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。

# Amazon Titan Text 模型
<a name="model-parameters-titan-text"></a>

Amazon Titan Text 支援以下推論參數。

如需 Titan Text 提示工程指導方針的詳細資訊，請參閱 [Titan Text 提示工程指導方針](https://d2eo22ngex1n9g.cloudfront.net/Documentation/User+Guides/Titan/Amazon+Titan+Text+Prompt+Engineering+Guidelines.pdf)。

如需 Titan 模型的詳細資訊，請參閱 [Amazon Titan 模型概觀](titan-models.md)。

**Topics**
+ [

## 請求與回應
](#model-parameters-titan-request-response)
+ [

## 程式碼範例
](#inference-titan-code)

## 請求與回應
<a name="model-parameters-titan-request-response"></a>

請求內文在 [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) 或 [InvokeModelWithResponseStream](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html) 請求的 `body` 欄位中傳遞。

------
#### [ Request ]

```
{
    "inputText": string,
    "textGenerationConfig": {
        "temperature": float,  
        "topP": float,
        "maxTokenCount": int,
        "stopSequences": [string]
    }
}
```

下列是必要參數：
+ **inputText** – 可提供模型以產生回應的提示。若要以交談風格產生回應，請使用下列格式提交提示：

  ```
  "inputText": "User: <theUserPrompt>\nBot:"
  ```

  此格式會向模型指出，在使用者提供提示之後，應該在新行上回應。

`textGenerationConfig` 為選用。您可以用於設定以下[推論參數](inference-parameters.md)：
+ **temperature** – 使用較低的值來降低回應中的隨機性。  
****    
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/zh_tw/bedrock/latest/userguide/model-parameters-titan-text.html)
+ **topP** – 使用的值越低，會忽略較不可能的選項，並減少回應的多樣性。  
****    
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/zh_tw/bedrock/latest/userguide/model-parameters-titan-text.html)
+ **maxTokenCount** – 指定要在回應中產生的字符數目上限。字符限制上限會嚴格強制執行。  
****    
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/zh_tw/bedrock/latest/userguide/model-parameters-titan-text.html)
+ **stopSequences** – 指定字元序列以指定模型應在何處停止。

------
#### [ InvokeModel Response ]

```
{
    "inputTextTokenCount": int,
    "results": [{
        "tokenCount": int,
        "outputText": "\n<response>\n",
        "completionReason": "string"
    }]
}
```

回應內文包含以下欄位：
+ **inputTextTokenCount** – 提示中的字符數量。
+ **results** – 一個項目的陣列，亦即包含下列欄位的物件：
  + **tokenCount** – 回應中的字符數量。
  + **outputText** – 回應中的文字。
  + **completionReason** – 完成產生回應的原因。以下是可能的原因：
    + FINISHED – 回應已完全產生。
    + LENGTH – 回應因您設定的回應長度而被截斷。
    + STOP\$1CRITERIA\$1MET – 由於已達到停止條件，因此回應遭到截斷。
    + RAG\$1QUERY\$1WHEN\$1RAG\$1DISABLED – 此功能已停用且無法完成查詢。
    + CONTENT\$1FILTERED – 內容已由套用的內容篩選條件篩選或移除。

------
#### [ InvokeModelWithResponseStream Response ]

回應串流的內文中每個區塊的文字格式如下所示。您必須解碼 `bytes` 欄位 (請參閱 [使用 InvokeModel 提交單一提示](inference-invoke.md) 中的範例)。

```
{
    "chunk": {
        "bytes": b'{
            "index": int,
            "inputTextTokenCount": int,
            "totalOutputTextTokenCount": int,
            "outputText": "<response-chunk>",
            "completionReason": "string"
        }'
    }
}
```
+ **index** – 串流回應中區塊的索引。
+ **inputTextTokenCount** – 提示中的字符數量。
+ **totalOutputTextTokenCount** – 回應中的字符數量。
+ **outputText** – 回應中的文字。
+ **completionReason** – 完成產生回應的原因。以下是可能的原因。
  + FINISHED – 回應已完全產生。
  + LENGTH – 回應因您設定的回應長度而被截斷。
  + STOP\$1CRITERIA\$1MET – 由於已達到停止條件，因此回應遭到截斷。
  + RAG\$1QUERY\$1WHEN\$1RAG\$1DISABLED – 此功能已停用且無法完成查詢。
  + CONTENT\$1FILTERED – 內容已由套用的篩選條件篩選或移除。

------

## 程式碼範例
<a name="inference-titan-code"></a>

下列範例展示如何使用具 Python SDK 的 Amazon Titan Text Premier 模型進行推論。

```
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to create a list of action items from a meeting transcript
with the Amazon Titan Text model (on demand).
"""
import json
import logging
import boto3

from botocore.exceptions import ClientError


class ImageError(Exception):
    "Custom exception for errors returned by Amazon Titan Text models"

    def __init__(self, message):
        self.message = message


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_text(model_id, body):
    """
    Generate text using Amazon Titan Text models on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (json): The response from the model.
    """

    logger.info(
        "Generating text with Amazon Titan Text model %s", model_id)

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

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Text generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated text with Amazon Titan Text model %s", model_id)

    return response_body


def main():
    """
    Entrypoint for Amazon Titan Text model example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        # You can replace the model_id with any other Titan Text Models
        # Titan Text Model family model_id is as mentioned below:
        # amazon.titan-text-premier-v1:0, amazon.titan-text-express-v1, amazon.titan-text-lite-v1
        model_id = 'amazon.titan-text-premier-v1:0'

        prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream  
            for our new product launch Brant: Sure Miguel, is there anything in particular you want
            to discuss? Miguel: Yes, I want to talk about how users enter into the product.
            Brant: Ok, in that case let me add in Namita. Namita: Hey everyone 
            Brant: Hi Namita, Miguel wants to discuss how users enter into the product.
            Miguel: its too complicated and we should remove friction.  
            for example, why do I need to fill out additional forms?  
            I also find it difficult to find where to access the product
            when I first land on the landing page. Brant: I would also add that
            I think there are too many steps. Namita: Ok, I can work on the
            landing page to make the product more discoverable but brant
            can you work on the additonal forms? Brant: Yes but I would need 
            to work with James from another team as he needs to unblock the sign up workflow.
            Miguel can you document any other concerns so that I can discuss with James only once?
            Miguel: Sure.
            From the meeting transcript above, Create a list of action items for each person. """

        body = json.dumps({
            "inputText": prompt,
            "textGenerationConfig": {
                "maxTokenCount": 3072,
                "stopSequences": [],
                "temperature": 0.7,
                "topP": 0.9
            }
        })

        response_body = generate_text(model_id, body)
        print(f"Input token count: {response_body['inputTextTokenCount']}")

        for result in response_body['results']:
            print(f"Token count: {result['tokenCount']}")
            print(f"Output text: {result['outputText']}")
            print(f"Completion reason: {result['completionReason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating text with the Amazon Titan Text Premier model {model_id}.")


if __name__ == "__main__":
    main()
```

下列範例展示如何使用具 Python SDK 的 Amazon Titan Text G1 - Express 模型進行推論。

```
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to create a list of action items from a meeting transcript
with the Amazon &titan-text-express; model (on demand).
"""
import json
import logging
import boto3

from botocore.exceptions import ClientError


class ImageError(Exception):
    "Custom exception for errors returned by Amazon &titan-text-express; model"

    def __init__(self, message):
        self.message = message


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_text(model_id, body):
    """
    Generate text using Amazon &titan-text-express; model on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (json): The response from the model.
    """

    logger.info(
        "Generating text with Amazon &titan-text-express; model %s", model_id)

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

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Text generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated text with Amazon &titan-text-express; model %s", model_id)

    return response_body


def main():
    """
    Entrypoint for Amazon &titan-text-express; example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        model_id = 'amazon.titan-text-express-v1'

        prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream  
            for our new product launch Brant: Sure Miguel, is there anything in particular you want
            to discuss? Miguel: Yes, I want to talk about how users enter into the product.
            Brant: Ok, in that case let me add in Namita. Namita: Hey everyone 
            Brant: Hi Namita, Miguel wants to discuss how users enter into the product.
            Miguel: its too complicated and we should remove friction.  
            for example, why do I need to fill out additional forms?  
            I also find it difficult to find where to access the product
            when I first land on the landing page. Brant: I would also add that
            I think there are too many steps. Namita: Ok, I can work on the
            landing page to make the product more discoverable but brant
            can you work on the additonal forms? Brant: Yes but I would need 
            to work with James from another team as he needs to unblock the sign up workflow.
            Miguel can you document any other concerns so that I can discuss with James only once?
            Miguel: Sure.
            From the meeting transcript above, Create a list of action items for each person. """

        body = json.dumps({
            "inputText": prompt,
            "textGenerationConfig": {
                "maxTokenCount": 4096,
                "stopSequences": [],
                "temperature": 0,
                "topP": 1
            }
        })

        response_body = generate_text(model_id, body)
        print(f"Input token count: {response_body['inputTextTokenCount']}")

        for result in response_body['results']:
            print(f"Token count: {result['tokenCount']}")
            print(f"Output text: {result['outputText']}")
            print(f"Completion reason: {result['completionReason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating text with the Amazon &titan-text-express; model {model_id}.")


if __name__ == "__main__":
    main()
```