Light RAG: Simple and Fast Retrieval Augmented Generation
A comprehensive guide to Light RAG, a new retrieval augmented generation method for efficient and cost-effective document querying.
Course Timeline
🎥 Introduction to Light RAG
Overview of Light RAG, its advantages over Graph RAG, and its open-source availability.
💡 Understanding the Need for Light RAG
Explores the limitations of standard RAG systems and the benefits of knowledge graph-based approaches like Light RAG and Graph RAG.
⚙️ Light RAG Algorithm: Indexing and Retrieval
Detailed explanation of Light RAG's two-part algorithm: indexing (entity and relationship extraction) and retrieval (low-level, high-level, and hybrid).
📊 Light RAG vs. Graph RAG: Performance and Cost Comparison
Comparison of Light RAG and Graph RAG performance across various datasets, highlighting Light RAG's cost-effectiveness.
🚀 Setting up Light RAG Locally
Step-by-step guide on installing Light RAG using Git clone or pip, including virtual environment setup and package installation.
💻 Running Light RAG: Code Example and Explanation
Walkthrough of a sample Python code, demonstrating how to use Light RAG for querying, including data loading, indexing, and query execution in different modes (naive, local, global, hybrid).
🔍 Analyzing Light RAG's Output and Results
Interpretation of Light RAG's output, including the generated knowledge graph, query results from different retrieval modes, and token usage comparison with Graph RAG.
Outro
Summary of Light RAG's capabilities and a recommendation to explore the project's GitHub repository for more details.