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# 运行提示管理器示例代码
要尝试一些用于 Prompt 管理的代码示例，请选择首选方法的选项卡，然后按照以下步骤操作：以下代码示例假设您已设置凭据以使用 AWS API。如果您尚未设置，请参阅 [开始使用 API](getting-started-api.md)。
------
#### [ Python ]
1. 运行以下代码片段 适用于 Python (Boto3) 的 AWS SDK，通过创建[适用于 Amazon Bedrock 的 A [CreatePrompt](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_CreatePrompt.html)gents 构建时终端节点，使用两个变量（`genre`和`number`）来加载、创建客户端，并创建用于](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)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")
```
1. 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](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_ListPrompts.html)代理构建](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)时终端节点：
```
# List prompts that you've created
client.list_prompts()
```
1. 您应该在 `promptSummaries` 字段中的对象中的 `id` 字段中看到您创建的提示的 ID。运行以下代码片段以显示您通过创建 [Amazon Bedrock [GetPrompt](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_GetPrompt.html)代理构建时终端节点创建的](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)提示信息：
```
# Get information about the prompt that you created
client.get_prompt(promptIdentifier=prompt_id)
```
1. 通过运行以下代码片段创建[适用于 Amazon Bedrock 的[CreatePromptVersion](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_CreatePromptVersion.html)代理构建](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)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")
```
1. 通过运行以下代码片段创建[适用于 Amazon Bedrock 的[ListPrompts](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_ListPrompts.html)代理构建](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)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)
```
1. 通过运行以下代码片段创建适用于 [Amazon Bedrock 的[GetPrompt](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_GetPrompt.html)代理构建时终端节点，查看您刚刚创建的](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)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
)
```
1. 按照[运行 Amazon Bedrock 流代码示例](flows-code-ex.md)中的步骤将提示添加到流来测试提示。在创建流的第一步中，请改为运行以下代码段以使用您创建的提示，而不是在流中定义内联提示（将 `promptARN` 字段中提示版本的 ARN 替换为您创建的提示版本的 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
}
)
```
1. 通过运行以下代码片段来创建[适用于 Amazon Bedrock 的[DeletePrompt](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_DeletePrompt.html)代理构建时终端节点，删除您刚刚创建的](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)提示版本：
```
# Delete the prompt version that you created
client.delete_prompt(
promptIdentifier=prompt_id,
promptVersion=prompt_version
)
```
1. 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](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_DeletePrompt.html)代理构建时终端节点而](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt)创建的提示：
```
# Delete the prompt that you created
client.delete_prompt(
promptIdentifier=prompt_id
)
```
------
## 调用托管提示符
在创建提示并对其进行版本控制后，您可以使用 C [onverse](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) API 调用它进行推理。将提示 ARN 指定为，并为`modelId`字段中的任何提示变量提供值。`promptVariables`
```
import boto3, json
bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-east-1")
# Use the prompt ARN (with version) as the modelId
prompt_arn = "arn:aws:bedrock:us-east-1:123456789012:prompt/PROMPT_ID:VERSION"
response = bedrock_runtime.converse(
modelId=prompt_arn,
promptVariables={
"genre": {"text": "jazz"},
"number": {"text": "5"}
}
)
# Print the response
print(response["output"]["message"]["content"][0]["text"])
```
**注意**
调用托管提示时，您无需指定`messages`或`system`字段，这些字段是在提示模板中定义的。您只需要为提示中定义的变量提供值即可。