors does X research area?”
db.query("get_professor_by_research_area_name", {"research_area_name": "Computer Vision for Basketball"})
  1. “What professors are working in X University?”
QUERY get_professors_by_university_name (university_name: String) =>
    professors <- N<Professor>::WHERE(EXISTS(_::Out<HasUniversity>::WHERE(_::{name}::EQ(university_name))))
    RETURN professors
  1. “What professors are working in X Department?”
db.query("get_professors_by_department_name", {"department_name": "Computer Science"})
  1. “What professors are working in the X University and are working in X department?”
db.query("get_professors_by_university_and_department_name", {"university_name": "Uni X", "department_name": "Computer Science"})
  1. “Find me professors doing X research area”
query_vector = model.encode("Computer Vision for Basketball").astype(float).tolist()
db.query("search_similar_professors_by_research_area", {"query_vector": query_vector, "k": 1})
  1. “I want to find professors working in computer vision, in X university”
query_vector = model.encode("Computer Vision for Basketball").astype(float).tolist()
db.query("get_professor_semantically_and_by_university_name", {"query_vector": query_vector, "university_name": "Uni X", "k": 1})
⌘I