Add integration tile for DeepEval integration (#166)

* Create deepeval.md

* add deepeval logo

* Update list of supported metrics

integrations/deepeval.md

@@ -0,0 +1,87 @@
+---
+layout: integration
+name: DeepEval
+description: Use the DeepEval evaluation framework to calculate model-based metrics 
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/deepeval-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/deepeval
+type: Evaluation Framework
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/deepeval.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+    - [DeepEvalEvaluator](#DeepEvalEvaluator)
+
+## Overview
+
+[DeepEval](https://docs.confident-ai.com/) is an open source framework for model-based evaluation to evaluate your LLM applications by quantifying their performance on aspects such as faithfulness, answer relevancy, contextual recall etc. More information can be found on the [documentation page](https://docs.haystack.deepset.ai/v2.0/docs/deepevalevaluator).
+
+## Installation
+
+Install the DeepEval integration:
+```bash
+pip install deepeval-haystack
+```
+
+## Usage
+
+Once installed, you will have access to a [DeepEvalEvaluator](https://docs.haystack.deepset.ai/v2.0/docs/deepevalevaluator) that supports a variety of model-based evaluation metrics: 
+- Answer Relevancy
+- Faithfulness
+- Contextual Precision
+- Contextual Recall
+- Contextual Relevance
+
+### DeepEvalEvaluator
+
+To use this integration for calculating model-based evaluation metrics, initialize a `DeepEvalEvaluator` with the metric name and metric input parameters: 
+
+```python
+from haystack import Pipeline
+from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric
+
+QUESTIONS = [
+    "Which is the most popular global sport?",
+    "Who created the Python language?",
+]
+CONTEXTS = [
+    [
+        "The popularity of sports can be measured in various ways, including TV viewership, social media presence, number of participants, and economic impact.",
+        "Football is undoubtedly the world's most popular sport with major events like the FIFA World Cup and sports personalities like Ronaldo and Messi, drawing a followership of more than 4 billion people.",
+    ],
+    [
+        "Python, created by Guido van Rossum in the late 1980s, is a high-level general-purpose programming language.",
+        "Its design philosophy emphasizes code readability, and its language constructs aim to help programmers write clear, logical code for both small and large-scale software projects.",
+    ],
+]
+RESPONSES = [
+    "Football is the most popular sport with around 4 billion followers worldwide",
+    "Python language was created by Guido van Rossum.",
+]
+
+pipeline = Pipeline()
+evaluator = DeepEvalEvaluator(
+    metric=DeepEvalMetric.FAITHFULNESS,
+    metric_params={"model": "gpt-4"},
+)
+pipeline.add_component("evaluator", evaluator)
+
+# Each metric expects a specific set of parameters as input. Refer to the
+# DeepEvalMetric class' documentation for more details.
+results = pipeline.run({"evaluator": {"questions": QUESTIONS, "contexts": CONTEXTS, "responses": RESPONSES}})
+
+for output in results["evaluator"]["results"]:
+    print(output)
+```