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k-NN vector field type
The k-NN plugin introduces a custom data type, the knn_vector
, that allows users to ingest their k-NN vectors into an OpenSearch index and perform different kinds of k-NN search. The knn_vector
field is highly configurable and can serve many different k-NN workloads. In general, a knn_vector
field can be built either by providing a method definition or specifying a model id.
Example
For example, to map my_vector1
as a knn_vector
, use the following request:
PUT test-index
{
"settings": {
"index": {
"knn": true,
"knn.algo_param.ef_search": 100
}
},
"mappings": {
"properties": {
"my_vector1": {
"type": "knn_vector",
"dimension": 3,
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "lucene",
"parameters": {
"ef_construction": 128,
"m": 24
}
}
}
}
}
}
Method definitions
Method definitions are used when the underlying approximate k-NN algorithm does not require training. For example, the following knn_vector
field specifies that nmslib’s implementation of hnsw should be used for approximate k-NN search. During indexing, nmslib will build the corresponding hnsw segment files.
"my_vector": {
"type": "knn_vector",
"dimension": 4,
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "nmslib",
"parameters": {
"ef_construction": 128,
"m": 24
}
}
}
Model IDs
Model IDs are used when the underlying Approximate k-NN algorithm requires a training step. As a prerequisite, the model has to be created with the Train API. The model contains the information needed to initialize the native library segment files.
"type": "knn_vector",
"model_id": "my-model"
}
However, if you intend to use Painless scripting or a k-NN score script, you only need to pass the dimension.
"type": "knn_vector",
"dimension": 128
}
Lucene byte vector
By default, k-NN vectors are float
vectors, where each dimension is 4 bytes. If you want to save storage space, you can use byte
vectors with the lucene
engine. In a byte
vector, each dimension is a signed 8-bit integer in the [-128, 127] range.
Byte vectors are supported only for the lucene
engine. They are not supported for the nmslib
and faiss
engines.
In k-NN benchmarking tests, the use of byte
rather than float
vectors resulted in a significant reduction in storage and memory usage as well as improved indexing throughput and reduced query latency. Additionally, precision on recall was not greatly affected (note that recall can depend on various factors, such as the quantization technique and data distribution).
When using byte
vectors, expect some loss of precision in the recall compared to using float
vectors. Byte vectors are useful in large-scale applications and use cases that prioritize a reduced memory footprint in exchange for a minimal loss of recall.
Introduced in k-NN plugin version 2.9, the optional data_type
parameter defines the data type of a vector. The default value of this parameter is float
.
To use a byte
vector, set the data_type
parameter to byte
when creating mappings for an index:
PUT test-index
{
"settings": {
"index": {
"knn": true,
"knn.algo_param.ef_search": 100
}
},
"mappings": {
"properties": {
"my_vector1": {
"type": "knn_vector",
"dimension": 3,
"data_type": "byte",
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "lucene",
"parameters": {
"ef_construction": 128,
"m": 24
}
}
}
}
}
}
Then ingest documents as usual. Make sure each dimension in the vector is in the supported [-128, 127] range:
PUT test-index/_doc/1
{
"my_vector1": [-126, 28, 127]
}
PUT test-index/_doc/2
{
"my_vector1": [100, -128, 0]
}
When querying, be sure to use a byte
vector:
GET test-index/_search
{
"size": 2,
"query": {
"knn": {
"my_vector1": {
"vector": [26, -120, 99],
"k": 2
}
}
}
}
Quantization techniques
If your vectors are of the type float
, you need to first convert them to the byte
type before ingesting the documents. This conversion is accomplished by quantizing the dataset—reducing the precision of its vectors. There are many quantization techniques, such as scalar quantization or product quantization (PQ), which is used in the Faiss engine. The choice of quantization technique depends on the type of data you’re using and can affect the accuracy of recall values. The following sections describe the scalar quantization algorithms that were used to quantize the k-NN benchmarking test data for the L2 and cosine similarity space types. The provided pseudocode is for illustration purposes only.
Scalar quantization for the L2 space type
The following example pseudocode illustrates the scalar quantization technique used for the benchmarking tests on Euclidean datasets with the L2 space type. Euclidean distance is shift invariant. If you shift both \(x\) and \(y\) by the same \(z\), then the distance remains the same (\(\lVert x-y\rVert =\lVert (x-z)-(y-z)\rVert\)).
# Random dataset (Example to create a random dataset)
dataset = np.random.uniform(-300, 300, (100, 10))
# Random query set (Example to create a random queryset)
queryset = np.random.uniform(-350, 350, (100, 10))
# Number of values
B = 256
# INDEXING:
# Get min and max
dataset_min = np.min(dataset)
dataset_max = np.max(dataset)
# Shift coordinates to be non-negative
dataset -= dataset_min
# Normalize into [0, 1]
dataset *= 1. / (dataset_max - dataset_min)
# Bucket into 256 values
dataset = np.floor(dataset * (B - 1)) - int(B / 2)
# QUERYING:
# Clip (if queryset range is out of datset range)
queryset = queryset.clip(dataset_min, dataset_max)
# Shift coordinates to be non-negative
queryset -= dataset_min
# Normalize
queryset *= 1. / (dataset_max - dataset_min)
# Bucket into 256 values
queryset = np.floor(queryset * (B - 1)) - int(B / 2)
Scalar quantization for the cosine similarity space type
The following example pseudocode illustrates the scalar quantization technique used for the benchmarking tests on angular datasets with the cosine similarity space type. Cosine similarity is not shift invariant (\(cos(x, y) \neq cos(x-z, y-z)\)).
The following pseudocode is for positive numbers:
# For Positive Numbers
# INDEXING and QUERYING:
# Get Max of train dataset
max = np.max(dataset)
min = 0
B = 127
# Normalize into [0,1]
val = (val - min) / (max - min)
val = (val * B)
# Get int and fraction values
int_part = floor(val)
frac_part = val - int_part
if 0.5 < frac_part:
bval = int_part + 1
else:
bval = int_part
return Byte(bval)
The following pseudocode is for negative numbers:
# For Negative Numbers
# INDEXING and QUERYING:
# Get Min of train dataset
min = 0
max = -np.min(dataset)
B = 128
# Normalize into [0,1]
val = (val - min) / (max - min)
val = (val * B)
# Get int and fraction values
int_part = floor(var)
frac_part = val - int_part
if 0.5 < frac_part:
bval = int_part + 1
else:
bval = int_part
return Byte(bval)