Sample code for ScoreModelGrader with audio

sdk/ai/azure-ai-projects/samples/evaluations/sample_evaluations_score_model_grader_with_audio.py

@@ -0,0 +1,199 @@
+# 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 evaluations and eval runs.
+
+    The OpenAI official tutorial is here: https://cookbook.openai.com/examples/evaluation/use-cases/evalsapi_audio_inputs
+
+USAGE:
+    python sample_evaluations_score_model_grader_with_audio.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.0.0b2" azure-identity python-dotenv
+
+    Set these environment variables with your own values:
+    1) AZURE_AI_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
+       Microsoft Foundry project. It has the form: https://<account_name>.services.ai.azure.com/api/projects/<project_name>.
+    2) AZURE_AI_MODEL_DEPLOYMENT_NAME - Required. The name of the model deployment to use for evaluation.
+    3) AZURE_AI_MODEL_DEPLOYMENT_NAME_FOR_AUDIO - Required. The name of the model deployment for audio to use for evaluation, recommend to use "gpt-4o-audio-preview"
+"""
+
+import os
+import base64
+
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+import time
+from pprint import pprint
+from openai.types.evals.create_eval_completions_run_data_source_param import (
+    CreateEvalCompletionsRunDataSourceParam,
+    SourceFileContent,
+    SourceFileContentContent,
+    InputMessagesTemplate,
+    InputMessagesTemplateTemplateEvalItem,
+)
+from openai.types.responses import EasyInputMessageParam, ResponseInputAudioParam
+from openai.types.eval_create_params import DataSourceConfigCustom
+from dotenv import load_dotenv
+
+
+load_dotenv()
+file_path = os.path.abspath(__file__)
+folder_path = os.path.dirname(file_path)
+
+endpoint = os.environ.get("AZURE_AI_PROJECT_ENDPOINT", "")
+model_deployment_name = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "")
+model_deployment_name_for_audio = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME_FOR_AUDIO", "")
+
+
+def audio_to_base64(audio_path: str) -> str:
+    """Read an audio file and return its base64-encoded content."""
+    with open(audio_path, "rb") as f:
+        return base64.b64encode(f.read()).decode()
+
+
+with (
+    DefaultAzureCredential() as credential,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
+    project_client.get_openai_client() as client,
+):
+
+    data_source_config = DataSourceConfigCustom(
+        {
+            "type": "custom",
+            "item_schema": {
+                "type": "object",
+                "properties": {
+                    "audio_data": {
+                        "type": "string",
+                        "description": "Base64-encoded WAV audio data."
+                    },
+                    "expected": {
+                        "type": "string",
+                        "description": "The expected content in the audio."
+                    }
+                },
+                "required": [
+                    "audio_data",
+                    "expected",
+                ],
+            },
+            "include_sample_schema": True,
+        }
+    )
+
+    testing_criteria = [
+        {
+            "type": "score_model",
+            "name": "score_grader",
+            "model": model_deployment_name,
+            "input": [
+                {
+                    "role": "system",
+                    "content": "You are an audio analyzer. Listen to the audio, return a float score in [0,1] where 1 means the audio has the same meaning as {{item.expected}}.",
+                },
+                {
+                    "role": "user",
+                    "content": "{{sample.output_text}}",
+                }
+            ],
+            "range": [
+                0.0,
+                1.0
+            ],
+            "pass_threshold": 0.5,
+        },
+    ]
+
+    print("Creating evaluation")
+    eval_object = client.evals.create(
+        name="OpenAI graders test",
+        data_source_config=data_source_config,
+        testing_criteria=testing_criteria,  # type: ignore
+    )
+    print(f"Evaluation created (id: {eval_object.id}, name: {eval_object.name})")
+
+    print("Get evaluation by Id")
+    eval_object_response = client.evals.retrieve(eval_object.id)
+    print("Evaluation Response:")
+    pprint(eval_object_response)
+
+    source_file_content_content = SourceFileContentContent(
+        item={
+            "audio_data": audio_to_base64(os.path.join(folder_path, "data_folder/sample_evaluations_score_model_grader_with_audio.wav")),
+            "expected": "Don't forget a jacket",
+        },
+    )
+    source_file_content = SourceFileContent(
+        type="file_content",
+        content=[source_file_content_content],
+    )
+    input_messages = InputMessagesTemplate(
+        type="template",
+        template=[
+            EasyInputMessageParam(
+                role="system",
+                content="You are an assistant that can analyze audio input. You will be given an audio input to analyze.",
+            ),
+            InputMessagesTemplateTemplateEvalItem(
+                role="user",
+                type="message",
+                content="Listen to the following audio and convert to text.",
+            ),
+            InputMessagesTemplateTemplateEvalItem(
+                role="user",
+                type="message",
+                content=ResponseInputAudioParam(
+                    type="input_audio",
+                    input_audio={
+                        "data": "{{item.audio_data}}",
+                        "format": "wav",
+                    }
+                )
+            )
+        ],
+    )
+
+    print("Creating Eval Run")
+    eval_run_object = client.evals.runs.create(
+        eval_id=eval_object.id,
+        name="Eval",
+        metadata={"team": "eval-exp", "scenario": "notifications-v1"},
+        data_source=CreateEvalCompletionsRunDataSourceParam(
+            type="completions",
+            source=source_file_content,
+            model=model_deployment_name_for_audio,
+            input_messages=input_messages,
+            sampling_params={
+                "temperature": 0.8,
+            },
+        )
+    )
+    print(f"Eval Run created (id: {eval_run_object.id}, name: {eval_run_object.name})")
+    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)
+
+    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 Report URL: {run.report_url}")
+
+            break
+        time.sleep(5)
+        print("Waiting for eval run to complete...")
+
+    client.evals.delete(eval_id=eval_object.id)
+    print("Evaluation deleted")