Azure · m7md7sien · Nov 3, 2025 · Nov 3, 2025 · Nov 3, 2025 · Nov 3, 2025
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+# 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
+    `openai.evals.*` methods to create, get and list eval group and and eval runs
+    using inline dataset content.
+
+USAGE:
+    python sample_coherence.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. It has the form: https://<account_name>.services.ai.azure.com/api/projects/<project_name>.
+    2) MODEL_DEPLOYMENT_NAME - Required. The name of the model deployment to use for evaluation.
+"""
+
+import os
+import json
+import time
+
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+from openai.types.evals.create_eval_jsonl_run_data_source_param import (
+    CreateEvalJSONLRunDataSourceParam,
+    SourceFileContent,
+    SourceFileContentContent
+)
+
+def main() -> None:
+    endpoint = os.environ[
+        "PROJECT_ENDPOINT"
+    ]  # Sample : https://<account_name>.services.ai.azure.com/api/projects/<project_name>
+    model_deployment_name = os.environ.get("MODEL_DEPLOYMENT_NAME", "")  # Sample : gpt-4o-mini
+
+    with DefaultAzureCredential() as credential:
+        with AIProjectClient(endpoint=endpoint, credential=credential, api_version="2025-11-15-preview") as project_client:
+            print("Creating an OpenAI client from the AI Project client")
+
+            client = project_client.get_openai_client()
+            client._custom_query = {"api-version": "2025-11-15-preview"}
+
+            data_source_config = {
+                "type": "custom",
+                "item_schema": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string"
+                        },
+                        "response": {
+                            "type": "string"
+                        }
+                    },
+                    "required": []
+                },
+                "include_sample_schema": True
+            }
+
+            testing_criteria = [
+                {
+                    "type": "azure_ai_evaluator",
+                    "name": "coherence",
+                    "evaluator_name": "builtin.coherence",
+                    "initialization_parameters": {
+                        "deployment_name": f"{model_deployment_name}"
+                    },
+                    "data_mapping": {
+                        "query": "{{item.query}}",
+                        "response": "{{item.response}}"
+                    }
+                }
+            ]
+
+            print("Creating Eval Group")
+            eval_object = client.evals.create(
+                name="label model test with inline data",
+                data_source_config=data_source_config,
+                testing_criteria=testing_criteria,
+            )
+            print(f"Eval Group created")
+
+            print("Get Eval Group by Id")
+            eval_object_response = client.evals.retrieve(eval_object.id)
+            print("Eval Run Response:")
+            pprint(eval_object_response)
+
+            # Sample inline data
+            success_query = "What is the capital of France?"
+            success_response = "The capital of France is Paris."
+
+            # Failure example - incoherent response
+            failure_query = "What is the capital of France?"
+            failure_response = "France capital is... well, the city where government sits is Paris but no wait, Lyon is bigger actually maybe Rome? The French people live in many cities but the main one, I think it's definitely Paris or maybe not, depends on what you mean by capital."
+
+            print("Creating Eval Run with Inline Data")
+            eval_run_object = client.evals.runs.create(
+                eval_id=eval_object.id,
+                name="inline_data_run",
+                metadata={
+                    "team": "eval-exp",
+                    "scenario": "inline-data-v1"
+                },
+                data_source=CreateEvalJSONLRunDataSourceParam(
+                    type="jsonl", 
+                    source=SourceFileContent(
+                        type="file_content",
+                        content= [
+                            # Success example - coherent response
+                            SourceFileContentContent(
+                                item= {
+                                    "query": success_query,
+                                    "response": success_response
+                                }
+                            ),
+                            # Failure example - incoherent response
+                            SourceFileContentContent(
+                                item= {
+                                    "query": failure_query,
+                                    "response": failure_response
+                                }
+                            )
+                        ]
+                    )
+                )
+            )
+
+            print(f"Eval Run created")
+            pprint(eval_run_object)
+
+            print("Get Eval Run by Id")
+            eval_run_response = client.evals.runs.retrieve(run_id=eval_run_object.id, eval_id=eval_object.id)
+            print("Eval Run Response:")
+            pprint(eval_run_response)
+
+            print("\n\n----Eval Run Output Items----\n\n")
+
+            while True:
+                run = client.evals.runs.retrieve(run_id=eval_run_response.id, eval_id=eval_object.id)
+                if run.status == "completed" or run.status == "failed": 
+                    output_items = list(client.evals.runs.output_items.list(
+                        run_id=run.id, eval_id=eval_object.id
+                    ))
+                    pprint(output_items)
+                    print(f"Eval Run Status: {run.status}")
+                    print(f"Eval Run Report URL: {run.report_url}")
+                    break
+                time.sleep(5)
+                print("Waiting for eval run to complete...")
+
+            # [END evaluations_sample]
+
+
+def _to_json_primitive(obj):
+    if obj is None or isinstance(obj, (str, int, float, bool)):
+        return obj
+    if isinstance(obj, (list, tuple)):
+        return [_to_json_primitive(i) for i in obj]
+    if isinstance(obj, dict):
+        return {k: _to_json_primitive(v) for k, v in obj.items()}
+    for method in ("to_dict", "as_dict", "dict", "serialize"):
+        if hasattr(obj, method):
+            try:
+                return _to_json_primitive(getattr(obj, method)())
+            except Exception:
+                pass
+    if hasattr(obj, "__dict__"):
+        return _to_json_primitive({k: v for k, v in vars(obj).items() if not k.startswith("_")})
+    return str(obj)
+
+def pprint(str) -> None:
+    print(json.dumps(_to_json_primitive(str), indent=2))
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,72 @@
+# 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
+    `openai.evals.*` methods to create, get and list eval group and and eval runs
+    for Any agentic evaluator using inline dataset content.
+
+USAGE:
+    python sample_generic_agentic_evaluator.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. It has the form: https://<account_name>.services.ai.azure.com/api/projects/<project_name>.
+    2) MODEL_DEPLOYMENT_NAME - Required. The name of the model deployment to use for evaluation.
+"""
+
+import os
+from utils import run_evaluator
+from schema_mappings import evaluator_to_data_source_config, evaluator_to_data_mapping
+from openai.types.evals.create_eval_jsonl_run_data_source_param import SourceFileContentContent
+
+
+def _get_evaluator_initialization_parameters(evaluator_name: str) -> dict[str, str]:
+    if evaluator_name == "task_navigation_efficiency":
+        return {
+            "matching_mode": "exact_match"  #  Can be "exact_match", "in_order_match", or "any_order_match"
+        }
+    else:
+        model_deployment_name = os.environ.get(
+            "MODEL_DEPLOYMENT_NAME", "")  # Sample : gpt-4o-mini
+        return {
+            "deployment_name": model_deployment_name
+        }
+
+
+def _get_evaluation_contents() -> list[SourceFileContentContent]:
+    # Sample inline data
+    # Change this to add more examples for evaluation
+    # Use the appropriate schema based on the evaluator being used
+    success_query = "What is the capital of France?"
+    success_response = "The capital of France is Paris."
+
+    evaluation_contents = [SourceFileContentContent(
+        item={
+            "query": success_query,
+            "response": success_response
+        }
+    )]
+
+    return evaluation_contents
+
+def main() -> None:
+    evaluator_name = "coherence"  # Change to any agentic evaluator name like "relevance", "response_completeness", "task_navigation_efficiency"
+    data_source_config = evaluator_to_data_source_config[evaluator_name]
+    initialization_parameters = _get_evaluator_initialization_parameters(evaluator_name)
+    data_mapping = evaluator_to_data_mapping[evaluator_name]
+    evaluation_contents = _get_evaluation_contents()
+
+    run_evaluator(evaluator_name, evaluation_contents, data_source_config, initialization_parameters, data_mapping)
+
+
+if __name__ == "__main__":
+    main()