bucket

S3 Vector Bucket Management

This module provides functionality for managing S3 vector buckets, including bucket creation with proper error handling. It integrates with the AWS S3 Vectors service to provide a Pythonic interface for bucket operations.

The module handles common AWS operations such as bucket creation and provides appropriate error handling for scenarios like bucket name conflicts.

Example:

>>> bucket = Bucket(name="my-vector-bucket")
>>> result = bucket.create(s3_vectors_client)

class s3vectorm.bucket.Bucket(*, name: str)

Represents an S3 vector bucket for storing and managing vector data.

This class provides a Pydantic model for S3 vector buckets with methods to create buckets in AWS S3 Vectors service. It handles common operations and error scenarios gracefully.

Attributes:

name: The name of the vector bucket

Example:

>>> bucket = Bucket(name="my-vector-bucket")
>>> result = bucket.create(s3_vectors_client)
>>> if result is not None:
...     print("Bucket created successfully")
... else:
...     print("Bucket already exists")

create(s3_vectors_client: S3VectorsClient, encryption_configuration: EncryptionConfiguration = OPT) → dict[str, Any] | None

Create the vector bucket in AWS S3 Vectors service.

This method attempts to create a new S3 vector bucket with the specified name and optional encryption configuration. It handles the common case where a bucket with the same name already exists by returning None instead of raising an exception.

Parameters:

• s3_vectors_client – The AWS S3 Vectors client to use for the operation

• encryption_configuration – Optional encryption settings for the bucket. If not provided, default encryption will be used.

Returns:

A dictionary containing the AWS response if the bucket was created successfully, or None if the bucket already exists.

Raises:

botocore.exceptions.ClientError: For AWS errors other than bucket

name conflicts (e.g., permission errors, invalid bucket names).

Example:

>>> bucket = Bucket(name="my-new-bucket")
>>> client = boto3.client('s3vectors')
>>> result = bucket.create(client)
>>> if result:
...     print(f"Created bucket: {result}")
... else:
...     print("Bucket already exists")

Reference:

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3vectors/client/create_vector_bucket.html

delete(s3_vectors_client: S3VectorsClient, vector_bucket_arn: str = OPT)

Note

You have to delete all indexes in bucket before you can delete the bucket.

Reference:

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3vectors/client/delete_vector_bucket.html

list_index(s3_vectors_client: S3VectorsClient, vector_bucket_arn: str = OPT, prefix: str = OPT, page_size: int = 100, max_items: int = 9999) → Generator[ListIndexesOutput, None, None]

List all indexes in the vector bucket with pagination support.

This method retrieves all vector indexes within the bucket using pagination to handle buckets with many indexes efficiently. It supports filtering by index name prefix.

Parameters:

• s3_vectors_client – The AWS S3 Vectors client to use for the operation

• vector_bucket_arn – Optional ARN of the vector bucket. If provided, takes precedence over bucket name

• prefix – Optional prefix to filter index names

• page_size – Number of indexes per page (default: 100)

• max_items – Maximum total number of indexes to retrieve (default: 9999)

Yields:

Dict responses containing paginated index results

Example:

>>> bucket = Bucket(name="my-bucket")

>>> for res in bucket.list_index(client):
...     for index_summary in res.indexes:
...         print(index_summary)

Reference:

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3vectors/paginator/ListIndexes.html

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].