ConverseStream
Sends messages to the specified Amazon Bedrock model and returns
the response in a stream. ConverseStream
provides a consistent API
that works with all Amazon Bedrock models that support messages.
This allows you to write code once and use it with different models. Should a
model have unique inference parameters, you can also pass those unique parameters to the
model.
To find out if a model supports streaming, call GetFoundationModel
and check the responseStreamingSupported
field in the response.
Note
The AWS CLI doesn't support streaming operations in Amazon Bedrock, including ConverseStream
.
Amazon Bedrock doesn't store any text, images, or documents that you provide as content. The data is only used to generate the response.
You can submit a prompt by including it in the messages
field, specifying the modelId
of a foundation model or inference profile to run inference on it, and including any other fields that are relevant to your use case.
You can also submit a prompt from Prompt management by specifying the ARN of the prompt version and including a map of variables to values in the promptVariables
field. You can append more messages to the prompt by using the messages
field. If you use a prompt from Prompt management, you can't include the following fields in the request: additionalModelRequestFields
, inferenceConfig
, system
, or toolConfig
. Instead, these fields must be defined through Prompt management. For more information, see Use a prompt from Prompt management.
For information about the Converse API, see Use the Converse API. To use a guardrail, see Use a guardrail with the Converse API. To use a tool with a model, see Tool use (Function calling).
For example code, see Conversation streaming example.
This operation requires permission for the bedrock:InvokeModelWithResponseStream
action.
Important
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 base inference actions (InvokeModel and InvokeModelWithResponseStream). For more information see Deny access for inference on specific models.
For troubleshooting some of the common errors you might encounter when using the ConverseStream
API,
see Troubleshooting Amazon Bedrock API Error Codes in the Amazon Bedrock User Guide
Request Syntax
POST /model/modelId
/converse-stream HTTP/1.1
Content-type: application/json
{
"additionalModelRequestFields": JSON value
,
"additionalModelResponseFieldPaths": [ "string
" ],
"guardrailConfig": {
"guardrailIdentifier": "string
",
"guardrailVersion": "string
",
"streamProcessingMode": "string
",
"trace": "string
"
},
"inferenceConfig": {
"maxTokens": number
,
"stopSequences": [ "string
" ],
"temperature": number
,
"topP": number
},
"messages": [
{
"content": [
{ ... }
],
"role": "string
"
}
],
"promptVariables": {
"string
" : { ... }
},
"system": [
{ ... }
],
"toolConfig": {
"toolChoice": { ... },
"tools": [
{ ... }
]
}
}
URI Request Parameters
The request uses the following URI parameters.
- modelId
-
Specifies the model or throughput with which to run inference, or the prompt resource to use in inference. The value depends on the resource 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.
-
To include a prompt that was defined in Prompt management, specify the ARN of the prompt version to use.
The Converse API doesn't support imported models.
Length Constraints: Minimum length of 1. Maximum length of 2048.
Pattern:
^(arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|([0-9]{12}:imported-model/[a-z0-9]{12})|([0-9]{12}:provisioned-model/[a-z0-9]{12})|([0-9]{12}:(inference-profile|application-inference-profile)/[a-zA-Z0-9-:.]+)))|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|(([0-9a-zA-Z][_-]?)+)|([a-zA-Z0-9-:.]+)|(^(arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:[0-9]{12}:prompt/[0-9a-zA-Z]{10}(?::[0-9]{1,5})?))$
Required: Yes
-
Request Body
The request accepts the following data in JSON format.
- additionalModelRequestFields
-
Additional inference parameters that the model supports, beyond the base set of inference parameters that
Converse
andConverseStream
support in theinferenceConfig
field. For more information, see Model parameters.Type: JSON value
Required: No
- additionalModelResponseFieldPaths
-
Additional model parameters field paths to return in the response.
Converse
andConverseStream
return the requested fields as a JSON Pointer object in theadditionalModelResponseFields
field. The following is example JSON foradditionalModelResponseFieldPaths
.[ "/stop_sequence" ]
For information about the JSON Pointer syntax, see the Internet Engineering Task Force (IETF)
documentation. Converse
andConverseStream
reject an empty JSON Pointer or incorrectly structured JSON Pointer with a400
error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored byConverse
.Type: Array of strings
Array Members: Minimum number of 0 items. Maximum number of 10 items.
Length Constraints: Minimum length of 1. Maximum length of 256.
Required: No
- guardrailConfig
-
Configuration information for a guardrail that you want to use in the request. If you include
guardContent
blocks in thecontent
field in themessages
field, the guardrail operates only on those messages. If you include noguardContent
blocks, the guardrail operates on all messages in the request body and in any included prompt resource.Type: GuardrailStreamConfiguration object
Required: No
- inferenceConfig
-
Inference parameters to pass to the model.
Converse
andConverseStream
support a base set of inference parameters. If you need to pass additional parameters that the model supports, use theadditionalModelRequestFields
request field.Type: InferenceConfiguration object
Required: No
- messages
-
The messages that you want to send to the model.
Type: Array of Message objects
Required: No
- promptVariables
-
Contains a map of variables in a prompt from Prompt management to objects containing the values to fill in for them when running model invocation. This field is ignored if you don't specify a prompt resource in the
modelId
field.Type: String to PromptVariableValues object map
Required: No
- system
-
A prompt that provides instructions or context to the model about the task it should perform, or the persona it should adopt during the conversation.
Type: Array of SystemContentBlock objects
Required: No
- toolConfig
-
Configuration information for the tools that the model can use when generating a response.
For information about models that support streaming tool use, see Supported models and model features.
Type: ToolConfiguration object
Required: No
Response Syntax
HTTP/1.1 200
Content-type: application/json
{
"contentBlockDelta": {
"contentBlockIndex": number,
"delta": { ... }
},
"contentBlockStart": {
"contentBlockIndex": number,
"start": { ... }
},
"contentBlockStop": {
"contentBlockIndex": number
},
"internalServerException": {
},
"messageStart": {
"role": "string"
},
"messageStop": {
"additionalModelResponseFields": JSON value,
"stopReason": "string"
},
"metadata": {
"metrics": {
"latencyMs": number
},
"trace": {
"guardrail": {
"inputAssessment": {
"string" : {
"contentPolicy": {
"filters": [
{
"action": "string",
"confidence": "string",
"filterStrength": "string",
"type": "string"
}
]
},
"contextualGroundingPolicy": {
"filters": [
{
"action": "string",
"score": number,
"threshold": number,
"type": "string"
}
]
},
"invocationMetrics": {
"guardrailCoverage": {
"textCharacters": {
"guarded": number,
"total": number
}
},
"guardrailProcessingLatency": number,
"usage": {
"contentPolicyUnits": number,
"contextualGroundingPolicyUnits": number,
"sensitiveInformationPolicyFreeUnits": number,
"sensitiveInformationPolicyUnits": number,
"topicPolicyUnits": number,
"wordPolicyUnits": number
}
},
"sensitiveInformationPolicy": {
"piiEntities": [
{
"action": "string",
"match": "string",
"type": "string"
}
],
"regexes": [
{
"action": "string",
"match": "string",
"name": "string",
"regex": "string"
}
]
},
"topicPolicy": {
"topics": [
{
"action": "string",
"name": "string",
"type": "string"
}
]
},
"wordPolicy": {
"customWords": [
{
"action": "string",
"match": "string"
}
],
"managedWordLists": [
{
"action": "string",
"match": "string",
"type": "string"
}
]
}
}
},
"modelOutput": [ "string" ],
"outputAssessments": {
"string" : [
{
"contentPolicy": {
"filters": [
{
"action": "string",
"confidence": "string",
"filterStrength": "string",
"type": "string"
}
]
},
"contextualGroundingPolicy": {
"filters": [
{
"action": "string",
"score": number,
"threshold": number,
"type": "string"
}
]
},
"invocationMetrics": {
"guardrailCoverage": {
"textCharacters": {
"guarded": number,
"total": number
}
},
"guardrailProcessingLatency": number,
"usage": {
"contentPolicyUnits": number,
"contextualGroundingPolicyUnits": number,
"sensitiveInformationPolicyFreeUnits": number,
"sensitiveInformationPolicyUnits": number,
"topicPolicyUnits": number,
"wordPolicyUnits": number
}
},
"sensitiveInformationPolicy": {
"piiEntities": [
{
"action": "string",
"match": "string",
"type": "string"
}
],
"regexes": [
{
"action": "string",
"match": "string",
"name": "string",
"regex": "string"
}
]
},
"topicPolicy": {
"topics": [
{
"action": "string",
"name": "string",
"type": "string"
}
]
},
"wordPolicy": {
"customWords": [
{
"action": "string",
"match": "string"
}
],
"managedWordLists": [
{
"action": "string",
"match": "string",
"type": "string"
}
]
}
}
]
}
}
},
"usage": {
"inputTokens": number,
"outputTokens": number,
"totalTokens": number
}
},
"modelStreamErrorException": {
},
"serviceUnavailableException": {
},
"throttlingException": {
},
"validationException": {
}
}
Response Elements
If the action is successful, the service sends back an HTTP 200 response.
The following data is returned in JSON format by the service.
- contentBlockDelta
-
The messages output content block delta.
Type: ContentBlockDeltaEvent object
- contentBlockStart
-
Start information for a content block.
Type: ContentBlockStartEvent object
- contentBlockStop
-
Stop information for a content block.
Type: ContentBlockStopEvent object
- internalServerException
-
An internal server error occurred. Retry your request.
Type: Exception
HTTP Status Code: 500 - messageStart
-
Message start information.
Type: MessageStartEvent object
- messageStop
-
Message stop information.
Type: MessageStopEvent object
- metadata
-
Metadata for the converse output stream.
Type: ConverseStreamMetadataEvent object
- modelStreamErrorException
-
A streaming error occurred. Retry your request.
Type: Exception
HTTP Status Code: 424 -
The service isn't currently available. For troubleshooting this error, see ServiceUnavailable in the Amazon Bedrock User Guide
Type: Exception
HTTP Status Code: 503 - throttlingException
-
Your request was denied due to exceeding the account quotas for Amazon Bedrock. For troubleshooting this error, see ThrottlingException in the Amazon Bedrock User Guide
Type: Exception
HTTP Status Code: 429 - validationException
-
The input fails to satisfy the constraints specified by Amazon Bedrock. For troubleshooting this error, see ValidationError in the Amazon Bedrock User Guide
Type: Exception
HTTP Status Code: 400
Errors
For information about the errors that are common to all actions, see Common Errors.
- AccessDeniedException
-
The request is denied because you do not have sufficient permissions to perform the requested action. For troubleshooting this error, see AccessDeniedException in the Amazon Bedrock User Guide
HTTP Status Code: 403
- InternalServerException
-
An internal server error occurred. For troubleshooting this error, see InternalFailure in the Amazon Bedrock User Guide
HTTP Status Code: 500
- ModelErrorException
-
The request failed due to an error while processing the model.
HTTP Status Code: 424
- ModelNotReadyException
-
The model specified in the request is not ready to serve inference requests. The AWS SDK will automatically retry the operation up to 5 times. For information about configuring automatic retries, see Retry behavior in the AWS SDKs and Tools reference guide.
HTTP Status Code: 429
- ModelTimeoutException
-
The request took too long to process. Processing time exceeded the model timeout length.
HTTP Status Code: 408
- ResourceNotFoundException
-
The specified resource ARN was not found. For troubleshooting this error, see ResourceNotFound in the Amazon Bedrock User Guide
HTTP Status Code: 404
- ServiceUnavailableException
-
The service isn't currently available. For troubleshooting this error, see ServiceUnavailable in the Amazon Bedrock User Guide
HTTP Status Code: 503
- ThrottlingException
-
Your request was denied due to exceeding the account quotas for Amazon Bedrock. For troubleshooting this error, see ThrottlingException in the Amazon Bedrock User Guide
HTTP Status Code: 429
- ValidationException
-
The input fails to satisfy the constraints specified by Amazon Bedrock. For troubleshooting this error, see ValidationError in the Amazon Bedrock User Guide
HTTP Status Code: 400
Examples
Send a message to a model and stream the response.
Send a message to Anthropic Claude Sonnet with ConverseStream
and stream the response.
Sample Request
POST /model/anthropic.claude-3-sonnet-20240229-v1:0/converse-stream HTTP/1.1
{
"messages": [
{
"role": "user",
"content": [
{
"text": "Write an article about impact of high inflation to GDP of a country"
}
]
}
],
"system": [{"text" : "You are an economist with access to lots of data"}],
"inferenceConfig": {
"maxTokens": 1000,
"temperature": 0.5
}
}
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following: