Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms:

Graph is increasingly driving the Agentic space, which I see as being a very good sign. Recently, a programmer asked why graphs would be superior to using…

Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.

Making LLM Knowledge Graph building fast & dynamic: challenges, design decisions, and solutions

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.

Showcase of Data Transformation Process, Breakdown of 25 dev hours involved, Schemas used, Questions & Responses, and Graph created

Introduction

A deep dive into LangChain’s implementation of graph construction with LLMs

A question-based extraction approach

Learn how to overcome them using knowledge graphs and structured tools

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

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

Applications of Graph approaches in RAG — current state

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

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

✨ 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.

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

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

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

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

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

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…

If powerful LLMs were all that was needed to get to great enterprise generative AI programs, then hundreds of thousands of open-source and closed-source LLMs… | 46 comments on LinkedIn

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps organizations find hidden relationships and patterns

Unlocking the Secrets of Scientific Discovery with AI and Knowledge Graphs ... Have you ever wondered how AI could revolutionize the way we conduct scientific… | 17 comments on LinkedIn