AgentsforBedrock / Client / get_knowledge_base

get_knowledge_base

AgentsforBedrock.Client.get_knowledge_base(**kwargs)

Gets information about a knoweldge base.

See also: AWS API Documentation

Request Syntax

response = client.get_knowledge_base(
    knowledgeBaseId='string'
)

Parameters:

knowledgeBaseId (string) –

[REQUIRED]

The unique identifier of the knowledge base for which to get information.

Return type:

dict

Returns:

Response Syntax

{
    'knowledgeBase': {
        'createdAt': datetime(2015, 1, 1),
        'description': 'string',
        'failureReasons': [
            'string',
        ],
        'knowledgeBaseArn': 'string',
        'knowledgeBaseConfiguration': {
            'type': 'VECTOR',
            'vectorKnowledgeBaseConfiguration': {
                'embeddingModelArn': 'string'
            }
        },
        'knowledgeBaseId': 'string',
        'name': 'string',
        'roleArn': 'string',
        'status': 'CREATING'|'ACTIVE'|'DELETING'|'UPDATING'|'FAILED'|'DELETE_UNSUCCESSFUL',
        'storageConfiguration': {
            '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'
        },
        'updatedAt': datetime(2015, 1, 1)
    }
}

Response Structure

• (dict) –

  • knowledgeBase (dict) –

    Contains details about the knowledge base.

    • createdAt (datetime) –

      The time at which 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 (dict) –

      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 (dict) –

        Contains details about the embeddings model that’sused to convert the data source.

        • embeddingModelArn (string) –

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

    • 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 (dict) –

      Contains details about the storage configuration of the knowledge base.

      • mongoDbAtlasConfiguration (dict) –

        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 (dict) –

          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 (dict) –

        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 (dict) –

          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 (dict) –

        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 (dict) –

          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 (dict) –

        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 (dict) –

          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 (dict) –

        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 (dict) –

          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 (datetime) –

      The time at which the knowledge base was last updated.

Exceptions

• AgentsforBedrock.Client.exceptions.ThrottlingException

• AgentsforBedrock.Client.exceptions.AccessDeniedException

• AgentsforBedrock.Client.exceptions.ValidationException

• AgentsforBedrock.Client.exceptions.InternalServerException

• AgentsforBedrock.Client.exceptions.ResourceNotFoundException