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sparse_vector field support #168186

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merged 10 commits into from
Oct 11, 2023
Merged

sparse_vector field support #168186

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1b87c3c
Add sparse_vector field support
carlosdelest Oct 5, 2023
ae9df2f
ML pipelines create sparse_vector fields instead of rank_features
carlosdelest Oct 5, 2023
d8086e8
dense_vector icon looks better on a sparse_vector field type
carlosdelest Oct 6, 2023
316ca69
Fix test snapshot
carlosdelest Oct 6, 2023
9a4c78f
Merge branch 'main' into carlosdelest/sparse_vector_field_support
carlosdelest Oct 6, 2023
51bf42f
Merge branch 'main' into carlosdelest/sparse_vector_field_support
carlosdelest Oct 6, 2023
995e2de
Merge branch 'main' into carlosdelest/sparse_vector_field_support
carlosdelest Oct 6, 2023
0a29bfe
Add sparse vector to mappings editor
carlosdelest Oct 10, 2023
3b9cb51
Merge remote-tracking branch 'carlosdelest/carlosdelest/sparse_vector…
carlosdelest Oct 10, 2023
829bf71
Merge branch 'main' into carlosdelest/sparse_vector_field_support
carlosdelest Oct 10, 2023
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1 change: 1 addition & 0 deletions packages/kbn-field-utils/src/utils/field_types.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@ export enum KNOWN_FIELD_TYPES {
RANK_FEATURES = 'rank_features',
POINT = 'point',
SHAPE = 'shape',
SPARSE_VECTOR = 'sparse_vector',
STRING = 'string',
TEXT = 'text',
VERSION = 'version',
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4 changes: 4 additions & 0 deletions packages/kbn-field-utils/src/utils/get_field_type_description.ts
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Expand Up @@ -118,6 +118,10 @@ export function getFieldTypeDescription(type?: string) {
return i18n.translate('fieldUtils.fieldNameDescription.shapeField', {
defaultMessage: 'Arbitrary cartesian geometries.',
});
case KNOWN_FIELD_TYPES.SPARSE_VECTOR:
return i18n.translate('fieldUtils.fieldNameDescription.sparseVectorField', {
defaultMessage: 'Records sparse vectors of float values.',
});
case KNOWN_FIELD_TYPES.STRING:
return i18n.translate('fieldUtils.fieldNameDescription.stringField', {
defaultMessage: 'Full text such as the body of an email or a product description.',
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4 changes: 4 additions & 0 deletions packages/kbn-field-utils/src/utils/get_field_type_name.ts
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Expand Up @@ -123,6 +123,10 @@ export function getFieldTypeName(type?: string) {
return i18n.translate('fieldUtils.fieldNameIcons.shapeFieldAriaLabel', {
defaultMessage: 'Shape',
});
case KNOWN_FIELD_TYPES.SPARSE_VECTOR:
return i18n.translate('fieldUtils.fieldNameIcons.sparseVectorFieldAriaLabel', {
defaultMessage: 'Sparse vector',
});
case KNOWN_FIELD_TYPES.STRING:
return i18n.translate('fieldUtils.fieldNameIcons.stringFieldAriaLabel', {
defaultMessage: 'String',
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10 changes: 10 additions & 0 deletions packages/kbn-react-field/src/field_icon/__snapshots__/field_icon.test.tsx.snap
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2 changes: 2 additions & 0 deletions packages/kbn-react-field/src/field_icon/field_icon.tsx
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Expand Up @@ -32,6 +32,7 @@ export interface FieldIconProps extends Omit<EuiTokenProps, 'iconType'> {
| '_source'
| 'point'
| 'shape'
| 'sparse_vector'
| 'string'
| string
| 'nested'
Expand Down Expand Up @@ -69,6 +70,7 @@ export const typeToEuiIconMap: Partial<Record<string, EuiTokenProps>> = {
_source: { iconType: 'editorCodeBlock', color: 'gray' },
point: { iconType: 'tokenShape' }, // there is no separate icon for `point` yet
shape: { iconType: 'tokenShape' },
sparse_vector: { iconType: 'tokenDenseVector' },
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Same question here, do we want to use tokenDenseVector or should we create a new token for sparse vector?

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@andreadelrio do we have an icon we can use or add to eui?

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Andrea is already pinged in elastic/eui#7257 (comment). 😉 My plan is to open a separate PR when that's ready.

string: { iconType: 'tokenString' },
text: { iconType: 'tokenString' },
keyword: { iconType: 'tokenKeyword' },
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4 changes: 2 additions & 2 deletions ...ise_search/server/lib/indices/pipelines/ml_inference/update_ml_inference_mappings.test.ts
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Expand Up @@ -44,7 +44,7 @@ describe('updateMlInferenceMappings', () => {
input_one_expanded: {
properties: {
predicted_value: {
type: 'rank_features',
type: 'sparse_vector',
},
model_id: {
type: 'keyword',
Expand All @@ -54,7 +54,7 @@ describe('updateMlInferenceMappings', () => {
input_two_expanded: {
properties: {
predicted_value: {
type: 'rank_features',
type: 'sparse_vector',
},
model_id: {
type: 'keyword',
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4 changes: 2 additions & 2 deletions ...terprise_search/server/lib/indices/pipelines/ml_inference/update_ml_inference_mappings.ts
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Expand Up @@ -77,7 +77,7 @@ const generateTextExpansionMappingProperties = (sourceFields: string[], targetFi
type: 'keyword',
},
predicted_value: {
type: 'rank_features',
type: 'sparse_vector',
},
},
},
Expand All @@ -97,7 +97,7 @@ const formDefaultElserMappingProps = (sourceFields: string[]) => {
type: 'keyword',
},
predicted_value: {
type: 'rank_features',
type: 'sparse_vector',
},
},
},
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17 changes: 17 additions & 0 deletions ...agement/public/application/components/mappings_editor/constants/data_types_definition.tsx
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Expand Up @@ -851,6 +851,23 @@ export const TYPE_DEFINITION: { [key in DataType]: DataTypeDefinition } = {
</p>
),
},
sparse_vector: {
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Hi @carlosdelest, just a question: why are we adding these changes to the Index management plugin?
Did you mean to add support for the sparse_vector field type in the Mappings editor?

Screenshot 2023-10-06 at 17 52 47

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@ElenaStoeva , thanks for pointing that out - I totally missed it.

I've made the changes here.

image

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Thanks for clarifying! Tested locally and LGTM.

label: i18n.translate('xpack.idxMgmt.mappingsEditor.dataType.sparseVectorDescription', {
defaultMessage: 'Sparse vector',
}),
value: 'sparse_vector',
documentation: {
main: 'sparse-vector.html',
},
description: () => (
<p>
<FormattedMessage
id="xpack.idxMgmt.mappingsEditor.dataType.sparseVectorLongDescription"
defaultMessage="Sparse vector fields index features and weights, useful for document scoring."
/>
</p>
),
},
point: {
label: i18n.translate('xpack.idxMgmt.mappingsEditor.dataType.pointDescription', {
defaultMessage: 'Point',
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1 change: 1 addition & 0 deletions ...s/index_management/public/application/components/mappings_editor/types/document_fields.ts
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Expand Up @@ -58,6 +58,7 @@ export type MainType =
| 'rank_features'
| 'shape'
| 'search_as_you_type'
| 'sparse_vector'
| 'date'
| 'date_nanos'
| 'geo_point'
Expand Down