avaliação do Microsoft Foundry

FoundryEvalsconecta as APIs de avaliação do Agent Framework ao serviço de avaliação gerenciada do Microsoft Foundry. Ele fornece avaliadores de qualidade, segurança, uso de ferramentas, comportamento do agente e rubrica, com relatórios armazenados disponíveis no portal do Foundry.

Para EvalItem, verificações locais, avaliadores personalizados e estratégias de divisão de conversa, consulte a avaliação do Agente.

Pré-requisitos

• Um projeto do Microsoft Foundry e uma implantação de modelo.
• Um ponto de extremidade do Foundry com escopo de projeto.
• Permissão para enviar avaliações e ler relatórios.

string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());

AIAgent agent = projectClient.AsAIAgent(
    model: deploymentName,
    instructions: "You are a helpful assistant that provides clear, accurate answers.",
    name: "QualityTestAgent");

// Configure Foundry evaluators.
FoundryEvals foundryEvals = new(projectClient, deploymentName, FoundryEvals.Relevance, FoundryEvals.Coherence);

// --- Pattern 1: Run agent, then evaluate pre-existing responses ---
string[] queries = ["What is photosynthesis?", "Explain gravity in simple terms."];

AgentResponse[] responses = new AgentResponse[queries.Length];
for (int i = 0; i < queries.Length; i++)
{
    responses[i] = await agent.RunAsync(queries[i]);
}

AgentEvaluationResults results1 = await agent.EvaluateAsync(responses, queries, foundryEvals);

Console.WriteLine("=== Pattern 1: Evaluate pre-existing responses ===");
PrintResults(results1, queries);

// --- Pattern 2: Run + evaluate in one call ---
string[] queries2 = ["What causes rain?", "Why is the sky blue?"];
AgentEvaluationResults results2 = await agent.EvaluateAsync(queries2, foundryEvals);

Console.WriteLine("=== Pattern 2: Run + evaluate in one call ===");
PrintResults(results2, queries2);

async def main() -> None:
    # 1. Set up the FoundryChatClient
    chat_client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ.get("FOUNDRY_MODEL", "gpt-4o"),
        credential=AzureCliCredential(),
    )

    # 2. Create an agent with tools
    agent = Agent(
        client=chat_client,
        name="travel-assistant",
        instructions=(
            "You are a helpful travel assistant. Use your tools to answer questions about weather and flights."
        ),
        tools=[get_weather, get_flight_price],
    )

    # 3. Create the evaluator — provider config goes here, once
    evals = FoundryEvals(client=chat_client)

    # =========================================================================
    # Pattern 1: evaluate_agent(responses=...) — evaluate a response you already have
    # =========================================================================
    print("=" * 60)
    print("Pattern 1: evaluate_agent(responses=...) — evaluate existing response")
    print("=" * 60)

    query = "How much does a flight from Seattle to Paris cost?"
    response = await agent.run(query)
    print(f"Agent said: {response.text[:100]}...")

    # Pass agent= so tool definitions are extracted, queries= for the eval item context
    results = await evaluate_agent(
        agent=agent,
        responses=response,
        queries=[query],
        evaluators=FoundryEvals(
            client=chat_client,
            evaluators=[FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY],
        ),
    )

    for r in results:
        print(f"Status: {r.status}")
        print(f"Results: {r.passed}/{r.total} passed")
        print(f"Portal: {r.report_url}")
        if r.all_passed:
            print("[PASS] All passed")
        else:
            print(f"[FAIL] {r.failed} failed")

Pontos de qualidade

Fixar conjuntos de dados, implantações de modelo, versões do avaliador e versões rubricas quando os resultados devem ser comparáveis entre execuções. Use auxiliares de declaração de resultado para falhar na CI quando as métricas necessárias regredirem.

Próximas Etapas