lightrag-cn-git/lightrag/evaluation/sample_retrieval_oracle.json

{
  "oracle": [
    {
      "question": "How does LightRAG solve the hallucination problem in large language models?",
      "expected_documents": ["01_lightrag_overview.md"]
    },
    {
      "question": "What are the three main components required in a RAG system?",
      "expected_documents": ["02_rag_architecture.md"]
    },
    {
      "question": "How does LightRAG's retrieval performance compare to traditional RAG approaches?",
      "expected_documents": ["03_lightrag_improvements.md"]
    },
    {
      "question": "What vector databases does LightRAG support and what are their key characteristics?",
      "expected_documents": ["04_supported_databases.md"]
    },
    {
      "question": "What are the four key metrics for evaluating RAG system quality and what does each metric measure?",
      "expected_documents": ["05_evaluation_and_deployment.md"]
    },
    {
      "question": "What are the core benefits of LightRAG and how does it improve upon traditional RAG systems?",
      "expected_documents": [
        "01_lightrag_overview.md",
        "03_lightrag_improvements.md"
      ]
    }
  ]
}