NewHNSWPQIndex()

This function creates an HNSW_PQ index configuration whose vectors are product-quantized to reduce memory usage.

Go

func NewHNSWPQIndex(metricType MetricType, m int, efConstruction int, pqM int, nbits int) *hnswPQIndex

PARAMETERS:

• metricType (MetricType)

  The distance metric type for similarity search (e.g., entity.COSINE, entity.L2, entity.IP).

• m (int)

  The number of bi-directional links for each element. Higher values improve recall but increase memory usage. Typical range: 4-64.

• efConstruction (int)

  The size of the dynamic candidate list during index construction. Higher values improve index quality but slow down build time. Typical range: 8-512.

• pqM (int)

  The number of PQ sub-quantizers (server default 32).

• nbits (int)

  The bits per sub-quantizer, in [1, 24] (server default 8).

RETURNS:

Index

An index configuration instance. Pass this to CreateIndex() via the index option.

BUILDER METHODS:

• WithRefineType(refineType string) *hnswPQIndex

  Enables the refine index and sets its precision. Knowhere accepts sq4u / sq6 / sq8 / fp16 / bf16 / fp32 / flat. The refine index keeps full-precision vectors alongside the quantized ones and re-ranks with them.

Example

Go

import (
	"github.com/milvus-io/milvus/client/v3/index"
	"github.com/milvus-io/milvus/client/v3/entity"
	"github.com/milvus-io/milvus/client/v3/milvusclient"
)

// Create index configuration
idx := index.NewHNSWPQIndex(entity.COSINE, 16, 200, 32, 8)

// Use with CreateIndex
createIdxOption := milvusclient.NewCreateIndexOption("collection_name", "vector_field", idx)
task, err := client.CreateIndex(ctx, createIdxOption)