Every time I write about why graph-based RAG produces more insightful and more accurate answers for Q&A / digital assistant AI applications, people ask — do… | 21 comments on LinkedIn

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:

Neo4j’s Philip Rathle on the Rise of GraphRAG and GQL.

End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages. - a-s-g93/neo4j-runway

RAG + Knowledge Graphs cut customer support resolution time by 29.6%. 📉 A case study from LinkedIn. 🤝💼 Conventional RAG methods treat historical issue… | 10 comments on LinkedIn

OMG! 341 papers have been published on the topic of RAG (Retrieval Augmented Generation) since Jan 1, 2024: Naive RAG, Advanced RAG, GraphRAG … !  Please tell…

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…

Graph RAG can perform much better than std RAG. Here’s when and how: When you want your LLM to understand the interconnection between your documents before…

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can...

Learn how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems.

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...

Wow, what a great farm-to-fork notebook by Jerry Liu that goes from 1) the exciting text of the San Francisco 2023 Budget Proposal (gnarly PDF!) all the way…

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.

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...

Introducing Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data ... Did you know that 80% of enterprise data resides in unstructured… | 13 comments on LinkedIn

When building GraphRAG, you may want to explicitly define the graph yourself, or use the LLM automatically extract the graph. Both have tradeoffs: the former… | 17 comments on LinkedIn

Over the past few weeks I’ve been researching, and building a framework that combines the power of Large Language Models for text parsing and transformation with the precision of structur…

Case Studies, how Public companies are using KGs today, and where to apply KGs in RAG

Build-your-own Graph RAG 🕸️ There are two prepackaged ways to do RAG with knowledge graphs: vector/keyword search with graph traversal, and text-to-cypher.… | 15 comments on LinkedIn

GNN-RAG Combines the language understanding abilities of LLMs with the reasoning abilities of GNNs in a RAG style. The GNN extracts useful and relevant…

🎉 Check out our new work on Transformer theory! (out today on arxiv) Key takeaways: 1️⃣ We show how 9 different algorithmic tasks map into a complexity… | 10 comments on LinkedIn

💡 Knowledge Graphs for mimicking human memory to integrate “new experiences” in LLMs. 🔬 In a paper entitled “HippoRAG: Neurobiologically Inspired Long-Term…

Subscribe • Previous Issues Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines By Ben Lorica and Prashanth Rao. GraphRAG (Graph-based Retrieval Augmented Generation) enhances the traditional Retrieval Augmented Generation (RAG) method by integrating knowledge graphs (

💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…

We’re excited to launch a huge feature making LlamaIndex the framework for building knowledge graphs with LLMs: The Property Graph Index 💫 (There’s a lot of… | 57 comments on LinkedIn

Catch Thomas Smoker of WhyHow.AI talking with Demetrios Brinkmann of MLOps Community about "Managing Small Knowledge Graphs for Multi-agent Systems" Key…