The Client class provides an interface to initialize, create, load and list Encrypted Indexes. Encrypted Indexes, in turn, expose data-related functionality such as upserting, querying, and deleting.

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Creating the Client

To create the client, you must define the backing store locations which it will use. At minimum, you must set an index_location (where the index will reside) and a config_location (where the index metadata will reside). For example:

import cyborg_vector_search_py as cvs
import secrets

# Using `memory` storage for this example
# `redis` and `postgres` are also supported

index_location = cvs.DBConfig("memory")  # Where encrypted index is stored (for queries)
config_location = cvs.DBConfig("memory") # Where encrypted index config is stored (for config/loading)

# Create a client
client = cvs.Client(index_location, config_location)

Bear in mind that all contents stored in the backing stores is end-to-end encrypted, meaning that no index contents will be stored in plaintext. For performance reasons, you may wish to use a “fast” backing store option for index_location, such as Redis.

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Creating the Client with Item Storage

If you wish to store encrypted items in the index, you will also need to set a item_location:

import cyborg_vector_search_py as cvs

# Using `redis` storage for the index and config
# Using `postgres` storage for the items
index_location = cvs.DBConfig(location="redis", connection_string="redis://localhost")
config_location = cvs.DBConfig(location="redis", connection_string="redis://localhost")
item_location = cvs.DBConfig(location="postgres", table_name="item_table", connection_string="host=localhost dbname=postgres")

# Construct the Client object
client = cvs.Client(index_location=index_location, 
                            config_location=config_location,
                            item_location=item_location)

Item contents will be encrypted prior to being uploaded to the backing store. In most applications, these are accessed less frequently than the index, so a “slower” backing store option can be used, such as PostgreSQL.

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Setting Device Configurations

Cyborg Vector Search can be accelerated in two ways:

• CPU Multithreading (via OpenMP) -> can scale with the number of CPU cores available
• GPU Acceleration (via CUDA & cuVS) -> can increase the speed of certain operations (e.g., ingestion) by an order of magnitude.

You can control either of these with the optional cpu_threads and gpu_accelerate flags:

# ... existing setup
client = cvs.Client(index_location=index_location, 
                            config_location=config_location,
                            item_location=item_location,
                            cpu_threads=4,
                            gpu_accelerate=False)

By default, cpu_threads will use all available cores, and gpu_accelerate will be set to False.

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API Reference

For more information on the Client class, refer to the API Reference: