Large Language Models (LLMs) are revolutionizing AI capabilities, but organizations face challenges in reducing inaccuracies and protecting valuable data. Knowledge Graphs offer a solution, helping improve LLM accuracy and safeguard organizational data assets. Learn how implementing Knowledge Graphs can address these critical issues and maintain competitiveness in the AI landscape.

A question-based extraction approach

We are launching a series of benchmarks to prove the value of GraphRAG for question & answering on complex documents. The process is simple, we ingest the…

Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...

🕸️Building a LangGraph agent with graph memory The following community examples demonstrates building an agent using LangGraph. Graphiti is used to… | 24 comments on LinkedIn

Learn about the different kinds of data involved in developing, using and monitoring foundation AI models and systems.

Learn how to overcome them using knowledge graphs and structured tools

Don’t complicate things with graph DBs, QLs, or graph analytics.

More Graph, More Agents: Scaling Graph Reasoning with LLMs Graph reasoning tasks have proven to be a tough nut to crack for Large Language Models (LLMs).…

Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn

Graph RAG answers the big questions where text embeddings won’t help you.

🚀 Exciting New Research: "Graph-constrained Reasoning (GCR)" - Enabling Faithful KG-grounded LLM Reasoning with Zero Hallucination! 🧠 🎉 Proud to share our… | 11 comments on LinkedIn

GraphRAG explained with an imaginary quiz game and not a single line of source code

Applications of Graph approaches in RAG — current state

A GCP native, fully serverless implementation that you will replicate in minutes

In this video, I introduce LightRAG, a new, cost-effective retrieval augmented generation (RAG) method that combines knowledge graphs and embedding-based ret...

⛔ The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context. 🟢…

Data Structures for Hyper-Personalized Lesson Plans

Document Metadata Generation, File Directory & Two-Step Information Retrieval Processes

Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.

Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that… | 19 comments on LinkedIn

The 3 layers of Agentic Graph RAG 💬 The 3 layers of agentic graph RAG represent a significant advancement in AI-driven knowledge systems. These layers… | 17 comments on LinkedIn

Researchers created a knowledge graph based on 58 million journal papers to fuel personalized research ideas for scientists. Over 4400 ideas generated by the… | 29 comments on LinkedIn

After a period of more than a year (can't believe time flew by so quick!), I had the pleasure of going back for a second time on the Practical AI Podcast with…

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Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immu...

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

Hey Knowledge Graph friends! As I imagine some of you are, I've been a bit annoyed that many "Knowledge Graph and AI" demos and tools—while definitely… | 13 comments on LinkedIn

Design patterns for improving GenAI applications with a graph.