GraphRAG

📦 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.

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

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.

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

❇️ Quickly build knowledge graphs from unstructured text documents Easily implement knowledge graph retrievers combining graph traversals and vector… — Neo4j (@neo4j)

Create a Neo4j GraphRAG workflow using LangChain and LangGraph, combining graph queries, vector search, and dynamic prompting for advanced RAG.

Building an agent with LangChain's LangGraph and Graphiti

Will we still need to learn query languages in the future?

GraphRAG (by way of Neo4j in this case) enhances faithfulness (a RAGAS metric most similar to precision) when compared to vector-based RAG, but does not significantly lift other RAGAS metrics related to retrieval; may not offer enough ROI to justify the hype of the accuracy benefits given the performance overhead.