Client is the main class exposed by cyborg_vector_search_py. It exposes the functionality necessary to create, load, list and delete indexes. Operations within encrypted indexes (such as upsert and query) are contained within the EncryptedIndex class returned by create_index and load_index.

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Constructor

Client(index_location: DBConfig,
       config_location: DBConfig,
       items_location: DBConfig,
       cpu_threads: int = 0,
       gpu_accelerate: bool = False)

Initializes a new Cyborg Vector Search Client instance.

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Parameters

ParameterTypeDefaultDescription
index_locationDBConfig-Configuration for index storage location. Use a dictionary with keys location, table_name, and connection_string.
config_locationDBConfig-Configuration for index metadata storage. Uses the same dictionary structure as index_location.
items_locationDBConfigNONE(Optional) Configuration for encrypted item storage. Uses the same dictionary structure as index_location.
cpu_threadsint0(Optional) Number of CPU threads to use for computations (defaults to 0 = all cores).
gpu_accelerateboolFalse(Optional) Indicates whether to use GPU acceleration (defaults to False).

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Exceptions

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Example Usage

import cyborg_vector_search_py as cvs

index_location = cvs.DBConfig(location='redis', connection_string="redis://localhost")
config_location = cvs.DBConfig(location='redis', connection_string="redis://localhost")
items_location = cvs.DBConfig(location='postgres', table_name="items", connection_string="host=localhost dbname=postgres")

# Construct the Client object
client = cvs.Client(index_location=index_location,
                    config_location=config_location,
                    items_location=items_location,
                    cpu_threads=4,
                    gpu_accelerate=True)

# Proceed with further operations