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test(workflow): add unit tests for workflow components (#33741)
Co-authored-by: CodingOnStar <hanxujiang@dify.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Coding On Star
GitHub
@ -0,0 +1,513 @@
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import type { KnowledgeBaseNodeType } from '../../types'
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import { act } from '@testing-library/react'
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import {
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createNode,
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createNodeDataFactory,
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} from '@/app/components/workflow/__tests__/fixtures'
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import { renderWorkflowFlowHook } from '@/app/components/workflow/__tests__/workflow-test-env'
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import { RerankingModeEnum } from '@/models/datasets'
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import {
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ChunkStructureEnum,
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HybridSearchModeEnum,
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IndexMethodEnum,
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RetrievalSearchMethodEnum,
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WeightedScoreEnum,
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} from '../../types'
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import { useConfig } from '../use-config'
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const mockHandleNodeDataUpdateWithSyncDraft = vi.hoisted(() => vi.fn())
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vi.mock('@/app/components/workflow/hooks', () => ({
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useNodeDataUpdate: () => ({
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handleNodeDataUpdateWithSyncDraft: mockHandleNodeDataUpdateWithSyncDraft,
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}),
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}))
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const createNodeData = createNodeDataFactory<KnowledgeBaseNodeType>({
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title: 'Knowledge Base',
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desc: '',
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type: 'knowledge-base' as KnowledgeBaseNodeType['type'],
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index_chunk_variable_selector: ['chunks', 'results'],
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chunk_structure: ChunkStructureEnum.general,
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indexing_technique: IndexMethodEnum.QUALIFIED,
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embedding_model: 'text-embedding-3-large',
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embedding_model_provider: 'openai',
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keyword_number: 3,
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.semantic,
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reranking_enable: false,
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reranking_mode: RerankingModeEnum.RerankingModel,
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reranking_model: {
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reranking_provider_name: '',
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reranking_model_name: '',
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},
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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summary_index_setting: {
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enable: false,
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summary_prompt: 'existing prompt',
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},
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})
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const renderConfigHook = (nodeData: KnowledgeBaseNodeType) =>
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renderWorkflowFlowHook(() => useConfig('knowledge-base-node'), {
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nodes: [
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createNode({
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id: 'knowledge-base-node',
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data: nodeData,
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}),
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],
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edges: [],
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})
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describe('useConfig', () => {
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beforeEach(() => {
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vi.clearAllMocks()
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})
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it('should preserve the current chunk variable selector when the chunk structure does not change', () => {
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const { result } = renderConfigHook(createNodeData())
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act(() => {
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result.current.handleChunkStructureChange(ChunkStructureEnum.general)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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chunk_structure: ChunkStructureEnum.general,
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index_chunk_variable_selector: ['chunks', 'results'],
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}),
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})
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})
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it('should reset chunk variables and keep a high-quality search method when switching chunk structures', () => {
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const { result } = renderConfigHook(createNodeData({
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.keywordSearch,
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reranking_enable: false,
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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}))
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act(() => {
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result.current.handleChunkStructureChange(ChunkStructureEnum.parent_child)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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chunk_structure: ChunkStructureEnum.parent_child,
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indexing_technique: IndexMethodEnum.QUALIFIED,
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index_chunk_variable_selector: [],
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.keywordSearch,
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}),
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}),
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})
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})
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it('should preserve semantic search when switching to a structured chunk mode from a high-quality search method', () => {
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const { result } = renderConfigHook(createNodeData({
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.semantic,
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reranking_enable: false,
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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}))
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act(() => {
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result.current.handleChunkStructureChange(ChunkStructureEnum.question_answer)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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chunk_structure: ChunkStructureEnum.question_answer,
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.semantic,
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}),
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}),
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})
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})
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it('should update the index method and keyword number', () => {
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const { result } = renderConfigHook(createNodeData())
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act(() => {
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result.current.handleIndexMethodChange(IndexMethodEnum.ECONOMICAL)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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indexing_technique: IndexMethodEnum.ECONOMICAL,
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.keywordSearch,
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}),
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}),
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})
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act(() => {
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result.current.handleIndexMethodChange(IndexMethodEnum.QUALIFIED)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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indexing_technique: IndexMethodEnum.QUALIFIED,
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.semantic,
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}),
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}),
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})
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act(() => {
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result.current.handleKeywordNumberChange(9)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: {
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keyword_number: 9,
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},
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})
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})
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it('should create default weights when embedding weights are missing and default reranking mode when switching away from hybrid', () => {
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const { result } = renderConfigHook(createNodeData({
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.semantic,
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reranking_enable: false,
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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}))
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act(() => {
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result.current.handleEmbeddingModelChange({
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embeddingModel: 'text-embedding-3-small',
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embeddingModelProvider: 'openai',
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})
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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weights: expect.objectContaining({
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vector_setting: expect.objectContaining({
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embedding_provider_name: 'openai',
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embedding_model_name: 'text-embedding-3-small',
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}),
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keyword_setting: expect.objectContaining({
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keyword_weight: 0.3,
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}),
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}),
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}),
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}),
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})
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act(() => {
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result.current.handleRetrievalSearchMethodChange(RetrievalSearchMethodEnum.fullText)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.fullText,
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reranking_mode: RerankingModeEnum.RerankingModel,
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}),
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}),
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})
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})
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it('should update embedding model weights and retrieval search method defaults', () => {
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const { result } = renderConfigHook(createNodeData({
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.semantic,
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reranking_enable: false,
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reranking_mode: RerankingModeEnum.RerankingModel,
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reranking_model: {
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reranking_provider_name: '',
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reranking_model_name: '',
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},
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weights: {
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weight_type: WeightedScoreEnum.Customized,
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vector_setting: {
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vector_weight: 0.8,
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embedding_provider_name: 'openai',
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embedding_model_name: 'text-embedding-3-large',
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},
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keyword_setting: {
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keyword_weight: 0.2,
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},
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},
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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}))
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act(() => {
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result.current.handleEmbeddingModelChange({
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embeddingModel: 'text-embedding-3-small',
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embeddingModelProvider: 'openai',
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})
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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embedding_model: 'text-embedding-3-small',
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embedding_model_provider: 'openai',
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retrieval_model: expect.objectContaining({
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weights: expect.objectContaining({
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vector_setting: expect.objectContaining({
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embedding_provider_name: 'openai',
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embedding_model_name: 'text-embedding-3-small',
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}),
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}),
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}),
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}),
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})
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act(() => {
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result.current.handleRetrievalSearchMethodChange(RetrievalSearchMethodEnum.hybrid)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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search_method: RetrievalSearchMethodEnum.hybrid,
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reranking_mode: RerankingModeEnum.RerankingModel,
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reranking_enable: true,
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}),
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}),
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})
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})
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it('should seed hybrid weights and propagate retrieval tuning updates', () => {
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const { result } = renderConfigHook(createNodeData({
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retrieval_model: {
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search_method: RetrievalSearchMethodEnum.hybrid,
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reranking_enable: false,
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top_k: 3,
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score_threshold_enabled: false,
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score_threshold: 0.5,
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},
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}))
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act(() => {
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result.current.handleHybridSearchModeChange(HybridSearchModeEnum.WeightedScore)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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reranking_mode: HybridSearchModeEnum.WeightedScore,
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reranking_enable: false,
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weights: expect.objectContaining({
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vector_setting: expect.objectContaining({
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embedding_provider_name: 'openai',
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embedding_model_name: 'text-embedding-3-large',
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}),
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}),
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}),
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}),
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})
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act(() => {
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result.current.handleRerankingModelEnabledChange(true)
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result.current.handleWeighedScoreChange({ value: [0.6, 0.4] })
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result.current.handleRerankingModelChange({
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reranking_provider_name: 'cohere',
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reranking_model_name: 'rerank-v3',
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})
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result.current.handleTopKChange(8)
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result.current.handleScoreThresholdChange(0.75)
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result.current.handleScoreThresholdEnabledChange(true)
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(2, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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reranking_enable: true,
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}),
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}),
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(3, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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weights: expect.objectContaining({
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weight_type: WeightedScoreEnum.Customized,
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vector_setting: expect.objectContaining({
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vector_weight: 0.6,
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}),
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keyword_setting: expect.objectContaining({
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keyword_weight: 0.4,
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}),
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}),
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}),
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}),
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(4, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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reranking_model: {
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reranking_provider_name: 'cohere',
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reranking_model_name: 'rerank-v3',
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},
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}),
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}),
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(5, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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top_k: 8,
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}),
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||||
}),
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})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(6, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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score_threshold: 0.75,
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}),
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||||
}),
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||||
})
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expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenNthCalledWith(7, {
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id: 'knowledge-base-node',
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data: expect.objectContaining({
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retrieval_model: expect.objectContaining({
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score_threshold_enabled: true,
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}),
|
||||
}),
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||||
})
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||||
})
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it('should reuse existing hybrid weights and allow empty embedding defaults', () => {
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const { result } = renderConfigHook(createNodeData({
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embedding_model: undefined,
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embedding_model_provider: undefined,
|
||||
retrieval_model: {
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||||
search_method: RetrievalSearchMethodEnum.hybrid,
|
||||
reranking_enable: false,
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||||
reranking_mode: RerankingModeEnum.WeightedScore,
|
||||
weights: {
|
||||
weight_type: WeightedScoreEnum.Customized,
|
||||
vector_setting: {
|
||||
vector_weight: 0.9,
|
||||
embedding_provider_name: 'existing-provider',
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||||
embedding_model_name: 'existing-model',
|
||||
},
|
||||
keyword_setting: {
|
||||
keyword_weight: 0.1,
|
||||
},
|
||||
},
|
||||
top_k: 3,
|
||||
score_threshold_enabled: false,
|
||||
score_threshold: 0.5,
|
||||
},
|
||||
}))
|
||||
|
||||
act(() => {
|
||||
result.current.handleHybridSearchModeChange(HybridSearchModeEnum.RerankingModel)
|
||||
})
|
||||
|
||||
expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
|
||||
id: 'knowledge-base-node',
|
||||
data: expect.objectContaining({
|
||||
retrieval_model: expect.objectContaining({
|
||||
reranking_mode: HybridSearchModeEnum.RerankingModel,
|
||||
reranking_enable: true,
|
||||
weights: expect.objectContaining({
|
||||
vector_setting: expect.objectContaining({
|
||||
embedding_provider_name: 'existing-provider',
|
||||
embedding_model_name: 'existing-model',
|
||||
}),
|
||||
}),
|
||||
}),
|
||||
}),
|
||||
})
|
||||
|
||||
act(() => {
|
||||
result.current.handleEmbeddingModelChange({
|
||||
embeddingModel: 'fallback-model',
|
||||
embeddingModelProvider: '',
|
||||
})
|
||||
})
|
||||
|
||||
expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
|
||||
id: 'knowledge-base-node',
|
||||
data: expect.objectContaining({
|
||||
embedding_model: 'fallback-model',
|
||||
embedding_model_provider: '',
|
||||
retrieval_model: expect.objectContaining({
|
||||
weights: expect.objectContaining({
|
||||
vector_setting: expect.objectContaining({
|
||||
embedding_provider_name: '',
|
||||
embedding_model_name: 'fallback-model',
|
||||
}),
|
||||
}),
|
||||
}),
|
||||
}),
|
||||
})
|
||||
})
|
||||
|
||||
it('should normalize input variables and merge summary index settings', () => {
|
||||
const { result } = renderConfigHook(createNodeData())
|
||||
|
||||
act(() => {
|
||||
result.current.handleInputVariableChange('chunks')
|
||||
})
|
||||
|
||||
expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
|
||||
id: 'knowledge-base-node',
|
||||
data: {
|
||||
index_chunk_variable_selector: [],
|
||||
},
|
||||
})
|
||||
|
||||
act(() => {
|
||||
result.current.handleInputVariableChange(['payload', 'chunks'])
|
||||
})
|
||||
|
||||
expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
|
||||
id: 'knowledge-base-node',
|
||||
data: {
|
||||
index_chunk_variable_selector: ['payload', 'chunks'],
|
||||
},
|
||||
})
|
||||
|
||||
act(() => {
|
||||
result.current.handleSummaryIndexSettingChange({
|
||||
enable: true,
|
||||
})
|
||||
})
|
||||
|
||||
expect(mockHandleNodeDataUpdateWithSyncDraft).toHaveBeenLastCalledWith({
|
||||
id: 'knowledge-base-node',
|
||||
data: {
|
||||
summary_index_setting: {
|
||||
enable: true,
|
||||
summary_prompt: 'existing prompt',
|
||||
},
|
||||
},
|
||||
})
|
||||
})
|
||||
})
|
||||
@ -0,0 +1,81 @@
|
||||
import { renderHook } from '@testing-library/react'
|
||||
import { ModelTypeEnum } from '@/app/components/header/account-setting/model-provider-page/declarations'
|
||||
import {
|
||||
createCredentialState,
|
||||
createModel,
|
||||
createModelItem,
|
||||
createProviderMeta,
|
||||
} from '@/app/components/workflow/__tests__/model-provider-fixtures'
|
||||
import { useEmbeddingModelStatus } from '../use-embedding-model-status'
|
||||
|
||||
const mockUseCredentialPanelState = vi.hoisted(() => vi.fn())
|
||||
const mockUseProviderContext = vi.hoisted(() => vi.fn())
|
||||
|
||||
vi.mock('@/app/components/header/account-setting/model-provider-page/provider-added-card/use-credential-panel-state', () => ({
|
||||
useCredentialPanelState: mockUseCredentialPanelState,
|
||||
}))
|
||||
|
||||
vi.mock('@/context/provider-context', () => ({
|
||||
useProviderContext: mockUseProviderContext,
|
||||
}))
|
||||
|
||||
describe('useEmbeddingModelStatus', () => {
|
||||
beforeEach(() => {
|
||||
vi.clearAllMocks()
|
||||
mockUseProviderContext.mockReturnValue({
|
||||
modelProviders: [createProviderMeta({
|
||||
supported_model_types: [ModelTypeEnum.textEmbedding],
|
||||
})],
|
||||
})
|
||||
mockUseCredentialPanelState.mockReturnValue(createCredentialState())
|
||||
})
|
||||
|
||||
// The hook should resolve provider and model metadata before deriving the final status.
|
||||
describe('Resolution', () => {
|
||||
it('should return the matched provider, current model, and active status', () => {
|
||||
const embeddingModelList = [createModel()]
|
||||
|
||||
const { result } = renderHook(() => useEmbeddingModelStatus({
|
||||
embeddingModel: 'text-embedding-3-large',
|
||||
embeddingModelProvider: 'openai',
|
||||
embeddingModelList,
|
||||
}))
|
||||
|
||||
expect(result.current.providerMeta?.provider).toBe('openai')
|
||||
expect(result.current.modelProvider?.provider).toBe('openai')
|
||||
expect(result.current.currentModel?.model).toBe('text-embedding-3-large')
|
||||
expect(result.current.status).toBe('active')
|
||||
})
|
||||
|
||||
it('should return incompatible when the provider exists but the selected model is missing', () => {
|
||||
const embeddingModelList = [
|
||||
createModel({
|
||||
models: [createModelItem({ model: 'another-model' })],
|
||||
}),
|
||||
]
|
||||
|
||||
const { result } = renderHook(() => useEmbeddingModelStatus({
|
||||
embeddingModel: 'text-embedding-3-large',
|
||||
embeddingModelProvider: 'openai',
|
||||
embeddingModelList,
|
||||
}))
|
||||
|
||||
expect(result.current.providerMeta?.provider).toBe('openai')
|
||||
expect(result.current.currentModel).toBeUndefined()
|
||||
expect(result.current.status).toBe('incompatible')
|
||||
})
|
||||
|
||||
it('should return empty when no embedding model is configured', () => {
|
||||
const { result } = renderHook(() => useEmbeddingModelStatus({
|
||||
embeddingModel: undefined,
|
||||
embeddingModelProvider: undefined,
|
||||
embeddingModelList: [],
|
||||
}))
|
||||
|
||||
expect(result.current.providerMeta).toBeUndefined()
|
||||
expect(result.current.modelProvider).toBeUndefined()
|
||||
expect(result.current.currentModel).toBeUndefined()
|
||||
expect(result.current.status).toBe('empty')
|
||||
})
|
||||
})
|
||||
})
|
||||
@ -0,0 +1,26 @@
|
||||
import { renderHook } from '@testing-library/react'
|
||||
import {
|
||||
IndexMethodEnum,
|
||||
RetrievalSearchMethodEnum,
|
||||
} from '../../types'
|
||||
import { useSettingsDisplay } from '../use-settings-display'
|
||||
|
||||
describe('useSettingsDisplay', () => {
|
||||
beforeEach(() => {
|
||||
vi.clearAllMocks()
|
||||
})
|
||||
|
||||
// The display map should expose translated labels for all index and retrieval settings.
|
||||
describe('Translations', () => {
|
||||
it('should return translated labels for each supported setting key', () => {
|
||||
const { result } = renderHook(() => useSettingsDisplay())
|
||||
|
||||
expect(result.current[IndexMethodEnum.QUALIFIED]).toBe('datasetCreation.stepTwo.qualified')
|
||||
expect(result.current[IndexMethodEnum.ECONOMICAL]).toBe('datasetSettings.form.indexMethodEconomy')
|
||||
expect(result.current[RetrievalSearchMethodEnum.semantic]).toBe('dataset.retrieval.semantic_search.title')
|
||||
expect(result.current[RetrievalSearchMethodEnum.fullText]).toBe('dataset.retrieval.full_text_search.title')
|
||||
expect(result.current[RetrievalSearchMethodEnum.hybrid]).toBe('dataset.retrieval.hybrid_search.title')
|
||||
expect(result.current[RetrievalSearchMethodEnum.keywordSearch]).toBe('dataset.retrieval.keyword_search.title')
|
||||
})
|
||||
})
|
||||
})
|
||||
Reference in New Issue
Block a user