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

Introduction to Graph RAG and LLM RoutersSetting Up the Development EnvironmentBuilding the Knowledge GraphData Preparation and IngestionGraph Database Selection and SetupExample usageExample usageImplementing the LLM RouterDefining Router LogicIntegrating with LangChainConnecting Graph RAG with the RouterImplementing Advanced RAG TechniquesScaling and OptimizationConclusion and Future Directions Introduction to Graph RAG and LLM Routers Graph RAG, short for Retrieval-Augmented

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...

An easy trick to improve your LLM results without fine-tuning. Many people know "Few-Shot prompting" or "Chain of Thought prompting". A new (better) method was… | 77 comments on LinkedIn

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-Native Graph Creation have several misconceptions from traditional graph creation. We look to tackle some of these misconceptions.

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

Additionally, there are evaluation methods where LLMs serve as the evaluators, including single point, reference-based, and pairwise-based…

The Advantages of Small Graphs for Construction, Retrieval, Maintenance and Scaling KG RAG systems.

When we progress from data to knowledge, there is what physicists call a phase change like the change from water to ice or from mud to brick. The ingredients are the same throughout the transition, but we compress and restructure these ingredients into something entirely new with dramatically differ

Ten Arguments

🥳 The Wait is Over! As promised from my last post (https://lnkd.in/g9_-9i8D), I took MSFT open-source GraphRAG for a 🏎️💨 road test via my JAM4RAG (Just… | 12 comments on LinkedIn

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

Examples of KG + RAG & Schemas that are used by various systems across different domains, for inspirational purposes.

We switched from building simple LLM agents to graph-based AI solutions this year. In our experience, agentic graphs are the only way to 1) ensure high… | 44 comments on LinkedIn