Retrieval and RAG
Recall@k
Implemented
rag.recall_at_kDefinition
Share of all relevant documents that appear in the top k, averaged over queries.
Formula
|relevant ∩ top_k| / |relevant|
Range: [0, 1]
Inputs and outputs
- relevant: per query, relevant ids or id -> grade
- retrieved: per query, ranked ids
Returns: MetricResult (float, or per-example array with average=None)
Assumptions
No assumptions beyond valid, aligned inputs of the documented types.
Limitations
No metric-specific limitations are documented yet. Interpret the value alongside the task, data, and other metrics.
Python API
import evalsuite as es
es.recall_at_k(relevant, retrieved, k=5)References
- Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge University Press; 2008. Chapter 8.