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Look inside
Upgrade your RAG applications with the power of knowledge graphs.
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 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 you’ll learn:
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 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 you’ll learn:
- The benefits of using Knowledge Graphs in a RAG system
- How to implement a GraphRAG system from scratch
- The process of building a fully working production RAG system
- Constructing knowledge graphs using LLMs
- Evaluating performance of a RAG pipeline
about the book
Knowledge Graph-Enhanced RAG teaches you to implement accurate, performant, and traceable RAG by structuring the context data as a knowledge graph. Filled with practical techniques, this book teaches you how to build RAG on both unstructured and structured data. You’ll go hands-on to build a vector similarity search retrieval tool, an Agentic RAG application, extract information from text to create a Knowledge Graph, evaluate performance and accuracy, and more.about the reader
For readers with intermediate Python skills and some experience with a graph database like Neo4j.about the authors
Tomaz Bratanic has extensive experience with graphs, machine learning, and generative AI. He has written an in-depth book about using graph algorithms in practical examples. Nowadays, he focuses on generative AI and LLMs by contributing to popular frameworks like LangChain and LlamaIndex and writing blog posts about LLM-based applications.Oskar Hane is a Senior Staff Software Engineer at Neo4j. He has over 20 years of experience as a Software Engineer and 10 years of experience working with Neo4j and knowledge graphs. He is currently leading the Generative AI engineering team within Neo4j, with the focus to provide the best possible experience for other developers to build GenAI applications with Neo4j.
Fantastic source to learn how to tune LLM AI models to meet specific use cases.
The way you think and interact with LLMs will never be the same again.
A perfect resource to get started with RAG using Neo4j.
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