tests/eval_samples.json

[
  {
    "question": "What is Retrieval-Augmented Generation (RAG)?",
    "expected_answer": "RAG is a technique that combines information retrieval with language model generation. Instead of relying solely on a model's parametric knowledge, RAG retrieves relevant documents from an external knowledge base and uses them as context for generating answers.",
    "relevant_sources": [],
    "collection": "default"
  },
  {
    "question": "What chunking strategies are available in this RAG system?",
    "expected_answer": "The system supports three chunking strategies: recursive character splitting (default), semantic chunking based on embedding similarity, and hierarchical parent-child chunking for multi-granularity retrieval.",
    "relevant_sources": [],
    "collection": "default"
  }
]