Lightweight evaluation metrics for RAG (Hit@k, Recall@k, Precision@k, MRR, nDCG, and more)
Project description
rag-eval-lite
Lightweight retrieval evaluation metrics for RAG systems.
Install
pip install rag-eval-lite
Usage
from rag_eval import evaluate_dataset
results = evaluate_dataset(data, k=3)
print(results)
Input Format
Each item in data must be:
{
"question": "string",
"golden_chunk_ids": ["chunk_1"],
"retrieved_chunk_ids": ["chunk_1", "chunk_3", "chunk_7"]
}
golden_chunk_ids: ground truth relevant chunksretrieved_chunk_ids: retriever output (ordered by rank)
Output
{
"hit@k": float,
"precision@k": float,
"recall@k": float,
"mrr": float,
"ndcg@k": float,
"num_failures": int,
"failures": [...]
}
Notes
- Order of retrieved chunks matters
- Supports single and multi-chunk evaluation
- Failures include full debug info
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