# 开始使用
<a name="datasets-getting-started"></a>

本主题提供了创建、填充和发布数据集的端到端工作流程。

## 数据集架构
<a name="datasets-gs-schema-overview"></a>

每个数据集`schemaType`在创建时都声明一个。 AgentCore 在接受每个示例之前，会根据声明的架构对其进行验证。支持两种架构类型：
+  **AGENTCORE\_EVALUATION\_PREDEFINED\_V1 — 用于根据预先写好的对话回合**测试代理。必填字段：`scenario_id`，`turns`（非空列表；每回合必须包含`input`）。
+  **AGENTCORE\_EVALUATION\_SIMULATION\_V1 — 用于生成合成**对话。必填字段：`scenario_id`、`actor_profile`（带有必填`context`和的对象`goal`）、`input`。

有关完整的架构字段定义、示例和实况映射，请参阅[数据集架构](dataset-evaluations-schema.md)。

## End-to-end 工作流程
<a name="datasets-gs-workflow"></a>

以下示例演示了完整的数据集生命周期：创建、添加示例、列出示例、发布版本和清理。

**Example**  

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
   ```

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)
   ```