GraphRAG

Bridging the gap between organisational data, implicit organisational reasoning, and AI reasoning with Knowledge Graphs purpose-built for AI.

The only viable successor to Kuzu. Built for agentic solutions in highly regulated industries.

📦 Package Links & Code Examples:- 🐍 PyPI: https://pypi.org/project/graph_nd/- 📁 GitHub: https://github.com/zach-blumenfeld/graph-nd- 📚 Docs: https://grap...

Extract and generate a knowledge graph from educational curriculum information without writing a single line of code.

Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

Take a deep dive into Neo4j Aura Graph Analytics, and learn how to run PageRank, community detection, and other algorithms directly in your workflow.

Knowledge graphs are powerful tools for representing relationships between entities in a structured format. They are widely used in various…

Streamline the process of working with legal contracts by implementing an end-to-end solution using agentic GraphRAG.

Learn when and how to use GraphRAG and how it can improve on some search tasks

GraphRAG is a powerful retrieval mechanism that improves Generative AI applications by taking advantage of the rich context in graph data structures.

Discover how to build a knowledge graph in 7 simple steps, from defining your use case to creating a model to ingesting your data.

Discover the best graph visualization tools for visualizing your Neo4j graph database, including development, exploration, dashboarding, and embedded tools.

What are the key ontology standards you should have in mind? Ontology standards are crucial for knowledge representation and reasoning in AI and data… | 100 comments on LinkedIn

GraphRAG: Enhancing Retrieval-Augmented Generation with SurrealDB, Gemini and DeepSeek

Learn more about integrating Neo4j with Qdrant for external vector searches by spinning up both locally and loading with sample data.

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Upgrade your RAG applications with the power of knowledge graphs./b Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Knowledge Graph-Enhanced RAG/i shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Knowledge Graph-Enhanced RAG/i you’ll learn: The benefits of using Knowledge Graphs in a RAG system/li How to implement a GraphRAG system from scratch/li The process of building a fully working production RAG system/li Constructing knowledge graphs using LLMs/li Evaluating performance of a RAG pipeline/li /ul Knowledge Graph-Enhanced RAG/i is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.

In this blog post, we’ll be focusing on how to write the retrieval query that supplements or grounds the LLM’s answer.

Neo4j’s LLM Graph Builder is an app for automatically constructing knowledge graphs from unstructured data sources. It can be run locally…

Graphs are ubiquitous data structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. Graph neural networks (GNNs) have become...

A question-based extraction approach

Entity resolution, the process of determining which digital descriptions correspond to the same real-world entities, is an important graph use case. It is also a crucial precursor to many graph data science projects. In this session, you will learn steps that the Neo4j professional services team has used in many entity resolution projects. The steps include designing a graph data model that highlights shared identifiers, standardizing the format of node properties, identifying outlier nodes that should be excluded from the matching process, using graph data science algorithms to identify duplicate entities, using string similarity to identify misspellings, and capturing the results of entity resolution in your graph. Get certified with GraphAcademy: https://dev.neo4j.com/learngraph Neo4j AuraDB https://dev.neo4j.com/auradb Knowledge Graph Builder https://dev.neo4j.com/KGBuilder Neo4j GenAI https://dev.neo4j.com/graphrag

The Programmable Cypher-based 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

Learn how to overcome them using knowledge graphs and structured tools