Indexes in KDB.AI
This section contains details of the available indexes in KDB.AI and key parameters that can be tailored to specific use cases.
FLAT
The Flat search performs an exhaustive search against all vectors in the search space. You can configure it with a number of distance metrics. As the search is exhaustive, it finds the exact nearest neighbors without approximations.
Build Parameters
Option | Description | Type | Required | Default |
---|---|---|---|---|
dims |
Number of dimensions | long | true | 8 |
IVF
When using a Flat Inverted File search, first you train the index on a set of points that are used to generate cluster centroids using a k-means algorithm. The data is not partitioned into a cluster based on centroid distance. The search is performed by running a flat search against the most relevant clusters. As only a subset of the data is searched, the results are returned much quicker, but as a consequence can be less accurate.
Build Parameters
Option | Description | Type | Required | Default |
---|---|---|---|---|
nclusters |
Number of clusters | int | false | 8 |
Search Parameters
index_options
atsearch()
(anddense_index_options
athybrid_search()
)
Option | Description | Type | Required | Default |
---|---|---|---|---|
clusters |
The number of clusters to be traversed in the search | int | false | 2 |
Training is required to initialize the IVF index.
IVFPQ
You can compress input data using the product quantization method before applying the IVF schema above. This is known as IVFPQ. This can greatly reduce the size of the index held in memory and improve search speeds.
Build Parameters
Option | Description | Type | Required | Default |
---|---|---|---|---|
nclusters |
Number of clusters | int | false | 8 |
nsplits |
Number of splits | int | false | 8 |
nbits |
Number of bits | int | false | 8 |
Search Parameters
index_options
atsearch()
(anddense_index_options
athybrid_search()
)
Option | Description | Type | Required | Default |
---|---|---|---|---|
clusters |
The number of clusters to be traversed in the search | int | false | 2 |
Training is required, to initialize the IVFPQ index.
HNSW
A HNSW index establishes connections between vertices in the graph based on their distances. These links are instrumental in enabling efficient traversal and navigation through the hierarchical graph during the search process.
The HNSW index can be searched to navigate through the layers of the graph to find increasingly similar data in the graph. This approach is extremely efficient with search performance a measure of the complexity of the graph.
Build Parameters
Option | Description | Type | Required | Default |
---|---|---|---|---|
dims |
Number of dimensions | long | true | 8 |
efConstruction |
Nodes at each step (construction) | int | false | 8 |
M |
Valence of each node in graph | int | false | 8 |
Search Parameters
index_options
atsearch()
(anddense_index_options
athybrid_search()
)
Option | Description | Type | Required | Default |
---|---|---|---|---|
efSearch |
Nodes considered at each step (search) | int | false | 8 |
For coding example of using the argument index_options
at search()
(and dense_index_options
at hybrid_search()
), see the Python API Client page.