Indexes

The struct types on this page implement the method signatures of the entity.Index interface.

entity.GenericIndex

This struct type applies to general purposes without any constraint on the index type. You can use entity.NewGenericIndex() to create one for the client.CreateIndex() request as follows:

index := entity.NewGenericIndex(name string, it entity.IndexType, params map[string]string)

Parameter	Description	Type
`name`	Name of the index.	`string`
`it`	Type of the index.	`entity.IndexType`
`params`	Index parameters corresponding to the specified index type.	`map[string]string`

entity.IndexAUTOINDEX

This struct type creates an AUTOINDEX for the specified field. You can use entity.NewIndexAUTOINDEX() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexAUTOINDEX(metricType MetricType)

Parameter	Description	Type
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`

entity.IndexBinFlat

This struct type creates a BIN_FLAT index for the specified field. You can use entity.NewIndexBinFlat() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexBinFlat(metricType MetricType, nlist int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`

entity.IndexBinIvfFlat

This struct type creates a BIN_IVF_FLAT index for the specified field. You can use entity.NewIndexBinIvfFlat() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexBinIvfFlat(metricType MetricType, nlist int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`

entity.IndexDISKANN

This struct type creates a DiskANN index for the specified field. You can use entity.NewIndexDISKANN() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexDISKANN(metricType MetricType)

Parameter	Description	Type
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`

entity.IndexFlat

This struct type creates a FLAT index for the specified field. You can use entity.NewIndexIvfFlat() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexFlat(metricType MetricType)

Parameter	Description	Type
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`

entity.IndexGPUBruteForce

This struct type creates a GPU_BRUTE_FORCE index for the specified field. You can use entity.NewIndexGPUBruteForce() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexGPUBruteForce(metricType MetricType)

Parameter	Description	Type
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`

entity.IndexGPUCagra

This struct type creates a GPU_CAGRA index for the specified field. You can use entity.NewIndexGPUCagra() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexBinIvfFlat(metricType MetricType, intermediateGraphDegree, graphDegree int)

Parameter

Description

Type

Value Range

metricType

Metric type for Milvus to follow when it measures similarities between vector embeddings.

entity.MetricType

N/A

intermediateGraphDegree

Number of k-nearest neighbors (k) of this intermediate k-NN graph, trade off the quality of the final searchable CAGRA graph;

It affects recall and build time by determining the graph's degree before pruning. Recommended values are 32 or 64.

int

graphDegree

CAGRA's optimized graph fixed-degree number.

It affects search performance and recall by setting the graph's degree after pruning. A larger difference between these two degrees results in a longer build time. Its value must be smaller than the value of intermediateGraphDegree.

int

entity.IndexGPUIvfFlat

This struct type creates a GPU_IVF_FLAT index for the specified field. You can use entity.NewIndexGPUIvfFlat() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexGPUIvfFlat(metricType MetricType, nlist int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`

entity.IndexGPUIvfPQ

This struct type creates a GPU_IVF_PQ index for the specified field. You can use entity.NewIndexGPUIvfPQ() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexGPUIvfPQ(metricType MetricType, nlist int, m int, nbits int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`
`m`	Number of factors of product quantization	`int`	`dim mod m or = 0`
`nbits`	Number of bits in which each low-dimensional vector is stored.	`int`	`[1, 16]`

entity.IndexHNSW

This struct type creates an HNSW index for the specified field. You can use entity.NewIndexHNSW() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexHNSW(metricType MetricType, M int, efConstruction int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`M`	Maximum number of outgoing connections in the graph. Higher M leads to higher accuracy/run_time at fixed ef/efConstruction.	`int`	`[2, 2048]`
`efConstruction`	Index search speed/build speed tradeoff. Increasing this parameter may enhance index quality, but it also tends to lengthen the indexing time.	`int`	`[1, int_max]`

entity.IndexIvfFlat

This struct type creates an IVF_FLAT index for the specified field. You can use entity.NewIndexIvfFlat() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexIvfFlat(metricType MetricType, nlist int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`

In addition to the methods defined in the entity.Index interface, this struct type also defines the following methods.

Method	Return Type	Description
`SupportBinary()`	`bool`	Returns whether

entity.IndexIvfPQ

This struct type creates an IVF_PQ index for the specified field. You can use entity.NewIndexGPUIvfPQ() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexIvfPQ(metricType MetricType, nlist int, m int, nbits int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`
`m`	Number of factors of product quantization	`int`	`dim mod m or = 0`
`nbits`	Number of bits in which each low-dimensional vector is stored.	`int`	`[1, 16]`

entity.IndexIvfSQ8

This struct type creates an IVF_SQ8 index for the specified field. You can use entity.NewIndexIvfSQ8() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexIvfSQ8(metricType MetricType, nlist int)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`

entity.IndexSCANN

This struct type creates a SCANN index for the specified field. You can use entity.NewIndexSCANN() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexSCANN(metricType MetricType, nlist int, with_raw_data bool)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`nlist`	Number of cluster units.	`int`	`[1, 65536]`
`with_raw_data`	Whether to include raw data when creating the index.	`bool`	N/A

entity.IndexSparseInverted

This struct type creates a SPARSE_INVERTED_INDEX index for the specified field. You can use entity.NewIndexSparseInverted() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexSparseInverted(metricType MetricType, dropRatio float64)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`dropRatio`	Proportion of small vector values that are excluded during the indexing process. This option allows fine-tuning of the indexing process, making a trade-off between efficiency and accuracy by disregarding small values when building the index.	`float64`	`[0, 1]`

entity.IndexSparseWAND

This struct type creates a SPARSE_WAND index for the specified field. You can use entity.NewIndexSparseWAND() to create one for the client.CreateIndex() request as follows:

index := entity.NewIndexSparseWAND(metricType MetricType, dropRatio float64)

Parameter	Description	Type	Value Range
`metricType`	Metric type for Milvus to follow when it measures similarities between vector embeddings.	`entity.MetricType`	N/A
`dropRatio`	Proportion of small vector values that are excluded during the indexing process. This option allows fine-tuning of the indexing process, making a trade-off between efficiency and accuracy by disregarding small values when building the index.	`float64`	`[0, 1]`

entity.IndexType

This string type defines all applicable index types.

const (
    Flat       IndexType = "FLAT" //faiss
    BinFlat    IndexType = "BIN_FLAT"
    IvfFlat    IndexType = "IVF_FLAT" //faiss
    BinIvfFlat IndexType = "BIN_IVF_FLAT"
    IvfPQ      IndexType = "IVF_PQ" //faiss
    IvfSQ8     IndexType = "IVF_SQ8"
    HNSW       IndexType = "HNSW"
    AUTOINDEX  IndexType = "AUTOINDEX"
    DISKANN    IndexType = "DISKANN"
    SCANN      IndexType = "SCANN"

    GPUIvfFlat IndexType = "GPU_IVF_FLAT"
    GPUIvfPQ   IndexType = "GPU_IVF_PQ"

    GPUCagra      IndexType = "GPU_CAGRA"
    GPUBruteForce IndexType = "GPU_BRUTE_FORCE"

    // Sparse
    SparseInverted IndexType = "SPARSE_INVERTED_INDEX"
    SparseWAND     IndexType = "SPARSE_WAND"

    // DEPRECATED
    Scalar IndexType = ""

    Trie     IndexType = "Trie"
    Sorted   IndexType = "STL_SORT"
    Inverted IndexType = "INVERTED"
)

entity.GenericIndex​

entity.IndexAUTOINDEX​

entity.IndexBinFlat​

entity.IndexBinIvfFlat​

entity.IndexDISKANN​

entity.IndexFlat​

entity.IndexGPUBruteForce​

entity.IndexGPUCagra​

entity.IndexGPUIvfFlat​

entity.IndexGPUIvfPQ​

entity.IndexHNSW​

entity.IndexIvfFlat​

entity.IndexIvfPQ​

entity.IndexIvfSQ8​

entity.IndexSCANN​

entity.IndexSparseInverted​

entity.IndexSparseWAND​

entity.IndexType​