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# Amazon Titan Embeddings G1 - Text
Titan Embeddings G1 - Text 不支持使用推理参数。下文详细介绍了请求和响应格式，并提供了一个代码示例。
**Topics**
+ [请求和响应](#model-parameters-titan-embed-text-request-response)
+ [代码示例](#api-inference-examples-titan-embed-text)
## 请求和响应
请求正文在 [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) 请求的 `body` 字段中传递。
------
#### [ V2 Request ]
inputText 为必需参数。normalize 和 dimensions 为可选参数。
+ inputText – 输入要转换为嵌入的文本。
+ normalize –（可选）标记，用于表示是否对输出嵌入进行规范化。默认值为 true。
+ dimensions –（可选）输出嵌入应具有的维度数。接受以下值：1024（默认）、512、256。
+ embeddingTypes –（可选）接受包含“float”、“binary”或同时包含两者的列表。默认值为 `float`。
```
{
"inputText": string,
"dimensions": int,
"normalize": boolean,
"embeddingTypes": list
}
```
------
#### [ V2 Response ]
字段如下所述。
+ embedding – 一个数组，表示您提供的输入的嵌入向量。其类型始终为 `float`。
+ inputTextTokenCount – 输入中的词元数量。
+ embeddingsByType – 嵌入列表的字典或映射。根据输入的不同会列出“float”、“binary”或同时列出两者。
+ 示例：`"embeddingsByType": {"binary": [int,..], "float": [float,...]}`
+ 该字段将始终显示。即使您没有在输入中指定 `embeddingTypes`，也会显示“float”。示例：`"embeddingsByType": {"float": [float,...]}`
```
{
"embedding": [float, float, ...],
"inputTextTokenCount": int,
"embeddingsByType": {"binary": [int,..], "float": [float,...]}
}
```
------
#### [ G1 Request ]
唯一可用的字段是 `inputText`，您可以在其中输入要转换为嵌入的文本。
```
{
"inputText": string
}
```
------
#### [ G1 Response ]
响应的 `body` 包含以下字段。
```
{
"embedding": [float, float, ...],
"inputTextTokenCount": int
}
```
字段如下所述。
+ **embedding** – 一个数组，表示您提供的输入的嵌入向量。
+ **inputTextTokenCount** – 输入中的词元数量。
------
## 代码示例
以下示例展示了如何调用 Amazon Titan 嵌入模型来生成嵌入。选择与您要使用的模型相对应的选项卡：
------
#### [ 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 Text Embeddings V2 ]
使用 Titan Text Embeddings V2 时，如果 `embeddingTypes` 仅包含 `binary`，则 `embedding` 字段不会出现在响应中。
```
# 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()
```
------