Values.CreateKnowledgeBaseRequestSourceCreates a knowledge base. A knowledge base contains your data sources so that Large Language Models (LLMs) can use your data. To create a knowledge base, you must first set up your data sources and configure a supported vector store. For more information, see Set up a knowledge base. If you prefer to let Amazon Bedrock create and manage a vector store for you in Amazon OpenSearch Service, use the console. For more information, see Create a knowledge base. Provide the name and an optional description. Provide the Amazon Resource Name (ARN) with permissions to create a knowledge base in the roleArn field. Provide the embedding model to use in the embeddingModelArn field in the knowledgeBaseConfiguration object. Provide the configuration for your vector store in the storageConfiguration object. For an Amazon OpenSearch Service database, use the opensearchServerlessConfiguration object. For more information, see Create a vector store in Amazon OpenSearch Service. For an Amazon Aurora database, use the RdsConfiguration object. For more information, see Create a vector store in Amazon Aurora. For a Pinecone database, use the pineconeConfiguration object. For more information, see Create a vector store in Pinecone. For a Redis Enterprise Cloud database, use the redisEnterpriseCloudConfiguration object. For more information, see Create a vector store in Redis Enterprise Cloud.
type nonrec t = {clientToken : ClientToken.t option;A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see Ensuring idempotency.
*)name : Name.t;A name for the knowledge base.
*)description : Description.t option;A description of the knowledge base.
*)roleArn : KnowledgeBaseRoleArn.t;The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.
*)knowledgeBaseConfiguration : KnowledgeBaseConfiguration.t;Contains details about the embeddings model used for the knowledge base.
*)storageConfiguration : StorageConfiguration.t option;Contains details about the configuration of the vector database used for the knowledge base.
*)}val make :
?clientToken:??? ->
?description:??? ->
?storageConfiguration:??? ->
?tags:??? ->
name:Name.t ->
roleArn:KnowledgeBaseRoleArn.t ->
knowledgeBaseConfiguration:KnowledgeBaseConfiguration.t ->
unit ->
tval to_value :
t ->
[> `Structure of
(string
* [> `Map of
([> `String of TagKey.t ] * [> `String of TagValue.t ]) list
| `String of ClientToken.t
| `Structure of
(string
* [> `Enum of string
| `Structure of
(string
* [> `Enum of string
| `String of BedrockEmbeddingModelArn.t
| `Structure of
(string
* [> `List of
[> `Structure of
(string
* [> `Enum of string
| `Structure of
(string
* [> `List of
[> `String of
AwsDataCatalogTableName.t ]
list
| `String of S3BucketUri.t ])
list ])
list ]
list
| `String of FieldName.t
| `Structure of
(string
* [> `Enum of string
| `Integer of Dimensions.t
| `List of
[> `Structure of
(string
* [> `Structure of
(string
* [> `Integer of
AudioSegmentationConfigurationFixedLengthDurationInteger.t ])
list ])
list ]
list
| `Structure of
(string
* [> `List of
[> `Structure of
(string
* [> `Enum of string
| `List of
[> `Structure of
(string
* [> `Enum of string
| `String of
QueryGenerationColumnName.t ])
list ]
list
| `String of
QueryGenerationTableName.t ])
list ]
list
| `String of WorkgroupArn.t
| `Structure of
(string
* [> `Enum of string
| `String of SecretArn.t ])
list ])
list ])
list ])
list ])
list ])
list ])
list ]