io.lettuce.core

Class VAddArgs.Builder

java.lang.Object

io.lettuce.core.VAddArgs.Builder

Enclosing class:

VAddArgs

public static class VAddArgs.Builder
extends Object

Builder entry points for VAddArgs.

These static methods provide a convenient way to create new instances of VAddArgs with specific options set. Each method creates a new instance and sets the corresponding option for the VADD command.

Method Summary

Modifier and Type	Method and Description
static VAddArgs	attributes(String attributes) Creates new VAddArgs with the specified attributes (SETATTR).
static VAddArgs	checkAndSet(boolean checkAndSet) Creates new VAddArgs and sets the CAS flag.
static VAddArgs	explorationFactor(Long explorationFactor) Creates new VAddArgs and setting EF (exploration factor).
static VAddArgs	maxNodes(Long maxNodes) Creates new VAddArgs and setting M (maximum links).
static VAddArgs	quantizationType(QuantizationType quantType) Creates new VAddArgs and setting the quantization type.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

checkAndSet

public static VAddArgs checkAndSet(boolean checkAndSet)

Creates new VAddArgs and sets the CAS flag.

The CAS option performs the operation partially using threads, in a check-and-set style. The neighbor candidates collection, which is slow, is performed in the background, while the command is executed in the main thread.

Parameters:

checkAndSet - whether to perform a check and set operation.

Returns:

new VAddArgs with CAS set.

See Also:

VAddArgs.checkAndSet(boolean)

quantizationType

public static VAddArgs quantizationType(QuantizationType quantType)

Creates new VAddArgs and setting the quantization type.

Quantization affects how vectors are stored and impacts memory usage, performance, and recall quality:

• Q8 (default) - Uses signed 8-bit quantization, balancing memory usage and recall quality
• NOQUANT - Stores vectors without quantization, using more memory but preserving full precision
• BIN - Uses binary quantization, which is faster and uses less memory, but impacts recall quality

Note that these options are mutually exclusive.

Parameters:

quantType - the quantization type for the vector.

Returns:

new VAddArgs with the quantization type set.

See Also:

VAddArgs.quantizationType(QuantizationType)

explorationFactor

public static VAddArgs explorationFactor(Long explorationFactor)

Creates new VAddArgs and setting EF (exploration factor).

The EF option plays a role in the effort made to find good candidates when connecting the new node to the existing Hierarchical Navigable Small World (HNSW) graph.

The default is 200. Using a larger value may help in achieving a better recall, but will increase the time needed to add vectors to the set.

Parameters:

explorationFactor - the exploration factor for the vector search (default: 200).

Returns:

new VAddArgs with EF set.

See Also:

VAddArgs.explorationFactor(Long)

maxNodes

public static VAddArgs maxNodes(Long maxNodes)

Creates new VAddArgs and setting M (maximum links).

The M option specifies the maximum number of connections that each node of the graph will have with other nodes.

The default is 16. More connections means more memory, but provides for more efficient graph exploration. Nodes at layer zero (every node exists at least at layer zero) have M * 2 connections, while the other layers only have M connections.

If you don't have a recall quality problem, the default is acceptable and uses a minimal amount of memory.

Parameters:

maxNodes - the maximum number of connections per node (default: 16).

Returns:

new VAddArgs with M set.

See Also:

VAddArgs.maxNodes(Long)

attributes

public static VAddArgs attributes(String attributes)

Creates new VAddArgs with the specified attributes (SETATTR).

The SETATTR option associates attributes in the form of a JavaScript object to the newly created entry or updates the attributes (if they already exist). It is the same as calling the VSETATTR command separately.

Attributes can be used for filtering during similarity searches with the VSIM command.

Parameters:

attributes - the attributes for the vector as JSON string.

Returns:

new VAddArgs with the attributes set.

See Also:

VAddArgs.attributes(String)