Get Evaluation Job
| bedrock_get_evaluation_job | R Documentation |
Gets information about an evaluation job, such as the status of the job¶
Description¶
Gets information about an evaluation job, such as the status of the job.
Usage¶
Arguments¶
jobIdentifier[required] The Amazon Resource Name (ARN) of the evaluation job you want get information on.
Value¶
A list with the following syntax:
list(
jobName = "string",
status = "InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|"Deleting",
jobArn = "string",
jobDescription = "string",
roleArn = "string",
customerEncryptionKeyId = "string",
jobType = "Human"|"Automated",
applicationType = "ModelEvaluation"|"RagEvaluation",
evaluationConfig = list(
automated = list(
datasetMetricConfigs = list(
list(
taskType = "Summarization"|"Classification"|"QuestionAndAnswer"|"Generation"|"Custom",
dataset = list(
name = "string",
datasetLocation = list(
s3Uri = "string"
)
),
metricNames = list(
"string"
)
)
),
evaluatorModelConfig = list(
bedrockEvaluatorModels = list(
list(
modelIdentifier = "string"
)
)
)
),
human = list(
humanWorkflowConfig = list(
flowDefinitionArn = "string",
instructions = "string"
),
customMetrics = list(
list(
name = "string",
description = "string",
ratingMethod = "string"
)
),
datasetMetricConfigs = list(
list(
taskType = "Summarization"|"Classification"|"QuestionAndAnswer"|"Generation"|"Custom",
dataset = list(
name = "string",
datasetLocation = list(
s3Uri = "string"
)
),
metricNames = list(
"string"
)
)
)
)
),
inferenceConfig = list(
models = list(
list(
bedrockModel = list(
modelIdentifier = "string",
inferenceParams = "string",
performanceConfig = list(
latency = "standard"|"optimized"
)
)
)
),
ragConfigs = list(
list(
knowledgeBaseConfig = list(
retrieveConfig = list(
knowledgeBaseId = "string",
knowledgeBaseRetrievalConfiguration = list(
vectorSearchConfiguration = list(
numberOfResults = 123,
overrideSearchType = "HYBRID"|"SEMANTIC",
filter = list(
equals = list(
key = "string",
value = list()
),
notEquals = list(
key = "string",
value = list()
),
greaterThan = list(
key = "string",
value = list()
),
greaterThanOrEquals = list(
key = "string",
value = list()
),
lessThan = list(
key = "string",
value = list()
),
lessThanOrEquals = list(
key = "string",
value = list()
),
in = list(
key = "string",
value = list()
),
notIn = list(
key = "string",
value = list()
),
startsWith = list(
key = "string",
value = list()
),
listContains = list(
key = "string",
value = list()
),
stringContains = list(
key = "string",
value = list()
),
andAll = list(
list()
),
orAll = list(
list()
)
)
)
)
),
retrieveAndGenerateConfig = list(
type = "KNOWLEDGE_BASE"|"EXTERNAL_SOURCES",
knowledgeBaseConfiguration = list(
knowledgeBaseId = "string",
modelArn = "string",
retrievalConfiguration = list(
vectorSearchConfiguration = list(
numberOfResults = 123,
overrideSearchType = "HYBRID"|"SEMANTIC",
filter = list(
equals = list(
key = "string",
value = list()
),
notEquals = list(
key = "string",
value = list()
),
greaterThan = list(
key = "string",
value = list()
),
greaterThanOrEquals = list(
key = "string",
value = list()
),
lessThan = list(
key = "string",
value = list()
),
lessThanOrEquals = list(
key = "string",
value = list()
),
in = list(
key = "string",
value = list()
),
notIn = list(
key = "string",
value = list()
),
startsWith = list(
key = "string",
value = list()
),
listContains = list(
key = "string",
value = list()
),
stringContains = list(
key = "string",
value = list()
),
andAll = list(
list()
),
orAll = list(
list()
)
)
)
),
generationConfiguration = list(
promptTemplate = list(
textPromptTemplate = "string"
),
guardrailConfiguration = list(
guardrailId = "string",
guardrailVersion = "string"
),
kbInferenceConfig = list(
textInferenceConfig = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list(
list()
)
),
orchestrationConfiguration = list(
queryTransformationConfiguration = list(
type = "QUERY_DECOMPOSITION"
)
)
),
externalSourcesConfiguration = list(
modelArn = "string",
sources = list(
list(
sourceType = "S3"|"BYTE_CONTENT",
s3Location = list(
uri = "string"
),
byteContent = list(
identifier = "string",
contentType = "string",
data = raw
)
)
),
generationConfiguration = list(
promptTemplate = list(
textPromptTemplate = "string"
),
guardrailConfiguration = list(
guardrailId = "string",
guardrailVersion = "string"
),
kbInferenceConfig = list(
textInferenceConfig = list(
temperature = 123.0,
topP = 123.0,
maxTokens = 123,
stopSequences = list(
"string"
)
)
),
additionalModelRequestFields = list(
list()
)
)
)
)
)
)
)
),
outputDataConfig = list(
s3Uri = "string"
),
creationTime = as.POSIXct(
"2015-01-01"
),
lastModifiedTime = as.POSIXct(
"2015-01-01"
),
failureMessages = list(
"string"
)
)