deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/evaluators/deepevalevaluator.mdx

---
title: "DeepEvalEvaluator"
id: deepevalevaluator
slug: "/deepevalevaluator"
description: "The DeepEvalEvaluator evaluates Haystack pipelines using LLM-based metrics. It supports metrics like answer relevancy, faithfulness, contextual relevance, and more."
---

# DeepEvalEvaluator

The DeepEvalEvaluator evaluates Haystack pipelines using LLM-based metrics. It supports metrics like answer relevancy, faithfulness, contextual relevance, and more.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | On its own or in an evaluation pipeline. To be used after a separate pipeline has generated the inputs for the Evaluator. |
| **Mandatory init variables** | `metric`: One of the DeepEval metrics to use for evaluation |
| **Mandatory run variables** | `**inputs`: A keyword arguments dictionary containing the expected inputs. The expected inputs will change based on the metric you are evaluating. See below for more details. |
| **Output variables** | `results`: A nested list of metric results. There can be one or more results, depending on the metric. Each result is a dictionary containing:  <br /> <br />- `name` - The name of the metric  <br />- `score` - The score of the metric  <br />- `explanation` - An optional explanation of the score |
| **API reference** | [DeepEval](/reference/integrations-deepeval) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/deepeval |

</div>

DeepEval is an evaluation framework that provides a number of LLM-based evaluation metrics. You can use the `DeepEvalEvaluator` component to evaluate a Haystack pipeline, such as a retrieval-augmented generated pipeline, against one of the metrics provided by DeepEval.

## Supported Metrics

DeepEval supports a number of metrics, which we expose through the [DeepEval metric enumeration.](/reference/integrations-deepeval#deepevalmetric) [`DeepEvalEvaluator`](/reference/integrations-deepeval#deepevalevaluator) in Haystack supports the metrics listed below with the expected `metric_params` while initializing the Evaluator. Many metrics use OpenAI models and require you to set an environment variable `OPENAI_API_KEY`. For a complete guide on these metrics, visit the [DeepEval documentation](https://docs.confident-ai.com/docs/getting-started).

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | On its own or in an evaluation pipeline. To be used after a separate pipeline has generated the inputs for the Evaluator. |
| **Mandatory init variables** | `metric`: One of the DeepEval metrics to use for evaluation |
| **Mandatory run variables** | “\*\*inputs”: A keyword arguments dictionary containing the expected inputs. The expected inputs will change based on the metric you are evaluating. See below for more details. |
| **Output variables** | `results`: A nested list of metric results. There can be one or more results, depending on the metric. Each result is a dictionary containing:  <br /> <br />- `name` - The name of the metric  <br />- `score` - The score of the metric  <br />- `explanation` - An optional explanation of the score |
| **API reference** | [DeepEval](/reference/integrations-deepeval) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/deepeval |

</div>

## Parameters Overview

To initialize a `DeepEvalEvaluator`, you need to provide the following parameters :

- `metric`: A `DeepEvalMetric`.
- `metric_params`: Optionally, if the metric calls for any additional parameters, you should provide them here.

## Usage

To use the `DeepEvalEvaluator`, you first need to install the integration:

```bash
pip install deepeval-haystack
```

To use the `DeepEvalEvaluator` you need to follow these steps:

1. Initialize the `DeepEvalEvaluator` while providing the correct `metric_params` for the metric you are using.
2. Run the `DeepEvalEvaluator` on its own or in a pipeline by providing the expected input for the metric you are using.

### Examples

**Evaluate Faithfulness**

To create a faithfulness evaluation pipeline:

```python
from haystack import Pipeline
from haystack_integrations.components.evaluators.deepeval import (
    DeepEvalEvaluator,
    DeepEvalMetric,
)

pipeline = Pipeline()
evaluator = DeepEvalEvaluator(
    metric=DeepEvalMetric.FAITHFULNESS,
    metric_params={"model": "gpt-4"},
)
pipeline.add_component("evaluator", evaluator)
```

To run the evaluation pipeline, you should have the _expected inputs_ for the metric ready at hand. This metric expects a list of `questions` and `contexts`. These should come from the results of the pipeline you want to evaluate.

```python
results = pipeline.run(
    {
        "evaluator": {
            "questions": [
                "When was the Rhodes Statue built?",
                "Where is the Pyramid of Giza?",
            ],
            "contexts": [["Context for question 1"], ["Context for question 2"]],
            "responses": ["Response for question 1", "response for question 2"],
        },
    },
)
```

## Additional References

🧑‍🍳 Cookbook: [RAG Pipeline Evaluation Using DeepEval](https://haystack.deepset.ai/cookbook/rag_eval_deep_eval)