Symulowanie konwersacji przy użyciu zestawu SDK Microsoft Foundry (wersja zapoznawcza)

import os
from openai.types.eval_create_params import DataSourceConfigCustom
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import TestingCriterionAzureAIEvaluator, PromptAgentDefinition

endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
model_deployment_name = os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
simulator_model = os.environ["AZURE_AI_SIMULATOR_MODEL"]
agent_name = os.environ.get("FOUNDRY_AGENT_NAME", "")

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
    project_client.get_openai_client() as openai_client,
):
    # Simulation uses the same "custom" eval group type as dataset evaluation (S1),
    # since the generated conversations follow the same messages schema.
    data_source_config = DataSourceConfigCustom(
        type="custom",
        item_schema={
            "type": "object",
            "properties": {
                "messages": {"type": "array"},
            },
            "required": ["messages"],
        },
        include_sample_schema=False,
    )

    testing_criteria = [
        TestingCriterionAzureAIEvaluator(
            type="azure_ai_evaluator",
            name="customer_satisfaction",
            evaluator_name="builtin.customer_satisfaction",
            initialization_parameters={"model": model_deployment_name},
            data_mapping={"messages": "{{item.messages}}"},
        ),
    ]

  object dataSourceConfig = new
  {
    type = "custom",
    item_schema = new
    {
      type = "object",
      properties = new { messages = new { type = "array" } },
      required = new[] { "messages" }
    },
    include_sample_schema = false
  };
  object[] testingCriteria =
  [
    new
    {
      type = "azure_ai_evaluator",
      name = "customer_satisfaction",
      evaluator_name = "builtin.customer_satisfaction",
      initialization_parameters = new { model = modelDeploymentName },
      data_mapping = new { messages = "{{item.messages}}" }
    }
  ];

Bieżące przykłady zestawu JAVAScript/TypeScript SDK nie demonstrują symulacji konwersacji. Użyj zakładki Python lub cURL w tym przepływie.

curl --request POST \
  --url "https://${ACCOUNT}.services.ai.azure.com/api/projects/${PROJECT}/openai/evals?api-version=2025-11-15-preview" \
  --header "Authorization: ******" \
  --header "Content-Type: application/json" \
  --data '{
    "name": "conversation-simulation-evaluation",
    "data_source_config": {
      "type": "custom",
      "item_schema": {
        "type": "object",
        "properties": {
          "messages": {"type": "array"}
        },
        "required": ["messages"]
      },
      "include_sample_schema": false
    },
    "testing_criteria": [
      {
        "type": "azure_ai_evaluator",
        "name": "customer_satisfaction",
        "evaluator_name": "builtin.customer_satisfaction",
        "initialization_parameters": {"model": "gpt-5-mini"},
        "data_mapping": {"messages": "{{item.messages}}"}
      }
    ]
  }'

Zapisz kanoniczne wiersze JSONL z sekcji Przygotowywanie danych scenariusza jako simulation_scenarios.jsonl.

# Create (or update) an agent to simulate against
agent = project_client.agents.create_version(
    agent_name=agent_name,
    definition=PromptAgentDefinition(
        model=model_deployment_name,
        instructions="You are a helpful customer service agent. Be empathetic and solution-oriented.",
    ),
)

# Upload scenario data
scenarios_id = project_client.datasets.upload_file(
    name="simulation-scenarios",
    version="1",
    file_path="./simulation_scenarios.jsonl",
).id

# Create the evaluation
eval_object = openai_client.evals.create(
    name="Multi-turn Conversation Simulation",
    data_source_config=data_source_config,
    testing_criteria=testing_criteria,
)

# Create a simulation run. AZURE_AI_SIMULATOR_MODEL uses the format
# {connectionName}/{modelDeploymentName}.
eval_run = openai_client.evals.runs.create(
    eval_id=eval_object.id,
    name="conversation-simulation-run",
    data_source={
        "type": "azure_ai_user_conversation_simulation_preview",
        "source": {
            "type": "file_id",
            "id": scenarios_id,
        },
        "target": {
            "type": "azure_ai_agent",
            "name": agent.name,
            "version": agent.version,
        },
        "model_configuration": {
            "model": simulator_model,
            "sampling_params": {
                "temperature": 0.7,
                "top_p": 1.0,
                "max_completion_tokens": 800,
            },
        },
        "default_simulation_configuration": {
            "max_num_turns": 8,
            "conversation_repetitions": 2,
            "desired_num_turns": 5,
            "enable_conversation_dataset_generation": True,
            "output_conversation_dataset_name": "support-simulations",
        },
    },
    extra_body={"evaluation_level": "conversation"},
)

Ustaw wartości FOUNDRY_AGENT_NAME, FOUNDRY_AGENT_VERSIONi AZURE_AI_SIMULATOR_MODEL. Zapisz kanoniczne wiersze JSONL z sekcji Przygotowywanie danych scenariusza jako simulation_scenarios.jsonl.

  var agentName = Environment.GetEnvironmentVariable("FOUNDRY_AGENT_NAME")
    ?? throw new InvalidOperationException("FOUNDRY_AGENT_NAME isn't set.");
  var agentVersion = Environment.GetEnvironmentVariable(
    "FOUNDRY_AGENT_VERSION")
    ?? throw new InvalidOperationException("FOUNDRY_AGENT_VERSION isn't set.");
  var simulatorModel = Environment.GetEnvironmentVariable(
    "AZURE_AI_SIMULATOR_MODEL")
    ?? throw new InvalidOperationException("AZURE_AI_SIMULATOR_MODEL isn't set.");
  FileDataset scenarios = await projectClient.Datasets.UploadFileAsync(
    name: "simulation-scenarios",
    version: "1",
    filePath: "./simulation_scenarios.jsonl");

  BinaryData evaluationData = BinaryData.FromObjectAsJson(new
  {
    name = "Multi-turn Conversation Simulation",
    data_source_config = dataSourceConfig,
    testing_criteria = testingCriteria
  });
  using BinaryContent evaluationContent = BinaryContent.Create(evaluationData);
  ClientResult evaluation = await evaluationClient.CreateEvaluationAsync(
    evaluationContent);
  string evaluationId = GetString(evaluation, "id");

  BinaryData runData = BinaryData.FromObjectAsJson(new
  {
    name = "conversation-simulation-run",
    evaluation_level = "conversation",
    data_source = new
    {
      type = "azure_ai_user_conversation_simulation_preview",
      source = new { type = "file_id", id = scenarios.Id },
      target = new
      {
        type = "azure_ai_agent",
        name = agentName,
        version = agentVersion
      },
      model_configuration = new
      {
        model = simulatorModel,
        sampling_params = new
        {
          temperature = 0.7f,
          top_p = 1.0f,
          max_completion_tokens = 800
        }
      },
      default_simulation_configuration = new
      {
        max_num_turns = 8,
        conversation_repetitions = 2,
        desired_num_turns = 5,
        enable_conversation_dataset_generation = true,
        output_conversation_dataset_name = "support-simulations"
      }
    }
  });
  using BinaryContent runContent = BinaryContent.Create(runData);
  ClientResult evaluationRun = await evaluationClient.CreateEvaluationRunAsync(
    evaluationId: evaluationId,
    content: runContent);
  Console.WriteLine($"Evaluation run created: {GetString(evaluationRun, "id")}");

Dokumentacja: AIProjectDatasetsOperations.UploadFileAsync i EvaluationClient metody protokołu.

Bieżące przykłady zestawu JAVAScript/TypeScript SDK nie demonstrują symulacji konwersacji. Użyj zakładki Python lub cURL w tym przepływie.

curl --request POST \
  --url "https://${ACCOUNT}.services.ai.azure.com/api/projects/${PROJECT}/openai/evals/${EVAL_ID}/runs?api-version=2025-11-15-preview" \
  --header "Authorization: ******" \
  --header "Content-Type: application/json" \
  --data '{
    "name": "conversation-simulation-run",
    "evaluation_level": "conversation",
    "data_source": {
      "type": "azure_ai_user_conversation_simulation_preview",
      "source": {
        "type": "file_id",
        "id": "YOUR_SCENARIOS_DATASET_ID"
      },
      "target": {
        "type": "azure_ai_agent",
        "name": "my-agent",
        "version": "1"
      },
      "model_configuration": {
        "model": "my-connection/gpt-4.1",
        "sampling_params": {
          "temperature": 0.7,
          "top_p": 1.0,
          "max_completion_tokens": 800
        },
        "voice_model": {
          "type": "azure-standard",
          "name": "en-US-AvaMultilingualNeural",
          "temperature": 0.7
        }
      },
      "default_simulation_configuration": {
        "max_num_turns": 10,
        "conversation_repetitions": 2,
        "audio_effects": {
          "effects": ["street_traffic", "telephonic_voice"],
          "volume_percentage": 20
        },
        "user_behavior": {
          "interruption": {"type": "default"}
        },
        "enable_conversation_dataset_generation": true,
        "output_conversation_dataset_name": "voice-support-simulations"
      }
    }
  }'

inline_source = {
    "type": "inline_user_conversation_simulation",
    "test_cases": [
        {
            "test_case_id": "refund-delay",
            "test_case_description": "A frustrated customer wants an update on a delayed refund.",
            "simulation_configuration": {
                "desired_num_turns": 6,
            },
        }
    ],
}

# In the run request:
# data_source={..., "source": inline_source}

object inlineSource = new
{
  type = "inline_user_conversation_simulation",
  test_cases = new[]
  {
    new
    {
      test_case_id = "refund-delay",
      test_case_description =
        "A frustrated customer wants an update on a delayed refund.",
      simulation_configuration = new { desired_num_turns = 6 }
    }
  }
};

// In the run request:
// data_source = new { ..., source = inlineSource }

const inlineSource = {
  type: "inline_user_conversation_simulation",
  test_cases: [
    {
      test_case_id: "refund-delay",
      test_case_description:
        "A frustrated customer wants an update on a delayed refund.",
      simulation_configuration: {
        desired_num_turns: 6,
      },
    },
  ],
};

// In the run request:
// data_source: { ...dataSource, source: inlineSource }

{
  "source": {
    "type": "inline_user_conversation_simulation",
    "test_cases": [
      {
        "test_case_id": "refund-delay",
        "test_case_description": "A frustrated customer wants an update on a delayed refund.",
        "simulation_configuration": {
          "desired_num_turns": 6
        }
      }
    ]
  }
}