Iniciar la evaluación del lote

La CLI resuelve serviceNames logGroupNames automáticamente a partir de la configuración del proyecto cuando se utiliza--runtime:

agentcore run batch-evaluation \
  --runtime MyAgent \
  --evaluator Builtin.GoalSuccessRate Builtin.Helpfulness Builtin.Faithfulness

Con indicadores opcionales:

# Custom name and lookback window
agentcore run batch-evaluation \
  --runtime MyAgent \
  --evaluator Builtin.GoalSuccessRate \
  --name my_baseline_eval \
  --lookback-days 1

# Specific sessions
agentcore run batch-evaluation \
  --runtime MyAgent \
  --evaluator Builtin.GoalSuccessRate \
  --session-ids session-abc123 session-def456

# With ground truth
agentcore run batch-evaluation \
  --runtime MyAgent \
  --evaluator Builtin.GoalSuccessRate Builtin.Correctness \
  --ground-truth ground-truth.json

De forma predeterminada, el comando inicia el trabajo y lo devuelve inmediatamente. Pase --wait a bloquear hasta que el trabajo alcance un estado terminal (COMPLETED, oSTOPPED)FAILED, tras lo cual la CLI muestra las puntuaciones medias por evaluador y guarda los resultados en. .cli/jobs/batch-eval-results/

agentcore run batch-evaluationtambién admite los siguientes indicadores:

import boto3
import uuid
import time
import json

client = boto3.client("bedrock-agentcore", region_name="us-west-2")

# All sessions in the log group
response = client.start_batch_evaluation(
    batchEvaluationName=f"baseline_eval_{uuid.uuid4().hex[:8]}",
    evaluators=[
        {"evaluatorId": "Builtin.GoalSuccessRate"},
        {"evaluatorId": "Builtin.Helpfulness"},
        {"evaluatorId": "Builtin.Faithfulness"},
    ],
    dataSourceConfig={
        "cloudWatchLogs": {
            "serviceNames": ["MyAgent.DEFAULT"],
            "logGroupNames": ["/aws/bedrock-agentcore/runtimes/MyAgent-abc123-DEFAULT"],
        }
    },
    clientToken=str(uuid.uuid4()),
)

batch_eval_id = response["batchEvaluationId"]
print(f"Started: {batch_eval_id}")

# Poll until complete
while True:
    result = client.get_batch_evaluation(batchEvaluationId=batch_eval_id)
    status = result["status"]
    print(f"Status: {status}")

    if status in ("COMPLETED", "COMPLETED_WITH_ERRORS", "FAILED", "STOPPED"):
        break
    time.sleep(30)

print(json.dumps(result, indent=4, default=str))

Con el filtrado de ID de sesión:

response = client.start_batch_evaluation(
    batchEvaluationName=f"targeted-eval-{uuid.uuid4().hex[:8]}",
    evaluators=[
        {"evaluatorId": "Builtin.GoalSuccessRate"},
    ],
    dataSourceConfig={
        "cloudWatchLogs": {
            "serviceNames": ["MyAgent.DEFAULT"],
            "logGroupNames": ["/aws/bedrock-agentcore/runtimes/MyAgent-abc123-DEFAULT"],
            "filterConfig": {
                "sessionIds": ["session-001", "session-002", "session-003"]
            },
        }
    },
    clientToken=str(uuid.uuid4()),
)

Con filtrado por intervalos de tiempo:

from datetime import datetime, timedelta, timezone

now = datetime.now(timezone.utc)
response = client.start_batch_evaluation(
    batchEvaluationName=f"weekly-eval-{uuid.uuid4().hex[:8]}",
    evaluators=[
        {"evaluatorId": "Builtin.GoalSuccessRate"},
    ],
    dataSourceConfig={
        "cloudWatchLogs": {
            "serviceNames": ["MyAgent.DEFAULT"],
            "logGroupNames": ["/aws/bedrock-agentcore/runtimes/MyAgent-abc123-DEFAULT"],
            "filterConfig": {
                "timeRange": {
                    "startTime": (now - timedelta(days=7)).isoformat(),
                    "endTime": now.isoformat(),
                }
            },
        }
    },
    clientToken=str(uuid.uuid4()),
)