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開始使用 - Amazon Bedrock AgentCore
資料集結構描述端對端工作流程

開始使用

本主題提供建立end-to-end工作流程。

資料集結構描述

每個資料集schemaType都會在建立時宣告 。AgentCore 會在接受每個範例之前，針對宣告的結構描述進行驗證。支援兩種結構描述類型：

  • AGENTCORE_EVALUATION_PREDEFINED_V1 — 用於針對預先編寫的對話輪換測試客服人員。必要欄位：scenario_id、 turns（非空白清單；每個回合必須包含 input)。

  • AGENTCORE_EVALUATION_SIMULATED_V1 — 用於產生合成對話。必要欄位：scenario_id、 actor_profile（具有必要 context和 的物件goal)、input。

如需完整的結構描述欄位定義、範例和 Ground Truth 映射，請參閱資料集結構描述。

端對端工作流程

下列範例示範完整的資料集生命週期：建立、新增範例、列出範例、發佈版本和清除。

範例
AgentCore CLI

  1. # 1. Create dataset
agentcore add dataset --name my_eval_dataset \
    --schema-type AGENTCORE_EVALUATION_PREDEFINED_V1

# 2. Add your scenarios to the JSONL file
#    File: agentcore/datasets/my_eval_dataset.jsonl

# 3. Deploy to create the dataset and sync examples
agentcore deploy

# 4. Publish version 1
agentcore dataset publish-version --name my_eval_dataset

# 5. Check status (shows versions and example count)
agentcore status --type dataset

# 6. Download a published version to local file
agentcore dataset download --name my_eval_dataset --version 1

# 7. Cleanup
agentcore remove dataset --name my_eval_dataset
agentcore deploy
AgentCore SDK

  1. from bedrock_agentcore.evaluation import DatasetClient

client = DatasetClient(region_name="us-west-2")

# 1. Create dataset (polls until ACTIVE)
ds = client.create_dataset_and_wait(
    datasetName="my_eval_dataset",
    schemaType="AGENTCORE_EVALUATION_PREDEFINED_V1",
    source={
        "inlineExamples": {
            "examples": [
                {
                    "scenario_id": "TC-01",
                    "turns": [{"input": "What is my balance?", "expected_response": "Your balance is $50."}],
                    "assertions": ["Response includes a dollar amount"],
                }
            ]
        }
    },
)
dataset_id = ds["datasetId"]
print(f"Created: {dataset_id}, status={ds['status']}")

# 2. Add more examples
ds = client.add_examples_and_wait(
    datasetId=dataset_id,
    source={
        "inlineExamples": {
            "examples": [
                {"scenario_id": "TC-02", "turns": [{"input": "Transfer $100", "expected_response": "Transfer complete."}]}
            ]
        }
    },
)
print(f"Example count: {ds['exampleCount']}")

# 3. List examples
resp = client.list_dataset_examples(datasetId=dataset_id)
for example in resp["examples"]:
    print(f"  {example['exampleId']}: {example['scenario_id']}")

# 4. Publish version 1
ds = client.create_dataset_version_and_wait(datasetId=dataset_id)
print(f"Published, draftStatus: {ds.get('draftStatus')}")

# 5. List versions
resp = client.list_dataset_versions(datasetId=dataset_id)
for v in resp["versions"]:
    print(f"  Version {v['datasetVersion']}: {v['exampleCount']} examples")

# 6. Cleanup
client.delete_dataset_and_wait(datasetId=dataset_id)