Build an Index on Vectors

This guide describes how to build an index on vectors in Milvus.

Vector indexes are an organizational unit of metadata used to accelerate vector similarity search. You need to create an index before you can perform ANN searches against your Milvus.

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

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

Prepare index parameter

Prepare the index parameters as follows:

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 }),
};

idx, err := entity.NewIndexIvfFlat(   // NewIndex func
    entity.L2,                        // metricType
    1024,                             // ConstructParams
)
if err != nil {
  log.Fatal("fail to create ivf flat index parameter:", err.Error())
}

final IndexType INDEX_TYPE = IndexType.IVF_FLAT;   // IndexType
final String INDEX_PARAM = "{\"nlist\":1024}";     // ExtraParam

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): IVF_FLAT

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

Index params nlist: 1024

Timeout []:

curl -X 'POST' \
  'http://localhost:9091/api/v1/index' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "collection_name": "book",
    "field_name": "book_intro",
    "extra_params":[
      {"key": "metric_type", "value": "L2"},
      {"key": "index_type", "value": "IVF_FLAT"},
      {"key": "params", "value": "{\"nlist\":1024}"}
    ]
  }'

Build index

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

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

utility.index_building_progress("book")
# Output: {'total_rows': 0, 'indexed_rows': 0}

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

err := milvusClient.CreateIndex(
  context.Background(),        // ctx
  "book",                      // CollectionName
  "book_intro",                // fieldName
  idx,                         // entity.Index
  false,                       // async
)
if err != nil {
  log.Fatal("fail to create index:", err.Error())
}

milvusClient.createIndex(
  CreateIndexParam.newBuilder()
    .withCollectionName("book")
    .withFieldName("book_intro")
    .withIndexType(INDEX_TYPE)
    .withMetricType(MetricType.L2)
    .withExtraParam(INDEX_PARAM)
    .withSyncMode(Boolean.FALSE)
    .build()
);

# Follow the previous step.

# Follow the previous step.

What's next

• Learn more basic operations of Milvus:
  • Conduct a vector search
  • Conduct a hybrid search