创建用于仅限检索 RAG 评测作业的提示数据集 - Amazon Bedrock
为 Amazon Bedrock 在其中调用知识库的“仅检索”评测作业准备数据集使用自己的推理响应数据准备用于“仅检索”评测作业的数据集

创建用于仅限检索 RAG 评测作业的提示数据集

“仅检索”评测作业需要采用 JSON 行格式的提示数据集。数据集最多可包含 1000 条提示。

为 Amazon Bedrock 在其中调用知识库的“仅检索”评测作业准备数据集

要创建 Amazon Bedrock 在其中调用知识库的“仅检索”评测作业，您的提示数据集必须包含以下键值对：

  • referenceResponses – 此父键用于指定您期望端到端 RAG 系统返回的基础事实响应。此参数并不代表您期望从知识库中检索到的目标段落或分块。在 text 键中指定基础事实。如果您在评测作业中选择上下文覆盖程度指标，则 referenceResponses 是必需的。

  • prompt – 此父键用于指定您希望 RAG 系统进行响应的提示（用户查询）。

下面是一个包含 6 个输入并使用了 JSON 行格式的自定义数据集示例。

{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"Provide the prompt you want to use during inference"}]},"referenceResponses":[{"content":[{"text":"Specify a ground-truth response"}]}]}]}

为清楚起见，已展开以下提示。在实际提示数据集内，每一行（提示）都必须是一个有效的 JSON 对象。

{
    "conversationTurns": [
        {
            "prompt": {
                "content": [
                    {
                        "text": "What is the recommended service interval for your product?"
                    }
                ]
            },
            "referenceResponses": [
                {
                    "content": [
                        {
                            "text": "The recommended service interval for our product is two years."
                        }
                    ]
                }
            ]
        }
    ]
}

使用自己的推理响应数据准备用于“仅检索”评测作业的数据集

要创建您在其中提供自己的推理响应数据的“仅检索”评测作业，您的提示数据集必须包含：

  • prompt – 此父键用于指定用来生成推理响应数据的提示（用户查询）。

  • referenceResponses – 此父键用于指定您期望端到端 RAG 系统返回的基础事实响应。此参数并不代表您期望从知识库中检索到的目标段落或分块。在 text 键中指定基础事实。如果您在评测作业中选择上下文覆盖程度指标，则 referenceResponses 是必需的。

  • referenceContexts（可选）– 此可选父键用于指定您希望从 RAG 来源中检索到的基础事实段落。如果您需要在自己的自定义评测指标中使用此键，只需包含此键即可。Amazon Bedrock 提供的内置指标不使用此属性。

  • knowledgeBaseIdentifier – 客户定义的字符串，可标识用于生成检索结果的 RAG 来源。

  • retrievedResults – 包含检索结果列表的 JSON 对象。对于每个结果，您可以提供以键值对的形式指定的可选 name 和可选 metadata。

下面是一个包含 6 个输入并使用了 JSON 行格式的自定义数据集示例。

{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}
{"conversationTurns":[{"prompt":{"content":[{"text":"The prompt you used to generate your response"}]},"referenceResponses":[{"content":[{"text":"A ground-truth response"}]}],"referenceContexts":[{"content":[{"text":"A ground truth for a received passage"}]}],"output":{"knowledgeBaseIdentifier":"A string identifying your RAG source","retrievedResults":{"retrievalResults":[{"name":"(Optional) a name for your reference context","content":{"text":"The output from your RAG inference"},"metadata":{"(Optional) a key for your metadata":"(Optional) a metadata value"}}]}}}]}

为清楚起见，已展开以下提示。在实际提示数据集内，每一行（提示）都必须是一个有效的 JSON 对象。

{
  "conversationTurns": [
    {
      "prompt": {
        "content": [
          {
            "text": "What is the recommended service interval for your product?"
          }
        ]
      },
      "referenceResponses": [
        {
          "content": [
            {
              "text": "The recommended service interval for our product is two years."
            }
          ]
        }
      ],
      "referenceContexts": [
        {
          "content": [
            {
              "text": "A ground truth for a received passage"
            }
          ]
        }
      ],
       "output": {
        "knowledgeBaseIdentifier": "RAG source 1",
        "retrievedResults": {
          "retrievalResults": [
            {
              "name": "(Optional) a name for your retrieval",
              "content": {
                "text": "The recommended service interval for our product is two years."
              },
              "metadata": {
                "(Optional) a key for your metadata": "(Optional) a value for your metadata"
              }
            }
          ]
        }
      }
    }
  ]
}