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
Related Scorers
SemanticSimilarityScorer
Computes semantic similarity between query and retrieved context using sentence embeddings. Uses cos...
Basic RAGContextualPrecisionScorerPP
Computes precision for retrieved chunks using numerical relevance scoring. Precision = (Number of re...
Basic RAGRetrievalF1Scorer
F1 score combining precision and recall for contextual evaluation. Computes harmonic mean of precisi...
Basic RAGRetrievalDiversityScorer
Evaluates diversity of retrieved context chunks using cosine distance between embeddings. Higher sco...
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
Ready to try RetrievalRankingScorer?
Start evaluating your AI agents with Noveum.ai's comprehensive scorer library.
Explore More Scorers
Discover 106 calibrated LLM-as-Judge scorers for comprehensive AI evaluation