Types

DBConfig

The DBConfig class specifies the storage location for the index, with options for in-memory storage, databases, or file-based storage.

Parameters

Parameter	Type	Default	Description
`location`	`string`	-	DB location (`redis`, `postgres`, `memory`)
`table_name`	`string`	None	(Optional) Table name (`postgres`-only)
`connection_string`	`string`	None	(Optional) Connection string to access DB.

The supported location options are:

"redis": Use for high-speed, in-memory storage (recommended for index_location).
"postgres": Use for reliable, SQL-based storage (recommended for config_location).
"memory" Use for temporary in-memory storage (for benchmarking and evaluation purposes).

Example Usage

import cyborg_vector_search_py as cvs

index_location = cvs.DBConfig(location="redis",
                          connection_string="redis://localhost")

config_location = cvs.DBConfig(location="postgres",
                           table_name="config_table", connection_string="host=localhost dbname=postgres")

For more info, you can read about supported backing stores here.

DistanceMetric

DistanceMetric is a string representing the distance metric used for the index. Options include:

"cosine": Cosine similarity.
"euclidean": Euclidean distance.
"squared_euclidean": Squared Euclidean distance.

IndexConfig

The IndexConfig class defines the parameters for the type of index to be created. Each index type (e.g., ivf, ivfflat, ivfpq) has unique configuration options:

IndexIVF

Ideal for large-scale datasets where fast retrieval is prioritized over high recall:

Speed	Recall	Index Size
Fastest	Lowest	Smallest

Parameters

Parameter	Type	Description
`dimension`	`int`	Dimensionality of vector embeddings.
`n_lists`	`int`	Number of inverted index lists to create in the index (recommended base-2 value).
`metric`	`str`	(Optional) Distance metric to use for index build and queries.

Example Usage

import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVF(dimension=128, n_lists=1024, metric="euclidean")

IndexIVFFlat

Suitable for applications requiring high recall with less concern for memory usage:

Speed	Recall	Index Size
Fast	Highest	Biggest

Parameters

Parameter	Type	Description
`dimension`	`int`	Dimensionality of vector embeddings.
`n_lists`	`int`	Number of inverted index lists to create in the index (recommended base-2 value).
`metric`	`str`	(Optional) Distance metric to use for index build and queries.

Example Usage

import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVFFlat(dimension=128, n_lists=1024, metric="euclidean")

IndexIVFPQ

Product Quantization compresses embeddings, making it suitable for balancing memory use and recall:

Speed	Recall	Index Size
Fast	High	Medium

Parameters

Parameter	Type	Description
`dimension`	`int`	Dimensionality of vector embeddings.
`n_lists`	`int`	Number of inverted index lists to create in the index (recommended base-2 value).
`pq_dim`	`int`	Dimensionality of embeddings after quantization (less than or equal to `dimension`).
`pq_bits`	`int`	Number of bits per dimension for PQ embeddings (between 1 and 16).
`metric`	`str`	(Optional) Distance metric to use for index build and queries.

Example Usage

import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVFPQ(dimension=128, n_lists=1024, pq_dim=64, pq_bits=8, metric="euclidean")

Introduction

Client

Encrypted Index

Types & Getters

DBConfig

Parameters

Example Usage

DistanceMetric

IndexConfig

IndexIVF

Parameters

Example Usage

IndexIVFFlat

Parameters

Example Usage

IndexIVFPQ

Parameters

Example Usage

​DBConfig

​Parameters

​Example Usage

​DistanceMetric

​IndexConfig

​IndexIVF

​Parameters

​Example Usage

​IndexIVFFlat

​Parameters

​Example Usage

​IndexIVFPQ

​Parameters

​Example Usage

DBConfig

Parameters

Example Usage

DistanceMetric

IndexConfig

IndexIVF

Parameters

Example Usage

IndexIVFFlat

Parameters

Example Usage

IndexIVFPQ

Parameters

Example Usage