运行 Prompt 管理代码示例

运行以下代码片段来加载 AWS SDK for Python (Boto3)，创建一个客户端，然后通过创建 Amazon Bedrock CreatePrompt代理构建时端点来创建使用两个变量（genre和number）创建音乐播放列表的提示：

# Create a prompt in Prompt management
import boto3

# Create an Amazon Bedrock Agents client
client = boto3.client(service_name="bedrock-agent")

# Create the prompt
response = client.create_prompt(
    name="MakePlaylist",
    description="My first prompt.",
    variants=[
        { 
            "name": "Variant1",
            "modelId": "amazon.titan-text-express-v1",
            "templateType": "TEXT",
            "inferenceConfiguration": {
                "text": {
                    "temperature": 0.8
                }
            },
            "templateConfiguration": { 
                "text": {
                    "text": "Make me a {{genre}} playlist consisting of the following number of songs: {{number}}."
                }
            }
      }
    ]
)
                        
prompt_id = response.get("id")

运行以下代码片段，查看您刚刚创建的提示（以及您账户中的任何其他提示），以创建适用于 Amazon Bedrock 的ListPrompts代理构建时终端节点：

# List prompts that you've created
client.list_prompts()

您应该在字段对象的id字段中看到您创建的提示的 promptSummaries ID。运行以下代码片段以显示您通过创建 Amazon Bedrock GetPrompt代理构建时终端节点创建的提示信息：

# Get information about the prompt that you created
client.get_prompt(promptIdentifier=prompt_id)

通过运行以下代码片段创建适用于 Amazon Bedrock 的CreatePromptVersion代理构建时终端节点，创建提示符版本并获取其 ID：

# Create a version of the prompt that you created
response = client.create_prompt_version(promptIdentifier=prompt_id)
                        
prompt_version = response.get("version")
prompt_version_arn = response.get("arn")

通过运行以下代码片段创建适用于 Amazon Bedrock 的ListPrompts代理构建时终端节点，查看有关您刚刚创建的提示版本的信息以及有关草稿版本的信息：

# List versions of the prompt that you just created
client.list_prompts(promptIdentifier=prompt_id)

通过运行以下代码片段创建适用于 Amazon Bedrock 的GetPrompt代理构建时终端节点，查看您刚刚创建的提示版本的信息：

# Get information about the prompt version that you created
client.get_prompt(
    promptIdentifier=prompt_id, 
    promptVersion=prompt_version
)

按照中的步骤将提示添加到提示流中来测试提示运行提示流程代码示例。在创建流程的第一步中，请改为运行以下代码片段以使用您创建的提示，而不是在流程中定义行内提示（将promptARN字段中的提示版本替换为您创建的提示版本）：ARNARN

# Import Python SDK and create client
import boto3

client = boto3.client(service_name='bedrock-agent')

FLOWS_SERVICE_ROLE = "arn:aws:iam::123456789012:role/MyPromptFlowsRole" # Prompt flows service role that you created. For more information, see https://docs.aws.amazon.com/bedrock/latest/userguide/flows-permissions.html
PROMPT_ARN = prompt_version_arn # ARN of the prompt that you created, retrieved programatically during creation.

# Define each node

# The input node validates that the content of the InvokeFlow request is a JSON object.
input_node = {
    "type": "Input",
    "name": "FlowInput",
    "outputs": [
        {
            "name": "document",
            "type": "Object"
        }
    ]
}

# This prompt node contains a prompt that you defined in Prompt management.
# It validates that the input is a JSON object that minimally contains the fields "genre" and "number", which it will map to the prompt variables.
# The output must be named "modelCompletion" and be of the type "String".
prompt_node = {
    "type": "Prompt",
    "name": "MakePlaylist",
    "configuration": {
        "prompt": {
            "sourceConfiguration": {
                "resource": {
                    "promptArn": ""
                }
            }
        }
    },
    "inputs": [
        {
            "name": "genre",
            "type": "String",
            "expression": "$.data.genre"
        },
        {
            "name": "number",
            "type": "Number",
            "expression": "$.data.number"
        }
    ],
    "outputs": [
        {
            "name": "modelCompletion",
            "type": "String"
        }
    ]
}

# The output node validates that the output from the last node is a string and returns it as is. The name must be "document".
output_node = {
    "type": "Output",
    "name": "FlowOutput",
    "inputs": [
        {
            "name": "document",
            "type": "String",
            "expression": "$.data"
        }
    ]
}

# Create connections between the nodes
connections = []

#   First, create connections between the output of the flow input node and each input of the prompt node
for input in prompt_node["inputs"]:
    connections.append(
        {
            "name": "_".join([input_node["name"], prompt_node["name"], input["name"]]),
            "source": input_node["name"],
            "target": prompt_node["name"],
            "type": "Data",
            "configuration": {
                "data": {
                    "sourceOutput": input_node["outputs"][0]["name"],
                    "targetInput": input["name"]
                }
            }
        }
    )

# Then, create a connection between the output of the prompt node and the input of the flow output node
connections.append(
    {
        "name": "_".join([prompt_node["name"], output_node["name"]]),
        "source": prompt_node["name"],
        "target": output_node["name"],
        "type": "Data",
        "configuration": {
            "data": {
                "sourceOutput": prompt_node["outputs"][0]["name"],
                "targetInput": output_node["inputs"][0]["name"]
            }
        }
    }
)

# Create the flow from the nodes and connections
client.create_flow(
    name="FlowCreatePlaylist",
    description="A flow that creates a playlist given a genre and number of songs to include in the playlist.",
    executionRoleArn=FLOWS_SERVICE_ROLE,
    definition={
        "nodes": [input_node, prompt_node, output_node],
        "connections": connections
    }
)

通过运行以下代码片段来创建适用于 Amazon Bedrock 的DeletePrompt代理构建时终端节点，删除您刚刚创建的提示版本：

# Delete the prompt version that you created
client.delete_prompt(
    promptIdentifier=prompt_id, 
    promptVersion=prompt_version
)

完全删除您刚刚通过运行以下代码段创建适用于 Amazon Bedrock 的DeletePrompt代理构建时终端节点而创建的提示：

# Delete the prompt that you created
client.delete_prompt(
    promptIdentifier=prompt_id
)