Overview

You can use the DocumentNDCGEvaluator component to evaluate documents retrieved by a Haystack pipeline, such as a RAG pipeline, against ground truth labels. A higher NDCG is better and indicates that relevant documents appear at an earlier position in the list of retrieved documents.

If the ground truth documents have scores, a higher NDCG indicates that documents with a higher score appear at an earlier position in the list of retrieved documents. If the ground truth documents have no scores, binary relevance is assumed, meaning that all ground truth documents are equally relevant, and the order in which they are in the list of retrieved documents does not matter for the NDCG.

No parameters are required to initialize a DocumentNDCGEvaluator.

Usage

On its own

Below is an example where we use the DocumentNDCGEvaluator to evaluate documents retrieved for a query. There are two ground truth documents and three retrieved documents. All ground truth documents are retrieved, but one non-relevant document is ranked higher than one of the ground truth documents, which lowers the NDCG score.

from haystack import Document
from haystack.components.evaluators import DocumentNDCGEvaluator

evaluator = DocumentNDCGEvaluator()
result = evaluator.run(
    ground_truth_documents=[[Document(content="France", score=1.0), Document(content="Paris", score=0.5)]],
    retrieved_documents=[[Document(content="France"), Document(content="Germany"), Document(content="Paris")]],
)
print(result["individual_scores"])
# [0.8869]
print(result["score"])
# 0.8869

In a pipeline

Below is an example of using a DocumentNDCGEvaluator and DocumentMRREvaluator in a pipeline to evaluate retrieved documents and compare them to ground truth documents. Running a pipeline instead of the individual components simplifies calculating more than one metric.

from haystack import Document, Pipeline
from haystack.components.evaluators import DocumentMRREvaluator, DocumentNDCGEvaluator

pipeline = Pipeline()
pipeline.add_component("ndcg_evaluator", DocumentNDCGEvaluator())
pipeline.add_component("mrr_evaluator", DocumentMRREvaluator())

ground_truth_documents=[[Document(content="France", score=1.0), Document(content="Paris", score=0.5)]]
retrieved_documents=[[Document(content="France"), Document(content="Germany"), Document(content="Paris")]]

result = pipeline.run({
  "ndcg_evaluator": {
    "ground_truth_documents": ground_truth_documents,
    "retrieved_documents": retrieved_documents
  },
  "mrr_evaluator": {
    "ground_truth_documents": ground_truth_documents,
	  "retrieved_documents": retrieved_documents
	},
})

for evaluator in result:
    print(result[evaluator]["score"])
# 0.9502
# 1.0