Invoke Model With Response Stream
bedrockruntime_invoke_model_with_response_stream | R Documentation |
Invoke the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body¶
Description¶
Invoke the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body. The response is returned in a stream.
To see if a model supports streaming, call
GetFoundationModel
and check the responseStreamingSupported
field in the response.
The CLI doesn't support streaming operations in Amazon Bedrock,
including invoke_model_with_response_stream
.
For example code, see Invoke model with streaming code example in the Amazon Bedrock User Guide.
This operation requires permissions to perform the
bedrock:InvokeModelWithResponseStream
action.
To deny all inference access to resources that you specify in the
modelId field, you need to deny access to the bedrock:InvokeModel
and
bedrock:InvokeModelWithResponseStream
actions. Doing this also denies
access to the resource through the Converse API actions (converse
and
converse_stream
). For more information see Deny access for inference
on specific
models.
For troubleshooting some of the common errors you might encounter when
using the invoke_model_with_response_stream
API, see Troubleshooting
Amazon Bedrock API Error
Codes
in the Amazon Bedrock User Guide
Usage¶
bedrockruntime_invoke_model_with_response_stream(body, contentType,
accept, modelId, trace, guardrailIdentifier, guardrailVersion,
performanceConfigLatency)
Arguments¶
body
The prompt and inference parameters in the format specified in the
contentType
in the header. You must provide the body in JSON format. To see the format and content of the request and response bodies for different models, refer to Inference parameters. For more information, see Run inference in the Bedrock User Guide.contentType
The MIME type of the input data in the request. You must specify
application/json
.accept
The desired MIME type of the inference body in the response. The default value is
application/json
.modelId
[required] The unique identifier of the model to invoke to run inference.
The
modelId
to provide depends on the type of model or throughput that you use:If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.
trace
Specifies whether to enable or disable the Bedrock trace. If enabled, you can see the full Bedrock trace.
guardrailIdentifier
The unique identifier of the guardrail that you want to use. If you don't provide a value, no guardrail is applied to the invocation.
An error is thrown in the following situations.
You don't provide a guardrail identifier but you specify the
amazon-bedrock-guardrailConfig
field in the request body.You enable the guardrail but the
contentType
isn'tapplication/json
.You provide a guardrail identifier, but
guardrailVersion
isn't specified.
guardrailVersion
The version number for the guardrail. The value can also be
DRAFT
.performanceConfigLatency
Model performance settings for the request.
Value¶
A list with the following syntax:
list(
body = list(
chunk = list(
bytes = raw
),
internalServerException = list(
message = "string"
),
modelStreamErrorException = list(
message = "string",
originalStatusCode = 123,
originalMessage = "string"
),
validationException = list(
message = "string"
),
throttlingException = list(
message = "string"
),
modelTimeoutException = list(
message = "string"
),
serviceUnavailableException = list(
message = "string"
)
),
contentType = "string",
performanceConfigLatency = "standard"|"optimized"
)