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- Update sample_dataset.json with 3 improved, specific test questions - Shorten and correct evaluation README (removed outdated info about mock responses) - Add sample_documents reference with expected ~95% RAGAS score Test Results with sample documents: - Average RAGAS Score: 95.28% -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%
21 lines
813 B
Markdown
21 lines
813 B
Markdown
# Sample Documents for Evaluation
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These markdown files correspond to test questions in `../sample_dataset.json`.
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## Usage
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1. **Index documents** into LightRAG (via WebUI, API, or Python)
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2. **Run evaluation**: `python lightrag/evaluation/eval_rag_quality.py`
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3. **Expected results**: ~91-100% RAGAS score per question
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## Files
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- `01_lightrag_overview.md` - LightRAG framework and hallucination problem
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- `02_rag_architecture.md` - RAG system components
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- `03_lightrag_improvements.md` - LightRAG vs traditional RAG
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- `04_supported_databases.md` - Vector database support
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- `05_evaluation_and_deployment.md` - Metrics and deployment
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## Note
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Documents use clear entity-relationship patterns for LightRAG's default entity extraction prompts. For better results with your data, customize `lightrag/prompt.py`.
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