Retrieval and RAG
Precision@k
Implemented
rag.precision_at_kDefinition
Share of the top k retrieved documents that are relevant (k in the denominator even when fewer are returned), averaged over queries.
Formula
|relevant ∩ top_k| / k
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.precision_at_k(relevant, retrieved, k=5)References
- Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge University Press; 2008. Chapter 8.