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 Embeddings G1 - Text
Titan Embeddings G1 - Text 不支援使用推論參數。以下各節詳細說明請求和回應格式，並提供程式碼範例。
**Topics**
+ [請求與回應](#model-parameters-titan-embed-text-request-response)
+ [範例程式碼](#api-inference-examples-titan-embed-text)
## 請求與回應
請求內文在 [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) 請求的 `body` 欄位中傳遞。
------
#### [ V2 Request ]
inputText 參數為必要。標準化和維度參數是選用的。
+ inputText – 輸入文字以轉換為嵌入。
+ normalize – (選用) 指示是否標準化輸出嵌入的旗標。預設為 true。
+ dimensions – (選用) 輸出嵌入應具有的維度數量。接受下列值：1024 (預設值)、512、256。
+ embeddingTypes – (選用) 接受包含「浮點數」、「二進位」或兩者的清單。預設為 `float`。
```
{
"inputText": string,
"dimensions": int,
"normalize": boolean,
"embeddingTypes": list
}
```
------
#### [ V2 Response ]
這些欄位如下所述。
+ embedding – 代表您所提供輸入的嵌入向量陣列。這一律為類型 `float`。
+ inputTextTokenCount – 輸入中的字符數量。
+ embeddingsByType – 嵌入清單的字典或映射。根據輸入，列出「浮點數」、「二進位」或兩者。
+ 範例：`"embeddingsByType": {"binary": [int,..], "float": [float,...]}`
+ 此欄位一律會出現。即使您未在輸入中指定 `embeddingTypes`，仍然會有「浮點數」。範例：`"embeddingsByType": {"float": [float,...]}`
```
{
"embedding": [float, float, ...],
"inputTextTokenCount": int,
"embeddingsByType": {"binary": [int,..], "float": [float,...]}
}
```
------
#### [ G1 Request ]
唯一可用的欄位是 `inputText`，您可以在其中包含要轉換為嵌入的文字。
```
{
"inputText": string
}
```
------
#### [ G1 Response ]
回應的 `body` 包含下列欄位。
```
{
"embedding": [float, float, ...],
"inputTextTokenCount": int
}
```
這些欄位如下所述。
+ **embedding** – 代表您所提供輸入的嵌入向量陣列。
+ **inputTextTokenCount** – 輸入中的字符數量。
------
## 範例程式碼
下列範例示範如何呼叫 Amazon Titan Embeddings 模型來產生嵌入。選取對應至您正在使用之模型的索引標籤：
------
#### [ Amazon Titan Embeddings G1 - Text ]
```
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate an embedding with the Amazon Titan Embeddings G1 - Text model (on demand).
"""
import json
import logging
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def generate_embedding(model_id, body):
"""
Generate an embedding with the vector representation of a text input using Amazon Titan Embeddings G1 - Text on demand.
Args:
model_id (str): The model ID to use.
body (str) : The request body to use.
Returns:
response (JSON): The embedding created by the model and the number of input tokens.
"""
logger.info("Generating an embedding with Amazon Titan Embeddings G1 - 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())
return response_body
def main():
"""
Entrypoint for Amazon Titan Embeddings G1 - Text example.
"""
logging.basicConfig(level=logging.INFO,
format="%(levelname)s: %(message)s")
model_id = "amazon.titan-embed-text-v1"
input_text = "What are the different services that you offer?"
# Create request body.
body = json.dumps({
"inputText": input_text,
})
try:
response = generate_embedding(model_id, body)
print(f"Generated an embedding: {response['embedding']}")
print(f"Input Token count: {response['inputTextTokenCount']}")
except ClientError as err:
message = err.response["Error"]["Message"]
logger.error("A client error occurred: %s", message)
print("A client error occured: " +
format(message))
else:
print(f"Finished generating an embedding with Amazon Titan Embeddings G1 - Text model {model_id}.")
if __name__ == "__main__":
main()
```
------
#### [ Amazon Titan 文本嵌入 V2 ]
使用 Titan Text Embeddings V2 時，如果 `embeddingTypes` 只包含 `binary`，則 `embedding` 欄位不會在回應中。
```
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate an embedding with the Amazon Titan Text Embeddings V2 Model
"""
import json
import logging
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def generate_embedding(model_id, body):
"""
Generate an embedding with the vector representation of a text input using Amazon Titan Text Embeddings G1 on demand.
Args:
model_id (str): The model ID to use.
body (str) : The request body to use.
Returns:
response (JSON): The embedding created by the model and the number of input tokens.
"""
logger.info("Generating an embedding with Amazon Titan Text Embeddings V2 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())
return response_body
def main():
"""
Entrypoint for Amazon Titan Embeddings V2 - Text example.
"""
logging.basicConfig(level=logging.INFO,
format="%(levelname)s: %(message)s")
model_id = "amazon.titan-embed-text-v2:0"
input_text = "What are the different services that you offer?"
# Create request body.
body = json.dumps({
"inputText": input_text,
"embeddingTypes": ["binary"]
})
try:
response = generate_embedding(model_id, body)
print(f"Generated an embedding: {response['embeddingsByType']['binary']}") # returns binary embedding
print(f"Input text: {input_text}")
print(f"Input Token count: {response['inputTextTokenCount']}")
except ClientError as err:
message = err.response["Error"]["Message"]
logger.error("A client error occurred: %s", message)
print("A client error occured: " +
format(message))
else:
print(f"Finished generating an embedding with Amazon Titan Text Embeddings V2 model {model_id}.")
if __name__ == "__main__":
main()
```
------