AWS CLI Command Reference

[ aws . bedrock-agent ]

create-knowledge-base

Description

Creates 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 .

Note

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.

See also: AWS API Documentation

Synopsis

  create-knowledge-base
[--client-token <value>]
[--description <value>]
--knowledge-base-configuration <value>
--name <value>
--role-arn <value>
--storage-configuration <value>
[--tags <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--client-token (string)

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 .

--description (string)

A description of the knowledge base.

--knowledge-base-configuration (structure)

Contains details about the embeddings model used for the knowledge base.

type -> (string)

The type of data that the data source is converted into for the knowledge base.

vectorKnowledgeBaseConfiguration -> (structure)

Contains details about the model that’s used to convert the data source into vector embeddings.

embeddingModelArn -> (string)

The Amazon Resource Name (ARN) of the model or inference profile used to create vector embeddings for the knowledge base.

embeddingModelConfiguration -> (structure)

The embeddings model configuration details for the vector model used in Knowledge Base.

bedrockEmbeddingModelConfiguration -> (structure)

The vector configuration details on the Bedrock embeddings model.

dimensions -> (integer)

The dimensions details for the vector configuration used on the Bedrock embeddings model.

JSON Syntax:

{
  "type": "VECTOR",
  "vectorKnowledgeBaseConfiguration": {
    "embeddingModelArn": "string",
    "embeddingModelConfiguration": {
      "bedrockEmbeddingModelConfiguration": {
        "dimensions": integer
      }
    }
  }
}

--name (string)

A name for the knowledge base.

--role-arn (string)

The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.

--storage-configuration (structure)

Contains details about the configuration of the vector database used for the knowledge base.

mongoDbAtlasConfiguration -> (structure)

Contains the storage configuration of the knowledge base in MongoDB Atlas.

collectionName -> (string)

The collection name of the knowledge base in MongoDB Atlas.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that contains user credentials for your MongoDB Atlas cluster.

databaseName -> (string)

The database name in your MongoDB Atlas cluster for your knowledge base.

endpoint -> (string)

The endpoint URL of your MongoDB Atlas cluster for your knowledge base.

endpointServiceName -> (string)

The name of the VPC endpoint service in your account that is connected to your MongoDB Atlas cluster.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the MongoDB Atlas vector search index.

opensearchServerlessConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Amazon OpenSearch Service.

collectionArn -> (string)

The Amazon Resource Name (ARN) of the OpenSearch Service vector store.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the vector store.

pineconeConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Pinecone.

connectionString -> (string)

The endpoint URL for your index management page.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Pinecone API key.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

namespace -> (string)

The namespace to be used to write new data to your database.

rdsConfiguration -> (structure)

Contains details about the storage configuration of the knowledge base in Amazon RDS. For more information, see Create a vector index in Amazon RDS .

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Amazon RDS database.

databaseName -> (string)

The name of your Amazon RDS database.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

primaryKeyField -> (string)

The name of the field in which Amazon Bedrock stores the ID for each entry.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

resourceArn -> (string)

The Amazon Resource Name (ARN) of the vector store.

tableName -> (string)

The name of the table in the database.

redisEnterpriseCloudConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Redis Enterprise Cloud.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Redis Enterprise Cloud database.

endpoint -> (string)

The endpoint URL of the Redis Enterprise Cloud database.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the vector index.

type -> (string)

The vector store service in which the knowledge base is stored.

Shorthand Syntax:

mongoDbAtlasConfiguration={collectionName=string,credentialsSecretArn=string,databaseName=string,endpoint=string,endpointServiceName=string,fieldMapping={metadataField=string,textField=string,vectorField=string},vectorIndexName=string},opensearchServerlessConfiguration={collectionArn=string,fieldMapping={metadataField=string,textField=string,vectorField=string},vectorIndexName=string},pineconeConfiguration={connectionString=string,credentialsSecretArn=string,fieldMapping={metadataField=string,textField=string},namespace=string},rdsConfiguration={credentialsSecretArn=string,databaseName=string,fieldMapping={metadataField=string,primaryKeyField=string,textField=string,vectorField=string},resourceArn=string,tableName=string},redisEnterpriseCloudConfiguration={credentialsSecretArn=string,endpoint=string,fieldMapping={metadataField=string,textField=string,vectorField=string},vectorIndexName=string},type=string

JSON Syntax:

{
  "mongoDbAtlasConfiguration": {
    "collectionName": "string",
    "credentialsSecretArn": "string",
    "databaseName": "string",
    "endpoint": "string",
    "endpointServiceName": "string",
    "fieldMapping": {
      "metadataField": "string",
      "textField": "string",
      "vectorField": "string"
    },
    "vectorIndexName": "string"
  },
  "opensearchServerlessConfiguration": {
    "collectionArn": "string",
    "fieldMapping": {
      "metadataField": "string",
      "textField": "string",
      "vectorField": "string"
    },
    "vectorIndexName": "string"
  },
  "pineconeConfiguration": {
    "connectionString": "string",
    "credentialsSecretArn": "string",
    "fieldMapping": {
      "metadataField": "string",
      "textField": "string"
    },
    "namespace": "string"
  },
  "rdsConfiguration": {
    "credentialsSecretArn": "string",
    "databaseName": "string",
    "fieldMapping": {
      "metadataField": "string",
      "primaryKeyField": "string",
      "textField": "string",
      "vectorField": "string"
    },
    "resourceArn": "string",
    "tableName": "string"
  },
  "redisEnterpriseCloudConfiguration": {
    "credentialsSecretArn": "string",
    "endpoint": "string",
    "fieldMapping": {
      "metadataField": "string",
      "textField": "string",
      "vectorField": "string"
    },
    "vectorIndexName": "string"
  },
  "type": "OPENSEARCH_SERVERLESS"|"PINECONE"|"REDIS_ENTERPRISE_CLOUD"|"RDS"|"MONGO_DB_ATLAS"
}

--tags (map)

Specify the key-value pairs for the tags that you want to attach to your knowledge base in this object.

key -> (string)

value -> (string)

Shorthand Syntax:

KeyName1=string,KeyName2=string

JSON Syntax:

{"string": "string"
  ...}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.

--output (string)

The formatting style for command output.

  • json
  • text
  • table
  • yaml
  • yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

  • on
  • off
  • auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

  • base64
  • raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

knowledgeBase -> (structure)

Contains details about the knowledge base.

createdAt -> (timestamp)

The time the knowledge base was created.

description -> (string)

The description of the knowledge base.

failureReasons -> (list)

A list of reasons that the API operation on the knowledge base failed.

(string)

knowledgeBaseArn -> (string)

The Amazon Resource Name (ARN) of the knowledge base.

knowledgeBaseConfiguration -> (structure)

Contains details about the embeddings configuration of the knowledge base.

type -> (string)

The type of data that the data source is converted into for the knowledge base.

vectorKnowledgeBaseConfiguration -> (structure)

Contains details about the model that’s used to convert the data source into vector embeddings.

embeddingModelArn -> (string)

The Amazon Resource Name (ARN) of the model or inference profile used to create vector embeddings for the knowledge base.

embeddingModelConfiguration -> (structure)

The embeddings model configuration details for the vector model used in Knowledge Base.

bedrockEmbeddingModelConfiguration -> (structure)

The vector configuration details on the Bedrock embeddings model.

dimensions -> (integer)

The dimensions details for the vector configuration used on the Bedrock embeddings model.

knowledgeBaseId -> (string)

The unique identifier of the knowledge base.

name -> (string)

The name of the knowledge base.

roleArn -> (string)

The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.

status -> (string)

The status of the knowledge base. The following statuses are possible:

  • CREATING – The knowledge base is being created.
  • ACTIVE – The knowledge base is ready to be queried.
  • DELETING – The knowledge base is being deleted.
  • UPDATING – The knowledge base is being updated.
  • FAILED – The knowledge base API operation failed.

storageConfiguration -> (structure)

Contains details about the storage configuration of the knowledge base.

mongoDbAtlasConfiguration -> (structure)

Contains the storage configuration of the knowledge base in MongoDB Atlas.

collectionName -> (string)

The collection name of the knowledge base in MongoDB Atlas.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that contains user credentials for your MongoDB Atlas cluster.

databaseName -> (string)

The database name in your MongoDB Atlas cluster for your knowledge base.

endpoint -> (string)

The endpoint URL of your MongoDB Atlas cluster for your knowledge base.

endpointServiceName -> (string)

The name of the VPC endpoint service in your account that is connected to your MongoDB Atlas cluster.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the MongoDB Atlas vector search index.

opensearchServerlessConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Amazon OpenSearch Service.

collectionArn -> (string)

The Amazon Resource Name (ARN) of the OpenSearch Service vector store.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the vector store.

pineconeConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Pinecone.

connectionString -> (string)

The endpoint URL for your index management page.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Pinecone API key.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

namespace -> (string)

The namespace to be used to write new data to your database.

rdsConfiguration -> (structure)

Contains details about the storage configuration of the knowledge base in Amazon RDS. For more information, see Create a vector index in Amazon RDS .

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Amazon RDS database.

databaseName -> (string)

The name of your Amazon RDS database.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

primaryKeyField -> (string)

The name of the field in which Amazon Bedrock stores the ID for each entry.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

resourceArn -> (string)

The Amazon Resource Name (ARN) of the vector store.

tableName -> (string)

The name of the table in the database.

redisEnterpriseCloudConfiguration -> (structure)

Contains the storage configuration of the knowledge base in Redis Enterprise Cloud.

credentialsSecretArn -> (string)

The Amazon Resource Name (ARN) of the secret that you created in Secrets Manager that is linked to your Redis Enterprise Cloud database.

endpoint -> (string)

The endpoint URL of the Redis Enterprise Cloud database.

fieldMapping -> (structure)

Contains the names of the fields to which to map information about the vector store.

metadataField -> (string)

The name of the field in which Amazon Bedrock stores metadata about the vector store.

textField -> (string)

The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.

vectorField -> (string)

The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.

vectorIndexName -> (string)

The name of the vector index.

type -> (string)

The vector store service in which the knowledge base is stored.

updatedAt -> (timestamp)

The time the knowledge base was last updated.
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