Passing this along (because I think it shows how this field is evolving) but also to make a point. RAG is only half the story. Use RDF2Vec or a similar encoder…

😎 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…

When you need fast and high-throughput graph extraction with better quality than Claude 3.5 Sonnet.

Using SPARQL and the Knowledge Graph for RAG

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an...

Recent advancements in LLMs have sparked excitement about their potential to organize and utilize Knowledge Graphs. Microsoft's GraphRAG is a good starting… | 10 comments on LinkedIn

How MINPROMPT Uses Graph-Based Techniques to Optimize Data Augmentation ⁉ https://lnkd.in/e64JJyid The quality and quantity of training data remain…

Try out Artifacts created by Claude users

Microsoft's GraphRAG architecture surpasses traditional #RAG systems by integrating knowledge graphs with vector stores. By structuring information… | 24 comments on LinkedIn

The Necessary Multi-Step Retrieval Process in Graph RAG Systems 〽 Graph-based Retrieval-Augmented Generation (RAG) systems is a cutting-edge approach to… | 50 comments on LinkedIn

Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large...

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.

LLMs and Knowledge Graphs: A love story 💓 Researchers from University of Oxford recently released MedGraphRAG. At its core, MedGraphRAG is a framework…

The era of ChatGPT has arrived. It’s a transformative time, so much so that it could be called the third industrial revolution. Nowadays, even my mother uses ChatGPT for her […]

Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated...

✨ Attention Information Extraction Enthusiasts ✨ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edge… | 33 comments on LinkedIn

Let’s face it. If you have been working with generative AI (GenAI) and large language models (LLMs) in any serious way, you will have had to develop a strategy for minimizing hallucinations.

we spent 12 months figuring out that LLM text-to-SQL doesn't work. and so we re-architected our entire system. what we ended up building was an ontology… | 36 comments on LinkedIn

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…

We are excited to announce the Beta launch of the WhyHow.AI Knowledge Graph Studio Platform, the easiest way to build KG RAG systems.

Safety RAG (SRAG) is how we get to 100% hallucination-free answers.

RAG is popular but unfit for many enterprise use cases and I explain both of those things.

Utilizing knowledge graphs is one popular solution to drive up the performance of AI applications. We work closely together with other key players such as Emil…

An open-source model for 10x cheaper knowledge graph construction

Large Language Models (LLMs) have demonstrated unprecedented prowess across various natural language processing tasks in various application domains. Recent studies show that LLMs can be leveraged...

Using knowledge graphs to improve the performance of RAG systems by optimising retrieval design architectures and collaborative filtering.

Knowledge Graph Embedding (KGE) algorithms can improve the performance of LLMs in understanding relationships as a context for Q&As.

Learn how to use unstructured.io for PDF document parsing, extracting, and ingestion into the Neo4j graph database for GenAI applications.

Let’s talk about the new graph-based approach to processing complex datasets for context-rich LLM responses

𝗥𝗔𝗚 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗶𝗹 𝗗𝘂𝗲 𝗧𝗼 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗼𝗰𝘂𝘀 𝗢𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗻𝘁 𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎… | 12 comments on LinkedIn