🚀 FastAPI

📋 Prerequisites

• Python 3.12+
• Neo4j AuraDB (or a local Neo4j Desktop instance)
• Groq Cloud API Key (or a local LLM model API KEY)

🛠️ Installation & Setup

0. Clone the repository

 git clone https://github.com/nemanjaASE/NeSy.git

1. Navigate to the backend project root:

  cd NeSy/backend

2. Create a virtual environment:

  python -m venv .venv

  # Activate on Windows:
  .venv\Scripts\activate
  # Activate on Linux/macOS:
  source .venv/bin/activate

3. Install dependencies:

  pip install -r requirements.txt

4. Configuration (.env file):

Create a .env file in the backend/ directory:

# Environment variables for the Neuro-symbolic Diagnostic API
PROJECT_NAME="your-project-name"
ENVIRONMENT="environment-name" # e.g., development or production

# Neo4j connection settings
NEO4J_URL="your-neo4j-url"
NEO4J_USERNAME="your-neo4j-username"
NEO4J_PASSWORD="your-neo4j-password"

# LLM API settings
LLM_API_KEY="your-llm-api-key"
LLM_EXTRACTION_MODEL_NAME="your-llm-extraction-model-name"
LLM_XAI_MODEL_NAME="your-llm-xai-model-name"

# Embedding model
EMBEDDING_MODEL_NAME="your-embedding-model-name"

# CORS settings
ALLOWED_ORIGINS="your-allowed-origins" # e.g., http://localhost:3000
ALLOWED_METHODS="your-allowed-methods" # e.g., GET,POST,PUT,DELETE
ALLOWED_HEADERS="your-allowed-headers" # e.g., Content-Type,Authorization
ALLOW_CREDENTIALS="your-allow-credentials" # true or false

💻 Running the Application

1. Initialize the Knowledge Graph:

Before starting the API, you must populate the Neo4j database with the medical ontologies, calculate the Information Content (IC) weights, and generate symptom embeddings.

Note: The preparation pipeline is currently implemented as interactive Jupyter Notebooks. 📘 Click here to read the detailed Notebooks Setup Guide to learn how to configure your VS Code kernel and execute the graph enrichment steps.

2. Start the FastAPI Development Server

fastapi dev app/main.py