Integrating Neo4j with Flask for Graph Data Management

A Python-based project integrating Neo4j graph database functionalities with Flask REST API endpoints and data ingestion pipelines. It provides tools for managing graph data, including node creation, querying, and CSV/GraphML data uploads.

Features

• REST API server built with Flask for Neo4j interactions.
• Neo4j connection and query management classes.
• Data ingestion utilities for CSV and GraphML files into Neo4j.
• Neo4j data models defined using Neomodel.
• Docker Compose setup for local development with Neo4j and Flask.

Tech Stack

• Python 3
• Flask
• Neo4j (Graph Database)
• Neomodel (Object Graph Mapper for Neo4j)
• Docker & Docker Compose
• PySpark (for some data processing)

Getting Started

Prerequisites

• Docker and Docker Compose installed
• Python 3 environment

Installation

Clone the repository:

git clone https://github.com/justin-napolitano/supreme-court-neo.git
cd supreme-court-neo/neo4jAPI

Install Python dependencies (assuming a virtual environment):

pip install -r requirements.txt

Running the Application

To start the Flask REST API and Neo4j database using Docker Compose:

docker-compose -f docker-compose.dev.yml up --build

The Flask app will be available at http://localhost:5000.

Usage

• Access the root endpoint / for a basic health check.
• Use /init_neo_driver to initialize the Neo4j driver and run a test query.

Project Structure

neo4jAPI/
├── rest.py               # Flask REST API server
├── neo4j_connect.py      # Neo4j connection and query wrapper
├── neo_native_apy.py     # Neo4j driver wrapper with sample queries
├── NeoNodes.py           # Neomodel node definitions
├── docker-compose.dev.yml# Docker Compose config for dev
├── clean_loc_data.py     # Utility for file listing
├── upload_csv_to_neo.py  # Data ingestion pipeline for CSV
├── csv_to_neo.py         # CSV data upload functions
├── graphml_to_neo.py     # GraphML import utility
├── neo4j_connect_2.py    # Alternative Neo4j connection class
├── neo4jClasses.py       # Neo4j Aura and Sandbox API classes
├── neoModelAPI.py        # Neomodel API wrapper
├── stack_graph.py        # Cypher queries and graph processing logic
└── test_data/            # Sample test data

Future Work / Roadmap

• Add comprehensive documentation and usage examples.
• Implement more robust error handling and logging.
• Extend REST API with additional endpoints for CRUD operations on graph nodes.
• Integrate authentication and security for API access.
• Automate data pipeline workflows for scalable ingestion.
• Add unit and integration tests.

Note: Some credentials and connection details are placeholders or hardcoded for development and should be secured or parameterized for production.