A practical guide to building production-quality Retrieval Augmented Generation (RAG) systems using knowledge graphs for enhanced accuracy, traceability, and performance. Link: https://mng.bz/4Jy5
Essential GraphRAG demonstrates how to harness knowledge graphs to supercharge your RAG systems. While traditional RAG relies on vector similarity, GraphRAG leverages structured relationships in data to deliver more relevant and comprehensive context to LLMs.
- Benefits of using Knowledge Graphs in RAG systems
- Implementing GraphRAG systems from scratch
- Building production-ready RAG pipelines
- Constructing knowledge graphs using LLMs
- Evaluating RAG pipeline performance
- Vector similarity-based retrieval approaches
- Working with semantic layers and agentic RAG
- Generating Cypher statements for graph queries
- Practical Examples: Vector similarity search tools, Agentic RAG applications, and performance evaluation
- Production-Ready: Learn to extract structured knowledge from text and deploy real-world systems
- Hybrid Approach: Combine vector-based and graph-based retrieval methods
- Comprehensive: From basic concepts to advanced implementation techniques
Traditional RAG systems struggle with complex relationships and context. GraphRAG solves this by:
- Modeling data relationships explicitly in knowledge graphs
- Providing richer, more relevant prompts to LLMs
- Reducing hallucinations through structured context
- Enabling better traceability and explainability
This book is perfect for developers, data scientists, and AI engineers looking to build sophisticated RAG systems that go beyond simple vector similarity search.
Build RAG systems that truly understand your data's relationships and deliver accurate, contextual responses.