{
  "test_cases": [
    {
      "question": "What is LightRAG and what problem does it solve?",
      "ground_truth": "LightRAG is a Simple and Fast Retrieval-Augmented Generation framework developed by HKUDS. It solves the problem of efficiently combining large language models with external knowledge retrieval to provide accurate, contextual responses while reducing hallucinations.",
      "context": "general_rag_knowledge"
    },
    {
      "question": "What are the main components of a RAG system?",
      "ground_truth": "A RAG system consists of three main components: 1) A retrieval system (vector database or search engine) to find relevant documents, 2) An embedding model to convert text into vector representations, and 3) A large language model (LLM) to generate responses based on retrieved context.",
      "context": "rag_architecture"
    },
    {
      "question": "How does LightRAG improve upon traditional RAG approaches?",
      "ground_truth": "LightRAG improves upon traditional RAG by offering a simpler API, faster retrieval performance, better integration with various vector databases, and optimized prompting strategies. It focuses on ease of use while maintaining high quality results.",
      "context": "lightrag_features"
    },
    {
      "question": "What vector databases does LightRAG support?",
      "ground_truth": "LightRAG supports multiple vector databases including ChromaDB, Neo4j, Milvus, Qdrant, MongoDB Atlas Vector Search, and Redis. It also includes a built-in nano-vectordb for simple deployments.",
      "context": "supported_storage"
    },
    {
      "question": "What are the key metrics for evaluating RAG system quality?",
      "ground_truth": "Key RAG evaluation metrics include: 1) Faithfulness - whether answers are factually grounded in retrieved context, 2) Answer Relevance - how well answers address the question, 3) Context Recall - completeness of retrieval, and 4) Context Precision - quality and relevance of retrieved documents.",
      "context": "rag_evaluation"
    },
    {
      "question": "How can you deploy LightRAG in production?",
      "ground_truth": "LightRAG can be deployed in production using Docker containers, as a REST API server with FastAPI, or integrated directly into Python applications. It supports environment-based configuration, multiple LLM providers, and can scale horizontally.",
      "context": "deployment_options"
    },
    {
      "question": "What LLM providers does LightRAG support?",
      "ground_truth": "LightRAG supports multiple LLM providers including OpenAI (GPT-3.5, GPT-4), Anthropic Claude, Ollama for local models, Azure OpenAI, AWS Bedrock, and any OpenAI-compatible API endpoint.",
      "context": "llm_integration"
    },
    {
      "question": "What is the purpose of graph-based retrieval in RAG systems?",
      "ground_truth": "Graph-based retrieval in RAG systems enables relationship-aware context retrieval. It stores entities and their relationships as a knowledge graph, allowing the system to understand connections between concepts and retrieve more contextually relevant information beyond simple semantic similarity.",
      "context": "knowledge_graph_rag"
    }
  ]
}