--- title: Getting Started with Datadog description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog --- # aws_bedrock_evaluation_job{% #aws_bedrock_evaluation_job %} ## `account_id`{% #account_id %} **Type**: `STRING` ## `application_type`{% #application_type %} **Type**: `STRING`**Provider name**: `applicationType`**Description**: Specifies whether the evaluation job is for evaluating a model or evaluating a knowledge base (retrieval and response generation). ## `creation_time`{% #creation_time %} **Type**: `TIMESTAMP`**Provider name**: `creationTime`**Description**: The time the evaluation job was created. ## `customer_encryption_key_id`{% #customer_encryption_key_id %} **Type**: `STRING`**Provider name**: `customerEncryptionKeyId`**Description**: The Amazon Resource Name (ARN) of the customer managed encryption key specified when the evaluation job was created. ## `evaluation_config`{% #evaluation_config %} **Type**: `STRUCT`**Provider name**: `evaluationConfig`**Description**: Contains the configuration details of either an automated or human-based evaluation job. - `automated`**Type**: `STRUCT`**Provider name**: `automated`**Description**: Contains the configuration details of an automated evaluation job that computes metrics. - `custom_metric_config`**Type**: `STRUCT`**Provider name**: `customMetricConfig`**Description**: Defines the configuration of custom metrics to be used in an evaluation job. - `custom_metrics`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `customMetrics`**Description**: Defines a list of custom metrics to be used in an Amazon Bedrock evaluation job. - `custom_metric_definition`**Type**: `STRUCT`**Provider name**: `customMetricDefinition`**Description**: The definition of a custom metric for use in an Amazon Bedrock evaluation job. - `instructions`**Type**: `STRING`**Provider name**: `instructions`**Description**: The prompt for a custom metric that instructs the evaluator model how to rate the model or RAG source under evaluation. - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name for a custom metric. Names must be unique in your Amazon Web Services region. - `rating_scale`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ratingScale`**Description**: Defines the rating scale to be used for a custom metric. We recommend that you always define a ratings scale when creating a custom metric. If you don't define a scale, Amazon Bedrock won't be able to visually display the results of the evaluation in the console or calculate average values of numerical scores. For more information on specifying a rating scale, see [Specifying an output schema (rating scale)](https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-custom-metrics-prompt-formats.html#model-evaluation-custom-metrics-prompt-formats-schema). - `definition`**Type**: `STRING`**Provider name**: `definition`**Description**: Defines the definition for one rating in a custom metric rating scale. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: Defines the value for one rating in a custom metric rating scale. - `float_value`**Type**: `FLOAT`**Provider name**: `floatValue`**Description**: A floating point number representing the value for a rating in a custom metric rating scale. - `string_value`**Type**: `STRING`**Provider name**: `stringValue`**Description**: A string representing the value for a rating in a custom metric rating scale. - `evaluator_model_config`**Type**: `STRUCT`**Provider name**: `evaluatorModelConfig`**Description**: Configuration of the evaluator model you want to use to evaluate custom metrics in an Amazon Bedrock evaluation job. - `bedrock_evaluator_models`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `bedrockEvaluatorModels`**Description**: Defines the model you want to evaluate custom metrics in an Amazon Bedrock evaluation job. - `model_identifier`**Type**: `STRING`**Provider name**: `modelIdentifier`**Description**: The Amazon Resource Name (ARN) of the evaluator model for custom metrics. For a list of supported evaluator models, see [Evaluate model performance using another LLM as a judge](https://docs.aws.amazon.com/bedrock/latest/userguide/evaluation-judge.html) and [Evaluate the performance of RAG sources using Amazon Bedrock evaluations](https://docs.aws.amazon.com/bedrock/latest/userguide/evaluation-kb.html). - `dataset_metric_configs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `datasetMetricConfigs`**Description**: Configuration details of the prompt datasets and metrics you want to use for your evaluation job. - `dataset`**Type**: `STRUCT`**Provider name**: `dataset`**Description**: Specifies the prompt dataset. - `dataset_location`**Type**: `STRUCT`**Provider name**: `datasetLocation`**Description**: For custom prompt datasets, you must specify the location in Amazon S3 where the prompt dataset is saved. - `s3_uri`**Type**: `STRING`**Provider name**: `s3Uri`**Description**: The S3 URI of the S3 bucket specified in the job. - `name`**Type**: `STRING`**Provider name**: `name`**Description**: Used to specify supported built-in prompt datasets. Valid values are `Builtin.Bold`, `Builtin.BoolQ`, `Builtin.NaturalQuestions`, `Builtin.Gigaword`, `Builtin.RealToxicityPrompts`, `Builtin.TriviaQA`, `Builtin.T-Rex`, `Builtin.WomensEcommerceClothingReviews` and `Builtin.Wikitext2`. - `metric_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `metricNames`**Description**: The names of the metrics you want to use for your evaluation job. For knowledge base evaluation jobs that evaluate retrieval only, valid values are "`Builtin.ContextRelevance`", "`Builtin.ContextCoverage`". For knowledge base evaluation jobs that evaluate retrieval with response generation, valid values are "`Builtin.Correctness`", "`Builtin.Completeness`", "`Builtin.Helpfulness`", "`Builtin.LogicalCoherence`", "`Builtin.Faithfulness`", "`Builtin.Harmfulness`", "`Builtin.Stereotyping`", "`Builtin.Refusal`". For automated model evaluation jobs, valid values are "`Builtin.Accuracy`", "`Builtin.Robustness`", and "`Builtin.Toxicity`". In model evaluation jobs that use a LLM as judge you can specify "`Builtin.Correctness`", "`Builtin.Completeness"`, "`Builtin.Faithfulness"`, "`Builtin.Helpfulness`", "`Builtin.Coherence`", "`Builtin.Relevance`", "`Builtin.FollowingInstructions`", "`Builtin.ProfessionalStyleAndTone`", You can also specify the following responsible AI related metrics only for model evaluation job that use a LLM as judge "`Builtin.Harmfulness`", "`Builtin.Stereotyping`", and "`Builtin.Refusal`". For human-based model evaluation jobs, the list of strings must match the `name` parameter specified in `HumanEvaluationCustomMetric`. - `task_type`**Type**: `STRING`**Provider name**: `taskType`**Description**: The the type of task you want to evaluate for your evaluation job. This applies only to model evaluation jobs and is ignored for knowledge base evaluation jobs. - `evaluator_model_config`**Type**: `STRUCT`**Provider name**: `evaluatorModelConfig`**Description**: Contains the evaluator model configuration details. `EvaluatorModelConfig` is required for evaluation jobs that use a knowledge base or in model evaluation job that use a model as judge. This model computes all evaluation related metrics. - `bedrock_evaluator_models`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `bedrockEvaluatorModels`**Description**: The evaluator model used in knowledge base evaluation job or in model evaluation job that use a model as judge. This model computes all evaluation related metrics. - `model_identifier`**Type**: `STRING`**Provider name**: `modelIdentifier`**Description**: The Amazon Resource Name (ARN) of the evaluator model used used in knowledge base evaluation job or in model evaluation job that use a model as judge. - `human`**Type**: `STRUCT`**Provider name**: `human`**Description**: Contains the configuration details of an evaluation job that uses human workers. - `custom_metrics`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `customMetrics`**Description**: A `HumanEvaluationCustomMetric` object. It contains the names the metrics, how the metrics are to be evaluated, an optional description. - `description`**Type**: `STRING`**Provider name**: `description`**Description**: An optional description of the metric. Use this parameter to provide more details about the metric. - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the metric. Your human evaluators will see this name in the evaluation UI. - `rating_method`**Type**: `STRING`**Provider name**: `ratingMethod`**Description**: Choose how you want your human workers to evaluation your model. Valid values for rating methods are `ThumbsUpDown`, `IndividualLikertScale`,`ComparisonLikertScale`, `ComparisonChoice`, and `ComparisonRank` - `dataset_metric_configs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `datasetMetricConfigs`**Description**: Use to specify the metrics, task, and prompt dataset to be used in your model evaluation job. - `dataset`**Type**: `STRUCT`**Provider name**: `dataset`**Description**: Specifies the prompt dataset. - `dataset_location`**Type**: `STRUCT`**Provider name**: `datasetLocation`**Description**: For custom prompt datasets, you must specify the location in Amazon S3 where the prompt dataset is saved. - `s3_uri`**Type**: `STRING`**Provider name**: `s3Uri`**Description**: The S3 URI of the S3 bucket specified in the job. - `name`**Type**: `STRING`**Provider name**: `name`**Description**: Used to specify supported built-in prompt datasets. Valid values are `Builtin.Bold`, `Builtin.BoolQ`, `Builtin.NaturalQuestions`, `Builtin.Gigaword`, `Builtin.RealToxicityPrompts`, `Builtin.TriviaQA`, `Builtin.T-Rex`, `Builtin.WomensEcommerceClothingReviews` and `Builtin.Wikitext2`. - `metric_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `metricNames`**Description**: The names of the metrics you want to use for your evaluation job. For knowledge base evaluation jobs that evaluate retrieval only, valid values are "`Builtin.ContextRelevance`", "`Builtin.ContextCoverage`". For knowledge base evaluation jobs that evaluate retrieval with response generation, valid values are "`Builtin.Correctness`", "`Builtin.Completeness`", "`Builtin.Helpfulness`", "`Builtin.LogicalCoherence`", "`Builtin.Faithfulness`", "`Builtin.Harmfulness`", "`Builtin.Stereotyping`", "`Builtin.Refusal`". For automated model evaluation jobs, valid values are "`Builtin.Accuracy`", "`Builtin.Robustness`", and "`Builtin.Toxicity`". In model evaluation jobs that use a LLM as judge you can specify "`Builtin.Correctness`", "`Builtin.Completeness"`, "`Builtin.Faithfulness"`, "`Builtin.Helpfulness`", "`Builtin.Coherence`", "`Builtin.Relevance`", "`Builtin.FollowingInstructions`", "`Builtin.ProfessionalStyleAndTone`", You can also specify the following responsible AI related metrics only for model evaluation job that use a LLM as judge "`Builtin.Harmfulness`", "`Builtin.Stereotyping`", and "`Builtin.Refusal`". For human-based model evaluation jobs, the list of strings must match the `name` parameter specified in `HumanEvaluationCustomMetric`. - `task_type`**Type**: `STRING`**Provider name**: `taskType`**Description**: The the type of task you want to evaluate for your evaluation job. This applies only to model evaluation jobs and is ignored for knowledge base evaluation jobs. - `human_workflow_config`**Type**: `STRUCT`**Provider name**: `humanWorkflowConfig`**Description**: The parameters of the human workflow. - `flow_definition_arn`**Type**: `STRING`**Provider name**: `flowDefinitionArn`**Description**: The Amazon Resource Number (ARN) for the flow definition - `instructions`**Type**: `STRING`**Provider name**: `instructions`**Description**: Instructions for the flow definition ## `failure_messages`{% #failure_messages %} **Type**: `UNORDERED_LIST_STRING`**Provider name**: `failureMessages`**Description**: A list of strings that specify why the evaluation job failed to create. ## `inference_config`{% #inference_config %} **Type**: `STRUCT`**Provider name**: `inferenceConfig`**Description**: Contains the configuration details of the inference model used for the evaluation job. - `models`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `models`**Description**: Specifies the inference models. - `bedrock_model`**Type**: `STRUCT`**Provider name**: `bedrockModel`**Description**: Defines the Amazon Bedrock model or inference profile and inference parameters you want used. - `inference_params`**Type**: `STRING`**Provider name**: `inferenceParams`**Description**: Each Amazon Bedrock support different inference parameters that change how the model behaves during inference. - `model_identifier`**Type**: `STRING`**Provider name**: `modelIdentifier`**Description**: The ARN of the Amazon Bedrock model or inference profile specified. - `performance_config`**Type**: `STRUCT`**Provider name**: `performanceConfig`**Description**: Specifies performance settings for the model or inference profile. - `latency`**Type**: `STRING`**Provider name**: `latency`**Description**: Specifies whether to use the latency-optimized or standard version of a model or inference profile. - `precomputed_inference_source`**Type**: `STRUCT`**Provider name**: `precomputedInferenceSource`**Description**: Defines the model used to generate inference response data for a model evaluation job where you provide your own inference response data. - `inference_source_identifier`**Type**: `STRING`**Provider name**: `inferenceSourceIdentifier`**Description**: A label that identifies a model used in a model evaluation job where you provide your own inference response data. - `rag_configs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ragConfigs`**Description**: Contains the configuration details of the inference for a knowledge base evaluation job, including either the retrieval only configuration or the retrieval with response generation configuration. - `knowledge_base_config`**Type**: `STRUCT`**Provider name**: `knowledgeBaseConfig`**Description**: Contains configuration details for knowledge base retrieval and response generation. - `retrieve_and_generate_config`**Type**: `STRUCT`**Provider name**: `retrieveAndGenerateConfig`**Description**: Contains configuration details for retrieving information from a knowledge base and generating responses. - `external_sources_configuration`**Type**: `STRUCT`**Provider name**: `externalSourcesConfiguration`**Description**: The configuration for the external source wrapper object in the `retrieveAndGenerate` function. - `generation_configuration`**Type**: `STRUCT`**Provider name**: `generationConfiguration`**Description**: Contains configurations details for response generation based on retrieved text chunks. - `additional_model_request_fields`**Type**: `STRING`**Provider name**: `additionalModelRequestFields`**Description**: Additional model parameters and their corresponding values not included in the text inference configuration for an external source. Takes in custom model parameters specific to the language model being used. - `guardrail_configuration`**Type**: `STRUCT`**Provider name**: `guardrailConfiguration`**Description**: Configuration details for the guardrail. - `guardrail_id`**Type**: `STRING`**Provider name**: `guardrailId`**Description**: The unique identifier for the guardrail. - `guardrail_version`**Type**: `STRING`**Provider name**: `guardrailVersion`**Description**: The version of the guardrail. - `kb_inference_config`**Type**: `STRUCT`**Provider name**: `kbInferenceConfig`**Description**: Configuration details for inference when using `RetrieveAndGenerate` to generate responses while using an external source. - `text_inference_config`**Type**: `STRUCT`**Provider name**: `textInferenceConfig`**Description**: Contains configuration details for text generation using a language model via the `RetrieveAndGenerate` function. - `max_tokens`**Type**: `INT32`**Provider name**: `maxTokens`**Description**: The maximum number of tokens to generate in the output text. Do not use the minimum of 0 or the maximum of 65536. The limit values described here are arbitrary values, for actual values consult the limits defined by your specific model. - `stop_sequences`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `stopSequences`**Description**: A list of sequences of characters that, if generated, will cause the model to stop generating further tokens. Do not use a minimum length of 1 or a maximum length of 1000. The limit values described here are arbitrary values, for actual values consult the limits defined by your specific model. - `temperature`**Type**: `FLOAT`**Provider name**: `temperature`**Description**: Controls the random-ness of text generated by the language model, influencing how much the model sticks to the most predictable next words versus exploring more surprising options. A lower temperature value (e.g. 0.2 or 0.3) makes model outputs more deterministic or predictable, while a higher temperature (e.g. 0.8 or 0.9) makes the outputs more creative or unpredictable. - `top_p`**Type**: `FLOAT`**Provider name**: `topP`**Description**: A probability distribution threshold which controls what the model considers for the set of possible next tokens. The model will only consider the top p% of the probability distribution when generating the next token. - `prompt_template`**Type**: `STRUCT`**Provider name**: `promptTemplate`**Description**: Contains the template for the prompt for the external source wrapper object. - `text_prompt_template`**Type**: `STRING`**Provider name**: `textPromptTemplate`**Description**: The template for the prompt that's sent to the model for response generation. You can include prompt placeholders, which become replaced before the prompt is sent to the model to provide instructions and context to the model. In addition, you can include XML tags to delineate meaningful sections of the prompt template. For more information, see [Knowledge base prompt template](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-config.html) and [Use XML tags with Anthropic Claude models](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags). - `model_arn`**Type**: `STRING`**Provider name**: `modelArn`**Description**: The Amazon Resource Name (ARN) of the foundation model or [inference profile](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html) used to generate responses. - `sources`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `sources`**Description**: The document for the external source wrapper object in the `retrieveAndGenerate` function. - `byte_content`**Type**: `STRUCT`**Provider name**: `byteContent`**Description**: The identifier, content type, and data of the external source wrapper object. - `content_type`**Type**: `STRING`**Provider name**: `contentType`**Description**: The MIME type of the document contained in the wrapper object. - `identifier`**Type**: `STRING`**Provider name**: `identifier`**Description**: The file name of the document contained in the wrapper object. - `s3_location`**Type**: `STRUCT`**Provider name**: `s3Location`**Description**: The S3 location of the external source wrapper object. - `uri`**Type**: `STRING`**Provider name**: `uri`**Description**: The S3 URI location for the wrapper object of the document. - `source_type`**Type**: `STRING`**Provider name**: `sourceType`**Description**: The source type of the external source wrapper object. - `knowledge_base_configuration`**Type**: `STRUCT`**Provider name**: `knowledgeBaseConfiguration`**Description**: Contains configuration details for the knowledge base retrieval and response generation. - `generation_configuration`**Type**: `STRUCT`**Provider name**: `generationConfiguration`**Description**: Contains configurations details for response generation based on retrieved text chunks. - `additional_model_request_fields`**Type**: `STRING`**Provider name**: `additionalModelRequestFields`**Description**: Additional model parameters and corresponding values not included in the `textInferenceConfig` structure for a knowledge base. This allows you to provide custom model parameters specific to the language model being used. - `guardrail_configuration`**Type**: `STRUCT`**Provider name**: `guardrailConfiguration`**Description**: Contains configuration details for the guardrail. - `guardrail_id`**Type**: `STRING`**Provider name**: `guardrailId`**Description**: The unique identifier for the guardrail. - `guardrail_version`**Type**: `STRING`**Provider name**: `guardrailVersion`**Description**: The version of the guardrail. - `kb_inference_config`**Type**: `STRUCT`**Provider name**: `kbInferenceConfig`**Description**: Contains configuration details for inference for knowledge base retrieval and response generation. - `text_inference_config`**Type**: `STRUCT`**Provider name**: `textInferenceConfig`**Description**: Contains configuration details for text generation using a language model via the `RetrieveAndGenerate` function. - `max_tokens`**Type**: `INT32`**Provider name**: `maxTokens`**Description**: The maximum number of tokens to generate in the output text. Do not use the minimum of 0 or the maximum of 65536. The limit values described here are arbitrary values, for actual values consult the limits defined by your specific model. - `stop_sequences`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `stopSequences`**Description**: A list of sequences of characters that, if generated, will cause the model to stop generating further tokens. Do not use a minimum length of 1 or a maximum length of 1000. The limit values described here are arbitrary values, for actual values consult the limits defined by your specific model. - `temperature`**Type**: `FLOAT`**Provider name**: `temperature`**Description**: Controls the random-ness of text generated by the language model, influencing how much the model sticks to the most predictable next words versus exploring more surprising options. A lower temperature value (e.g. 0.2 or 0.3) makes model outputs more deterministic or predictable, while a higher temperature (e.g. 0.8 or 0.9) makes the outputs more creative or unpredictable. - `top_p`**Type**: `FLOAT`**Provider name**: `topP`**Description**: A probability distribution threshold which controls what the model considers for the set of possible next tokens. The model will only consider the top p% of the probability distribution when generating the next token. - `prompt_template`**Type**: `STRUCT`**Provider name**: `promptTemplate`**Description**: Contains the template for the prompt that's sent to the model for response generation. - `text_prompt_template`**Type**: `STRING`**Provider name**: `textPromptTemplate`**Description**: The template for the prompt that's sent to the model for response generation. You can include prompt placeholders, which become replaced before the prompt is sent to the model to provide instructions and context to the model. In addition, you can include XML tags to delineate meaningful sections of the prompt template. For more information, see [Knowledge base prompt template](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-config.html) and [Use XML tags with Anthropic Claude models](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags). - `knowledge_base_id`**Type**: `STRING`**Provider name**: `knowledgeBaseId`**Description**: The unique identifier of the knowledge base. - `model_arn`**Type**: `STRING`**Provider name**: `modelArn`**Description**: The Amazon Resource Name (ARN) of the foundation model or [inference profile](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html) used to generate responses. - `orchestration_configuration`**Type**: `STRUCT`**Provider name**: `orchestrationConfiguration`**Description**: Contains configuration details for the model to process the prompt prior to retrieval and response generation. - `query_transformation_configuration`**Type**: `STRUCT`**Provider name**: `queryTransformationConfiguration`**Description**: Contains configuration details for transforming the prompt. - `type`**Type**: `STRING`**Provider name**: `type`**Description**: The type of transformation to apply to the prompt. - `retrieval_configuration`**Type**: `STRUCT`**Provider name**: `retrievalConfiguration`**Description**: Contains configuration details for retrieving text chunks. - `vector_search_configuration`**Type**: `STRUCT`**Provider name**: `vectorSearchConfiguration`**Description**: Contains configuration details for returning the results from the vector search. - `filter`**Type**: `STRUCT`**Provider name**: `filter`**Description**: Specifies the filters to use on the metadata fields in the knowledge base data sources before returning results. - `equals`**Type**: `STRUCT`**Provider name**: `equals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `greater_than`**Type**: `STRUCT`**Provider name**: `greaterThan`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `greater_than_or_equals`**Type**: `STRUCT`**Provider name**: `greaterThanOrEquals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `in`**Type**: `STRUCT`**Provider name**: `in`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `less_than`**Type**: `STRUCT`**Provider name**: `lessThan`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `less_than_or_equals`**Type**: `STRUCT`**Provider name**: `lessThanOrEquals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `list_contains`**Type**: `STRUCT`**Provider name**: `listContains`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `not_equals`**Type**: `STRUCT`**Provider name**: `notEquals`**Description**: Knowledge base data sources that contain a metadata attribute whose name matches the key and whose value doesn't match the value in this object are returned. The following example would return data sources that don't contain an animal attribute whose value is 'cat': `"notEquals": { "key": "animal", "value": "cat" }` - `key`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `not_in`**Type**: `STRUCT`**Provider name**: `notIn`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `starts_with`**Type**: `STRUCT`**Provider name**: `startsWith`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `string_contains`**Type**: `STRUCT`**Provider name**: `stringContains`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `number_of_results`**Type**: `INT32`**Provider name**: `numberOfResults`**Description**: The number of text chunks to retrieve; the number of results to return. - `override_search_type`**Type**: `STRING`**Provider name**: `overrideSearchType`**Description**: 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. - `type`**Type**: `STRING`**Provider name**: `type`**Description**: The type of resource that contains your data for retrieving information and generating responses. If you choose to use `EXTERNAL_SOURCES`, then currently only Claude 3 Sonnet models for knowledge bases are supported. - `retrieve_config`**Type**: `STRUCT`**Provider name**: `retrieveConfig`**Description**: Contains configuration details for retrieving information from a knowledge base. - `knowledge_base_id`**Type**: `STRING`**Provider name**: `knowledgeBaseId`**Description**: The unique identifier of the knowledge base. - `knowledge_base_retrieval_configuration`**Type**: `STRUCT`**Provider name**: `knowledgeBaseRetrievalConfiguration`**Description**: Contains configuration details for knowledge base retrieval. - `vector_search_configuration`**Type**: `STRUCT`**Provider name**: `vectorSearchConfiguration`**Description**: Contains configuration details for returning the results from the vector search. - `filter`**Type**: `STRUCT`**Provider name**: `filter`**Description**: Specifies the filters to use on the metadata fields in the knowledge base data sources before returning results. - `equals`**Type**: `STRUCT`**Provider name**: `equals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `greater_than`**Type**: `STRUCT`**Provider name**: `greaterThan`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `greater_than_or_equals`**Type**: `STRUCT`**Provider name**: `greaterThanOrEquals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `in`**Type**: `STRUCT`**Provider name**: `in`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `less_than`**Type**: `STRUCT`**Provider name**: `lessThan`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `less_than_or_equals`**Type**: `STRUCT`**Provider name**: `lessThanOrEquals`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `list_contains`**Type**: `STRUCT`**Provider name**: `listContains`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `not_equals`**Type**: `STRUCT`**Provider name**: `notEquals`**Description**: Knowledge base data sources that contain a metadata attribute whose name matches the key and whose value doesn't match the value in this object are returned. The following example would return data sources that don't contain an animal attribute whose value is 'cat': `"notEquals": { "key": "animal", "value": "cat" }` - `key`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `not_in`**Type**: `STRUCT`**Provider name**: `notIn`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `starts_with`**Type**: `STRUCT`**Provider name**: `startsWith`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `string_contains`**Type**: `STRUCT`**Provider name**: `stringContains`**Description**: 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`**Type**: `STRING`**Provider name**: `key`**Description**: The name of metadata attribute/field, which must match the name in your data source/document metadata. - `value`**Type**: `STRUCT`**Provider name**: `value`**Description**: The value of the metadata attribute/field. - `number_of_results`**Type**: `INT32`**Provider name**: `numberOfResults`**Description**: The number of text chunks to retrieve; the number of results to return. - `override_search_type`**Type**: `STRING`**Provider name**: `overrideSearchType`**Description**: 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. - `precomputed_rag_source_config`**Type**: `STRUCT`**Provider name**: `precomputedRagSourceConfig`**Description**: Contains configuration details about the RAG source used to generate inference response data for a Knowledge Base evaluation job. - `retrieve_and_generate_source_config`**Type**: `STRUCT`**Provider name**: `retrieveAndGenerateSourceConfig`**Description**: A summary of a RAG source used for a retrieve-and-generate Knowledge Base evaluation job where you provide your own inference response data. - `rag_source_identifier`**Type**: `STRING`**Provider name**: `ragSourceIdentifier`**Description**: A label that identifies the RAG source used for a retrieve-and-generate Knowledge Base evaluation job where you provide your own inference response data. - `retrieve_source_config`**Type**: `STRUCT`**Provider name**: `retrieveSourceConfig`**Description**: A summary of a RAG source used for a retrieve-only Knowledge Base evaluation job where you provide your own inference response data. - `rag_source_identifier`**Type**: `STRING`**Provider name**: `ragSourceIdentifier`**Description**: A label that identifies the RAG source used for a retrieve-only Knowledge Base evaluation job where you provide your own inference response data. ## `job_arn`{% #job_arn %} **Type**: `STRING`**Provider name**: `jobArn`**Description**: The Amazon Resource Name (ARN) of the evaluation job. ## `job_description`{% #job_description %} **Type**: `STRING`**Provider name**: `jobDescription`**Description**: The description of the evaluation job. ## `job_name`{% #job_name %} **Type**: `STRING`**Provider name**: `jobName`**Description**: The name for the evaluation job. ## `job_type`{% #job_type %} **Type**: `STRING`**Provider name**: `jobType`**Description**: Specifies whether the evaluation job is automated or human-based. ## `last_modified_time`{% #last_modified_time %} **Type**: `TIMESTAMP`**Provider name**: `lastModifiedTime`**Description**: The time the evaluation job was last modified. ## `output_data_config`{% #output_data_config %} **Type**: `STRUCT`**Provider name**: `outputDataConfig`**Description**: Contains the configuration details of the Amazon S3 bucket for storing the results of the evaluation job. - `s3_uri`**Type**: `STRING`**Provider name**: `s3Uri`**Description**: The Amazon S3 URI where the results of the evaluation job are saved. ## `role_arn`{% #role_arn %} **Type**: `STRING`**Provider name**: `roleArn`**Description**: The Amazon Resource Name (ARN) of the IAM service role used in the evaluation job. ## `status`{% #status %} **Type**: `STRING`**Provider name**: `status`**Description**: The current status of the evaluation job. ## `tags`{% #tags %} **Type**: `UNORDERED_LIST_STRING`