> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ContextRelevance

> Evaluator to assess how relevant retrieved documents are to a user query.

Evaluator to assess how relevant retrieved documents are to a user query.

The ContextRelevance evaluator measures whether retrieved documents provide
sufficient context to answer a given question. This is a critical metric for
RAG (Retrieval-Augmented Generation) pipelines to ensure the retrieval step
is fetching useful information.

Key Features:

* **Retrieval Assessment**: Determines if retrieved documents support the query
* **Three-Level Scoring**: Returns high (1.0), medium (0.5), or low (0.0) relevance scores
* **RAG Pipeline Evaluation**: Specifically designed for evaluating retrieval quality
* **Detailed Reasoning**: Provides explanation for the relevance assessment
* **Fiddler API Integration**: Uses Fiddler's built-in context relevance model

Use Cases:

* **RAG Systems**: Evaluating retrieval quality in RAG pipelines
* **Search Systems**: Assessing if search results are relevant to queries
* **Document Q\&A**: Verifying retrieved context supports the question
* **Knowledge Base Evaluation**: Testing retrieval effectiveness

Scoring Logic:

* **1.0 (High)**: Retrieved documents provide all necessary information to answer the query
* **0.5 (Medium)**: Retrieved documents are on topic but don't fully support a complete answer
* **0.0 (Low)**: Retrieved documents are not relevant to the query

## Parameters

* **user\_query** (*str*) – The question or query being asked.
* **retrieved\_documents** (*list* \*\[\**str* *]*) – The documents retrieved as context.
* **model** (*str*)
* **credential** (*str* *|* *None*)
* **kwargs** (*Any*)

## Returns

<ResponseField type="Score">
  A Score object containing:

  * value: 1.0 for high, 0.5 for medium, 0.0 for low relevance
  * label: "high", "medium", or "low"
  * reasoning: Detailed explanation of the assessment
</ResponseField>

## Example

```python theme={null}
from fiddler_evals.evaluators import ContextRelevance
evaluator = ContextRelevance(model="openai/gpt-4o")

# High context relevance
score = evaluator.score(
    user_query="What is the capital of France?",
    retrieved_documents=[
        "France is a country in Western Europe.",
        "Paris is the capital and largest city of France."
    ]
)
print(f"Context Relevance: {score.label}")  # "high"
print(f"Score: {score.value}")              # 1.0

# Low context relevance
score = evaluator.score(
    user_query="What is the capital of France?",
    retrieved_documents=[
        "Pizza is a popular Italian dish.",
        "The weather is nice today."
    ]
)
print(f"Context Relevance: {score.label}")  # "low"
```

<Info>
  This evaluator uses Fiddler's built-in context relevance assessment model
  and requires an active connection to the Fiddler API.
</Info>

## name *= 'context\_relevance'*

## score()

Score the relevance of retrieved documents to a query.

### Parameters

<ParamField path="user_query" type="str" required={true}>
  The question or query being asked.
</ParamField>

<ParamField path="retrieved_documents" type="list[str]" required={true}>
  The documents retrieved as context.
</ParamField>

### Returns

<ResponseField type="Score">
  A Score object containing:

  * value: 1.0 for high, 0.5 for medium, 0.0 for low relevance
  * label: "high", "medium", or "low"
  * reasoning: Detailed explanation of the assessment
</ResponseField>