執行提示管理程式碼範例

執行下列程式碼片段載入 適用於 Python (Boto3) 的 AWS SDK、建立用戶端，並透過產生 CreatePrompt Amazon Bedrock 代理程式建置時期端點建立一個提示，該提示會使用兩個變數 (genre 和 number) 建立音樂播放清單：

# 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")

執行下列程式碼片段產生 ListPrompts Amazon Bedrock 代理程式建置時期端點，以查看您剛建立的提示 (以及您帳戶中的任何其他提示)：

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

您應該會在 promptSummaries 欄位的物件中，看到您在 id 欄位中建立的提示 ID。執行下列程式碼片段，以透過產生 GetPrompt Amazon Bedrock 代理程式建置時期端點，來顯示您所建立提示的資訊：

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

透過執行下列程式碼片段產生 CreatePromptVersion Amazon Bedrock 代理程式建置時期端點，以建立提示的版本並取得其 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")

透過執行下列程式碼片段產生 ListPrompts Amazon Bedrock 代理程式建置時期端點，以檢視您剛建立的提示版本相關資訊，及草稿版本相關資訊：

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

透過執行下列程式碼片段產生 GetPrompt Amazon Bedrock 代理程式建置時期端點，以檢視您剛建立的提示版本相關資訊：

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

透過遵循 執行 Amazon Bedrock 流程程式碼範例 的步驟將提示新增至流程，來測試該提示。在第一個步驟中，當您建立流程時，以改為執行下列程式碼片段來使用您建立的提示，而不是在流程中定義內嵌提示 (將 promptARN 欄位中提示版本的 ARN 取代為您所建立提示版本的 ARN)：

# Import Python SDK and create client
import boto3

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

FLOWS_SERVICE_ROLE = "arn:aws:iam::123456789012:role/MyPromptFlowsRole" # 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
    }
)

透過執行下列程式碼片段產生 DeletePrompt Amazon Bedrock 代理程式建置時期端點，以刪除您剛建立的提示版本：

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

透過執行下列程式碼片段產生 DeletePrompt Amazon Bedrock 代理程式建置時期端點，以完全刪除您剛建立的提示：

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