refactor: remove lagecy tools
This commit is contained in:
Louis
parent
cc90c1e86e
commit
1027059a6b
@ -1,5 +0,0 @@
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it('should not throw any errors when imported', () => {
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expect(() => require('./index')).not.toThrow();
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})
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@ -1,2 +0,0 @@
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export * from './manager'
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export * from './tool'
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@ -1,47 +0,0 @@
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import { AssistantTool, MessageRequest } from '../../types'
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import { InferenceTool } from './tool'
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/**
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* Manages the registration and retrieval of inference tools.
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*/
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export class ToolManager {
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public tools = new Map<string, InferenceTool>()
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/**
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* Registers a tool.
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* @param tool - The tool to register.
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*/
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register<T extends InferenceTool>(tool: T) {
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this.tools.set(tool.name, tool)
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}
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/**
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* Retrieves a tool by it's name.
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* @param name - The name of the tool to retrieve.
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* @returns The tool, if found.
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*/
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get<T extends InferenceTool>(name: string): T | undefined {
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return this.tools.get(name) as T | undefined
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}
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/*
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** Process the message request with the tools.
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*/
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process(request: MessageRequest, tools: AssistantTool[]): Promise<MessageRequest> {
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return tools.reduce((prevPromise, currentTool) => {
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return prevPromise.then((prevResult) => {
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return currentTool.enabled
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? this.get(currentTool.type)?.process(prevResult, currentTool) ??
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Promise.resolve(prevResult)
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: Promise.resolve(prevResult)
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})
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}, Promise.resolve(request))
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}
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/**
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* The instance of the tool manager.
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*/
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static instance(): ToolManager {
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return (window.core?.toolManager as ToolManager) ?? new ToolManager()
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}
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}
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@ -1,63 +0,0 @@
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import { ToolManager } from '../../browser/tools/manager'
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import { InferenceTool } from '../../browser/tools/tool'
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import { AssistantTool, MessageRequest } from '../../types'
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class MockInferenceTool implements InferenceTool {
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name = 'mockTool'
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process(request: MessageRequest, tool: AssistantTool): Promise<MessageRequest> {
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return Promise.resolve(request)
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}
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}
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it('should register a tool', () => {
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const manager = new ToolManager()
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const tool = new MockInferenceTool()
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manager.register(tool)
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expect(manager.get(tool.name)).toBe(tool)
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})
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it('should retrieve a tool by its name', () => {
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const manager = new ToolManager()
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const tool = new MockInferenceTool()
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manager.register(tool)
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const retrievedTool = manager.get(tool.name)
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expect(retrievedTool).toBe(tool)
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})
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it('should return undefined for a non-existent tool', () => {
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const manager = new ToolManager()
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const retrievedTool = manager.get('nonExistentTool')
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expect(retrievedTool).toBeUndefined()
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})
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it('should process the message request with enabled tools', async () => {
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const manager = new ToolManager()
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const tool = new MockInferenceTool()
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manager.register(tool)
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const request: MessageRequest = { message: 'test' } as any
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const tools: AssistantTool[] = [{ type: 'mockTool', enabled: true }] as any
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const result = await manager.process(request, tools)
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expect(result).toBe(request)
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})
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it('should skip processing for disabled tools', async () => {
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const manager = new ToolManager()
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const tool = new MockInferenceTool()
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manager.register(tool)
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const request: MessageRequest = { message: 'test' } as any
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const tools: AssistantTool[] = [{ type: 'mockTool', enabled: false }] as any
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const result = await manager.process(request, tools)
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expect(result).toBe(request)
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})
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it('should throw an error when process is called without implementation', () => {
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class TestTool extends InferenceTool {
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name = 'testTool'
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}
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const tool = new TestTool()
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expect(() => tool.process({} as MessageRequest)).toThrowError()
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})
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@ -1,12 +0,0 @@
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import { AssistantTool, MessageRequest } from '../../types'
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/**
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* Represents a base inference tool.
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*/
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export abstract class InferenceTool {
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abstract name: string
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/*
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** Process a message request and return the processed message request.
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*/
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abstract process(request: MessageRequest, tool?: AssistantTool): Promise<MessageRequest>
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}
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@ -1,12 +1,7 @@
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import { Assistant, AssistantExtension, ToolManager } from '@janhq/core'
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import { Assistant, AssistantExtension } from '@janhq/core'
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import { RetrievalTool } from './tools/retrieval'
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export default class JanAssistantExtension extends AssistantExtension {
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export default class JanAssistantExtension extends AssistantExtension {
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async onLoad() {}
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async onLoad() {
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// Register the retrieval tool
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ToolManager.instance().register(new RetrievalTool())
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}
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/**
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/**
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* Called when the extension is unloaded.
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* Called when the extension is unloaded.
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@ -1,45 +0,0 @@
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import { getJanDataFolderPath } from '@janhq/core/node'
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import { retrieval } from './retrieval'
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import path from 'path'
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export function toolRetrievalUpdateTextSplitter(
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chunkSize: number,
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chunkOverlap: number
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) {
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retrieval.updateTextSplitter(chunkSize, chunkOverlap)
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}
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export async function toolRetrievalIngestNewDocument(
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thread: string,
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file: string,
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model: string,
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engine: string,
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useTimeWeighted: boolean
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) {
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const threadPath = path.join(getJanDataFolderPath(), 'threads', thread)
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const filePath = path.join(getJanDataFolderPath(), 'files', file)
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retrieval.updateEmbeddingEngine(model, engine)
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return retrieval
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.ingestAgentKnowledge(filePath, `${threadPath}/memory`, useTimeWeighted)
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.catch((err) => {
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console.error(err)
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})
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}
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export async function toolRetrievalLoadThreadMemory(threadId: string) {
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return retrieval
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.loadRetrievalAgent(
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path.join(getJanDataFolderPath(), 'threads', threadId, 'memory')
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)
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.catch((err) => {
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console.error(err)
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})
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}
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export async function toolRetrievalQueryResult(
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query: string,
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useTimeWeighted: boolean = false
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) {
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return retrieval.generateResult(query, useTimeWeighted).catch((err) => {
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console.error(err)
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})
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}
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@ -1,121 +0,0 @@
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import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter'
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import { formatDocumentsAsString } from 'langchain/util/document'
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import { PDFLoader } from 'langchain/document_loaders/fs/pdf'
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import { TimeWeightedVectorStoreRetriever } from 'langchain/retrievers/time_weighted'
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import { MemoryVectorStore } from 'langchain/vectorstores/memory'
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import { HNSWLib } from 'langchain/vectorstores/hnswlib'
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import { OpenAIEmbeddings } from 'langchain/embeddings/openai'
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export class Retrieval {
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public chunkSize: number = 100
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public chunkOverlap?: number = 0
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private retriever: any
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private embeddingModel?: OpenAIEmbeddings = undefined
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private textSplitter?: RecursiveCharacterTextSplitter
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// to support time-weighted retrieval
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private timeWeightedVectorStore: MemoryVectorStore
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private timeWeightedretriever: any | TimeWeightedVectorStoreRetriever
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constructor(chunkSize: number = 4000, chunkOverlap: number = 200) {
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this.updateTextSplitter(chunkSize, chunkOverlap)
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this.initialize()
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}
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private async initialize() {
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const apiKey = await window.core?.api.appToken()
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// declare time-weighted retriever and storage
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this.timeWeightedVectorStore = new MemoryVectorStore(
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new OpenAIEmbeddings(
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{ openAIApiKey: apiKey },
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{ basePath: `${CORTEX_API_URL}/v1` }
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)
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)
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this.timeWeightedretriever = new TimeWeightedVectorStoreRetriever({
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vectorStore: this.timeWeightedVectorStore,
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memoryStream: [],
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searchKwargs: 2,
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})
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}
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public updateTextSplitter(chunkSize: number, chunkOverlap: number): void {
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this.chunkSize = chunkSize
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this.chunkOverlap = chunkOverlap
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this.textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize: chunkSize,
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chunkOverlap: chunkOverlap,
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})
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}
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public async updateEmbeddingEngine(model: string, engine: string) {
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const apiKey = await window.core?.api.appToken()
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this.embeddingModel = new OpenAIEmbeddings(
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{ openAIApiKey: apiKey, model },
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// TODO: Raw settings
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{ basePath: `${CORTEX_API_URL}/v1` }
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)
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// update time-weighted embedding model
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this.timeWeightedVectorStore.embeddings = this.embeddingModel
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}
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public ingestAgentKnowledge = async (
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filePath: string,
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memoryPath: string,
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useTimeWeighted: boolean
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): Promise<any> => {
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const loader = new PDFLoader(filePath, {
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splitPages: true,
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})
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if (!this.embeddingModel) return Promise.reject()
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const doc = await loader.load()
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const docs = await this.textSplitter!.splitDocuments(doc)
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const vectorStore = await HNSWLib.fromDocuments(docs, this.embeddingModel)
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// add documents with metadata by using the time-weighted retriever in order to support time-weighted retrieval
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if (useTimeWeighted && this.timeWeightedretriever) {
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await (
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this.timeWeightedretriever as TimeWeightedVectorStoreRetriever
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).addDocuments(docs)
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}
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return vectorStore.save(memoryPath)
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}
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public loadRetrievalAgent = async (memoryPath: string): Promise<void> => {
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if (!this.embeddingModel) return Promise.reject()
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const vectorStore = await HNSWLib.load(memoryPath, this.embeddingModel)
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this.retriever = vectorStore.asRetriever(2)
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return Promise.resolve()
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}
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public generateResult = async (
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query: string,
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useTimeWeighted: boolean
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): Promise<string> => {
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if (useTimeWeighted) {
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if (!this.timeWeightedretriever) {
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return Promise.resolve(' ')
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}
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// use invoke because getRelevantDocuments is deprecated
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const relevantDocs = await this.timeWeightedretriever.invoke(query)
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const serializedDoc = formatDocumentsAsString(relevantDocs)
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return Promise.resolve(serializedDoc)
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}
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if (!this.retriever) {
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return Promise.resolve(' ')
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}
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// should use invoke(query) because getRelevantDocuments is deprecated
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const relevantDocs = await this.retriever.getRelevantDocuments(query)
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const serializedDoc = formatDocumentsAsString(relevantDocs)
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return Promise.resolve(serializedDoc)
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}
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}
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export const retrieval = new Retrieval()
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import {
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AssistantTool,
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executeOnMain,
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fs,
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InferenceTool,
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joinPath,
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MessageRequest,
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} from '@janhq/core'
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export class RetrievalTool extends InferenceTool {
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private _threadDir = 'file://threads'
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private retrievalThreadId: string | undefined = undefined
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name: string = 'retrieval'
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async process(
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data: MessageRequest,
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tool?: AssistantTool
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): Promise<MessageRequest> {
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if (!data.model || !data.messages) {
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return Promise.resolve(data)
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}
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const latestMessage = data.messages[data.messages.length - 1]
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// 1. Ingest the document if needed
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if (
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latestMessage &&
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latestMessage.content &&
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typeof latestMessage.content !== 'string' &&
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latestMessage.content.length > 1
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) {
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const docFile = latestMessage.content[1]?.doc_url?.url
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if (docFile) {
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await executeOnMain(
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NODE,
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'toolRetrievalIngestNewDocument',
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data.thread?.id,
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docFile,
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data.model?.id,
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data.model?.engine,
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tool?.useTimeWeightedRetriever ?? false
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)
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} else {
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return Promise.resolve(data)
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}
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} else if (
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// Check whether we need to ingest document or not
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// Otherwise wrong context will be sent
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!(await fs.existsSync(
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await joinPath([this._threadDir, data.threadId, 'memory'])
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))
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) {
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// No document ingested, reroute the result to inference engine
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return Promise.resolve(data)
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}
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// 2. Load agent on thread changed
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if (this.retrievalThreadId !== data.threadId) {
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await executeOnMain(NODE, 'toolRetrievalLoadThreadMemory', data.threadId)
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this.retrievalThreadId = data.threadId
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// Update the text splitter
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await executeOnMain(
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NODE,
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'toolRetrievalUpdateTextSplitter',
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tool?.settings?.chunk_size ?? 4000,
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tool?.settings?.chunk_overlap ?? 200
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)
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}
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// 3. Using the retrieval template with the result and query
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if (latestMessage.content) {
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const prompt =
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typeof latestMessage.content === 'string'
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? latestMessage.content
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: latestMessage.content[0].text
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// Retrieve the result
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const retrievalResult = await executeOnMain(
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NODE,
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'toolRetrievalQueryResult',
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prompt,
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tool?.useTimeWeightedRetriever ?? false
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)
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console.debug('toolRetrievalQueryResult', retrievalResult)
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// Update message content
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if (retrievalResult)
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data.messages[data.messages.length - 1].content =
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tool?.settings?.retrieval_template
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|
||||||
?.replace('{CONTEXT}', retrievalResult)
|
|
||||||
.replace('{QUESTION}', prompt)
|
|
||||||
}
|
|
||||||
|
|
||||||
// 4. Reroute the result to inference engine
|
|
||||||
return Promise.resolve(this.normalize(data))
|
|
||||||
}
|
|
||||||
|
|
||||||
// Filter out all the messages that are not text
|
|
||||||
// TODO: Remove it until engines can handle multiple content types
|
|
||||||
normalize(request: MessageRequest): MessageRequest {
|
|
||||||
request.messages = request.messages?.map((message) => {
|
|
||||||
if (
|
|
||||||
message.content &&
|
|
||||||
typeof message.content !== 'string' &&
|
|
||||||
(message.content.length ?? 0) > 0
|
|
||||||
) {
|
|
||||||
return {
|
|
||||||
...message,
|
|
||||||
content: [message.content[0]],
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return message
|
|
||||||
})
|
|
||||||
return request
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@ -11,20 +11,19 @@ import {
|
|||||||
events,
|
events,
|
||||||
MessageEvent,
|
MessageEvent,
|
||||||
ContentType,
|
ContentType,
|
||||||
|
EngineManager,
|
||||||
|
InferenceEngine,
|
||||||
} from '@janhq/core'
|
} from '@janhq/core'
|
||||||
import { extractInferenceParams, extractModelLoadParams } from '@janhq/core'
|
import { extractInferenceParams, extractModelLoadParams } from '@janhq/core'
|
||||||
import { atom, useAtom, useAtomValue, useSetAtom } from 'jotai'
|
import { atom, useAtom, useAtomValue, useSetAtom } from 'jotai'
|
||||||
import { OpenAI } from 'openai'
|
import { OpenAI } from 'openai'
|
||||||
|
|
||||||
import {
|
import {
|
||||||
ChatCompletionMessage,
|
|
||||||
ChatCompletionMessageParam,
|
ChatCompletionMessageParam,
|
||||||
ChatCompletionRole,
|
ChatCompletionRole,
|
||||||
ChatCompletionTool,
|
ChatCompletionTool,
|
||||||
} from 'openai/resources/chat'
|
} from 'openai/resources/chat'
|
||||||
|
|
||||||
import { Tool } from 'openai/resources/responses/responses'
|
|
||||||
|
|
||||||
import { ulid } from 'ulidx'
|
import { ulid } from 'ulidx'
|
||||||
|
|
||||||
import { modelDropdownStateAtom } from '@/containers/ModelDropdown'
|
import { modelDropdownStateAtom } from '@/containers/ModelDropdown'
|
||||||
@ -250,100 +249,41 @@ export default function useSendChatMessage() {
|
|||||||
}
|
}
|
||||||
setIsGeneratingResponse(true)
|
setIsGeneratingResponse(true)
|
||||||
|
|
||||||
let isDone = false
|
if (requestBuilder.tools && requestBuilder.tools.length) {
|
||||||
const openai = new OpenAI({
|
let isDone = false
|
||||||
apiKey: await window.core.api.appToken(),
|
const openai = new OpenAI({
|
||||||
baseURL: `${API_BASE_URL}/v1`,
|
apiKey: await window.core.api.appToken(),
|
||||||
dangerouslyAllowBrowser: true,
|
baseURL: `${API_BASE_URL}/v1`,
|
||||||
})
|
dangerouslyAllowBrowser: true,
|
||||||
while (!isDone) {
|
|
||||||
const data = requestBuilder.build()
|
|
||||||
const response = await openai.chat.completions.create({
|
|
||||||
messages: (data.messages ?? []).map((e) => {
|
|
||||||
return {
|
|
||||||
role: e.role as ChatCompletionRole,
|
|
||||||
content: e.content,
|
|
||||||
}
|
|
||||||
}) as ChatCompletionMessageParam[],
|
|
||||||
model: data.model?.id ?? '',
|
|
||||||
tools: data.tools as ChatCompletionTool[],
|
|
||||||
stream: false,
|
|
||||||
})
|
})
|
||||||
if (response.choices[0]?.message.content) {
|
while (!isDone) {
|
||||||
const newMessage: ThreadMessage = {
|
const data = requestBuilder.build()
|
||||||
id: ulid(),
|
const response = await openai.chat.completions.create({
|
||||||
object: 'message',
|
messages: (data.messages ?? []).map((e) => {
|
||||||
thread_id: activeThreadRef.current.id,
|
return {
|
||||||
assistant_id: activeAssistantRef.current.assistant_id,
|
role: e.role as ChatCompletionRole,
|
||||||
attachments: [],
|
content: e.content,
|
||||||
role: response.choices[0].message.role as any,
|
}
|
||||||
content: [
|
}) as ChatCompletionMessageParam[],
|
||||||
{
|
model: data.model?.id ?? '',
|
||||||
type: ContentType.Text,
|
tools: data.tools as ChatCompletionTool[],
|
||||||
text: {
|
stream: false,
|
||||||
value: response.choices[0].message.content
|
})
|
||||||
? (response.choices[0].message.content as any)
|
if (response.choices[0]?.message.content) {
|
||||||
: '',
|
const newMessage: ThreadMessage = {
|
||||||
annotations: [],
|
id: ulid(),
|
||||||
},
|
|
||||||
},
|
|
||||||
],
|
|
||||||
status: 'ready' as any,
|
|
||||||
created_at: Date.now(),
|
|
||||||
completed_at: Date.now(),
|
|
||||||
}
|
|
||||||
requestBuilder.pushAssistantMessage(
|
|
||||||
(response.choices[0].message.content as any) ?? ''
|
|
||||||
)
|
|
||||||
events.emit(MessageEvent.OnMessageUpdate, newMessage)
|
|
||||||
}
|
|
||||||
|
|
||||||
if (response.choices[0]?.message.tool_calls) {
|
|
||||||
for (const toolCall of response.choices[0].message.tool_calls) {
|
|
||||||
const id = ulid()
|
|
||||||
const toolMessage: ThreadMessage = {
|
|
||||||
id: id,
|
|
||||||
object: 'message',
|
object: 'message',
|
||||||
thread_id: activeThreadRef.current.id,
|
thread_id: activeThreadRef.current.id,
|
||||||
assistant_id: activeAssistantRef.current.assistant_id,
|
assistant_id: activeAssistantRef.current.assistant_id,
|
||||||
attachments: [],
|
attachments: [],
|
||||||
role: 'assistant' as any,
|
role: response.choices[0].message.role as any,
|
||||||
content: [
|
content: [
|
||||||
{
|
{
|
||||||
type: ContentType.Text,
|
type: ContentType.Text,
|
||||||
text: {
|
text: {
|
||||||
value: `<think>Executing Tool ${toolCall.function.name} with arguments ${toolCall.function.arguments}`,
|
value: response.choices[0].message.content
|
||||||
annotations: [],
|
? (response.choices[0].message.content as any)
|
||||||
},
|
: '',
|
||||||
},
|
|
||||||
],
|
|
||||||
status: 'pending' as any,
|
|
||||||
created_at: Date.now(),
|
|
||||||
completed_at: Date.now(),
|
|
||||||
}
|
|
||||||
events.emit(MessageEvent.OnMessageUpdate, toolMessage)
|
|
||||||
const result = await window.core.api.callTool({
|
|
||||||
toolName: toolCall.function.name,
|
|
||||||
arguments: JSON.parse(toolCall.function.arguments),
|
|
||||||
})
|
|
||||||
if (result.error) {
|
|
||||||
console.error(result.error)
|
|
||||||
break
|
|
||||||
}
|
|
||||||
const message: ThreadMessage = {
|
|
||||||
id: id,
|
|
||||||
object: 'message',
|
|
||||||
thread_id: activeThreadRef.current.id,
|
|
||||||
assistant_id: activeAssistantRef.current.assistant_id,
|
|
||||||
attachments: [],
|
|
||||||
role: 'assistant' as any,
|
|
||||||
content: [
|
|
||||||
{
|
|
||||||
type: ContentType.Text,
|
|
||||||
text: {
|
|
||||||
value:
|
|
||||||
`<think>Executing Tool ${toolCall.function.name} with arguments ${toolCall.function.arguments}</think>` +
|
|
||||||
(result.content[0]?.text ?? ''),
|
|
||||||
annotations: [],
|
annotations: [],
|
||||||
},
|
},
|
||||||
},
|
},
|
||||||
@ -352,15 +292,81 @@ export default function useSendChatMessage() {
|
|||||||
created_at: Date.now(),
|
created_at: Date.now(),
|
||||||
completed_at: Date.now(),
|
completed_at: Date.now(),
|
||||||
}
|
}
|
||||||
requestBuilder.pushAssistantMessage(result.content[0]?.text ?? '')
|
requestBuilder.pushAssistantMessage(
|
||||||
requestBuilder.pushMessage('Go for the next step')
|
(response.choices[0].message.content as any) ?? ''
|
||||||
events.emit(MessageEvent.OnMessageUpdate, message)
|
)
|
||||||
|
events.emit(MessageEvent.OnMessageUpdate, newMessage)
|
||||||
}
|
}
|
||||||
}
|
|
||||||
|
|
||||||
isDone =
|
if (response.choices[0]?.message.tool_calls) {
|
||||||
!response.choices[0]?.message.tool_calls ||
|
for (const toolCall of response.choices[0].message.tool_calls) {
|
||||||
!response.choices[0]?.message.tool_calls.length
|
const id = ulid()
|
||||||
|
const toolMessage: ThreadMessage = {
|
||||||
|
id: id,
|
||||||
|
object: 'message',
|
||||||
|
thread_id: activeThreadRef.current.id,
|
||||||
|
assistant_id: activeAssistantRef.current.assistant_id,
|
||||||
|
attachments: [],
|
||||||
|
role: 'assistant' as any,
|
||||||
|
content: [
|
||||||
|
{
|
||||||
|
type: ContentType.Text,
|
||||||
|
text: {
|
||||||
|
value: `<think>Executing Tool ${toolCall.function.name} with arguments ${toolCall.function.arguments}`,
|
||||||
|
annotations: [],
|
||||||
|
},
|
||||||
|
},
|
||||||
|
],
|
||||||
|
status: 'pending' as any,
|
||||||
|
created_at: Date.now(),
|
||||||
|
completed_at: Date.now(),
|
||||||
|
}
|
||||||
|
events.emit(MessageEvent.OnMessageUpdate, toolMessage)
|
||||||
|
const result = await window.core.api.callTool({
|
||||||
|
toolName: toolCall.function.name,
|
||||||
|
arguments: JSON.parse(toolCall.function.arguments),
|
||||||
|
})
|
||||||
|
if (result.error) {
|
||||||
|
console.error(result.error)
|
||||||
|
break
|
||||||
|
}
|
||||||
|
const message: ThreadMessage = {
|
||||||
|
id: id,
|
||||||
|
object: 'message',
|
||||||
|
thread_id: activeThreadRef.current.id,
|
||||||
|
assistant_id: activeAssistantRef.current.assistant_id,
|
||||||
|
attachments: [],
|
||||||
|
role: 'assistant' as any,
|
||||||
|
content: [
|
||||||
|
{
|
||||||
|
type: ContentType.Text,
|
||||||
|
text: {
|
||||||
|
value:
|
||||||
|
`<think>Executing Tool ${toolCall.function.name} with arguments ${toolCall.function.arguments}</think>` +
|
||||||
|
(result.content[0]?.text ?? ''),
|
||||||
|
annotations: [],
|
||||||
|
},
|
||||||
|
},
|
||||||
|
],
|
||||||
|
status: 'ready' as any,
|
||||||
|
created_at: Date.now(),
|
||||||
|
completed_at: Date.now(),
|
||||||
|
}
|
||||||
|
requestBuilder.pushAssistantMessage(result.content[0]?.text ?? '')
|
||||||
|
requestBuilder.pushMessage('Go for the next step')
|
||||||
|
events.emit(MessageEvent.OnMessageUpdate, message)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
isDone =
|
||||||
|
!response.choices[0]?.message.tool_calls ||
|
||||||
|
!response.choices[0]?.message.tool_calls.length
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
// Request for inference
|
||||||
|
EngineManager.instance()
|
||||||
|
.get(InferenceEngine.cortex)
|
||||||
|
?.inference(requestBuilder.build())
|
||||||
}
|
}
|
||||||
|
|
||||||
// Reset states
|
// Reset states
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
import { EngineManager, ToolManager } from '@janhq/core'
|
import { EngineManager } from '@janhq/core'
|
||||||
|
|
||||||
import { appService } from './appService'
|
import { appService } from './appService'
|
||||||
import { EventEmitter } from './eventsService'
|
import { EventEmitter } from './eventsService'
|
||||||
@ -16,7 +16,6 @@ export const setupCoreServices = () => {
|
|||||||
window.core = {
|
window.core = {
|
||||||
events: new EventEmitter(),
|
events: new EventEmitter(),
|
||||||
engineManager: new EngineManager(),
|
engineManager: new EngineManager(),
|
||||||
toolManager: new ToolManager(),
|
|
||||||
api: {
|
api: {
|
||||||
...(window.electronAPI ?? (IS_TAURI ? tauriAPI : restAPI)),
|
...(window.electronAPI ?? (IS_TAURI ? tauriAPI : restAPI)),
|
||||||
...appService,
|
...appService,
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user