Sample code for score model grader with image

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

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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 evaluation and and eval runs.
+
+USAGE:
+    python sample_evaluations_score_model_grader_with_image.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.0.0b1" 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.
+"""
+
+import os
+
+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,
+    InputMessagesTemplateTemplateEvalItemContentInputImage,
+)
+from openai.types.responses import EasyInputMessageParam
+from openai.types.eval_create_params import DataSourceConfigCustom
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+model_deployment_name = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "")
+
+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": {
+                    "image_url": {
+                        "type": "string",
+                        "description": "The URL of the image to be evaluated."
+                    },
+                    "caption": {
+                        "type": "string",
+                        "description": "The caption describing the image."
+                    },
+                },
+                "required": [
+                    "image_url",
+                    "caption",
+                ],
+            },
+            "include_sample_schema": True,
+        }
+    )
+
+    testing_criteria = [
+        {
+            "type": "score_model",
+            "name": "score_grader",
+            "model": model_deployment_name,
+            "input": [
+                {
+                    "role": "system",
+                    "content": "You are an expert grader. Judge how well the model response {{sample.output_text}} describes the image as well as matches the caption {{item.caption}}. Output a score of 1 if it's an excellent match with both. If it's somewhat compatible, output a score around 0.5. Otherwise, give a score of 0."
+                },
+                {
+                    "role": "user",
+                    "content":
+                        {
+                            "type": "input_image", 
+                            "image_url": "{{item.image_url}}"
+                        }
+                }
+            ],
+            "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)
+
+    sourceFileContentContent = SourceFileContentContent(
+        item={
+            "image_url": "http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800",
+            "caption": "a very typical bus station",
+        },
+    )
+    sourceFileContent = SourceFileContent(
+        type="file_content",
+        content=[sourceFileContentContent],
+    )
+    inputMessages = InputMessagesTemplate(
+        type="template",
+        template=[
+            EasyInputMessageParam(
+                role="system",
+                content="You are an assistant that analyzes images and provides captions that accurately describe the content of the image.",
+            ),
+            InputMessagesTemplateTemplateEvalItem(
+                role="user",
+                type="message",
+                content=InputMessagesTemplateTemplateEvalItemContentInputImage(
+                    type="input_image",
+                    image_url="{{item.image_url}}",
+                    detail="auto",
+                )
+            ),
+        ],
+    )
+
+    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=sourceFileContent,
+            model=model_deployment_name,
+            input_messages=inputMessages,
+            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")