2025/04/30 - Agents for Amazon Bedrock Runtime - 1 updated api methods
Changes You can now specify a cross region inference profile as a teacher model for the CreateModelCustomizationJob API. Additionally, the GetModelCustomizationJob API has been enhanced to return the sub-task statuses of a customization job within the StatusDetails response field.
{'agentName': 'string',
'customOrchestration': {'executor': {'lambda': 'string'}},
'orchestrationType': 'DEFAULT | CUSTOM_ORCHESTRATION'}
Response {'completion': {'trace': {'callerChain': [{'agentAliasArn': 'string'}],
'collaboratorName': 'string',
'eventTime': 'timestamp'}}}
Invokes an inline Amazon Bedrock agent using the configurations you provide with the request.
Specify the following fields for security purposes.
(Optional) customerEncryptionKeyArn – The Amazon Resource Name (ARN) of a KMS key to encrypt the creation of the agent.
(Optional) idleSessionTTLinSeconds – Specify the number of seconds for which the agent should maintain session information. After this time expires, the subsequent InvokeInlineAgent request begins a new session.
To override the default prompt behavior for agent orchestration and to use advanced prompts, include a promptOverrideConfiguration object. For more information, see Advanced prompts.
The agent instructions will not be honored if your agent has only one knowledge base, uses default prompts, has no action group, and user input is disabled.
See also: AWS API Documentation
Request Syntax
client.invoke_inline_agent(
actionGroups=[
{
'actionGroupExecutor': {
'customControl': 'RETURN_CONTROL',
'lambda': 'string'
},
'actionGroupName': 'string',
'apiSchema': {
'payload': 'string',
's3': {
's3BucketName': 'string',
's3ObjectKey': 'string'
}
},
'description': 'string',
'functionSchema': {
'functions': [
{
'description': 'string',
'name': 'string',
'parameters': {
'string': {
'description': 'string',
'required': True|False,
'type': 'string'|'number'|'integer'|'boolean'|'array'
}
},
'requireConfirmation': 'ENABLED'|'DISABLED'
},
]
},
'parentActionGroupSignature': 'AMAZON.UserInput'|'AMAZON.CodeInterpreter'|'ANTHROPIC.Computer'|'ANTHROPIC.Bash'|'ANTHROPIC.TextEditor',
'parentActionGroupSignatureParams': {
'string': 'string'
}
},
],
agentCollaboration='SUPERVISOR'|'SUPERVISOR_ROUTER'|'DISABLED',
agentName='string',
bedrockModelConfigurations={
'performanceConfig': {
'latency': 'standard'|'optimized'
}
},
collaboratorConfigurations=[
{
'agentAliasArn': 'string',
'collaboratorInstruction': 'string',
'collaboratorName': 'string',
'relayConversationHistory': 'TO_COLLABORATOR'|'DISABLED'
},
],
collaborators=[
{
'actionGroups': [
{
'actionGroupExecutor': {
'customControl': 'RETURN_CONTROL',
'lambda': 'string'
},
'actionGroupName': 'string',
'apiSchema': {
'payload': 'string',
's3': {
's3BucketName': 'string',
's3ObjectKey': 'string'
}
},
'description': 'string',
'functionSchema': {
'functions': [
{
'description': 'string',
'name': 'string',
'parameters': {
'string': {
'description': 'string',
'required': True|False,
'type': 'string'|'number'|'integer'|'boolean'|'array'
}
},
'requireConfirmation': 'ENABLED'|'DISABLED'
},
]
},
'parentActionGroupSignature': 'AMAZON.UserInput'|'AMAZON.CodeInterpreter'|'ANTHROPIC.Computer'|'ANTHROPIC.Bash'|'ANTHROPIC.TextEditor',
'parentActionGroupSignatureParams': {
'string': 'string'
}
},
],
'agentCollaboration': 'SUPERVISOR'|'SUPERVISOR_ROUTER'|'DISABLED',
'agentName': 'string',
'collaboratorConfigurations': [
{
'agentAliasArn': 'string',
'collaboratorInstruction': 'string',
'collaboratorName': 'string',
'relayConversationHistory': 'TO_COLLABORATOR'|'DISABLED'
},
],
'customerEncryptionKeyArn': 'string',
'foundationModel': 'string',
'guardrailConfiguration': {
'guardrailIdentifier': 'string',
'guardrailVersion': 'string'
},
'idleSessionTTLInSeconds': 123,
'instruction': 'string',
'knowledgeBases': [
{
'description': 'string',
'knowledgeBaseId': 'string',
'retrievalConfiguration': {
'vectorSearchConfiguration': {
'filter': {
'andAll': [
{'... recursive ...'},
],
'equals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'greaterThan': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'greaterThanOrEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'in': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'lessThan': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'lessThanOrEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'listContains': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'notEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'notIn': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'orAll': [
{'... recursive ...'},
],
'startsWith': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'stringContains': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
}
},
'implicitFilterConfiguration': {
'metadataAttributes': [
{
'description': 'string',
'key': 'string',
'type': 'STRING'|'NUMBER'|'BOOLEAN'|'STRING_LIST'
},
],
'modelArn': 'string'
},
'numberOfResults': 123,
'overrideSearchType': 'HYBRID'|'SEMANTIC',
'rerankingConfiguration': {
'bedrockRerankingConfiguration': {
'metadataConfiguration': {
'selectionMode': 'SELECTIVE'|'ALL',
'selectiveModeConfiguration': {
'fieldsToExclude': [
{
'fieldName': 'string'
},
],
'fieldsToInclude': [
{
'fieldName': 'string'
},
]
}
},
'modelConfiguration': {
'additionalModelRequestFields': {
'string': {...}|[...]|123|123.4|'string'|True|None
},
'modelArn': 'string'
},
'numberOfRerankedResults': 123
},
'type': 'BEDROCK_RERANKING_MODEL'
}
}
}
},
],
'promptOverrideConfiguration': {
'overrideLambda': 'string',
'promptConfigurations': [
{
'additionalModelRequestFields': {...}|[...]|123|123.4|'string'|True|None,
'basePromptTemplate': 'string',
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'promptState': 'ENABLED'|'DISABLED',
'promptType': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
]
}
},
],
customOrchestration={
'executor': {
'lambda': 'string'
}
},
customerEncryptionKeyArn='string',
enableTrace=True|False,
endSession=True|False,
foundationModel='string',
guardrailConfiguration={
'guardrailIdentifier': 'string',
'guardrailVersion': 'string'
},
idleSessionTTLInSeconds=123,
inlineSessionState={
'conversationHistory': {
'messages': [
{
'content': [
{
'text': 'string'
},
],
'role': 'user'|'assistant'
},
]
},
'files': [
{
'name': 'string',
'source': {
'byteContent': {
'data': b'bytes',
'mediaType': 'string'
},
's3Location': {
'uri': 'string'
},
'sourceType': 'S3'|'BYTE_CONTENT'
},
'useCase': 'CODE_INTERPRETER'|'CHAT'
},
],
'invocationId': 'string',
'promptSessionAttributes': {
'string': 'string'
},
'returnControlInvocationResults': [
{
'apiResult': {
'actionGroup': 'string',
'agentId': 'string',
'apiPath': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'httpMethod': 'string',
'httpStatusCode': 123,
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
},
'functionResult': {
'actionGroup': 'string',
'agentId': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'function': 'string',
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
}
},
],
'sessionAttributes': {
'string': 'string'
}
},
inputText='string',
instruction='string',
knowledgeBases=[
{
'description': 'string',
'knowledgeBaseId': 'string',
'retrievalConfiguration': {
'vectorSearchConfiguration': {
'filter': {
'andAll': [
{'... recursive ...'},
],
'equals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'greaterThan': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'greaterThanOrEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'in': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'lessThan': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'lessThanOrEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'listContains': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'notEquals': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'notIn': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'orAll': [
{'... recursive ...'},
],
'startsWith': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
},
'stringContains': {
'key': 'string',
'value': {...}|[...]|123|123.4|'string'|True|None
}
},
'implicitFilterConfiguration': {
'metadataAttributes': [
{
'description': 'string',
'key': 'string',
'type': 'STRING'|'NUMBER'|'BOOLEAN'|'STRING_LIST'
},
],
'modelArn': 'string'
},
'numberOfResults': 123,
'overrideSearchType': 'HYBRID'|'SEMANTIC',
'rerankingConfiguration': {
'bedrockRerankingConfiguration': {
'metadataConfiguration': {
'selectionMode': 'SELECTIVE'|'ALL',
'selectiveModeConfiguration': {
'fieldsToExclude': [
{
'fieldName': 'string'
},
],
'fieldsToInclude': [
{
'fieldName': 'string'
},
]
}
},
'modelConfiguration': {
'additionalModelRequestFields': {
'string': {...}|[...]|123|123.4|'string'|True|None
},
'modelArn': 'string'
},
'numberOfRerankedResults': 123
},
'type': 'BEDROCK_RERANKING_MODEL'
}
}
}
},
],
orchestrationType='DEFAULT'|'CUSTOM_ORCHESTRATION',
promptOverrideConfiguration={
'overrideLambda': 'string',
'promptConfigurations': [
{
'additionalModelRequestFields': {...}|[...]|123|123.4|'string'|True|None,
'basePromptTemplate': 'string',
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'promptState': 'ENABLED'|'DISABLED',
'promptType': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
]
},
sessionId='string',
streamingConfigurations={
'applyGuardrailInterval': 123,
'streamFinalResponse': True|False
}
)
list
A list of action groups with each action group defining the action the inline agent needs to carry out.
(dict) --
Contains details of the inline agent's action group.
actionGroupExecutor (dict) --
The Amazon Resource Name (ARN) of the Lambda function containing the business logic that is carried out upon invoking the action or the custom control method for handling the information elicited from the user.
customControl (string) --
To return the action group invocation results directly in the InvokeInlineAgent response, specify RETURN_CONTROL.
lambda (string) --
The Amazon Resource Name (ARN) of the Lambda function containing the business logic that is carried out upon invoking the action.
actionGroupName (string) -- [REQUIRED]
The name of the action group.
apiSchema (dict) --
Contains either details about the S3 object containing the OpenAPI schema for the action group or the JSON or YAML-formatted payload defining the schema. For more information, see Action group OpenAPI schemas.
payload (string) --
The JSON or YAML-formatted payload defining the OpenAPI schema for the action group.
s3 (dict) --
Contains details about the S3 object containing the OpenAPI schema for the action group.
s3BucketName (string) --
The name of the S3 bucket.
s3ObjectKey (string) --
The S3 object key for the S3 resource.
description (string) --
A description of the action group.
functionSchema (dict) --
Contains details about the function schema for the action group or the JSON or YAML-formatted payload defining the schema.
functions (list) --
A list of functions that each define an action in the action group.
(dict) --
Defines parameters that the agent needs to invoke from the user to complete the function. Corresponds to an action in an action group.
description (string) --
A description of the function and its purpose.
name (string) -- [REQUIRED]
A name for the function.
parameters (dict) --
The parameters that the agent elicits from the user to fulfill the function.
(string) --
(dict) --
Contains details about a parameter in a function for an action group.
description (string) --
A description of the parameter. Helps the foundation model determine how to elicit the parameters from the user.
required (boolean) --
Whether the parameter is required for the agent to complete the function for action group invocation.
type (string) -- [REQUIRED]
The data type of the parameter.
requireConfirmation (string) --
Contains information if user confirmation is required to invoke the function.
parentActionGroupSignature (string) --
Specify a built-in or computer use action for this action group. If you specify a value, you must leave the description, apiSchema, and actionGroupExecutor fields empty for this action group.
To allow your agent to request the user for additional information when trying to complete a task, set this field to AMAZON.UserInput.
To allow your agent to generate, run, and troubleshoot code when trying to complete a task, set this field to AMAZON.CodeInterpreter.
To allow your agent to use an Anthropic computer use tool, specify one of the following values.
parentActionGroupSignatureParams (dict) --
The configuration settings for a computer use action.
(string) --
(string) --
string
Defines how the inline collaborator agent handles information across multiple collaborator agents to coordinate a final response. The inline collaborator agent can also be the supervisor.
string
The name for the agent.
dict
Model settings for the request.
performanceConfig (dict) --
The latency configuration for the model.
latency (string) --
To use a latency-optimized version of the model, set to optimized.
list
Settings for an inline agent collaborator called with InvokeInlineAgent.
(dict) --
Settings of an inline collaborator agent.
agentAliasArn (string) --
The Amazon Resource Name (ARN) of the inline collaborator agent.
collaboratorInstruction (string) -- [REQUIRED]
Instructions that tell the inline collaborator agent what it should do and how it should interact with users.
collaboratorName (string) -- [REQUIRED]
Name of the inline collaborator agent which must be the same name as specified for agentName.
relayConversationHistory (string) --
A relay conversation history for the inline collaborator agent.
list
List of collaborator inline agents.
(dict) --
List of inline collaborators.
actionGroups (list) --
List of action groups with each action group defining tasks the inline collaborator agent needs to carry out.
(dict) --
Contains details of the inline agent's action group.
actionGroupExecutor (dict) --
The Amazon Resource Name (ARN) of the Lambda function containing the business logic that is carried out upon invoking the action or the custom control method for handling the information elicited from the user.
customControl (string) --
To return the action group invocation results directly in the InvokeInlineAgent response, specify RETURN_CONTROL.
lambda (string) --
The Amazon Resource Name (ARN) of the Lambda function containing the business logic that is carried out upon invoking the action.
actionGroupName (string) -- [REQUIRED]
The name of the action group.
apiSchema (dict) --
Contains either details about the S3 object containing the OpenAPI schema for the action group or the JSON or YAML-formatted payload defining the schema. For more information, see Action group OpenAPI schemas.
payload (string) --
The JSON or YAML-formatted payload defining the OpenAPI schema for the action group.
s3 (dict) --
Contains details about the S3 object containing the OpenAPI schema for the action group.
s3BucketName (string) --
The name of the S3 bucket.
s3ObjectKey (string) --
The S3 object key for the S3 resource.
description (string) --
A description of the action group.
functionSchema (dict) --
Contains details about the function schema for the action group or the JSON or YAML-formatted payload defining the schema.
functions (list) --
A list of functions that each define an action in the action group.
(dict) --
Defines parameters that the agent needs to invoke from the user to complete the function. Corresponds to an action in an action group.
description (string) --
A description of the function and its purpose.
name (string) -- [REQUIRED]
A name for the function.
parameters (dict) --
The parameters that the agent elicits from the user to fulfill the function.
(string) --
(dict) --
Contains details about a parameter in a function for an action group.
description (string) --
A description of the parameter. Helps the foundation model determine how to elicit the parameters from the user.
required (boolean) --
Whether the parameter is required for the agent to complete the function for action group invocation.
type (string) -- [REQUIRED]
The data type of the parameter.
requireConfirmation (string) --
Contains information if user confirmation is required to invoke the function.
parentActionGroupSignature (string) --
Specify a built-in or computer use action for this action group. If you specify a value, you must leave the description, apiSchema, and actionGroupExecutor fields empty for this action group.
To allow your agent to request the user for additional information when trying to complete a task, set this field to AMAZON.UserInput.
To allow your agent to generate, run, and troubleshoot code when trying to complete a task, set this field to AMAZON.CodeInterpreter.
To allow your agent to use an Anthropic computer use tool, specify one of the following values.
parentActionGroupSignatureParams (dict) --
The configuration settings for a computer use action.
(string) --
(string) --
agentCollaboration (string) --
Defines how the inline supervisor agent handles information across multiple collaborator agents to coordinate a final response.
agentName (string) --
Name of the inline collaborator agent which must be the same name as specified for collaboratorName.
collaboratorConfigurations (list) --
Settings of the collaborator agent.
(dict) --
Settings of an inline collaborator agent.
agentAliasArn (string) --
The Amazon Resource Name (ARN) of the inline collaborator agent.
collaboratorInstruction (string) -- [REQUIRED]
Instructions that tell the inline collaborator agent what it should do and how it should interact with users.
collaboratorName (string) -- [REQUIRED]
Name of the inline collaborator agent which must be the same name as specified for agentName.
relayConversationHistory (string) --
A relay conversation history for the inline collaborator agent.
customerEncryptionKeyArn (string) --
The Amazon Resource Name (ARN) of the AWS KMS key that encrypts the inline collaborator.
foundationModel (string) -- [REQUIRED]
The foundation model used by the inline collaborator agent.
guardrailConfiguration (dict) --
Details of the guardwrail associated with the inline collaborator.
guardrailIdentifier (string) -- [REQUIRED]
The unique identifier for the guardrail.
guardrailVersion (string) -- [REQUIRED]
The version of the guardrail.
idleSessionTTLInSeconds (integer) --
The number of seconds for which the Amazon Bedrock keeps information about the user's conversation with the inline collaborator agent.
A user interaction remains active for the amount of time specified. If no conversation occurs during this time, the session expires and Amazon Bedrock deletes any data provided before the timeout.
instruction (string) -- [REQUIRED]
Instruction that tell the inline collaborator agent what it should do and how it should interact with users.
knowledgeBases (list) --
Knowledge base associated with the inline collaborator agent.
(dict) --
Details of the knowledge base associated withe inline agent.
description (string) -- [REQUIRED]
The description of the knowledge base associated with the inline agent.
knowledgeBaseId (string) -- [REQUIRED]
The unique identifier for a knowledge base associated with the inline agent.
retrievalConfiguration (dict) --
The configurations to apply to the knowledge base during query. For more information, see Query configurations.
vectorSearchConfiguration (dict) -- [REQUIRED]
Contains details about how the results from the vector search should be returned. For more information, see Query configurations.
filter (dict) --
Specifies the filters to use on the metadata in the knowledge base data sources before returning results. For more information, see Query configurations.
andAll (list) --
Knowledge base data sources are returned if their metadata attributes fulfill all the filter conditions inside this list.
(dict) --
Specifies the filters to use on the metadata attributes in the knowledge base data sources before returning results. For more information, see Query configurations. See the examples below to see how to use these filters.
This data type is used in the following API operations:
Retrieve request – in the filter field
RetrieveAndGenerate request – in the filter field
equals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value matches the value in this object.
The following example would return data sources with an animal attribute whose value is cat:
"equals": { "key": "animal", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
greaterThan (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is greater than the value in this object.
The following example would return data sources with an year attribute whose value is greater than 1989:
"greaterThan": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
greaterThanOrEquals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is greater than or equal to the value in this object.
The following example would return data sources with an year attribute whose value is greater than or equal to 1989:
"greaterThanOrEquals": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
in (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is in the list specified in the value in this object.
The following example would return data sources with an animal attribute that is either cat or dog:
"in": { "key": "animal", "value": ["cat", "dog"] }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
lessThan (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is less than the value in this object.
The following example would return data sources with an year attribute whose value is less than to 1989.
"lessThan": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
lessThanOrEquals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is less than or equal to the value in this object.
The following example would return data sources with an year attribute whose value is less than or equal to 1989.
"lessThanOrEquals": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
listContains (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is a list that contains the value as one of its members.
The following example would return data sources with an animals attribute that is a list containing a cat member (for example ["dog", "cat"]).
"listContains": { "key": "animals", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
notEquals (dict) --
Knowledge base data sources are returned when:
It contains a metadata attribute whose name matches the key and whose value doesn't match the value in this object.
The key is not present in the document.
The following example would return data sources that don't contain an animal attribute whose value is cat.
"notEquals": { "key": "animal", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
notIn (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value isn't in the list specified in the value in this object.
The following example would return data sources whose animal attribute is neither cat nor dog.
"notIn": { "key": "animal", "value": ["cat", "dog"] }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
orAll (list) --
Knowledge base data sources are returned if their metadata attributes fulfill at least one of the filter conditions inside this list.
(dict) --
Specifies the filters to use on the metadata attributes in the knowledge base data sources before returning results. For more information, see Query configurations. See the examples below to see how to use these filters.
This data type is used in the following API operations:
Retrieve request – in the filter field
RetrieveAndGenerate request – in the filter field
startsWith (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value starts with the value in this object. This filter is currently only supported for Amazon OpenSearch Serverless vector stores.
The following example would return data sources with an animal attribute starts with ca (for example, cat or camel).
"startsWith": { "key": "animal", "value": "ca" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
stringContains (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is one of the following:
A string that contains the value as a substring. The following example would return data sources with an animal attribute that contains the substring at (for example cat). "stringContains": { "key": "animal", "value": "at" }
A list with a member that contains the value as a substring. The following example would return data sources with an animals attribute that is a list containing a member that contains the substring at (for example ["dog", "cat"]). "stringContains": { "key": "animals", "value": "at" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
implicitFilterConfiguration (dict) --
Settings for implicit filtering.
metadataAttributes (list) -- [REQUIRED]
Metadata that can be used in a filter.
(dict) --
Details about a metadata attribute.
description (string) -- [REQUIRED]
The attribute's description.
key (string) -- [REQUIRED]
The attribute's key.
type (string) -- [REQUIRED]
The attribute's type.
modelArn (string) -- [REQUIRED]
The model that generates the filter.
numberOfResults (integer) --
The number of source chunks to retrieve.
overrideSearchType (string) --
By default, Amazon Bedrock decides a search strategy for you. If you're using an Amazon OpenSearch Serverless vector store that contains a filterable text field, you can specify whether to query the knowledge base with a HYBRID search using both vector embeddings and raw text, or SEMANTIC search using only vector embeddings. For other vector store configurations, only SEMANTIC search is available. For more information, see Test a knowledge base.
rerankingConfiguration (dict) --
Contains configurations for reranking the retrieved results. For more information, see Improve the relevance of query responses with a reranker model.
bedrockRerankingConfiguration (dict) --
Contains configurations for an Amazon Bedrock reranker model.
metadataConfiguration (dict) --
Contains configurations for the metadata to use in reranking.
selectionMode (string) -- [REQUIRED]
Specifies whether to consider all metadata when reranking, or only the metadata that you select. If you specify SELECTIVE, include the selectiveModeConfiguration field.
selectiveModeConfiguration (dict) --
Contains configurations for the metadata fields to include or exclude when considering reranking.
fieldsToExclude (list) --
An array of objects, each of which specifies a metadata field to exclude from consideration when reranking.
(dict) --
Contains information for a metadata field to include in or exclude from consideration when reranking.
fieldName (string) -- [REQUIRED]
The name of a metadata field to include in or exclude from consideration when reranking.
fieldsToInclude (list) --
An array of objects, each of which specifies a metadata field to include in consideration when reranking. The remaining metadata fields are ignored.
(dict) --
Contains information for a metadata field to include in or exclude from consideration when reranking.
fieldName (string) -- [REQUIRED]
The name of a metadata field to include in or exclude from consideration when reranking.
modelConfiguration (dict) -- [REQUIRED]
Contains configurations for the reranker model.
additionalModelRequestFields (dict) --
A JSON object whose keys are request fields for the model and whose values are values for those fields.
(string) --
(:ref:`document<document>`) --
modelArn (string) -- [REQUIRED]
The ARN of the reranker model to use.
numberOfRerankedResults (integer) --
The number of results to return after reranking.
type (string) -- [REQUIRED]
The type of reranker model.
promptOverrideConfiguration (dict) --
Contains configurations to override prompt templates in different parts of an inline collaborator sequence. For more information, see Advanced prompts.
overrideLambda (string) --
The ARN of the Lambda function to use when parsing the raw foundation model output in parts of the agent sequence. If you specify this field, at least one of the promptConfigurations must contain a parserMode value that is set to OVERRIDDEN. For more information, see Parser Lambda function in Amazon Bedrock Agents.
promptConfigurations (list) -- [REQUIRED]
Contains configurations to override a prompt template in one part of an agent sequence. For more information, see Advanced prompts.
(dict) --
Contains configurations to override a prompt template in one part of an agent sequence. For more information, see Advanced prompts.
additionalModelRequestFields (:ref:`document<document>`) --
If the Converse or ConverseStream operations support the model, additionalModelRequestFields contains additional inference parameters, beyond the base set of inference parameters in the inferenceConfiguration field.
For more information, see Inference request parameters and response fields for foundation models in the Amazon Bedrock user guide.
basePromptTemplate (string) --
Defines the prompt template with which to replace the default prompt template. You can use placeholder variables in the base prompt template to customize the prompt. For more information, see Prompt template placeholder variables. For more information, see Configure the prompt templates.
foundationModel (string) --
The foundation model to use.
inferenceConfiguration (dict) --
Contains inference parameters to use when the agent invokes a foundation model in the part of the agent sequence defined by the promptType. For more information, see Inference parameters for foundation models.
maximumLength (integer) --
The maximum number of tokens allowed in the generated response.
stopSequences (list) --
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
(string) --
temperature (float) --
The likelihood of the model selecting higher-probability options while generating a response. A lower value makes the model more likely to choose higher-probability options, while a higher value makes the model more likely to choose lower-probability options.
topK (integer) --
While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for topK is the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topK to 50, the model selects the next token from among the top 50 most likely choices.
topP (float) --
While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for Top P determines the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topP to 0.8, the model only selects the next token from the top 80% of the probability distribution of next tokens.
parserMode (string) --
Specifies whether to override the default parser Lambda function when parsing the raw foundation model output in the part of the agent sequence defined by the promptType. If you set the field as OVERRIDDEN, the overrideLambda field in the PromptOverrideConfiguration must be specified with the ARN of a Lambda function.
promptCreationMode (string) --
Specifies whether to override the default prompt template for this promptType. Set this value to OVERRIDDEN to use the prompt that you provide in the basePromptTemplate. If you leave it as DEFAULT, the agent uses a default prompt template.
promptState (string) --
Specifies whether to allow the inline agent to carry out the step specified in the promptType. If you set this value to DISABLED, the agent skips that step. The default state for each promptType is as follows.
PRE_PROCESSING – ENABLED
ORCHESTRATION – ENABLED
KNOWLEDGE_BASE_RESPONSE_GENERATION – ENABLED
POST_PROCESSING – DISABLED
promptType (string) --
The step in the agent sequence that this prompt configuration applies to.
dict
Contains details of the custom orchestration configured for the agent.
executor (dict) --
The structure of the executor invoking the actions in custom orchestration.
lambda (string) --
The Amazon Resource Name (ARN) of the Lambda function containing the business logic that is carried out upon invoking the action.
string
The Amazon Resource Name (ARN) of the Amazon Web Services KMS key to use to encrypt your inline agent.
boolean
Specifies whether to turn on the trace or not to track the agent's reasoning process. For more information, see Using trace.
boolean
Specifies whether to end the session with the inline agent or not.
string
[REQUIRED]
The model identifier (ID) of the model to use for orchestration by the inline agent. For example, meta.llama3-1-70b-instruct-v1:0.
dict
The guardrails to assign to the inline agent.
guardrailIdentifier (string) -- [REQUIRED]
The unique identifier for the guardrail.
guardrailVersion (string) -- [REQUIRED]
The version of the guardrail.
integer
The number of seconds for which the inline agent should maintain session information. After this time expires, the subsequent InvokeInlineAgent request begins a new session.
A user interaction remains active for the amount of time specified. If no conversation occurs during this time, the session expires and the data provided before the timeout is deleted.
dict
Parameters that specify the various attributes of a sessions. You can include attributes for the session or prompt or, if you configured an action group to return control, results from invocation of the action group. For more information, see Control session context.
conversationHistory (dict) --
Contains the conversation history that persist across sessions.
messages (list) --
The conversation's messages.
(dict) --
Details about a message.
content (list) -- [REQUIRED]
The message's content.
(dict) --
A content block.
text (string) --
The block's text.
role (string) -- [REQUIRED]
The message's role.
files (list) --
Contains information about the files used by code interpreter.
(dict) --
Contains details of the source files.
name (string) -- [REQUIRED]
The name of the source file.
source (dict) -- [REQUIRED]
Specifies where the files are located.
byteContent (dict) --
The data and the text of the attached files.
data (bytes) -- [REQUIRED]
The raw bytes of the file to attach. The maximum size of all files that is attached is 10MB. You can attach a maximum of 5 files.
mediaType (string) -- [REQUIRED]
The MIME type of data contained in the file used for chat.
s3Location (dict) --
The s3 location of the files to attach.
uri (string) -- [REQUIRED]
The uri of the s3 object.
sourceType (string) -- [REQUIRED]
The source type of the files to attach.
useCase (string) -- [REQUIRED]
Specifies how the source files will be used by the code interpreter.
invocationId (string) --
The identifier of the invocation of an action. This value must match the invocationId returned in the InvokeInlineAgent response for the action whose results are provided in the returnControlInvocationResults field. For more information, see Return control to the agent developer.
promptSessionAttributes (dict) --
Contains attributes that persist across a session and the values of those attributes.
(string) --
(string) --
returnControlInvocationResults (list) --
Contains information about the results from the action group invocation. For more information, see Return control to the agent developer.
(dict) --
A result from the invocation of an action. For more information, see Return control to the agent developer and Control session context.
This data type is used in the following API operations:
apiResult (dict) --
The result from the API response from the action group invocation.
actionGroup (string) -- [REQUIRED]
The action group that the API operation belongs to.
agentId (string) --
The agent's ID.
apiPath (string) --
The path to the API operation.
confirmationState (string) --
Controls the API operations or functions to invoke based on the user confirmation.
httpMethod (string) --
The HTTP method for the API operation.
httpStatusCode (integer) --
http status code from API execution response (for example: 200, 400, 500).
responseBody (dict) --
The response body from the API operation. The key of the object is the content type (currently, only TEXT is supported). The response may be returned directly or from the Lambda function.
(string) --
(dict) --
Contains the body of the API response.
This data type is used in the following API operations:
In the returnControlInvocationResults field of the InvokeAgent request
body (string) --
The body of the API response.
images (list) --
Lists details, including format and source, for the image in the response from the function call. You can specify only one image and the function in the returnControlInvocationResults must be a computer use action. For more information, see Configure an Amazon Bedrock Agent to complete tasks with computer use tools.
(dict) --
Details about an image in the result from a function in the action group invocation. You can specify images only when the function is a computer use action. For more information, see Configure an Amazon Bedrock Agent to complete tasks with computer use tools.
format (string) -- [REQUIRED]
The type of image in the result.
source (dict) -- [REQUIRED]
The source of the image in the result.
bytes (bytes) --
The raw image bytes for the image. If you use an Amazon Web Services SDK, you don't need to encode the image bytes in base64.
responseState (string) --
Controls the final response state returned to end user when API/Function execution failed. When this state is FAILURE, the request would fail with dependency failure exception. When this state is REPROMPT, the API/function response will be sent to model for re-prompt
functionResult (dict) --
The result from the function from the action group invocation.
actionGroup (string) -- [REQUIRED]
The action group that the function belongs to.
agentId (string) --
The agent's ID.
confirmationState (string) --
Contains the user confirmation information about the function that was called.
function (string) --
The name of the function that was called.
responseBody (dict) --
The response from the function call using the parameters. The response might be returned directly or from the Lambda function. Specify TEXT or IMAGES. The key of the object is the content type. You can only specify one type. If you specify IMAGES, you can specify only one image. You can specify images only when the function in the returnControlInvocationResults is a computer use action. For more information, see Configure an Amazon Bedrock Agent to complete tasks with computer use tools.
(string) --
(dict) --
Contains the body of the API response.
This data type is used in the following API operations:
In the returnControlInvocationResults field of the InvokeAgent request
body (string) --
The body of the API response.
images (list) --
Lists details, including format and source, for the image in the response from the function call. You can specify only one image and the function in the returnControlInvocationResults must be a computer use action. For more information, see Configure an Amazon Bedrock Agent to complete tasks with computer use tools.
(dict) --
Details about an image in the result from a function in the action group invocation. You can specify images only when the function is a computer use action. For more information, see Configure an Amazon Bedrock Agent to complete tasks with computer use tools.
format (string) -- [REQUIRED]
The type of image in the result.
source (dict) -- [REQUIRED]
The source of the image in the result.
bytes (bytes) --
The raw image bytes for the image. If you use an Amazon Web Services SDK, you don't need to encode the image bytes in base64.
responseState (string) --
Controls the final response state returned to end user when API/Function execution failed. When this state is FAILURE, the request would fail with dependency failure exception. When this state is REPROMPT, the API/function response will be sent to model for re-prompt
sessionAttributes (dict) --
Contains attributes that persist across a session and the values of those attributes.
(string) --
(string) --
string
The prompt text to send to the agent.
string
[REQUIRED]
The instructions that tell the inline agent what it should do and how it should interact with users.
list
Contains information of the knowledge bases to associate with.
(dict) --
Details of the knowledge base associated withe inline agent.
description (string) -- [REQUIRED]
The description of the knowledge base associated with the inline agent.
knowledgeBaseId (string) -- [REQUIRED]
The unique identifier for a knowledge base associated with the inline agent.
retrievalConfiguration (dict) --
The configurations to apply to the knowledge base during query. For more information, see Query configurations.
vectorSearchConfiguration (dict) -- [REQUIRED]
Contains details about how the results from the vector search should be returned. For more information, see Query configurations.
filter (dict) --
Specifies the filters to use on the metadata in the knowledge base data sources before returning results. For more information, see Query configurations.
andAll (list) --
Knowledge base data sources are returned if their metadata attributes fulfill all the filter conditions inside this list.
(dict) --
Specifies the filters to use on the metadata attributes in the knowledge base data sources before returning results. For more information, see Query configurations. See the examples below to see how to use these filters.
This data type is used in the following API operations:
Retrieve request – in the filter field
RetrieveAndGenerate request – in the filter field
equals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value matches the value in this object.
The following example would return data sources with an animal attribute whose value is cat:
"equals": { "key": "animal", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
greaterThan (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is greater than the value in this object.
The following example would return data sources with an year attribute whose value is greater than 1989:
"greaterThan": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
greaterThanOrEquals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is greater than or equal to the value in this object.
The following example would return data sources with an year attribute whose value is greater than or equal to 1989:
"greaterThanOrEquals": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
in (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is in the list specified in the value in this object.
The following example would return data sources with an animal attribute that is either cat or dog:
"in": { "key": "animal", "value": ["cat", "dog"] }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
lessThan (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is less than the value in this object.
The following example would return data sources with an year attribute whose value is less than to 1989.
"lessThan": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
lessThanOrEquals (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is less than or equal to the value in this object.
The following example would return data sources with an year attribute whose value is less than or equal to 1989.
"lessThanOrEquals": { "key": "year", "value": 1989 }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
listContains (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is a list that contains the value as one of its members.
The following example would return data sources with an animals attribute that is a list containing a cat member (for example ["dog", "cat"]).
"listContains": { "key": "animals", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
notEquals (dict) --
Knowledge base data sources are returned when:
It contains a metadata attribute whose name matches the key and whose value doesn't match the value in this object.
The key is not present in the document.
The following example would return data sources that don't contain an animal attribute whose value is cat.
"notEquals": { "key": "animal", "value": "cat" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
notIn (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value isn't in the list specified in the value in this object.
The following example would return data sources whose animal attribute is neither cat nor dog.
"notIn": { "key": "animal", "value": ["cat", "dog"] }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
orAll (list) --
Knowledge base data sources are returned if their metadata attributes fulfill at least one of the filter conditions inside this list.
(dict) --
Specifies the filters to use on the metadata attributes in the knowledge base data sources before returning results. For more information, see Query configurations. See the examples below to see how to use these filters.
This data type is used in the following API operations:
Retrieve request – in the filter field
RetrieveAndGenerate request – in the filter field
startsWith (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value starts with the value in this object. This filter is currently only supported for Amazon OpenSearch Serverless vector stores.
The following example would return data sources with an animal attribute starts with ca (for example, cat or camel).
"startsWith": { "key": "animal", "value": "ca" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
stringContains (dict) --
Knowledge base data sources are returned if they contain a metadata attribute whose name matches the key and whose value is one of the following:
A string that contains the value as a substring. The following example would return data sources with an animal attribute that contains the substring at (for example cat). "stringContains": { "key": "animal", "value": "at" }
A list with a member that contains the value as a substring. The following example would return data sources with an animals attribute that is a list containing a member that contains the substring at (for example ["dog", "cat"]). "stringContains": { "key": "animals", "value": "at" }
key (string) -- [REQUIRED]
The name that the metadata attribute must match.
value (:ref:`document<document>`) -- [REQUIRED]
The value to whcih to compare the value of the metadata attribute.
implicitFilterConfiguration (dict) --
Settings for implicit filtering.
metadataAttributes (list) -- [REQUIRED]
Metadata that can be used in a filter.
(dict) --
Details about a metadata attribute.
description (string) -- [REQUIRED]
The attribute's description.
key (string) -- [REQUIRED]
The attribute's key.
type (string) -- [REQUIRED]
The attribute's type.
modelArn (string) -- [REQUIRED]
The model that generates the filter.
numberOfResults (integer) --
The number of source chunks to retrieve.
overrideSearchType (string) --
By default, Amazon Bedrock decides a search strategy for you. If you're using an Amazon OpenSearch Serverless vector store that contains a filterable text field, you can specify whether to query the knowledge base with a HYBRID search using both vector embeddings and raw text, or SEMANTIC search using only vector embeddings. For other vector store configurations, only SEMANTIC search is available. For more information, see Test a knowledge base.
rerankingConfiguration (dict) --
Contains configurations for reranking the retrieved results. For more information, see Improve the relevance of query responses with a reranker model.
bedrockRerankingConfiguration (dict) --
Contains configurations for an Amazon Bedrock reranker model.
metadataConfiguration (dict) --
Contains configurations for the metadata to use in reranking.
selectionMode (string) -- [REQUIRED]
Specifies whether to consider all metadata when reranking, or only the metadata that you select. If you specify SELECTIVE, include the selectiveModeConfiguration field.
selectiveModeConfiguration (dict) --
Contains configurations for the metadata fields to include or exclude when considering reranking.
fieldsToExclude (list) --
An array of objects, each of which specifies a metadata field to exclude from consideration when reranking.
(dict) --
Contains information for a metadata field to include in or exclude from consideration when reranking.
fieldName (string) -- [REQUIRED]
The name of a metadata field to include in or exclude from consideration when reranking.
fieldsToInclude (list) --
An array of objects, each of which specifies a metadata field to include in consideration when reranking. The remaining metadata fields are ignored.
(dict) --
Contains information for a metadata field to include in or exclude from consideration when reranking.
fieldName (string) -- [REQUIRED]
The name of a metadata field to include in or exclude from consideration when reranking.
modelConfiguration (dict) -- [REQUIRED]
Contains configurations for the reranker model.
additionalModelRequestFields (dict) --
A JSON object whose keys are request fields for the model and whose values are values for those fields.
(string) --
(:ref:`document<document>`) --
modelArn (string) -- [REQUIRED]
The ARN of the reranker model to use.
numberOfRerankedResults (integer) --
The number of results to return after reranking.
type (string) -- [REQUIRED]
The type of reranker model.
string
Specifies the type of orchestration strategy for the agent. This is set to DEFAULT orchestration type, by default.
dict
Configurations for advanced prompts used to override the default prompts to enhance the accuracy of the inline agent.
overrideLambda (string) --
The ARN of the Lambda function to use when parsing the raw foundation model output in parts of the agent sequence. If you specify this field, at least one of the promptConfigurations must contain a parserMode value that is set to OVERRIDDEN. For more information, see Parser Lambda function in Amazon Bedrock Agents.
promptConfigurations (list) -- [REQUIRED]
Contains configurations to override a prompt template in one part of an agent sequence. For more information, see Advanced prompts.
(dict) --
Contains configurations to override a prompt template in one part of an agent sequence. For more information, see Advanced prompts.
additionalModelRequestFields (:ref:`document<document>`) --
If the Converse or ConverseStream operations support the model, additionalModelRequestFields contains additional inference parameters, beyond the base set of inference parameters in the inferenceConfiguration field.
For more information, see Inference request parameters and response fields for foundation models in the Amazon Bedrock user guide.
basePromptTemplate (string) --
Defines the prompt template with which to replace the default prompt template. You can use placeholder variables in the base prompt template to customize the prompt. For more information, see Prompt template placeholder variables. For more information, see Configure the prompt templates.
foundationModel (string) --
The foundation model to use.
inferenceConfiguration (dict) --
Contains inference parameters to use when the agent invokes a foundation model in the part of the agent sequence defined by the promptType. For more information, see Inference parameters for foundation models.
maximumLength (integer) --
The maximum number of tokens allowed in the generated response.
stopSequences (list) --
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
(string) --
temperature (float) --
The likelihood of the model selecting higher-probability options while generating a response. A lower value makes the model more likely to choose higher-probability options, while a higher value makes the model more likely to choose lower-probability options.
topK (integer) --
While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for topK is the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topK to 50, the model selects the next token from among the top 50 most likely choices.
topP (float) --
While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for Top P determines the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topP to 0.8, the model only selects the next token from the top 80% of the probability distribution of next tokens.
parserMode (string) --
Specifies whether to override the default parser Lambda function when parsing the raw foundation model output in the part of the agent sequence defined by the promptType. If you set the field as OVERRIDDEN, the overrideLambda field in the PromptOverrideConfiguration must be specified with the ARN of a Lambda function.
promptCreationMode (string) --
Specifies whether to override the default prompt template for this promptType. Set this value to OVERRIDDEN to use the prompt that you provide in the basePromptTemplate. If you leave it as DEFAULT, the agent uses a default prompt template.
promptState (string) --
Specifies whether to allow the inline agent to carry out the step specified in the promptType. If you set this value to DISABLED, the agent skips that step. The default state for each promptType is as follows.
PRE_PROCESSING – ENABLED
ORCHESTRATION – ENABLED
KNOWLEDGE_BASE_RESPONSE_GENERATION – ENABLED
POST_PROCESSING – DISABLED
promptType (string) --
The step in the agent sequence that this prompt configuration applies to.
string
[REQUIRED]
The unique identifier of the session. Use the same value across requests to continue the same conversation.
dict
Specifies the configurations for streaming.
applyGuardrailInterval (integer) --
The guardrail interval to apply as response is generated. By default, the guardrail interval is set to 50 characters. If a larger interval is specified, the response will be generated in larger chunks with fewer ApplyGuardrail calls. The following examples show the response generated for Hello, I am an agent input string.
Example response in chunks: Interval set to 3 characters
'Hel', 'lo, ','I am', ' an', ' Age', 'nt'
Each chunk has at least 3 characters except for the last chunk
Example response in chunks: Interval set to 20 or more characters
Hello, I am an Agent
streamFinalResponse (boolean) --
Specifies whether to enable streaming for the final response. This is set to false by default.
dict
The response of this operation contains an :class:`.EventStream` member. When iterated the :class:`.EventStream` will yield events based on the structure below, where only one of the top level keys will be present for any given event.
Response Syntax
{
'completion': EventStream({
'accessDeniedException': {
'message': 'string'
},
'badGatewayException': {
'message': 'string',
'resourceName': 'string'
},
'chunk': {
'attribution': {
'citations': [
{
'generatedResponsePart': {
'textResponsePart': {
'span': {
'end': 123,
'start': 123
},
'text': 'string'
}
},
'retrievedReferences': [
{
'content': {
'byteContent': 'string',
'row': [
{
'columnName': 'string',
'columnValue': 'string',
'type': 'BLOB'|'BOOLEAN'|'DOUBLE'|'NULL'|'LONG'|'STRING'
},
],
'text': 'string',
'type': 'TEXT'|'IMAGE'|'ROW'
},
'location': {
'confluenceLocation': {
'url': 'string'
},
'customDocumentLocation': {
'id': 'string'
},
'kendraDocumentLocation': {
'uri': 'string'
},
's3Location': {
'uri': 'string'
},
'salesforceLocation': {
'url': 'string'
},
'sharePointLocation': {
'url': 'string'
},
'sqlLocation': {
'query': 'string'
},
'type': 'S3'|'WEB'|'CONFLUENCE'|'SALESFORCE'|'SHAREPOINT'|'CUSTOM'|'KENDRA'|'SQL',
'webLocation': {
'url': 'string'
}
},
'metadata': {
'string': {...}|[...]|123|123.4|'string'|True|None
}
},
]
},
]
},
'bytes': b'bytes'
},
'conflictException': {
'message': 'string'
},
'dependencyFailedException': {
'message': 'string',
'resourceName': 'string'
},
'files': {
'files': [
{
'bytes': b'bytes',
'name': 'string',
'type': 'string'
},
]
},
'internalServerException': {
'message': 'string',
'reason': 'string'
},
'resourceNotFoundException': {
'message': 'string'
},
'returnControl': {
'invocationId': 'string',
'invocationInputs': [
{
'apiInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'apiPath': 'string',
'collaboratorName': 'string',
'httpMethod': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
],
'requestBody': {
'content': {
'string': {
'properties': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
}
}
},
'functionInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'collaboratorName': 'string',
'function': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
},
]
},
'serviceQuotaExceededException': {
'message': 'string'
},
'throttlingException': {
'message': 'string'
},
'trace': {
'callerChain': [
{
'agentAliasArn': 'string'
},
],
'collaboratorName': 'string',
'eventTime': datetime(2015, 1, 1),
'sessionId': 'string',
'trace': {
'customOrchestrationTrace': {
'event': {
'text': 'string'
},
'traceId': 'string'
},
'failureTrace': {
'failureReason': 'string',
'traceId': 'string'
},
'guardrailTrace': {
'action': 'INTERVENED'|'NONE',
'inputAssessments': [
{
'contentPolicy': {
'filters': [
{
'action': 'BLOCKED',
'confidence': 'NONE'|'LOW'|'MEDIUM'|'HIGH',
'type': 'INSULTS'|'HATE'|'SEXUAL'|'VIOLENCE'|'MISCONDUCT'|'PROMPT_ATTACK'
},
]
},
'sensitiveInformationPolicy': {
'piiEntities': [
{
'action': 'BLOCKED'|'ANONYMIZED',
'match': 'string',
'type': 'ADDRESS'|'AGE'|'AWS_ACCESS_KEY'|'AWS_SECRET_KEY'|'CA_HEALTH_NUMBER'|'CA_SOCIAL_INSURANCE_NUMBER'|'CREDIT_DEBIT_CARD_CVV'|'CREDIT_DEBIT_CARD_EXPIRY'|'CREDIT_DEBIT_CARD_NUMBER'|'DRIVER_ID'|'EMAIL'|'INTERNATIONAL_BANK_ACCOUNT_NUMBER'|'IP_ADDRESS'|'LICENSE_PLATE'|'MAC_ADDRESS'|'NAME'|'PASSWORD'|'PHONE'|'PIN'|'SWIFT_CODE'|'UK_NATIONAL_HEALTH_SERVICE_NUMBER'|'UK_NATIONAL_INSURANCE_NUMBER'|'UK_UNIQUE_TAXPAYER_REFERENCE_NUMBER'|'URL'|'USERNAME'|'US_BANK_ACCOUNT_NUMBER'|'US_BANK_ROUTING_NUMBER'|'US_INDIVIDUAL_TAX_IDENTIFICATION_NUMBER'|'US_PASSPORT_NUMBER'|'US_SOCIAL_SECURITY_NUMBER'|'VEHICLE_IDENTIFICATION_NUMBER'
},
],
'regexes': [
{
'action': 'BLOCKED'|'ANONYMIZED',
'match': 'string',
'name': 'string',
'regex': 'string'
},
]
},
'topicPolicy': {
'topics': [
{
'action': 'BLOCKED',
'name': 'string',
'type': 'DENY'
},
]
},
'wordPolicy': {
'customWords': [
{
'action': 'BLOCKED',
'match': 'string'
},
],
'managedWordLists': [
{
'action': 'BLOCKED',
'match': 'string',
'type': 'PROFANITY'
},
]
}
},
],
'outputAssessments': [
{
'contentPolicy': {
'filters': [
{
'action': 'BLOCKED',
'confidence': 'NONE'|'LOW'|'MEDIUM'|'HIGH',
'type': 'INSULTS'|'HATE'|'SEXUAL'|'VIOLENCE'|'MISCONDUCT'|'PROMPT_ATTACK'
},
]
},
'sensitiveInformationPolicy': {
'piiEntities': [
{
'action': 'BLOCKED'|'ANONYMIZED',
'match': 'string',
'type': 'ADDRESS'|'AGE'|'AWS_ACCESS_KEY'|'AWS_SECRET_KEY'|'CA_HEALTH_NUMBER'|'CA_SOCIAL_INSURANCE_NUMBER'|'CREDIT_DEBIT_CARD_CVV'|'CREDIT_DEBIT_CARD_EXPIRY'|'CREDIT_DEBIT_CARD_NUMBER'|'DRIVER_ID'|'EMAIL'|'INTERNATIONAL_BANK_ACCOUNT_NUMBER'|'IP_ADDRESS'|'LICENSE_PLATE'|'MAC_ADDRESS'|'NAME'|'PASSWORD'|'PHONE'|'PIN'|'SWIFT_CODE'|'UK_NATIONAL_HEALTH_SERVICE_NUMBER'|'UK_NATIONAL_INSURANCE_NUMBER'|'UK_UNIQUE_TAXPAYER_REFERENCE_NUMBER'|'URL'|'USERNAME'|'US_BANK_ACCOUNT_NUMBER'|'US_BANK_ROUTING_NUMBER'|'US_INDIVIDUAL_TAX_IDENTIFICATION_NUMBER'|'US_PASSPORT_NUMBER'|'US_SOCIAL_SECURITY_NUMBER'|'VEHICLE_IDENTIFICATION_NUMBER'
},
],
'regexes': [
{
'action': 'BLOCKED'|'ANONYMIZED',
'match': 'string',
'name': 'string',
'regex': 'string'
},
]
},
'topicPolicy': {
'topics': [
{
'action': 'BLOCKED',
'name': 'string',
'type': 'DENY'
},
]
},
'wordPolicy': {
'customWords': [
{
'action': 'BLOCKED',
'match': 'string'
},
],
'managedWordLists': [
{
'action': 'BLOCKED',
'match': 'string',
'type': 'PROFANITY'
},
]
}
},
],
'traceId': 'string'
},
'orchestrationTrace': {
'invocationInput': {
'actionGroupInvocationInput': {
'actionGroupName': 'string',
'apiPath': 'string',
'executionType': 'LAMBDA'|'RETURN_CONTROL',
'function': 'string',
'invocationId': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
],
'requestBody': {
'content': {
'string': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
},
'verb': 'string'
},
'agentCollaboratorInvocationInput': {
'agentCollaboratorAliasArn': 'string',
'agentCollaboratorName': 'string',
'input': {
'returnControlResults': {
'invocationId': 'string',
'returnControlInvocationResults': [
{
'apiResult': {
'actionGroup': 'string',
'agentId': 'string',
'apiPath': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'httpMethod': 'string',
'httpStatusCode': 123,
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
},
'functionResult': {
'actionGroup': 'string',
'agentId': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'function': 'string',
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
}
},
]
},
'text': 'string',
'type': 'TEXT'|'RETURN_CONTROL'
}
},
'codeInterpreterInvocationInput': {
'code': 'string',
'files': [
'string',
]
},
'invocationType': 'ACTION_GROUP'|'KNOWLEDGE_BASE'|'FINISH'|'ACTION_GROUP_CODE_INTERPRETER'|'AGENT_COLLABORATOR',
'knowledgeBaseLookupInput': {
'knowledgeBaseId': 'string',
'text': 'string'
},
'traceId': 'string'
},
'modelInvocationInput': {
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'overrideLambda': 'string',
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'text': 'string',
'traceId': 'string',
'type': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
'modelInvocationOutput': {
'metadata': {
'usage': {
'inputTokens': 123,
'outputTokens': 123
}
},
'rawResponse': {
'content': 'string'
},
'reasoningContent': {
'reasoningText': {
'signature': 'string',
'text': 'string'
},
'redactedContent': b'bytes'
},
'traceId': 'string'
},
'observation': {
'actionGroupInvocationOutput': {
'text': 'string'
},
'agentCollaboratorInvocationOutput': {
'agentCollaboratorAliasArn': 'string',
'agentCollaboratorName': 'string',
'output': {
'returnControlPayload': {
'invocationId': 'string',
'invocationInputs': [
{
'apiInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'apiPath': 'string',
'collaboratorName': 'string',
'httpMethod': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
],
'requestBody': {
'content': {
'string': {
'properties': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
}
}
},
'functionInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'collaboratorName': 'string',
'function': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
},
]
},
'text': 'string',
'type': 'TEXT'|'RETURN_CONTROL'
}
},
'codeInterpreterInvocationOutput': {
'executionError': 'string',
'executionOutput': 'string',
'executionTimeout': True|False,
'files': [
'string',
]
},
'finalResponse': {
'text': 'string'
},
'knowledgeBaseLookupOutput': {
'retrievedReferences': [
{
'content': {
'byteContent': 'string',
'row': [
{
'columnName': 'string',
'columnValue': 'string',
'type': 'BLOB'|'BOOLEAN'|'DOUBLE'|'NULL'|'LONG'|'STRING'
},
],
'text': 'string',
'type': 'TEXT'|'IMAGE'|'ROW'
},
'location': {
'confluenceLocation': {
'url': 'string'
},
'customDocumentLocation': {
'id': 'string'
},
'kendraDocumentLocation': {
'uri': 'string'
},
's3Location': {
'uri': 'string'
},
'salesforceLocation': {
'url': 'string'
},
'sharePointLocation': {
'url': 'string'
},
'sqlLocation': {
'query': 'string'
},
'type': 'S3'|'WEB'|'CONFLUENCE'|'SALESFORCE'|'SHAREPOINT'|'CUSTOM'|'KENDRA'|'SQL',
'webLocation': {
'url': 'string'
}
},
'metadata': {
'string': {...}|[...]|123|123.4|'string'|True|None
}
},
]
},
'repromptResponse': {
'source': 'ACTION_GROUP'|'KNOWLEDGE_BASE'|'PARSER',
'text': 'string'
},
'traceId': 'string',
'type': 'ACTION_GROUP'|'AGENT_COLLABORATOR'|'KNOWLEDGE_BASE'|'FINISH'|'ASK_USER'|'REPROMPT'
},
'rationale': {
'text': 'string',
'traceId': 'string'
}
},
'postProcessingTrace': {
'modelInvocationInput': {
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'overrideLambda': 'string',
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'text': 'string',
'traceId': 'string',
'type': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
'modelInvocationOutput': {
'metadata': {
'usage': {
'inputTokens': 123,
'outputTokens': 123
}
},
'parsedResponse': {
'text': 'string'
},
'rawResponse': {
'content': 'string'
},
'reasoningContent': {
'reasoningText': {
'signature': 'string',
'text': 'string'
},
'redactedContent': b'bytes'
},
'traceId': 'string'
}
},
'preProcessingTrace': {
'modelInvocationInput': {
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'overrideLambda': 'string',
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'text': 'string',
'traceId': 'string',
'type': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
'modelInvocationOutput': {
'metadata': {
'usage': {
'inputTokens': 123,
'outputTokens': 123
}
},
'parsedResponse': {
'isValid': True|False,
'rationale': 'string'
},
'rawResponse': {
'content': 'string'
},
'reasoningContent': {
'reasoningText': {
'signature': 'string',
'text': 'string'
},
'redactedContent': b'bytes'
},
'traceId': 'string'
}
},
'routingClassifierTrace': {
'invocationInput': {
'actionGroupInvocationInput': {
'actionGroupName': 'string',
'apiPath': 'string',
'executionType': 'LAMBDA'|'RETURN_CONTROL',
'function': 'string',
'invocationId': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
],
'requestBody': {
'content': {
'string': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
},
'verb': 'string'
},
'agentCollaboratorInvocationInput': {
'agentCollaboratorAliasArn': 'string',
'agentCollaboratorName': 'string',
'input': {
'returnControlResults': {
'invocationId': 'string',
'returnControlInvocationResults': [
{
'apiResult': {
'actionGroup': 'string',
'agentId': 'string',
'apiPath': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'httpMethod': 'string',
'httpStatusCode': 123,
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
},
'functionResult': {
'actionGroup': 'string',
'agentId': 'string',
'confirmationState': 'CONFIRM'|'DENY',
'function': 'string',
'responseBody': {
'string': {
'body': 'string',
'images': [
{
'format': 'png'|'jpeg'|'gif'|'webp',
'source': {
'bytes': b'bytes'
}
},
]
}
},
'responseState': 'FAILURE'|'REPROMPT'
}
},
]
},
'text': 'string',
'type': 'TEXT'|'RETURN_CONTROL'
}
},
'codeInterpreterInvocationInput': {
'code': 'string',
'files': [
'string',
]
},
'invocationType': 'ACTION_GROUP'|'KNOWLEDGE_BASE'|'FINISH'|'ACTION_GROUP_CODE_INTERPRETER'|'AGENT_COLLABORATOR',
'knowledgeBaseLookupInput': {
'knowledgeBaseId': 'string',
'text': 'string'
},
'traceId': 'string'
},
'modelInvocationInput': {
'foundationModel': 'string',
'inferenceConfiguration': {
'maximumLength': 123,
'stopSequences': [
'string',
],
'temperature': ...,
'topK': 123,
'topP': ...
},
'overrideLambda': 'string',
'parserMode': 'DEFAULT'|'OVERRIDDEN',
'promptCreationMode': 'DEFAULT'|'OVERRIDDEN',
'text': 'string',
'traceId': 'string',
'type': 'PRE_PROCESSING'|'ORCHESTRATION'|'KNOWLEDGE_BASE_RESPONSE_GENERATION'|'POST_PROCESSING'|'ROUTING_CLASSIFIER'
},
'modelInvocationOutput': {
'metadata': {
'usage': {
'inputTokens': 123,
'outputTokens': 123
}
},
'rawResponse': {
'content': 'string'
},
'traceId': 'string'
},
'observation': {
'actionGroupInvocationOutput': {
'text': 'string'
},
'agentCollaboratorInvocationOutput': {
'agentCollaboratorAliasArn': 'string',
'agentCollaboratorName': 'string',
'output': {
'returnControlPayload': {
'invocationId': 'string',
'invocationInputs': [
{
'apiInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'apiPath': 'string',
'collaboratorName': 'string',
'httpMethod': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
],
'requestBody': {
'content': {
'string': {
'properties': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
}
}
},
'functionInvocationInput': {
'actionGroup': 'string',
'actionInvocationType': 'RESULT'|'USER_CONFIRMATION'|'USER_CONFIRMATION_AND_RESULT',
'agentId': 'string',
'collaboratorName': 'string',
'function': 'string',
'parameters': [
{
'name': 'string',
'type': 'string',
'value': 'string'
},
]
}
},
]
},
'text': 'string',
'type': 'TEXT'|'RETURN_CONTROL'
}
},
'codeInterpreterInvocationOutput': {
'executionError': 'string',
'executionOutput': 'string',
'executionTimeout': True|False,
'files': [
'string',
]
},
'finalResponse': {
'text': 'string'
},
'knowledgeBaseLookupOutput': {
'retrievedReferences': [
{
'content': {
'byteContent': 'string',
'row': [
{
'columnName': 'string',
'columnValue': 'string',
'type': 'BLOB'|'BOOLEAN'|'DOUBLE'|'NULL'|'LONG'|'STRING'
},
],
'text': 'string',
'type': 'TEXT'|'IMAGE'|'ROW'
},
'location': {
'confluenceLocation': {
'url': 'string'
},
'customDocumentLocation': {
'id': 'string'
},
'kendraDocumentLocation': {
'uri': 'string'
},
's3Location': {
'uri': 'string'
},
'salesforceLocation': {
'url': 'string'
},
'sharePointLocation': {
'url': 'string'
},
'sqlLocation': {
'query': 'string'
},
'type': 'S3'|'WEB'|'CONFLUENCE'|'SALESFORCE'|'SHAREPOINT'|'CUSTOM'|'KENDRA'|'SQL',
'webLocation': {
'url': 'string'
}
},
'metadata': {
'string': {...}|[...]|123|123.4|'string'|True|None
}
},
]
},
'repromptResponse': {
'source': 'ACTION_GROUP'|'KNOWLEDGE_BASE'|'PARSER',
'text': 'string'
},
'traceId': 'string',
'type': 'ACTION_GROUP'|'AGENT_COLLABORATOR'|'KNOWLEDGE_BASE'|'FINISH'|'ASK_USER'|'REPROMPT'
}
}
}
},
'validationException': {
'message': 'string'
}
}),
'contentType': 'string',
'sessionId': 'string'
}
**Response Structure**
::
# This section is too large to render.
# Please see the AWS API Documentation linked below.
`AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/bedrock-agent-runtime-2023-07-26/InvokeInlineAgent>`_