Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
This week is again heavy on the GraphRAG / Knowledge Graph side, with a hands-on video with Tomaz Bratanic, detailed GraphRAG challenges and recommendations, GraphRAG with a chatbot, and Knowledge Graphs as the foundation for innovative GenAI Apps.
I hope you enjoy this issue,
Alexander Erdl
COMING UP NEXT WEEK!
- Livestream: GraphAcademy Live: Cypher Fundamentals on July 04
- All Neo4j Events: Webinars and More
- GraphSummit Series: Get Connected With Graphs
FEATURED COMMUNITY MEMBER: Ashleigh Faith
Ashleigh Faith has her PhD in Advanced Semantics and over 15 years of experience working on graph solutions across the STEM, government, and finance industries. She also hosts the YouTube channel IsA DataThing, where she tries to demystify the graph space.
Connect with her on LinkedIn.
Ashleigh is already confirmed to speak at NODES 2024. In her session, she will walk through an architecture to add statement verification to your Knowledge Graph processes. Especially with LLMs now grounding off the data in KGs, its even more important to know how confident you can be in the data in your graph.
LLAMAINDEX: Advanced RAG with Knowledge Graphs
In this video, Tomaz Bratanic uses LlamaIndex property graph abstractions. In a previous blog post, he also explains how to implement entity deduplication and custom retrieval methods to increase GraphRAG accuracy.GRAPHRAG: GraphRAG: Design Patterns, Challenges, Recommendations
In this article, Ben Lorica and Prashanth Rao explore the design patterns, challenges, and recommendations for integrating knowledge graphs with Retrieval Augmented Generation (RAG) systems, enhancing the accuracy and contextual relevance of LLM responses by using structured graph data.CHATBOT: From Ancient Epic to Modern Marvel: Demystifying the Mahabharata Chatbot with GraphRAG (Part 3)
In the third part, Siddhant Agarwal delves deeper into bringing the Mahabharata to life with a context-rich and intuitive chatbot. He dissects the inner workings of this innovative system, focusing on a revolutionary approach called GraphRAG.KNOWLEDGE GRAPH: The RAG Stack: Featuring Knowledge Graphs
As RAG becomes a core technique for enterprise adoption of Generative AI, the RAG stack and knowledge graphs, in particular, will become integral to imposing degrees of determinism on probabilistic large models. This article by Chia Jeng Yang and Akash Bajwa details why knowledge graphs can serve as critical infrastructure to enable future generative AI innovation, such as AI multi-agent systems.POST OF THE WEEK: Adam Chan
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