test: adds tests for query service

• translator
• converter: already there, now using vite

src/tests/query/queryTranslator/queryTranslator.test.ts

+import { expect, describe, it } from 'vitest';
+import type { QueryMultiGraph } from 'ts-common/src/model/graphology';
+import { MLTypesEnum } from 'ts-common/src/model/query/machineLearningModel';
+import { type QueryBuilderSettings } from 'ts-common/src/model/query/queryBuilderModel';
+import { Query2BackendQuery } from './../../../utils/reactflow/query2backend';
+import type { MachineLearning } from 'ts-common/src/model/query/queryRequestModel';
+import { type BackendQueryFormat } from 'ts-common';
+import { createQueryMultiGraphFromData, settingsBase, ss_id } from './testData';
+import { visualQuery_1 } from './testData';
+import { expectedResult_1 } from './testData';
+import { Logger } from 'ts-common';
+
+Logger.excludedOwners.push('ts-common');
+Logger.excludedOwners.push('query-service');
+
+describe('query2backend', () => {
+  it('should return correctly a node - 0', () => {
+    const nodesData = [
+      {
+        id: 'Movie',
+        schemaKey: 'Movie',
+        type: 'entity',
+        width: 100,
+        height: 100,
+        x: 50,
+        y: 50,
+        name: 'Movie',
+        attributes: [
+          { name: '(# Connection)', type: 'float' },
+          { name: 'tagline', type: 'string' },
+          { name: 'votes', type: 'int' },
+          { name: 'title', type: 'string' },
+          { name: 'released', type: 'int' },
+        ],
+      },
+    ];
+
+    const visualQuery: QueryMultiGraph = createQueryMultiGraphFromData(nodesData, []);
+
+    const ml: MachineLearning[] = [
+      { type: MLTypesEnum.linkPrediction, parameters: [], id: 1 },
+      { type: MLTypesEnum.centrality, parameters: [], id: 2 },
+      { type: MLTypesEnum.communityDetection, parameters: [], id: 3 },
+      { type: MLTypesEnum.shortestPath, parameters: [], id: 4 },
+    ];
+
+    const result = Query2BackendQuery(ss_id, visualQuery, settingsBase, ml);
+    const expectedResult: BackendQueryFormat = {
+      saveStateID: 'test',
+      query: [
+        {
+          id: 'path_0',
+          node: {
+            label: 'Movie',
+            id: 'Movie',
+            relation: undefined,
+          },
+        },
+      ],
+      machineLearning: [
+        { type: MLTypesEnum.linkPrediction, parameters: [], id: 1 },
+        { type: MLTypesEnum.centrality, parameters: [], id: 2 },
+        { type: MLTypesEnum.communityDetection, parameters: [], id: 3 },
+        { type: MLTypesEnum.shortestPath, parameters: [], id: 4 },
+      ],
+      limit: 500,
+      return: ['*'],
+      cached: false,
+      logic: undefined,
+    };
+    expect(result).toEqual(expectedResult);
+  });
+  it('should return correctly on a simple query with multiple paths - 1', () => {
+    const ml: MachineLearning[] = [
+      { type: MLTypesEnum.linkPrediction, parameters: [], id: 1 },
+      { type: MLTypesEnum.centrality, parameters: [], id: 2 },
+      { type: MLTypesEnum.communityDetection, parameters: [], id: 3 },
+      { type: MLTypesEnum.shortestPath, parameters: [], id: 4 },
+    ];
+
+    const result = Query2BackendQuery(ss_id, visualQuery_1, settingsBase, ml);
+
+    expect(result).toEqual(expectedResult_1);
+  });
+  /*
+  it('should return correctly on a complex query with logic', () => {});
+  it('should return correctly on a query with group by logic', () => {});
+  it('should return correctly on a query with no label', () => {});
+  it('should return correctly on a query with no depth', () => {});
+  it('should return correctly on a query with average calculation', () => {});
+  it('should return correctly on a query with average calculation and multiple paths', () => {});
+  it('should return correctly on a single entity query with lower like logic', () => {});
+  it('should return correctly on a query with like logic', () => {});
+  it('should return correctly on a query with both direction relation', () => {});
+*/
+});

[Dennis Collaris]

-        id: 'Movie',
-        schemaKey: 'Movie',
-        type: 'entity',
-        width: 100,
-        height: 100,
-        x: 50,
-        y: 50,
-        name: 'Movie',
-        attributes: [
-          { name: '(# Connection)', type: 'float' },
-          { name: 'tagline', type: 'string' },
-          { name: 'votes', type: 'int' },
-          { name: 'title', type: 'string' },
-          { name: 'released', type: 'int' },
-        ],
+        id: 'Movie',
+        type: 'entity',
+        name: 'Movie',
+        attributes: [],

Lets keep this as minimal as possible. For what its testing at the moment, only these keys are necessary. The rest does not appear in the result, and would not need to be provided

[Dennis Collaris]

-      machineLearning: [
-        { type: MLTypesEnum.linkPrediction, parameters: [], id: 1 },
-        { type: MLTypesEnum.centrality, parameters: [], id: 2 },
-        { type: MLTypesEnum.communityDetection, parameters: [], id: 3 },
-        { type: MLTypesEnum.shortestPath, parameters: [], id: 4 },
-      ],
+      machineLearning: ml

I think this would also work

[Dennis Collaris]

logic: undefined seems.. wrong? It also appears in the other test. Did you check if this is expected behavior?

[Dennis Collaris]

This one is duplicated, we can maybe define it once, globally?