本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 需要結構化輸出
為了確保一致且結構化的輸出格式，您可以使用*結構化輸出*，包括 XML、JSON 或 Markdown 等格式。此方法可讓下游使用案例更有效地使用和處理模型產生的輸出。透過提供明確指示給模型，模型會以遵循預先定義之結構描述的方式產生回應。建議您提供 `output schema` 供模型遵循。
例如，如果下游解析器預期 JSON 物件中的鍵采用特定命名慣例，則您應該在查詢的**輸出結構描述**欄位中指定此點。此外，如果您偏好回應為 JSON 格式，而且沒有任何前序文字，請相應地指示模型。也就是說，明確陳述「**請只產生 JSON 輸出。請勿提供任何前序。**」。
**使用預先填入協助模型啟動**
有效的替代方案是預先填入 `assistant` 內容，藉此*引導*模型的回應。此技術可讓您引導模型的動作、略過前序，以及強制執行特定輸出格式，例如 JSON 和 XML。例如，如果您使用 `"{"` 或 `"```json"` 預先填入助理內容，則該輸入可以引導模型產生 JSON 物件，而無需提供其他資訊。
**提示**
如果您明確想要擷取 JSON，一種常見的觀察到的模式是使用 ````json` 預先填充 JSON，並在 ````` 上新增停止序列。這可確保模型輸出的 JSON 物件可透過程式設計方式進行解析。
以下是一些常見格式結構描述的範例。
------
#### [ JSON ]
```
JSON_schema = """Make sure your final response is a valid JSON schema follow the below Response Schema:
##Response Schema:
```json
{
"key1": "value1",
"key2": "value2",
key3: [{
"key3_1": "value_3_1,
"key3_2": "value_3_2,
...}```
"""
```
------
#### [ XML ]
```
XML_format = """Make sure your final response is a valid XML schema follow the below Response Schema:
##Response Schema:
( your thoughts go hee )
"""
```
------
#### [ Markdown ]
```
markdown_schema = """Make sure your final response is a valid Markdown schema follow the below Response Schema:
##Response Schema:
## Introduction
( 2-3 line intro)
## Design Guidance
(Bulleted list of design guidance)
## Step by Step Instructions on Execution
( Bulleted list of instructions with each with bold title.
## Conclusion
( conclusion )
"""
```
------
以下程式碼示範如何使用 API 進行預先填充：
```
import boto3
import json
# Create a Bedrock Runtime client.
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1"
)
request_body = {
"system": [
{"text": "You write JSON objects based on the given instructions"}
],
"messages": [
{
"role": "user",
"content": [{"text": "Provide details about the best selling full-frame cameras in past three years.
Answer in JSON format with keys like name, brand, price and a summary."}]
},
{
"role": "assistant",
"content": [{"text": " Here is the JSON response: ```json"}]
},
],
"inferenceConfig": {
"maxTokens": 300,
"temperature": 0,
}
}
# Invoke the model and extract the response body.
response = client.invoke_model(
modelId="amazon.nova-micro-v1:0",
body=json.dumps(request_body)
)
model_response = json.loads(response["body"].read())
```
**注意**
對於結構化輸出，無論是否利用了工具的使用，我們都建議使用貪婪解碼參數。也就是 `temperature=0`。
## 範例 1：新增輸出結構描述
在輸出結構描述區段新增適當的結構描述，可讓模型偏好符合該結構描述。不過，模型輸出並非確定性的，並且可能與輸出結構描述不同。
| Role | | 提示詞 |
| --- |--- |--- |
| 使用者 | 提供過去三年中最暢銷的全片幅相機的詳細資訊。使用名稱、品牌、價格和摘要等鍵，以 JSON 格式回答。 | 提供過去三年中最暢銷的全片幅相機的詳細資訊。您必須僅以 JSON 格式回答。請遵循以下輸出結構描述。
**輸出結構描述：**[{
"name": "name goes here",
"brand": "brand goes here",
"price": "price goes here",
"summary": "summary goes here"
}] |
| 助理 | [
{
"name": "Sony Alpha A7R IV",
"brand": "Sony",
"price": "Approximately $3,500",
"summary": "The Sony Alpha A7R IV is renowned for its
exceptional image quality, thanks to a 61-megapixel
full-frame sensor. It also features 4K video recording
and a robust build, making it a favorite among professional
photographers and videographers."
},
{
"name": "Canon EOS R5",
"brand": "Canon",
"price": "Approximately $3,900",
"summary": "The Canon EOS R5 combines high-resolution
imaging with advanced video capabilities. It houses a
45-megapixel sensor, 8K video recording, and impressive
autofocus performance, appealing to both photography
and filmmaking professionals."
},
{
"name": "Nikon Z7 II",
"brand": "Nikon",
"price": "Approximately $2,800",
"summary": "The Nikon Z7 II offers a 45.7-megapixel
full-frame sensor and is praised for its high resolution
and excellent dynamic range. It also supports 4K video
recording and features a versatile Z-mount lens system,
making it a strong contender in the full-frame market."
},
{
"name": "Fujifilm GFX 100S",
"brand": "Fujifilm",
"price": "Approximately $7,000",
"summary": "While not a traditional full-frame camera,
the Fujifilm GFX 100S stands out with its 102-megapixel
medium format sensor. It is lauded for its stunning detail
and color reproduction, though it comes at a higher price
point and is aimed at specialized users."
}
]
| [
{
"name": "Nikon D850",
"brand": "Nikon",
"price": "$3,296.95",
"summary": "The Nikon D850 is a professional full-frame
DSLR camera known for its exceptional image quality,
45.7-megapixel resolution, 7 fps continuous shooting,
and 4K video capabilities."
},
{
"name": "Canon EOS 5D Mark IV",
"brand": "Canon",
"price": "$3,499.00",
"summary": "The Canon EOS 5D Mark IV is a popular
full-frame DSLR appreciated for its 30.4-megapixel sensor,
4K video recording, robust build quality, and reliable
autofocus system."
},
{
"name": "Sony A7R III",
"brand": "Sony",
"price": "$3,198.00",
"summary": "The Sony A7R III is a high-resolution
full-frame mirrorless camera renowned for its 42.4-megapixel
sensor, excellent low-light performance, and versatile
lens compatibility."
},
{
"name": "Fujifilm GFX 100S",
"brand": "Fujifilm",
"price": "$5,999.99",
"summary": "The Fujifilm GFX 100S is a medium format
mirrorless camera that stands out for its 102-megapixel
sensor, high dynamic range, and exceptional color reproduction."
},
{
"name": "Sony A7 III",
"brand": "Sony",
"price": "$1,998.00",
"summary": "The Sony A7 III is a full-frame mirrorless
camera popular among enthusiasts and professionals for
its 24.2-megapixel sensor, fast autofocus, and 4K video
capabilities."
}
]
|
## 範例 2：預先填入模型回應
另一種方法是透過在助理的訊息中包含所需的初始文字，來預先填入模型的回應。在此情況下，Amazon Nova 模型的回應將從助理訊息結束的位置繼續。
| Role | 提示詞 |
| --- |--- |
| 使用者 | 提供過去三年中最暢銷的全片幅相機的詳細資訊。您的回應應該採用 JSON 格式，並具有下列鍵：名稱、品牌、年份、價格、摘要。 |
| 助理*(預先填入)* | ```json |
| 助理 | {
"cameras": [
{
"name": "Sony Alpha A7 III",
"brand": "Sony",
"year": 2018,
"price": "$1999",
"summary": "The Sony Alpha A7 III is a highly acclaimed
full-frame mirrorless camera known for its excellent
image quality, advanced autofocus system, and 4K video
capabilities."
},
{
"name": "Canon EOS R5",
"brand": "Canon",
"year": 2020,
"price": "$3899",
"summary": "The Canon EOS R5 is a top-tier full-frame
mirrorless camera that offers stunning 45MP images, 8K
video recording, and exceptional performance in various
lighting conditions."
},
{
"name": "Nikon Z7 II",
"brand": "Nikon",
"year": 2020,
"price": "$2999",
"summary": "The Nikon Z7 II is a flagship full-frame
mirrorless camera with a 45.7MP sensor, superb image
quality, and advanced video recording features."
},
{
"name": "Fujifilm GFX 100S",
"brand": "Fujifilm",
"year": 2020,
"price": "$7999",
"summary": "The Fujifilm GFX 100S is a medium format
mirrorless camera known for its impressive 102MP sensor,
beautiful film simulations, and versatile lens options."
},
{
"name": "Sony Alpha A7R IV",
"brand": "Sony",
"year": 2019,
"price": "$3499",
"summary": "The Sony Alpha A7R IV features a 61MP
sensor, excellent low-light performance, and advanced
video capabilities, making it a favorite among
professional photographers."
}
]
}
``` |
## 範例 3：指定要使用的工具
第三種方法是透過在工具使用結構描述區段中包含*傳統 JSON 結構描述*，使用工具來強制模型回應的特定結構描述。您可以為提供的結構描述指定*工具選擇*，Amazon Nova 的回應將根據選取的工具進行結構化。若要進一步了解如何利用工具的使用，請參閱 [搭配 Amazon Nova 使用工具 (函數呼叫)](tool-use.md)。
| | |
| --- |--- |
| 使用者 | 從下列提供的查詢中，擷取相關實體Query: John works in BUILDING-0987
and has been in charge of product id 23564#.
His performance has been excellent in past
year and he is up for a raise.
Use the print_entities tool.
|
| ToolConfig | tool_config = {
"tools": [
{
"toolSpec": {
"name": "print_entities",
"description": "Extract the named entity based on provided input",
"inputSchema": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The extracted entity name. This should be a name of a person, place, animal or thing"
},
"location": {
"type": "string",
"description": "The extracted location name. This is a site name or a building name like SITE-001 or BUILDING-003"
},
"product": {
"type": "string",
"description": "The extracted product code, this is generally a 6 digit alphanumeric code such as 45623#, 234567"
}
},
"required": ["name", "location", "product"]
}
}
}
],
"toolChoice": {
"tool": {
"name": "print_entities"
}
}
} |