AWS Database Blog
Category: Amazon Neptune Analytics
Introducing the GraphRAG Toolkit
Amazon Neptune recently released the GraphRAG Toolkit, an open source Python library that makes it straightforward to build graph-enhanced Retrieval Augmented Generation (RAG) workflows. In this post, we describe how you can get started with the toolkit. We begin by looking at the benefits of adding a graph to your RAG application. Then we show you how to set up a quick start environment and install the toolkit. Lastly, we discuss some of the design considerations that led to the toolkit’s graph model and its approach to content retrieval.
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 2: Enhancing fraud detection with Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. In Part 1, we introduced the import and export functionalities, and walked you through how to quickly get started with them. In this post, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 1: Introducing Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. Part 1 introduces the import and export functionalities, and walks you through how to quickly get started with them. In Part 2, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
Triple your knowledge graph speed with RDF linked data and openCypher using Amazon Neptune Analytics
There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.
Introducing smaller capacity units for Amazon Neptune Analytics: Up to 75% cheaper to get started with graph analytics workloads
In this post, we show how you can reduce your cost by up to 75% when getting started with graph analytics workloads using the new 32 and 64 m-NCU capacities for Neptune Analytics. Many commonly used sample datasets can fit on 32 or 64 m-NCU, allowing you to work with the same data but at a lower cost. We also discuss how to monitor the graph size and resize m-NCUs without downtime.