Build an Index

This topic describes how to build an index for vectors in Milvus.

Vector indexes are an organizational unit of metadata used to accelerate vector similarity search. Without index built on vectors, Milvus will perform a brute-force search by default.

See Vector Index and Index Selection for more information about mechanism and varieties of vector indexes.

Current release of Milvus only supports index on vector field. Future releases will support index on scalar field.
By default, Milvus does not index a segment with less than 1,024 rows. To change this parameter, configure minSegmentSizeToEnableIndex in milvus.yaml.

The following example builds a 1024-cluster IVF_FLAT index with Euclidean distance (L2) as the similarity metrics. You can choose the index and metrics that suit your scenario. See Similarity Metrics for more information.

Prepare index parameter

Prepare the index parameters.

Python Node.js CLI

index_params = {
        "metric_type":"L2",
        "index_type":"IVF_FLAT",
        "params":{"nlist":1024}
    }

const index_params = {
  metric_type: "L2",
  index_type: "IVF_FLAT",
  params: JSON.stringify({ nlist: 1024 }),
};

create index

Collection name (book): book

The name of the field to create an index for (book_intro): book_intro

Index type (FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, RNSG, HNSW, ANNOY): IVF_FLAT

Index metric type (L2, IP, HAMMING, TANIMOTO): L2

Index params nlist: 1024

Timeout []:

Parameter Description Options

Parameter	Description	Options
`metric_type`	Type of metrics used to measure similarity of vectors.	For floating point vectors: `L2` (Euclidean distance) `IP` (Inner product) For binary vectors: `JACCARD` (Jaccard distance) `TANIMOTO` (Tanimoto distance) `HAMMING` (Hamming distance) `SUPERSTRUCTURE` (Superstructure) `SUBSTRUCTURE` (Substructure)
`index_type`	Type of index used to accelerate the vector search.	For floating point vectors: `FLAT` (FLAT) `IVF_FLAT` (IVF_FLAT) `IVF_SQ8` (IVF_SQ8) `IVF_PQ` (IVF_PQ) `HNSW` (HNSW) `ANNOY` (ANNOY) `RHNSW_FLAT` (RHNSW_FLAT) `RHNSW_PQ` (RHNSW_PQ) `RHNSW_SQ` (RHNSW_SQ) For binary vectors: `BIN_FLAT` (BIN_FLAT) `BIN_IVF_FLAT` (BIN_IVF_FLAT)
`params`	Building parameter(s) specific to the index. See Index Selection for more information.	See Index Selection for more information.

metric_type

Type of metrics used to measure similarity of vectors.

For floating point vectors:

L2 (Euclidean distance)
IP (Inner product)

For binary vectors:

JACCARD (Jaccard distance)
TANIMOTO (Tanimoto distance)
HAMMING (Hamming distance)
SUPERSTRUCTURE (Superstructure)
SUBSTRUCTURE (Substructure)

index_type

Type of index used to accelerate the vector search.

For floating point vectors:

FLAT (FLAT)
IVF_FLAT (IVF_FLAT)
IVF_SQ8 (IVF_SQ8)
IVF_PQ (IVF_PQ)
HNSW (HNSW)
ANNOY (ANNOY)
RHNSW_FLAT (RHNSW_FLAT)
RHNSW_PQ (RHNSW_PQ)
RHNSW_SQ (RHNSW_SQ)

For binary vectors:

BIN_FLAT (BIN_FLAT)
BIN_IVF_FLAT (BIN_IVF_FLAT)

params Building parameter(s) specific to the index. See Index Selection for more information. See Index Selection for more information.

Parameter Description Option

Parameter	Description	Option
`metric_type`	Type of metrics used to measure similarity of vectors.	For floating point vectors: `L2` (Euclidean distance) `IP` (Inner product) For binary vectors: `JACCARD` (Jaccard distance) `TANIMOTO` (Tanimoto distance) `HAMMING` (Hamming distance) `SUPERSTRUCTURE` (Superstructure) `SUBSTRUCTURE` (Substructure)
`index_type`	Type of index used to accelerate the vector search.	For floating point vectors: `FLAT` (FLAT) `IVF_FLAT` (IVF_FLAT) `IVF_SQ8` (IVF_SQ8) `IVF_PQ` (IVF_PQ) `HNSW` (HNSW) `ANNOY` (ANNOY) `RHNSW_FLAT` (RHNSW_FLAT) `RHNSW_PQ` (RHNSW_PQ) `RHNSW_SQ` (RHNSW_SQ) For binary vectors: `BIN_FLAT` (BIN_FLAT) `BIN_IVF_FLAT` (BIN_IVF_FLAT)
`params`	Building parameter(s) specific to the index. See Index Selection for more information.	See Index Selection for more information.

metric_type

Type of metrics used to measure similarity of vectors.

For floating point vectors:

L2 (Euclidean distance)
IP (Inner product)

For binary vectors:

JACCARD (Jaccard distance)
TANIMOTO (Tanimoto distance)
HAMMING (Hamming distance)
SUPERSTRUCTURE (Superstructure)
SUBSTRUCTURE (Substructure)

index_type

Type of index used to accelerate the vector search.

For floating point vectors:

FLAT (FLAT)
IVF_FLAT (IVF_FLAT)
IVF_SQ8 (IVF_SQ8)
IVF_PQ (IVF_PQ)
HNSW (HNSW)
ANNOY (ANNOY)
RHNSW_FLAT (RHNSW_FLAT)
RHNSW_PQ (RHNSW_PQ)
RHNSW_SQ (RHNSW_SQ)

For binary vectors:

BIN_FLAT (BIN_FLAT)
BIN_IVF_FLAT (BIN_IVF_FLAT)

params Building parameter(s) specific to the index. See Index Selection for more information. See Index Selection for more information.

Option	Description
--help	Displays help for using the command.

Build index

Build the index by specifying the vector field name and index parameters.

Python Node.js CLI

from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
collection.create_index(
    field_name="book_intro", 
    index_params=index_params
    )

Status(code=0, message='')

await milvusClient.indexManager.createIndex({
  collection_name: "book",
  field_name: "book_intro",
  extra_params: index_params,
});

# Follow the previous step.

Parameter	Description
`field_name`	Name of the vector field to build index on.
`index_params`	Parameters of the index to build.

Parameter	Description
`collection_name`	Name of the collection to build index in.
`field_name`	Name of the vector field to build index on.
`extra_params`	Parameters of the index to build.

What's next

Learn more basic operations of Milvus:
Explore API references for Milvus SDKs:
- PyMilvus API reference
- Node.js API reference