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rag-system / tests /eval_samples.json

Joshua Sears

Claude Sonnet 4.6

Initial commit — production RAG system

bd8abf4 6 months ago

799 Bytes

[
  {
    "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"
  }
]

	[
	{
	"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"
	}
	]