執行提示管理程式碼範例

注意

提示管理處於預覽狀態，可能會有所變更。

若要嘗試一些提示管理的程式碼範例，請選取與您所選方法對應的索引標籤，並遵循下列步驟：下列程式碼範例假設您已設定憑證來使用 AWS API。如果您尚未完成，請參閱 開始使用 AWS API。

Python

1. 執行下列程式碼片段以載入 AWS SDK for Python (Boto3)、建立用戶端，並建立提示，透過建立 Amazon Bedrock 建置時間端點的 CreatePrompt Agents，使用兩個變數 （genre 和 number） 建立音樂播放清單： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

   # 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")

2. 執行下列程式碼片段，以查看您剛建立的提示 （以及您帳戶中的任何其他提示），以為 Amazon Bedrock 建置時間端點建立ListPrompts代理程式： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

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

3. 您應該會在 id 欄位的 物件中，看到您在 promptSummaries 欄位中建立的提示 ID。執行下列程式碼片段，顯示您透過建立 Amazon Bedrock 建置時間端點的GetPrompt代理程式所建立提示的資訊： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

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

4. 建立提示的版本，並執行下列程式碼片段取得其 ID，以建立 Amazon Bedrock 建置時間端點的CreatePromptVersion代理程式： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

   # 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")

5. 檢視您剛建立的提示版本的相關資訊，以及草案版本的相關資訊，方法是執行下列程式碼片段，讓 ListPrompts Agents for Amazon Bedrock 建置時間端點：

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

6. 檢視您剛透過執行下列程式碼片段建立 Amazon Bedrock 建置時間端點的GetPrompt代理程式而建立的提示版本資訊： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

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

7. 請依照 中的步驟，將提示新增至提示流程來測試提示執行 Amazon Bedrock Prompt 流程程式碼範例。在建立流程的第一個步驟中，執行下列程式碼片段，而不是使用您建立的提示，而不是在流程中定義內嵌提示 （ARN將promptARN欄位中提示版本的 取代為您建立的提示ARN版本）：

   # 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
       }
   )

8. 執行下列程式碼片段以刪除您剛建立的提示版本，以建立 Amazon Bedrock 建置時間端點的DeletePrompt代理程式： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

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

9. 執行下列程式碼片段，以建立 Amazon Bedrock 建置時間端點的DeletePrompt代理程式，將剛建立的提示完全刪除： https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-bt

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