Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems

Amazon Neptune Analytics now supports openCypher queries over RDF graphs. When you build an application that uses a graph database such as Amazon Neptune, you’re typically faced with a technology choice at the start: There are two different types of graphs, Resource Description Framework (RDF) graphs and labeled property graphs (LPGs), and your choice of […]

Discover more about what's new at AWS with Amazon Neptune Analytics now supports openCypher queries over RDF Graphs

😎 Pretty proud to see Ontotext mentioned in more than 10 Gartner Hype Cycle reports over the last couple months! This demonstrates the success of our…

Using SPARQL and the Knowledge Graph for RAG

Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.

Many people have asked about loading Microsoft Research #GraphRAG data into Neo4j. I wrote a quick notebook last night to import Documents, Chunks (TextUnit)… | 27 comments on LinkedIn

The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

Learn how to build a technical support chatbot with deeper, more relevant retrieval.

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…

This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…

How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

At Semantic Partners, we wanted to build our informed opinion over the strengths and weaknesses of graph RAG for RDF triple stores. We considered a simple use case: matching a job opening with Curriculum Vitae. We show how we used Ontotext GraphDB to build a simple graph RAG retriever using open, offline LLM models – the graph acting like a domain expert for improving search accuracy.

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.

The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…

The W3C RDF-star WG is currently debating a design issue which may greatly affect our chances of having a better alignment between RDF graphs and Labeled… | 13 comments on LinkedIn

In response to my last blog, LLMs and Knowledge — the study continues, I got a lot of questions LinkedIn, which I decided to answer in…

Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them

Continuing our exploration of Knowledge Graphs and Ontologies, this next part delves into hands-on implementation. Let’s transform flat CSV…

Go beyond typical RAG strategies

This is the first of a series of blogs wherein I describe what we believe is a superior pattern for creating highly performant, real-world…

Reference Architectures for Graph Databases on AWS - aws-samples/aws-dbs-refarch-graph

Natural language processing made easy

Building enterprise knowledge graphs as property graphs of billions of nodes / edges holding organizational knowledge to power your…

Seamlessy implement information extraction pipeline with LangChain and Neo4j