Update Flow
| bedrockagent_update_flow | R Documentation |
Modifies a flow¶
Description¶
Modifies a flow. Include both fields that you want to keep and fields that you want to change. For more information, see How it works and Create a flow in Amazon Bedrock in the Amazon Bedrock User Guide.
Usage¶
bedrockagent_update_flow(name, description, executionRoleArn,
customerEncryptionKeyArn, definition, flowIdentifier)
Arguments¶
name |
[required] A name for the flow. |
description |
A description for the flow. |
executionRoleArn |
[required] The Amazon Resource Name (ARN) of the service role with permissions to create and manage a flow. For more information, see Create a service role for flows in Amazon Bedrock in the Amazon Bedrock User Guide. |
customerEncryptionKeyArn |
The Amazon Resource Name (ARN) of the KMS key to encrypt the flow. |
definition |
A definition of the nodes and the connections between the nodes in the flow. |
flowIdentifier |
[required] The unique identifier of the flow. |
Value¶
A list with the following syntax:
list(
name = "string",
description = "string",
executionRoleArn = "string",
customerEncryptionKeyArn = "string",
id = "string",
arn = "string",
status = "Failed"|"Prepared"|"Preparing"|"NotPrepared",
createdAt = as.POSIXct(
"2015-01-01"
),
updatedAt = as.POSIXct(
"2015-01-01"
),
version = "string",
definition = list(
nodes = list(
list(
name = "string",
type = "Input"|"Output"|"KnowledgeBase"|"Condition"|"Lex"|"Prompt"|"LambdaFunction"|"Storage"|"Agent"|"Retrieval"|"Iterator"|"Collector"|"InlineCode"|"Loop"|"LoopInput"|"LoopController",
configuration = list(
input = list(),
output = list(),
knowledgeBase = list(
knowledgeBaseId = "string",
modelId = "string",
guardrailConfiguration = list(
guardrailIdentifier = "string",
guardrailVersion = "string"
),
numberOfResults = 123,
promptTemplate = list(
textPromptTemplate = "string"
),
inferenceConfiguration = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
rerankingConfiguration = list(
type = "BEDROCK_RERANKING_MODEL",
bedrockRerankingConfiguration = list(
modelConfiguration = list(
modelArn = "string",
additionalModelRequestFields = list(
list()
)
),
numberOfRerankedResults = 123,
metadataConfiguration = list(
selectionMode = "SELECTIVE"|"ALL",
selectiveModeConfiguration = list(
fieldsToInclude = list(
list(
fieldName = "string"
)
),
fieldsToExclude = list(
list(
fieldName = "string"
)
)
)
)
)
),
orchestrationConfiguration = list(
promptTemplate = list(
textPromptTemplate = "string"
),
inferenceConfig = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list(
list()
),
performanceConfig = list(
latency = "standard"|"optimized"
)
)
),
condition = list(
conditions = list(
list(
name = "string",
expression = "string"
)
)
),
lex = list(
botAliasArn = "string",
localeId = "string"
),
prompt = list(
sourceConfiguration = list(
resource = list(
promptArn = "string"
),
inline = list(
templateType = "TEXT"|"CHAT",
templateConfiguration = list(
text = list(
text = "string",
cachePoint = list(
type = "default"
),
inputVariables = list(
list(
name = "string"
)
)
),
chat = list(
messages = list(
list(
role = "user"|"assistant",
content = list(
list(
text = "string",
cachePoint = list(
type = "default"
)
)
)
)
),
system = list(
list(
text = "string",
cachePoint = list(
type = "default"
)
)
),
inputVariables = list(
list(
name = "string"
)
),
toolConfiguration = list(
tools = list(
list(
toolSpec = list(
name = "string",
description = "string",
inputSchema = list(
json = list()
),
strict = TRUE|FALSE
),
cachePoint = list(
type = "default"
)
)
),
toolChoice = list(
auto = list(),
any = list(),
tool = list(
name = "string"
)
)
)
)
),
modelId = "string",
inferenceConfiguration = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list()
)
),
guardrailConfiguration = list(
guardrailIdentifier = "string",
guardrailVersion = "string"
)
),
lambdaFunction = list(
lambdaArn = "string"
),
storage = list(
serviceConfiguration = list(
s3 = list(
bucketName = "string"
)
)
),
agent = list(
agentAliasArn = "string"
),
retrieval = list(
serviceConfiguration = list(
s3 = list(
bucketName = "string"
)
)
),
iterator = list(),
collector = list(),
inlineCode = list(
code = "string",
language = "Python_3"
),
loop = list(
definition = list()
),
loopInput = list(),
loopController = list(
continueCondition = list(
name = "string",
expression = "string"
),
maxIterations = 123
)
),
inputs = list(
list(
name = "string",
type = "String"|"Number"|"Boolean"|"Object"|"Array",
expression = "string",
category = "LoopCondition"|"ReturnValueToLoopStart"|"ExitLoop"
)
),
outputs = list(
list(
name = "string",
type = "String"|"Number"|"Boolean"|"Object"|"Array"
)
)
)
),
connections = list(
list(
type = "Data"|"Conditional",
name = "string",
source = "string",
target = "string",
configuration = list(
data = list(
sourceOutput = "string",
targetInput = "string"
),
conditional = list(
condition = "string"
)
)
)
)
)
)
Request syntax¶
svc$update_flow(
name = "string",
description = "string",
executionRoleArn = "string",
customerEncryptionKeyArn = "string",
definition = list(
nodes = list(
list(
name = "string",
type = "Input"|"Output"|"KnowledgeBase"|"Condition"|"Lex"|"Prompt"|"LambdaFunction"|"Storage"|"Agent"|"Retrieval"|"Iterator"|"Collector"|"InlineCode"|"Loop"|"LoopInput"|"LoopController",
configuration = list(
input = list(),
output = list(),
knowledgeBase = list(
knowledgeBaseId = "string",
modelId = "string",
guardrailConfiguration = list(
guardrailIdentifier = "string",
guardrailVersion = "string"
),
numberOfResults = 123,
promptTemplate = list(
textPromptTemplate = "string"
),
inferenceConfiguration = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
rerankingConfiguration = list(
type = "BEDROCK_RERANKING_MODEL",
bedrockRerankingConfiguration = list(
modelConfiguration = list(
modelArn = "string",
additionalModelRequestFields = list(
list()
)
),
numberOfRerankedResults = 123,
metadataConfiguration = list(
selectionMode = "SELECTIVE"|"ALL",
selectiveModeConfiguration = list(
fieldsToInclude = list(
list(
fieldName = "string"
)
),
fieldsToExclude = list(
list(
fieldName = "string"
)
)
)
)
)
),
orchestrationConfiguration = list(
promptTemplate = list(
textPromptTemplate = "string"
),
inferenceConfig = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list(
list()
),
performanceConfig = list(
latency = "standard"|"optimized"
)
)
),
condition = list(
conditions = list(
list(
name = "string",
expression = "string"
)
)
),
lex = list(
botAliasArn = "string",
localeId = "string"
),
prompt = list(
sourceConfiguration = list(
resource = list(
promptArn = "string"
),
inline = list(
templateType = "TEXT"|"CHAT",
templateConfiguration = list(
text = list(
text = "string",
cachePoint = list(
type = "default"
),
inputVariables = list(
list(
name = "string"
)
)
),
chat = list(
messages = list(
list(
role = "user"|"assistant",
content = list(
list(
text = "string",
cachePoint = list(
type = "default"
)
)
)
)
),
system = list(
list(
text = "string",
cachePoint = list(
type = "default"
)
)
),
inputVariables = list(
list(
name = "string"
)
),
toolConfiguration = list(
tools = list(
list(
toolSpec = list(
name = "string",
description = "string",
inputSchema = list(
json = list()
),
strict = TRUE|FALSE
),
cachePoint = list(
type = "default"
)
)
),
toolChoice = list(
auto = list(),
any = list(),
tool = list(
name = "string"
)
)
)
)
),
modelId = "string",
inferenceConfiguration = list(
text = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list()
)
),
guardrailConfiguration = list(
guardrailIdentifier = "string",
guardrailVersion = "string"
)
),
lambdaFunction = list(
lambdaArn = "string"
),
storage = list(
serviceConfiguration = list(
s3 = list(
bucketName = "string"
)
)
),
agent = list(
agentAliasArn = "string"
),
retrieval = list(
serviceConfiguration = list(
s3 = list(
bucketName = "string"
)
)
),
iterator = list(),
collector = list(),
inlineCode = list(
code = "string",
language = "Python_3"
),
loop = list(
definition = list()
),
loopInput = list(),
loopController = list(
continueCondition = list(
name = "string",
expression = "string"
),
maxIterations = 123
)
),
inputs = list(
list(
name = "string",
type = "String"|"Number"|"Boolean"|"Object"|"Array",
expression = "string",
category = "LoopCondition"|"ReturnValueToLoopStart"|"ExitLoop"
)
),
outputs = list(
list(
name = "string",
type = "String"|"Number"|"Boolean"|"Object"|"Array"
)
)
)
),
connections = list(
list(
type = "Data"|"Conditional",
name = "string",
source = "string",
target = "string",
configuration = list(
data = list(
sourceOutput = "string",
targetInput = "string"
),
conditional = list(
condition = "string"
)
)
)
)
),
flowIdentifier = "string"
)