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

There is a lot of noise about #KnowledgeGraphs for LLM apps. But we haven't seen an enterprise-grade implementation. This approach could be one. "The… | 11 comments on LinkedIn

This is something very cool! 3. GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models "GraphReader addresses the…

See how to combine structured and unstructured semantic queries and to use large language models to orchestrate question answering over a knowledge graph.

Recently, Microsoft announced with a post their GraphRAG offering. This article provides a brief overview of their approach, how it compares to other Graph RAG varieties, what problems it can address and what it cannot.

Knowledge graphs (KGs) are a specific type of #data structure designed to represent entities and the connections between them. They move beyond simply storing… | 14 comments on LinkedIn

Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy

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

Ask your (research) question against 76 Million scientific articles: https://ask.orkg.org Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific…

Copyright 2024 Kurt Cagle / The Cagle Report Recently, I've spent a lot of time talking with clients about the need for knowledge graphs in LLMs, why they are not "graphlike," and why we may need to rethink the whole transformer model. I think this topic is worth exploring, and I'd like to have a po

Predicting the architecture of intelligent organizations

Our paper "Are Large Language Models a Good Replacement of Taxonomies?" was just accepted to VLDB'2024! This finished our last stroke of study on how knowledgeable LLMs are and confirmed our recommendation for the next generation of KGs. How knowledgeable are LLMs? 1.

Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…

What Could Go Wrong When We Start Using LLMs to Organize Knowledge? 7 Pain Points of GraphRAG Alright, tech enthusiasts and AI aficionados. We need to discuss… | 43 comments on LinkedIn

Generalist and Lightweight Model for Relation Extraction (Extract any relationship types from text) - jackboyla/GLiREL

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:

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...