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+# Prompt flow evaluators
+
+[![Python package](https://img.shields.io/pypi/v/promptflow-evals)](https://pypi.org/project/promptflow-evals/)
+[![License: MIT](https://img.shields.io/github/license/microsoft/promptflow)](https://github.com/microsoft/promptflow/blob/main/LICENSE)
+
+## Introduction
+Evaluators are custom or prebuilt promptflow flows that are designed to measure the quality of the outputs from language models.
+
+## Usage
+Users can create evaluator runs on the local machine as shown in the example below:
+
+```python
+import os
+from pprint import pprint
+
+from promptflow.core import AzureOpenAIModelConfiguration
+from promptflow.evals.evaluate import evaluate
+from promptflow.evals.evaluators import RelevanceEvaluator
+from promptflow.evals.evaluators.content_safety import ViolenceEvaluator
+
+
+def answer_length(answer, **kwargs):
+    return {"value": len(answer)}
+
+
+if __name__ == "__main__":
+    # Built-in evaluators
+    # Initialize Azure OpenAI Connection
+    model_config = AzureOpenAIModelConfiguration(
+        azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+        api_key=os.environ.get("AZURE_OPENAI_KEY"),
+        azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT"),
+    )
+
+    # Initialzing Relevance Evaluator
+    relevance_eval = RelevanceEvaluator(model_config)
+
+    # Running Relevance Evaluator on single input row
+    relevance_score = relevance_eval(
+        answer="The Alpine Explorer Tent is the most waterproof.",
+        context="From the our product list,"
+        " the alpine explorer tent is the most waterproof."
+        " The Adventure Dining Table has higher weight.",
+        question="Which tent is the most waterproof?",
+    )
+
+    pprint(relevance_score)
+    # {'gpt_relevance': 5.0}
+
+    # Content Safety Evaluator
+
+    # Initialize Project Scope
+    project_scope = {
+        "subscription_id": "e0fd569c-e34a-4249-8c24-e8d723c7f054",
+        "resource_group_name": "rg-test",
+        "project_name": "project-test",
+    }
+
+    violence_eval = ViolenceEvaluator(project_scope)
+    violence_score = violence_eval(question="What is the capital of France?", answer="Paris.")
+    pprint(violence_score)
+    # {'violence': 'Safe',
+    # 'violence_reason': "The system's response is a straightforward factual answer "
+    #                    'to a geography question. There is no violent content or '
+    #                    'language present.',
+    # 'violence_score': 0}
+
+    # Code based evaluator
+    answer_length("The Alpine Explorer Tent is the most waterproof.")
+    # {'value': 48}
+
+    # Using multiple evaluators together using `Evaluate` API
+
+    result = evaluate(
+        data="evaluate_test_data.jsonl",
+        evaluators={
+            "answer_length": answer_length,
+            "violence": violence_eval,
+        },
+    )
+
+    pprint(result)
+```
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