Index configuration parameters Stay organized with collections Save and categorize content based on your preferences.
To configure indexes for similarity searches, you need to configure thefollowing fields.
For instructions on how to configure an index, seeConfigure index parameters.
NearestNeighborSearch
| Fields | |
|---|---|
contentsDeltaUri |
Allows inserting, updating or deleting the contents of the Vector Search If you set this field when calling |
isCompleteOverwrite |
If this field is set together with |
config | The configuration of the Vector Search |
NearestNeighborSearchConfig
| Fields | |
|---|---|
dimensions |
Required. The number of dimensions of the input vectors. Used for dense embeddings only. |
approximateNeighborsCount |
Required if tree-AH algorithm is used. The default number of neighbors to find through approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered using a more expensive distance computation. |
ShardSize | ShardSizeThe size of each shard. When an index is large, it is sharded based on the specified shard size. During serving, each shard is served on a separate node and scales independently. |
distanceMeasureType | The distance measure used in nearest neighbor search. |
featureNormType | Type of normalization to be carried out on each vector. |
algorithmConfig | oneOf:The configuration for the algorithms that Vector Search uses for efficient search. Used for dense embeddings only.
|
DistanceMeasureType
| Enums | |
|---|---|
SQUARED_L2_DISTANCE | Euclidean (L2) Distance |
L1_DISTANCE | Manhattan (L1) Distance |
DOT_PRODUCT_DISTANCE | Default value. Defined as a negative of the dot product. |
COSINE_DISTANCE | Cosine Distance. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of the COSINE distance. Our algorithms have been more optimized for the DOT_PRODUCT distance, and when combined with UNIT_L2_NORM, it offers the same ranking and mathematical equivalence as the COSINE distance. |
ShardSize
| Enums | |
|---|---|
SHARD_SIZE_SMALL | 2 GiB per shard |
SHARD_SIZE_MEDIUM | 20 GiB per shard |
SHARD_SIZE_LARGE | 50 GiB per shard |
FeatureNormType
| Enums | |
|---|---|
UNIT_L2_NORM | Unit L2 normalization type. |
NONE | Default value. No normalization type is specified. |
TreeAhConfig
These are the fields to select for the tree-AH algorithm.
| Fields | |
|---|---|
fractionLeafNodesToSearch | double |
| The default fraction of leaf nodes that any query may be searched. Must be in range 0.0 - 1.0, exclusive. The default value is 0.05 if not set. | |
leafNodeEmbeddingCount | int32 |
| Number of embeddings on each leaf node. The default value is 1000 if not set. | |
leafNodesToSearchPercent | int32 |
Deprecated, usefractionLeafNodesToSearch.The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set. | |
BruteForceConfig
This option implements the standard linear search in the database foreach query. There are no fields to configure for a brute force search.To select this algorithm, pass an empty object forBruteForceConfigtoalgorithmConfig.
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Last updated 2025-12-15 UTC.