AgentsforBedrock / Client / get_knowledge_base

get_knowledge_base#

AgentsforBedrock.Client.get_knowledge_base(**kwargs)#

Gets information about a knoweldge base.

Request Syntax

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

Parameters:

knowledgeBaseId (string) –

[REQUIRED]

The unique identifier of the knowledge base you want to get information on.

Return type:

dict

Returns:

Response Syntax

{
    'knowledgeBase': {
        'createdAt': datetime(2015, 1, 1),
        'description': 'string',
        'failureReasons': [
            'string',
        ],
        'knowledgeBaseArn': 'string',
        'knowledgeBaseConfiguration': {
            'type': 'VECTOR',
            'vectorKnowledgeBaseConfiguration': {
                'embeddingModelArn': 'string',
                'embeddingModelConfiguration': {
                    'bedrockEmbeddingModelConfiguration': {
                        'dimensions': 123
                    }
                }
            }
        },
        '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 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 model that’s used to convert the data source into vector embeddings.
      - embeddingModelArn (string) –
        
        The Amazon Resource Name (ARN) of the model used to create vector embeddings for the knowledge base.
      - embeddingModelConfiguration (dict) –
        
        The embeddings model configuration details for the vector model used in Knowledge Base.
        
        bedrockEmbeddingModelConfiguration (dict) –
        
        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 (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 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