---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: a90e14a610d260a0a9a8508e0c925bdd2122e64f5c83f06dee274f2e08148971
  pageDirectory: concepts
  sources:
    - ragas-scorers-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - ragas-scorers-in-mlflow
    - RSIM
    - Scorers in MLflow
    - RAGAS scorer
    - RAGAS scorers
    - RAGAS scorers#Available RAGAS scorers
  citations:
    - file: ragas-scorers-databricks-on-aws.md
title: RAGAS Scorers in MLflow
description: MLflow integration that enables RAGAS metrics to be used as built-in scorers for evaluating LLM traces via mlflow.genai.evaluate() or direct invocation.
tags:
  - MLflow
  - evaluation
  - integration
timestamp: "2026-06-19T20:06:43.827Z"
---

# RAGAS Scorers in MLflow

RAGAS (Retrieval Augmented Generation Assessment) is an evaluation framework for LLM applications. MLflow integrates with RAGAS so that you can use RAGAS metrics as scorers for evaluating retrieval quality, answer generation, agent behavior, and text similarity. ^[ragas-scorers-databricks-on-aws.md]

## Requirements[​](#requirements)

To use RAGAS scorers, install the `ragas` package. ^[ragas-scorers-databricks-on-aws.md]

## Quick Start[​](#quick-start)

You can call a RAGAS scorer directly by instantiating a scorer class (e.g., `Faithfulness`) and passing a trace to it. The scorer returns a `feedback` object with a numeric `.value` (between 0.0 and 1.0) and a `.rationale` explaining the score. ^[ragas-scorers-databricks-on-aws.md]

```python
from mlflow.genai.scorers.ragas import Faithfulness

scorer = Faithfulness(model="databricks:/databricks-gpt-5-mini")
feedback = scorer(trace=trace)
print(feedback.value)  # Score between 0.0 and 1.0
print(feedback.rationale)  # Explanation of the score
```

You can also use RAGAS scorers with `mlflow.genai.evaluate()` by passing a list of scorers:

```python
import mlflow
from mlflow.genai.scorers.ragas import Faithfulness, ContextPrecision

traces = mlflow.search_traces()
results = mlflow.genai.evaluate(
    data=traces,
    scorers=[
        Faithfulness(model="databricks:/databricks-gpt-5-mini"),
        ContextPrecision(model="databricks:/databricks-gpt-5-mini"),
    ],
)
```

^[ragas-scorers-databricks-on-aws.md]

## Available RAGAS Scorers[​](#available-ragas-scorers)

MLflow provides the following RAGAS scorers, grouped by task category.

### RAG Metrics[​](#rag-metrics)

These scorers evaluate retrieval quality and answer generation in retrieval-augmented generation (RAG) applications. ^[ragas-scorers-databricks-on-aws.md]

### Agent and Tool Use Metrics[​](#agent-and-tool-use-metrics)

These scorers evaluate AI agent behavior, including tool invocation accuracy and goal achievement. ^[ragas-scorers-databricks-on-aws.md]

### Natural Language Comparison[​](#natural-language-comparison)

These scorers compare generated text against expected output using both semantic and deterministic methods. ^[ragas-scorers-databricks-on-aws.md]

### General Purpose[​](#general-purpose)

These scorers provide flexible, customizable evaluation logic. ^[ragas-scorers-databricks-on-aws.md]

### Other Tasks[​](#other-tasks)

Additional scorers for other evaluation tasks are also available. ^[ragas-scorers-databricks-on-aws.md]

## Create a Scorer by Name[​](#create-a-scorer-by-name)

You can dynamically create a scorer using `get_scorer` by passing the metric name as a string:

```python
from mlflow.genai.scorers.ragas import get_scorer

scorer = get_scorer(
    metric_name="Faithfulness",
    model="databricks:/databricks-gpt-5-mini",
)
feedback = scorer(trace=trace)
```

^[ragas-scorers-databricks-on-aws.md]

## Configuration[​](#configuration)

RAGAS scorers accept metric-specific parameters as keyword arguments to the constructor. LLM-based metrics require a `model` parameter. Non-LLM metrics do not require a model. For metric-specific parameters and advanced usage options, refer to the RAGAS documentation (external). ^[ragas-scorers-databricks-on-aws.md]

```python
from mlflow.genai.scorers.ragas import Faithfulness, ExactMatch

# LLM-based metric with model specification
scorer = Faithfulness(model="databricks:/databricks-gpt-5-mini")

# Non-LLM metric (no model required)
deterministic_scorer = ExactMatch()
```

^[ragas-scorers-databricks-on-aws.md]

## Sources[​](#sources)

- ragas-scorers-databricks-on-aws.md

## Related Concepts[​](#related-concepts)

- [MLflow GenAI Evaluation](/concepts/mlflow-genai-evaluation.md)
- RAGAS Framework
- [Retrieval Augmented Generation (RAG)](/concepts/retrieval-augmented-generation-rag.md)
- [LLM Evaluation](/concepts/llm-as-a-judge-evaluation.md)
- [[MLflow Trace|MLflow Traces]]
- [Serverless Budget Policy](/concepts/serverless-budget-policy.md) (for serverless workloads)

# Citations

1. [ragas-scorers-databricks-on-aws.md](/references/ragas-scorers-databricks-on-aws-5240ee43.md)
