Sample Documents for Evaluation

These markdown files correspond to test questions in ../sample_dataset.json.

Usage

1. Index documents into LightRAG (via WebUI, API, or Python)
2. Run evaluation: python lightrag/evaluation/eval_rag_quality.py
3. Expected results: ~91-100% RAGAS score per question

Files

• 01_lightrag_overview.md - LightRAG framework and hallucination problem
• 02_rag_architecture.md - RAG system components
• 03_lightrag_improvements.md - LightRAG vs traditional RAG
• 04_supported_databases.md - Vector database support
• 05_evaluation_and_deployment.md - Metrics and deployment

Note

Documents use clear entity-relationship patterns for LightRAG's default entity extraction prompts. For better results with your data, customize lightrag/prompt.py.