Adding sample for agent evaluation

sdk/ai/azure-ai-projects/samples/evaluation/sample_agent_evaluations.py

@@ -0,0 +1,68 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    Given an AIProjectClient, this sample demonstrates how to use the synchronous
+    `.evaluations` methods to submit evaluation for an agent run.
+
+USAGE:
+    python sample_agent_evaluations.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-identity
+
+    Set these environment variables with your own values:
+    1) PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
+       Azure AI Foundry project.
+"""
+
+import os
+
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    AgentEvaluationRequest,
+    InputDataset,
+    EvaluatorIds,
+    EvaluatorConfiguration,
+    AgentEvaluationSamplingConfiguration
+)
+from dotenv import load_dotenv
+
+load_dotenv()
+
+endpoint = os.environ["PROJECT_ENDPOINT"]
+
+with DefaultAzureCredential(exclude_interactive_browser_credential=False) as credential:
+
+    with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
+
+        # [START evaluations_agent_sample]
+
+        agent_evaluation_request = AgentEvaluationRequest(
+            run_id="run-id",
+            thread_id="thread-id",
+            evaluators={
+                "violence": EvaluatorConfiguration(
+                    id=EvaluatorIds.VIOLENCE,
+                )
+            },
+            sampling_configuration=AgentEvaluationSamplingConfiguration(
+                name="test",
+                sampling_percent=0.5,
+            ),
+            app_insights_connection_string="connection-string",
+        )
+
+        agent_evaluation_response = project_client.evaluations.create_agent_evaluation(
+            evaluation=agent_evaluation_request
+        )
+
+        print(agent_evaluation_response)
+
+        # [END evaluations_agent_sample]