用于加快模型推理速度的提示缓存

在此示例中，第一个 image 字段为模型提供图像，第二个 text 字段要求模型分析图像。只要 content 对象中 cachePoint 前面的词元数达到了模型的最小词元数，系统就会创建一个缓存检查点。

...
"messages": [
   {
        "role": "user",
        "content": [
            {
                "image": {
                    "bytes": "asfb14tscve..."
                }
            },
            {
                "text": "What's in this image?"
            },
            {
                "cachePoint": {
                    "type": "default"
                }
            }
      ]
  }
]
...

在此示例中，您将在 text 字段中提供系统提示。此外，您还可以添加一个 cachePoint 字段来缓存系统提示。

...
  "system": [ 
    {
        "text": "You are an app that creates play lists for a radio station that plays rock and pop music. Only return song names and the artist. "
    },
    {
        "cachePoint": {
            "type": "default"
        }
    }
  ],
...

在此示例中，您在 toolSpec 字段中提供工具定义。（或者，您可以调用先前定义的工具。 有关更多信息，请参阅使用工具完成 Amazon Bedrock 模型响应。） 之后，您可以添加 cachePoint 字段来缓存该工具。

...
toolConfig={
    "tools": [
        {
            "toolSpec": {
                "name": "top_song",
                "description": "Get the most popular song played on a radio station.",
                "inputSchema": {
                    "json": {
                        "type": "object",
                        "properties": {
                            "sign": {
                                "type": "string",
                                "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ and WKRP."
                            }
                        },
                        "required": [
                            "sign"
                        ]
                    }
                }
            }
        },
        {
                "cachePoint": {
                    "type": "default"
                }
        }
    ]
}
...

以下示例显示了如何构建 Anthropic Claude 3.5 Sonnet v2 模型 InvokeModel请求的正文。请注意， InvokeModel 请求正文的确切格式和字段可能会因您选择的模型而异。要查看不同模型的请求和响应正文的格式和内容，请参阅基础模型的推理请求参数和响应字段。

按如下方式指定所需的 ttl 值，如果未指定 ttl 值，则适用 5 分钟缓存的默认行为。

"cache_control" : {
    "type": "ephemeral",
    "ttl" : "5m | 1h"
}

body={
        "anthropic_version": "bedrock-2023-05-31",
        "system":"Reply concisely",
        "messages": [
            {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Describe the best way to learn programming."
                },
                {
                    "type": "text",
                    "text": "Add additional context here for the prompt that meets the minimum token requirement for your chosen model.",
                    "cache_control": {
                        "type": "ephemeral"
                    }
                }
            ]
            }
        ],
        "max_tokens": 2048,
        "temperature": 0.5,
        "top_p": 0.8,
        "stop_sequences": [
            "stop"
        ],
        "top_k": 250
}

以下示例说明如何构造Amazon Nova模型 InvokeModel请求的正文。请注意， InvokeModel 请求正文的确切格式和字段可能会因您选择的模型而异。要查看不同模型的请求和响应正文的格式和内容，请参阅基础模型的推理请求参数和响应字段。

{
    "system": [{
        "text": "Reply Concisely"
    }],
    "messages": [{
        "role": "user",
        "content": [{
            "text": "Describe the best way to learn programming"
        },
        {
            "text": "Add additional context here for the prompt that meets the minimum token requirement for your chosen model.",
            "cachePoint": {
                "type": "default"
            }
        }]
    }],
    "inferenceConfig": {
        "maxTokens": 300,
        "topP": 0.1,
        "topK": 20,
        "temperature": 0.3
    }
}