a172cf893d
Add 5 markdown documents that users can index to reproduce evaluation results. Changes: - Add sample_documents/ folder with 5 markdown files covering LightRAG features - Update sample_dataset.json with 3 improved, specific test questions - Shorten and correct evaluation README (removed outdated info about mock responses) - Add sample_documents reference with expected ~95% RAGAS score Test Results with sample documents: - Average RAGAS Score: 95.28% - Faithfulness: 100%, Answer Relevance: 96.67% - Context Recall: 88.89%, Context Precision: 95.56%
25 lines
1.5 KiB
Markdown
25 lines
1.5 KiB
Markdown
# LightRAG Improvements Over Traditional RAG
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## Key Improvements
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LightRAG improves upon traditional RAG approaches in several significant ways.
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### Simpler API Design
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LightRAG offers a simpler API compared to traditional RAG frameworks. The framework provides intuitive interfaces for developers. Traditional RAG systems often require complex configuration and setup. LightRAG focuses on ease of use while maintaining functionality.
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### Faster Retrieval Performance
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LightRAG delivers faster retrieval performance than traditional RAG approaches. The framework optimizes document retrieval operations for speed. Traditional RAG systems often suffer from slow query response times. LightRAG achieves high quality results with improved performance.
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### Better Vector Database Integration
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LightRAG provides better integration with various vector databases. The framework supports multiple vector database backends seamlessly. Traditional RAG approaches typically lock developers into specific database choices. LightRAG enables flexible storage backend selection.
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### Optimized Prompting Strategies
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LightRAG implements optimized prompting strategies for better results. The framework uses refined prompt templates for accurate responses. Traditional RAG systems often use generic prompting approaches. LightRAG balances simplicity with high quality output.
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## Design Philosophy
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LightRAG prioritizes ease of use without sacrificing quality. The framework combines speed with accuracy in retrieval operations. LightRAG maintains flexibility in database and model selection.
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