RetrievalRankingScorer
Computes ranking metrics for retrieved context including MRR (Mean Reciprocal Rank), NDCG, and MAP. Assesses how well the retrieval system ranks relevant documents higher than irrelevant ones.
Overview
Computes ranking metrics for retrieved context including MRR (Mean Reciprocal Rank), NDCG, and MAP. Assesses how well the retrieval system ranks relevant documents higher than irrelevant ones.
Use Cases
- RAG-based question answering systems
How It Works
This scorer uses deterministic rule-based evaluation to validate outputs against specific criteria. It applies predefined rules and patterns to assess the response, providing consistent and reproducible results without requiring LLM inference.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| context.rankings | list[int] | Yes | Chunk positions/rankings |
| context.relevance_scores | list[float] | Yes | Relevance scores per chunk |
Output Schema
| Field | Type | Description |
|---|---|---|
| mrr | float | Mean Reciprocal Rank |
| ndcg | float | Normalized Discounted Cumulative Gain |
| map | float | Mean Average Precision |
| avg_ranking | float | Average ranking position |
| combined | float | Combined ranking score |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use RetrievalRankingScorer when you need to evaluate rag and rule-based aspects of your AI outputs. It's particularly useful for rag-based question answering systems.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
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