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workflow_detail tests #6

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merged 1 commit into from
Sep 6, 2024
Merged

workflow_detail tests #6

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8 changes: 1 addition & 7 deletions public/pages/workflow_detail/workflow_detail.test.tsx
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Original file line number Diff line number Diff line change
Expand Up @@ -57,13 +57,7 @@ const workflowId = '12345';
const workflowName = 'test_workflow';

describe('WorkflowDetail', () => {
const workflowTypes = [
WORKFLOW_TYPE.CUSTOM,
WORKFLOW_TYPE.SEMANTIC_SEARCH,
WORKFLOW_TYPE.HYBRID_SEARCH,
];

workflowTypes.forEach((type) => {
Object.values(WORKFLOW_TYPE).forEach((type) => {
test(`renders the page with ${type} type`, () => {
const { getAllByText, getByText, getByRole } = renderWithRouter(
workflowId,
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8 changes: 4 additions & 4 deletions public/pages/workflows/new_workflow/utils.ts
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Expand Up @@ -60,7 +60,7 @@ export function enrichPresetWorkflowWithUiMetadata(
} as WorkflowTemplate;
}

function fetchEmptyMetadata(): UIState {
export function fetchEmptyMetadata(): UIState {
return {
type: WORKFLOW_TYPE.CUSTOM,
config: {
Expand Down Expand Up @@ -125,7 +125,7 @@ function fetchEmptyMetadata(): UIState {
};
}

function fetchSemanticSearchMetadata(): UIState {
export function fetchSemanticSearchMetadata(): UIState {
let baseState = fetchEmptyMetadata();
baseState.type = WORKFLOW_TYPE.SEMANTIC_SEARCH;
baseState.config.ingest.enrich.processors = [new MLIngestProcessor().toObj()];
Expand All @@ -143,7 +143,7 @@ function fetchSemanticSearchMetadata(): UIState {
return baseState;
}

function fetchMultimodalSearchMetadata(): UIState {
export function fetchMultimodalSearchMetadata(): UIState {
let baseState = fetchEmptyMetadata();
baseState.type = WORKFLOW_TYPE.MULTIMODAL_SEARCH;
baseState.config.ingest.enrich.processors = [new MLIngestProcessor().toObj()];
Expand All @@ -163,7 +163,7 @@ function fetchMultimodalSearchMetadata(): UIState {
return baseState;
}

function fetchHybridSearchMetadata(): UIState {
export function fetchHybridSearchMetadata(): UIState {
let baseState = fetchEmptyMetadata();
baseState.type = WORKFLOW_TYPE.HYBRID_SEARCH;
baseState.config.ingest.enrich.processors = [new MLIngestProcessor().toObj()];
Expand Down
246 changes: 28 additions & 218 deletions test/utils.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,14 @@
* SPDX-License-Identifier: Apache-2.0
*/

import { PROCESSOR_TYPE, WORKFLOW_TYPE } from '../common/constants';
import { WORKFLOW_TYPE } from '../common/constants';
import { UIState, Workflow } from '../common/interfaces';
import {
IProcessorConfig,
ProcessorsConfig,
Workflow,
} from '../common/interfaces';
fetchEmptyMetadata,
fetchHybridSearchMetadata,
fetchMultimodalSearchMetadata,
fetchSemanticSearchMetadata,
} from '../public/pages/workflows/new_workflow/utils';

export function mockStore(
workflowId: string,
Expand Down Expand Up @@ -49,222 +51,30 @@ function generateWorkflow(
id: workflowId,
name: workflowName,
version: { template: '1.0.0', compatibility: ['2.17.0', '3.0.0'] },
ui_metadata: {
type: workflowType,
config: {
search: {
pipelineName: {
id: 'pipelineName',
type: 'string',
value: 'search_pipeline',
},
request: {
id: 'request',
type: 'json',
value: '{\n "query": {\n "match_all": {}\n },\n "size": 1000\n}',
},
index: { name: { id: 'indexName', type: 'string' } },
enrichRequest: getRequestProcessor(workflowType),
enrichResponse: getResponseProcessor(workflowType),
},
ingest: {
pipelineName: {
id: 'pipelineName',
type: 'string',
value: 'ingest_pipeline',
},
enrich: getRequestProcessor(workflowType),
index: {
settings: { id: 'indexSettings', type: 'json' },
mappings: {
id: 'indexMappings',
type: 'json',
value: '{\n "properties": {}\n}',
},
name: {
id: 'indexName',
type: 'string',
value: 'my-new-index',
},
},
enabled: { id: 'enabled', type: 'boolean', value: true },
},
},
},
ui_metadata: getConfig(workflowType),
};
}

function getRequestProcessor(workflowType: WORKFLOW_TYPE): ProcessorsConfig {
if (
workflowType === WORKFLOW_TYPE.HYBRID_SEARCH ||
workflowType === WORKFLOW_TYPE.SEMANTIC_SEARCH
) {
// TODO: In the code below, only the ml_inference processor has been added. Other processors still need to be included.
const mlInferenceProcessor: IProcessorConfig = {
name: 'ML Inference Processor',
id: 'ml_processor_ingest',
fields: [
{
id: 'model',
type: 'model',
value: {
id: 'dfMPE5EB8_-RPNi-S0gD',
},
},
{
id: 'input_map',
type: 'mapArray',
value: [
[
{
value: 'my_text',
key: '',
},
],
],
},
{
id: 'output_map',
type: 'mapArray',
value: [
[
{
value: '',
key: 'my_embedding',
},
],
],
},
],
type: PROCESSOR_TYPE.ML,
optionalFields: [
{
id: 'query_template',
type: 'jsonString',
value: getQueryTemplate(workflowType),
},
{
id: 'description',
type: 'string',
},
{
id: 'model_config',
type: 'json',
},
{
id: 'full_response_path',
type: 'boolean',
value: false,
},
{
id: 'ignore_missing',
type: 'boolean',
value: false,
},
{
id: 'ignore_failure',
type: 'boolean',
value: false,
},
{
id: 'max_prediction_tasks',
type: 'number',
value: 10,
},
{
id: 'tag',
type: 'string',
},
],
};
return {
processors: [mlInferenceProcessor],
};
}

return { processors: [] };
}

// Function to get the query template based on workflow type
function getQueryTemplate(workflowType: WORKFLOW_TYPE) {
if (workflowType === WORKFLOW_TYPE.HYBRID_SEARCH) {
return `{
"_source": {
"excludes": ["my_embedding"]
},
"query": {
"hybrid": {
"queries": [
{
"match": {
"my_text": {
"query": "{{query_text}}"
}
}
},
{
"knn": {
"my_embedding": {
"vector": jest.fn(),
"k": 10
}
}
}
]
}
}
}`;
}

if (workflowType === WORKFLOW_TYPE.SEMANTIC_SEARCH) {
return `{
"_source": {
"excludes": ["my_embedding"]
},
"query": {
"knn": {
"my_embedding": {
"vector": jest.fn(),
"k": 10
}
}
}
}`;
function getConfig(workflowType: WORKFLOW_TYPE) {
let uiMetadata = {} as UIState;
switch (workflowType) {
case WORKFLOW_TYPE.SEMANTIC_SEARCH: {
uiMetadata = fetchSemanticSearchMetadata();
break;
}
case WORKFLOW_TYPE.MULTIMODAL_SEARCH: {
uiMetadata = fetchMultimodalSearchMetadata();
break;
}
case WORKFLOW_TYPE.HYBRID_SEARCH: {
uiMetadata = fetchHybridSearchMetadata();
break;
}
default: {
uiMetadata = fetchEmptyMetadata();
break;
}
}
}

function getResponseProcessor(workflowType: WORKFLOW_TYPE): ProcessorsConfig {
return workflowType === WORKFLOW_TYPE.HYBRID_SEARCH
? {
processors: [
{
id: 'normalization_processor',
name: 'Normalization Processor',
type: PROCESSOR_TYPE.NORMALIZATION,
fields: [],
optionalFields: [
{ id: 'weights', type: 'string', value: '0.5, 0.5' },
{
id: 'normalization_technique',
type: 'select',
selectOptions: ['min_max', 'l2'],
},
{
id: 'combination_technique',
type: 'select',
selectOptions: [
'arithmetic_mean',
'geometric_mean',
'harmonic_mean',
],
},
{ id: 'description', type: 'string' },
{ id: 'tag', type: 'string' },
],
},
],
}
: { processors: [] };
return uiMetadata;
}

export const resizeObserverMock = jest.fn().mockImplementation(() => ({
Expand Down
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