This repository provides a comprehensive collection of sample code, documentation, and utilities for leveraging Amazon Neptune, a managed graph database service. It caters to developers, data scientists, and researchers seeking to build and deploy graph-based applications, perform complex graph analytics, and integrate Neptune with various AWS services and machine learning frameworks.

How It Works

The samples demonstrate diverse approaches to interacting with Amazon Neptune, including using Gremlin and SPARQL query languages, integrating with AWS services like Lambda and Kinesis for data ingestion and processing, and applying graph machine learning techniques with tools like GraphStorm. The repository showcases practical use cases such as fraud detection, recommendation engines, knowledge graph construction, and data visualization.

Quick Start & Requirements

• Installation: Varies by sample; typically involves Python package installation (pip install ...), Docker, or direct use of AWS services.
• Prerequisites: An AWS account with Neptune access, Python (often 3.x), Boto3 SDK, Gremlin drivers (Python, Java, Node.js), and potentially specific ML libraries or visualization tools. Some samples may require specific AWS services like SageMaker, Comprehend, or Kinesis.
• Resources: Requires an active Amazon Neptune instance. Setup complexity and resource needs vary significantly per sample.
• Links: CONTRIBUTING.md

Highlighted Details

• NeptuneGS: Integrates Neptune data with GraphStorm for graph machine learning.
• Vector Similarity Search (VSS): Demonstrates VSS combined with graph traversal on the EDGAR dataset.
• Knowledge Graph Chatbot: Full-stack example with NLP integration using Amazon Comprehend and Lex.
• Neptune ML: Examples for fraud detection, recommendations, and anomaly detection.

Maintenance & Community

Maintained by the Amazon Neptune team. Links to related works and other Neptune open-source projects are provided.

Licensing & Compatibility

Licensed under a modified MIT license. Generally permissive for commercial use and integration with closed-source applications.

Limitations & Caveats

Samples are provided as "best-effort" and may require modifications for production deployment. Some projects are archived or have been moved to standalone repositories.