From 95325346ffb7696d492464075187859bcd5857f0 Mon Sep 17 00:00:00 2001 From: Archer <545436317@qq.com> Date: Fri, 22 Aug 2025 10:18:24 +0800 Subject: [PATCH] perf: vector format (#5516) * perf: vector format * feat: embedding batch size --- document/content/docs/upgrading/4-12/4122.mdx | 4 + document/data/doc-last-modified.json | 3 +- packages/global/core/ai/model.d.ts | 1 + .../global/core/workflow/node/constant.ts | 3 +- packages/service/core/ai/embedding/index.ts | 56 ++--- packages/web/i18n/en/account_model.json | 1 + packages/web/i18n/zh-CN/account_model.json | 1 + packages/web/i18n/zh-Hant/account_model.json | 1 + .../src/components/Select/AIModelSelector.tsx | 19 +- .../account/model/AddModelBox.tsx | 20 ++ .../Flow/hooks/useDebug.tsx | 2 + .../WorkflowComponents/context/index.tsx | 18 ++ .../chat/ChatWindow/HomeChatWindow.tsx | 1 + projects/app/src/web/core/workflow/utils.ts | 120 +++++++++- test/cases/service/core/ai/embedding.test.ts | 224 ++++++++++++++++++ 15 files changed, 436 insertions(+), 38 deletions(-) create mode 100644 test/cases/service/core/ai/embedding.test.ts diff --git a/document/content/docs/upgrading/4-12/4122.mdx b/document/content/docs/upgrading/4-12/4122.mdx index 784be4cb1..bffb0bb9c 100644 --- a/document/content/docs/upgrading/4-12/4122.mdx +++ b/document/content/docs/upgrading/4-12/4122.mdx @@ -6,9 +6,12 @@ description: 'FastGPT V4.12.2 更新说明' ## 🚀 新增内容 +1. 向量模型并发请求设置，不统一设置成 10，避免部分向量模型不支持并发，默认均为 1，可在模型配置中设置。 ## ⚙️ 优化 +1. 增加工作流**独立分支**异常检测。 +2. 向量模型超过 1536 维度进行截断时，强制进行归一化。其他维度是否归一化，完全由配置决定，减少自动判断的计算量。 ## 🐛 修复 @@ -17,6 +20,7 @@ description: 'FastGPT V4.12.2 更新说明' 3. 移动端，分享链接，异常加载了登录态对话页的导航。 4. 用户同步可能出现写冲突问题。 5. 无法完全关闭系统套餐，会存在空对象默认值，导致鉴权异常。 +6. 工作流，添加团队应用，搜索无效。 ## 🔨 工具更新 diff --git a/document/data/doc-last-modified.json b/document/data/doc-last-modified.json index d234268a4..d6ff18126 100644 --- a/document/data/doc-last-modified.json +++ b/document/data/doc-last-modified.json @@ -97,13 +97,14 @@ "document/content/docs/protocol/terms.en.mdx": "2025-08-03T22:37:45+08:00", "document/content/docs/protocol/terms.mdx": "2025-08-03T22:37:45+08:00", "document/content/docs/toc.en.mdx": "2025-08-04T13:42:36+08:00", - "document/content/docs/toc.mdx": "2025-08-13T14:29:13+08:00", + "document/content/docs/toc.mdx": "2025-08-20T21:58:13+08:00", "document/content/docs/upgrading/4-10/4100.mdx": "2025-08-02T19:38:37+08:00", "document/content/docs/upgrading/4-10/4101.mdx": "2025-08-02T19:38:37+08:00", "document/content/docs/upgrading/4-11/4110.mdx": "2025-08-05T23:20:39+08:00", "document/content/docs/upgrading/4-11/4111.mdx": "2025-08-07T22:49:09+08:00", "document/content/docs/upgrading/4-12/4120.mdx": "2025-08-12T22:45:19+08:00", "document/content/docs/upgrading/4-12/4121.mdx": "2025-08-15T22:53:06+08:00", + "document/content/docs/upgrading/4-12/4122.mdx": "2025-08-22T09:38:44+08:00", "document/content/docs/upgrading/4-8/40.mdx": "2025-08-02T19:38:37+08:00", "document/content/docs/upgrading/4-8/41.mdx": "2025-08-02T19:38:37+08:00", "document/content/docs/upgrading/4-8/42.mdx": "2025-08-02T19:38:37+08:00", diff --git a/packages/global/core/ai/model.d.ts b/packages/global/core/ai/model.d.ts index 4b5f4f802..694c63b48 100644 --- a/packages/global/core/ai/model.d.ts +++ b/packages/global/core/ai/model.d.ts @@ -65,6 +65,7 @@ export type EmbeddingModelItemType = PriceType & weight: number; // training weight hidden?: boolean; // Disallow creation normalization?: boolean; // normalization processing + batchSize?: number; defaultConfig?: Record; // post request config dbConfig?: Record; // Custom parameters for storage queryConfig?: Record; // Custom parameters for query diff --git a/packages/global/core/workflow/node/constant.ts b/packages/global/core/workflow/node/constant.ts index 667f244c5..eec34dc3a 100644 --- a/packages/global/core/workflow/node/constant.ts +++ b/packages/global/core/workflow/node/constant.ts @@ -110,6 +110,8 @@ export enum FlowNodeTypeEnum { systemConfig = 'userGuide', pluginConfig = 'pluginConfig', globalVariable = 'globalVariable', + comment = 'comment', + workflowStart = 'workflowStart', chatNode = 'chatNode', @@ -141,7 +143,6 @@ export enum FlowNodeTypeEnum { loopStart = 'loopStart', loopEnd = 'loopEnd', formInput = 'formInput', - comment = 'comment', tool = 'tool', toolSet = 'toolSet' } diff --git a/packages/service/core/ai/embedding/index.ts b/packages/service/core/ai/embedding/index.ts index f0ddd2325..03e3faa73 100644 --- a/packages/service/core/ai/embedding/index.ts +++ b/packages/service/core/ai/embedding/index.ts @@ -23,8 +23,9 @@ export async function getVectorsByText({ model, input, type, headers }: GetVecto const formatInput = Array.isArray(input) ? input : [input]; - // 20 size every request - const chunkSize = parseInt(process.env.EMBEDDING_CHUNK_SIZE || '10'); + let chunkSize = Number(model.batchSize || 1); + chunkSize = isNaN(chunkSize) ? 1 : chunkSize; + const chunks = []; for (let i = 0; i < formatInput.length; i += chunkSize) { chunks.push(formatInput.slice(i, i + chunkSize)); @@ -74,14 +75,7 @@ export async function getVectorsByText({ model, input, type, headers }: GetVecto const tokens = await Promise.all(chunk.map((item) => countPromptTokens(item))); return tokens.reduce((sum, item) => sum + item, 0); })(), - Promise.all( - res.data - .map((item) => unityDimensional(item.embedding)) - .map((item) => { - if (model.normalization) return normalization(item); - return item; - }) - ) + Promise.all(res.data.map((item) => formatVectors(item.embedding, model.normalization))) ]); return { @@ -105,29 +99,35 @@ export async function getVectorsByText({ model, input, type, headers }: GetVecto } } -function unityDimensional(vector: number[]) { - if (vector.length > 1536) { - console.log( - `The current vector dimension is ${vector.length}, and the vector dimension cannot exceed 1536. The first 1536 dimensions are automatically captured` - ); - return vector.slice(0, 1536); - } - let resultVector = vector; - const vectorLen = vector.length; - - const zeroVector = new Array(1536 - vectorLen).fill(0); - - return resultVector.concat(zeroVector); -} -// normalization processing -function normalization(vector: number[]) { - if (vector.some((item) => item > 1)) { +export function formatVectors(vector: number[], normalization = false) { + // normalization processing + function normalizationVector(vector: number[]) { // Calculate the Euclidean norm (L2 norm) const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0)); - + if (norm === 0) { + return vector; + } // Normalize the vector by dividing each component by the norm return vector.map((val) => val / norm); } + // 超过上限，截断，并强制归一化 + if (vector.length > 1536) { + console.log( + `The current vector dimension is ${vector.length}, and the vector dimension cannot exceed 1536. The first 1536 dimensions are automatically captured` + ); + return normalizationVector(vector.slice(0, 1536)); + } else if (vector.length < 1536) { + const vectorLen = vector.length; + + const zeroVector = new Array(1536 - vectorLen).fill(0); + + vector = vector.concat(zeroVector); + } + + if (normalization) { + return normalizationVector(vector); + } + return vector; } diff --git a/packages/web/i18n/en/account_model.json b/packages/web/i18n/en/account_model.json index 6b3d824df..7e03ece02 100644 --- a/packages/web/i18n/en/account_model.json +++ b/packages/web/i18n/en/account_model.json @@ -7,6 +7,7 @@ "avg_ttfb": "Average first word duration (seconds)", "azure": "Azure", "base_url": "Base url", + "batch_size": "Number of concurrent requests", "channel_name": "Channel", "channel_priority": "Priority", "channel_priority_tip": "The higher the priority channel, the easier it is to be requested", diff --git a/packages/web/i18n/zh-CN/account_model.json b/packages/web/i18n/zh-CN/account_model.json index 1b1ad8779..6d97ac6ef 100644 --- a/packages/web/i18n/zh-CN/account_model.json +++ b/packages/web/i18n/zh-CN/account_model.json @@ -7,6 +7,7 @@ "avg_ttfb": "平均首字时长 (秒)", "azure": "微软 Azure", "base_url": "代理地址", + "batch_size": "并发请求数", "channel_name": "渠道名", "channel_priority": "优先级", "channel_priority_tip": "优先级越高的渠道，越容易被请求到", diff --git a/packages/web/i18n/zh-Hant/account_model.json b/packages/web/i18n/zh-Hant/account_model.json index 38dc0a711..04047acba 100644 --- a/packages/web/i18n/zh-Hant/account_model.json +++ b/packages/web/i18n/zh-Hant/account_model.json @@ -7,6 +7,7 @@ "avg_ttfb": "平均首字時長 (秒)", "azure": "Azure", "base_url": "代理地址", + "batch_size": "並發請求數", "channel_name": "管道名稱", "channel_priority": "優先順序", "channel_priority_tip": "優先順序越高的管道，越容易被請求到", diff --git a/projects/app/src/components/Select/AIModelSelector.tsx b/projects/app/src/components/Select/AIModelSelector.tsx index 6e120d459..f0c22e10c 100644 --- a/projects/app/src/components/Select/AIModelSelector.tsx +++ b/projects/app/src/components/Select/AIModelSelector.tsx @@ -10,12 +10,14 @@ import MyTooltip from '@fastgpt/web/components/common/MyTooltip'; import { ModelProviderList } from '@fastgpt/global/core/ai/provider'; import MultipleRowSelect from '@fastgpt/web/components/common/MySelect/MultipleRowSelect'; import { getModelFromList } from '@fastgpt/global/core/ai/model'; +import type { ResponsiveValue } from '@chakra-ui/system'; type Props = SelectProps & { disableTip?: string; + noOfLines?: ResponsiveValue; }; -const OneRowSelector = ({ list, onChange, disableTip, ...props }: Props) => { +const OneRowSelector = ({ list, onChange, disableTip, noOfLines, ...props }: Props) => { const { t } = useTranslation(); const { llmModelList, embeddingModelList, ttsModelList, sttModelList, reRankModelList } = useSystemStore(); @@ -55,7 +57,7 @@ const OneRowSelector = ({ list, onChange, disableTip, ...props }: Props) => { fallbackSrc={HUGGING_FACE_ICON} /> - {modelData.name} + {modelData.name} ) }; @@ -99,7 +101,14 @@ const OneRowSelector = ({ list, onChange, disableTip, ...props }: Props) => { ); }; -const MultipleRowSelector = ({ list, onChange, disableTip, placeholder, ...props }: Props) => { +const MultipleRowSelector = ({ + list, + onChange, + disableTip, + placeholder, + noOfLines, + ...props +}: Props) => { const { t } = useTranslation(); const { llmModelList, embeddingModelList, ttsModelList, sttModelList, reRankModelList } = useSystemStore(); @@ -189,7 +198,7 @@ const MultipleRowSelector = ({ list, onChange, disableTip, placeholder, ...props fallbackSrc={HUGGING_FACE_ICON} w={avatarSize} /> - {modelData?.name} + {modelData?.name} ); }, [modelList, props.value, t, avatarSize]); @@ -222,7 +231,7 @@ const MultipleRowSelector = ({ list, onChange, disableTip, placeholder, ...props }; const AIModelSelector = (props: Props) => { - return props.list.length > 100 ? ( + return props.list.length > 10 ? ( ) : ( diff --git a/projects/app/src/pageComponents/account/model/AddModelBox.tsx b/projects/app/src/pageComponents/account/model/AddModelBox.tsx index cd3d9738e..8edc66f10 100644 --- a/projects/app/src/pageComponents/account/model/AddModelBox.tsx +++ b/projects/app/src/pageComponents/account/model/AddModelBox.tsx @@ -476,6 +476,26 @@ export const ModelEditModal = ({ + + + + {t('account_model:batch_size')} + + + + + + + + diff --git a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/hooks/useDebug.tsx b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/hooks/useDebug.tsx index ae398b766..28bb7bdd3 100644 --- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/hooks/useDebug.tsx +++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/hooks/useDebug.tsx @@ -48,6 +48,7 @@ export const useDebug = () => { const getNodes = useContextSelector(WorkflowNodeEdgeContext, (v) => v.getNodes); const edges = useContextSelector(WorkflowNodeEdgeContext, (v) => v.edges); const onUpdateNodeError = useContextSelector(WorkflowContext, (v) => v.onUpdateNodeError); + const onRemoveError = useContextSelector(WorkflowContext, (v) => v.onRemoveError); const onStartNodeDebug = useContextSelector(WorkflowContext, (v) => v.onStartNodeDebug); const appDetail = useContextSelector(AppContext, (v) => v.appDetail); @@ -80,6 +81,7 @@ export const useDebug = () => { const checkResults = checkWorkflowNodeAndConnection({ nodes, edges }); if (!checkResults) { + onRemoveError(); const storeNodes = uiWorkflow2StoreWorkflow({ nodes, edges }); return JSON.stringify(storeNodes); diff --git a/projects/app/src/pageComponents/app/detail/WorkflowComponents/context/index.tsx b/projects/app/src/pageComponents/app/detail/WorkflowComponents/context/index.tsx index d99351255..7781eec74 100644 --- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/context/index.tsx +++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/context/index.tsx @@ -157,6 +157,7 @@ type WorkflowContextType = { nodeList: FlowNodeItemType[]; onUpdateNodeError: (node: string, isError: Boolean) => void; + onRemoveError: () => void; onResetNode: (e: { id: string; node: FlowNodeTemplateType }) => void; onChangeNode: (e: FlowNodeChangeProps) => void; getNodeDynamicInputs: (nodeId: string) => FlowNodeInputItemType[]; @@ -401,6 +402,9 @@ export const WorkflowContext = createContext({ isSaved?: boolean; }): boolean { throw new Error('Function not implemented.'); + }, + onRemoveError: function (): void { + throw new Error('Function not implemented.'); } }); @@ -473,6 +477,17 @@ const WorkflowContextProvider = ({ }); }); }); + const onRemoveError = useMemoizedFn(() => { + setNodes((state) => { + return state.map((item) => { + if (item.data.isError) { + item.data.isError = false; + item.selected = false; + } + return item; + }); + }); + }); // reset a node data. delete edge and replace it const onResetNode = useMemoizedFn(({ id, node }: { id: string; node: FlowNodeTemplateType }) => { @@ -625,6 +640,7 @@ const WorkflowContextProvider = ({ const checkResults = checkWorkflowNodeAndConnection({ nodes, edges }); if (!checkResults) { + onRemoveError(); const storeWorkflow = uiWorkflow2StoreWorkflow({ nodes, edges }); return storeWorkflow; @@ -1025,6 +1041,7 @@ const WorkflowContextProvider = ({ // node nodeList, onUpdateNodeError, + onRemoveError, onResetNode, onChangeNode, getNodeDynamicInputs, @@ -1075,6 +1092,7 @@ const WorkflowContextProvider = ({ onChangeNode, onDelEdge, onNextNodeDebug, + onRemoveError, onResetNode, onStartNodeDebug, onStopNodeDebug, diff --git a/projects/app/src/pageComponents/chat/ChatWindow/HomeChatWindow.tsx b/projects/app/src/pageComponents/chat/ChatWindow/HomeChatWindow.tsx index 95d917189..75bfd6d05 100644 --- a/projects/app/src/pageComponents/chat/ChatWindow/HomeChatWindow.tsx +++ b/projects/app/src/pageComponents/chat/ChatWindow/HomeChatWindow.tsx @@ -248,6 +248,7 @@ const HomeChatWindow = ({ myApps }: Props) => { size="sm" bg={'myGray.50'} rounded="full" + noOfLines={[1, 3]} list={availableModels} value={selectedModel} onChange={async (model) => { diff --git a/projects/app/src/web/core/workflow/utils.ts b/projects/app/src/web/core/workflow/utils.ts index 3a8550939..05ef99765 100644 --- a/projects/app/src/web/core/workflow/utils.ts +++ b/projects/app/src/web/core/workflow/utils.ts @@ -381,6 +381,11 @@ export const getNodeAllSource = ({ }; /* ====== Connection ======= */ +// Connectivity check result type +type ConnectivityIssue = { + nodeId: string; + issue: 'isolated' | 'no_input' | 'unreachable_from_start'; +}; export const checkWorkflowNodeAndConnection = ({ nodes, edges @@ -388,7 +393,7 @@ export const checkWorkflowNodeAndConnection = ({ nodes: Node[]; edges: Edge[]; }): string[] | undefined => { - // 1. reference check. Required value + // Node check for (const node of nodes) { const data = node.data; const inputs = data.inputs; @@ -453,6 +458,15 @@ export const checkWorkflowNodeAndConnection = ({ return [data.nodeId]; } } + if (data.flowNodeType === FlowNodeTypeEnum.agent) { + const toolConnections = edges.filter( + (edge) => + edge.source === data.nodeId && edge.sourceHandle === NodeOutputKeyEnum.selectedTools + ); + if (toolConnections.length === 0) { + return [data.nodeId]; + } + } // check node input if ( @@ -506,7 +520,7 @@ export const checkWorkflowNodeAndConnection = ({ return [data.nodeId]; } - // filter tools node edge + // Check node has invalid edge const edgeFilted = edges.filter( (edge) => !( @@ -514,7 +528,7 @@ export const checkWorkflowNodeAndConnection = ({ edge.sourceHandle === NodeOutputKeyEnum.selectedTools ) ); - // check node has edge + // Check node has edge const hasEdge = edgeFilted.some( (edge) => edge.source === data.nodeId || edge.target === data.nodeId ); @@ -522,6 +536,106 @@ export const checkWorkflowNodeAndConnection = ({ return [data.nodeId]; } } + + // Edge check + + /** + * Check graph connectivity and identify connectivity issues + */ + const checkConnectivity = ( + nodes: Node[], + edges: Edge[] + ): string[] => { + // Find start node + const startNode = nodes.find( + (node) => + node.data.flowNodeType === FlowNodeTypeEnum.workflowStart || + node.data.flowNodeType === FlowNodeTypeEnum.pluginInput + ); + + if (!startNode) { + // No start node found - this is a critical issue + return nodes.map((node) => node.data.nodeId); + } + + const issues: ConnectivityIssue[] = []; + + // Build adjacency lists for both directions + const outgoing = new Map(); + const incoming = new Map(); + + nodes.forEach((node) => { + outgoing.set(node.data.nodeId, []); + incoming.set(node.data.nodeId, []); + }); + + edges.forEach((edge) => { + const outList = outgoing.get(edge.source) || []; + outList.push(edge.target); + outgoing.set(edge.source, outList); + + const inList = incoming.get(edge.target) || []; + inList.push(edge.source); + incoming.set(edge.target, inList); + }); + + // Check reachability from start node（Start node/Loop start 可以到达的地方） + const reachableFromStart = new Set(); + const dfsFromStart = (nodeId: string) => { + if (reachableFromStart.has(nodeId)) return; + reachableFromStart.add(nodeId); + + const neighbors = outgoing.get(nodeId) || []; + neighbors.forEach((neighbor) => dfsFromStart(neighbor)); + }; + dfsFromStart(startNode.data.nodeId); + nodes.forEach((node) => { + if (node.data.flowNodeType === FlowNodeTypeEnum.loopStart) { + dfsFromStart(node.data.nodeId); + } + }); + + // Check each node for connectivity issues + for (const node of nodes) { + const nodeId = node.data.nodeId; + const nodeType = node.data.flowNodeType; + + // Skip system nodes that don't need connectivity checks + if ( + nodeType === FlowNodeTypeEnum.systemConfig || + nodeType === FlowNodeTypeEnum.pluginConfig || + nodeType === FlowNodeTypeEnum.comment || + nodeType === FlowNodeTypeEnum.globalVariable || + nodeType === FlowNodeTypeEnum.emptyNode + ) { + continue; + } + + const hasIncoming = (incoming.get(nodeId) || []).length > 0; + const hasOutgoing = (outgoing.get(nodeId) || []).length > 0; + const isStartNode = [ + FlowNodeTypeEnum.workflowStart, + FlowNodeTypeEnum.pluginInput, + FlowNodeTypeEnum.loopStart + ].includes(nodeType); + + // Check if node is reachable from start + if (!isStartNode && !reachableFromStart.has(nodeId)) { + issues.push({ + nodeId, + issue: 'unreachable_from_start' + }); + break; + } + } + + return issues.map((issue) => issue.nodeId); + }; + + const connectivityIssues = checkConnectivity(nodes, edges); + if (connectivityIssues.length > 0) { + return connectivityIssues; + } }; /* ====== Variables ======= */ diff --git a/test/cases/service/core/ai/embedding.test.ts b/test/cases/service/core/ai/embedding.test.ts new file mode 100644 index 000000000..2e98d1bd6 --- /dev/null +++ b/test/cases/service/core/ai/embedding.test.ts @@ -0,0 +1,224 @@ +import { formatVectors } from '@fastgpt/service/core/ai/embedding/index'; +import { describe, expect, it, vi } from 'vitest'; + +describe('formatVectors function test', () => { + // Helper function to create a normalized vector (L2 norm = 1) + const createNormalizedVector = (length: number): number[] => { + const vector = Array.from({ length }, (_, i) => (i + 1) / length); + const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0)); + return vector.map((val) => val / norm); + }; + + // Helper function to create an unnormalized vector + const createUnnormalizedVector = (length: number): number[] => { + return Array.from({ length }, (_, i) => (i + 1) * 10); + }; + + // Helper function to calculate L2 norm + const calculateNorm = (vector: number[]): number => { + return Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0)); + }; + + // Helper function to check if vector is normalized (L2 norm H 1) + const isNormalized = (vector: number[]): boolean => { + const norm = calculateNorm(vector); + return Math.abs(norm - 1) < 1e-10; + }; + + describe('1536 dimension vectors', () => { + it('should handle normalized 1536-dim vector with normalization=true', () => { + const inputVector = createNormalizedVector(1536); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Since input is already normalized, result should be very similar + expect(result).toEqual( + expect.arrayContaining(inputVector.map((val) => expect.closeTo(val, 10))) + ); + }); + + it('should handle normalized 1536-dim vector with normalization=false', () => { + const inputVector = createNormalizedVector(1536); + const result = formatVectors(inputVector, false); + + expect(result).toHaveLength(1536); + expect(result).toEqual(inputVector); + expect(isNormalized(result)).toBe(true); + }); + + it('should handle unnormalized 1536-dim vector with normalization=true', () => { + const inputVector = createUnnormalizedVector(1536); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Result should be different from input (normalized) + expect(result).not.toEqual(inputVector); + }); + + it('should handle unnormalized 1536-dim vector with normalization=false', () => { + const inputVector = createUnnormalizedVector(1536); + const result = formatVectors(inputVector, false); + + expect(result).toHaveLength(1536); + expect(result).toEqual(inputVector); + expect(isNormalized(result)).toBe(false); + }); + }); + + describe('Greater than 1536 dimension vectors', () => { + it('should handle normalized >1536-dim vector with normalization=true', () => { + const inputVector = createNormalizedVector(2048); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Should be truncated to first 1536 elements and then normalized + expect(result).toEqual( + expect.arrayContaining(inputVector.slice(0, 1536).map((val) => expect.any(Number))) + ); + }); + + it('should handle normalized >1536-dim vector with normalization=false', () => { + const inputVector = createNormalizedVector(2048); + const result = formatVectors(inputVector, true); // Always normalized for >1536 dims + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Should be truncated and normalized regardless of normalization flag + }); + + it('should handle unnormalized >1536-dim vector with normalization=true', () => { + const inputVector = createUnnormalizedVector(2048); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Should be truncated to first 1536 elements and then normalized + }); + + it('should handle unnormalized >1536-dim vector with normalization=false', () => { + const inputVector = createUnnormalizedVector(2048); + const result = formatVectors(inputVector, false); // Always normalized for >1536 dims + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Should be truncated and normalized regardless of normalization flag + }); + + it('should log warning for vectors with length > 1536', () => { + const consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => {}); + const inputVector = createNormalizedVector(2000); + + formatVectors(inputVector, false); + + expect(consoleSpy).toHaveBeenCalledWith( + expect.stringContaining( + 'The current vector dimension is 2000, and the vector dimension cannot exceed 1536' + ) + ); + + consoleSpy.mockRestore(); + }); + }); + + describe('Less than 1536 dimension vectors', () => { + it('should handle normalized <1536-dim vector with normalization=true', () => { + const inputVector = createNormalizedVector(512); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // First 512 elements should match input, rest should be 0 + expect(result.slice(0, 512)).toEqual( + expect.arrayContaining(inputVector.map((val) => expect.any(Number))) + ); + expect(result.slice(512)).toEqual(new Array(1024).fill(0)); + }); + + it('should handle normalized <1536-dim vector with normalization=false', () => { + const inputVector = createNormalizedVector(512); + const result = formatVectors(inputVector, false); + + expect(result).toHaveLength(1536); + // First 512 elements should match input exactly, rest should be 0 + expect(result.slice(0, 512)).toEqual(inputVector); + expect(result.slice(512)).toEqual(new Array(1024).fill(0)); + // The result remains normalized because adding zeros doesn't change the L2 norm + expect(isNormalized(result)).toBe(true); + }); + + it('should handle unnormalized <1536-dim vector with normalization=true', () => { + const inputVector = createUnnormalizedVector(512); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + // Should be padded with zeros and then normalized + expect(result.slice(512)).toEqual(new Array(1024).fill(0)); + }); + + it('should handle unnormalized <1536-dim vector with normalization=false', () => { + const inputVector = createUnnormalizedVector(512); + const result = formatVectors(inputVector, false); + + expect(result).toHaveLength(1536); + // First 512 elements should match input exactly, rest should be 0 + expect(result.slice(0, 512)).toEqual(inputVector); + expect(result.slice(512)).toEqual(new Array(1024).fill(0)); + expect(isNormalized(result)).toBe(false); + }); + + it('should demonstrate that padding preserves normalization status', () => { + // Create a vector that becomes unnormalized after some scaling + const baseVector = [3, 4]; // norm = 5, not normalized + const result = formatVectors(baseVector, false); + + expect(result).toHaveLength(1536); + expect(result[0]).toBe(3); + expect(result[1]).toBe(4); + expect(result.slice(2)).toEqual(new Array(1534).fill(0)); + expect(isNormalized(result)).toBe(false); + expect(calculateNorm(result)).toBeCloseTo(5, 10); + }); + }); + + describe('Edge cases', () => { + it('should handle zero vector', () => { + const inputVector = new Array(1536).fill(0); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(result).toEqual(inputVector); // Zero vector remains zero after normalization + }); + + it('should handle single element vector', () => { + const inputVector = [5.0]; + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(result[0]).toBeCloseTo(1.0, 10); // Normalized single element should be 1 + expect(result.slice(1)).toEqual(new Array(1535).fill(0)); + }); + + it('should handle exactly 1536 dimension vector', () => { + const inputVector = createNormalizedVector(1536); + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + }); + + it('should handle vector with negative values', () => { + const inputVector = [-1, -2, -3]; + const result = formatVectors(inputVector, true); + + expect(result).toHaveLength(1536); + expect(isNormalized(result)).toBe(true); + expect(result[0]).toBeLessThan(0); // Should preserve negative values + expect(result[1]).toBeLessThan(0); + expect(result[2]).toBeLessThan(0); + }); + }); +});