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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 cyborgdb_core as cyborgdb

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

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

# Get your API key
api_key = "your_api_key_here"  # Replace with your actual API key

# Create a client
client = cyborgdb.Client(
    api_key=api_key, 
    index_location=index_location, 
    config_location=config_location
)
You can get an API key from the CyborgDB Admin Dashboard. For more info, follow this guide. 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 cyborgdb_core as cyborgdb

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

# Get your API key
api_key = "your_api_key_here"  # Replace with your actual API key

# Construct the Client object
client = cyborgdb.Client(
    api_key=api_key, 
    index_location=index_location, 
    config_location=config_location,
    items_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

CyborgDB 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_config parameters: 
# ... existing setup (with api_key defined above)

# Enable GPU for specific operations (upsert and train)
gpu_config = cyborgdb.GPUConfig(upsert=True, train=True)

client = cyborgdb.Client(
    api_key=api_key,
    index_location=index_location,
    config_location=config_location,
    items_location=item_location,
    cpu_threads=4,
    gpu_config=gpu_config
)
gpu_config can only be set if running on a CUDA-enabled system with the CUDA driver installed. Use GPUConfig to specify which operations (upsert, train, query) should use GPU acceleration.
By default, cpu_threads will use all available cores, and gpu_config will be None (no GPU acceleration).

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API Reference

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

Python API Reference

API reference for Client in Python

C++ API Reference

API reference for cyborg::Client in C++