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feat: add LMStudio & Mise support
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‎mise.toml‎

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[tools]
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node = "20"

‎packages/components/credentials/LmStudioApi.credential.ts‎

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import { INodeParams, INodeCredential } from '../src/Interface'
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class LMStudioApi implements INodeCredential {
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label: string
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name: string
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version: number
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inputs: INodeParams[]
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constructor() {
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this.label = 'LM Studio API'
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this.name = 'lmStudioApi'
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this.version = 1.0
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this.inputs = [
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{
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label: 'LM Studio Api Key',
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name: 'lmStudioApiKey',
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type: 'password',
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placeholder: '<LM_STUDIO_ACCESS_TOKEN>'
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}
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]
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}
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}
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module.exports = { credClass: LMStudioApi }

‎packages/components/nodes/chatmodels/ChatLmStudio/ChatLmStudio.ts‎

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import { ChatOpenAI as LangchainChatLmStudio, ChatOpenAIFields as ChatLmStudioFields } from '@langchain/openai'
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import { BaseCache } from '@langchain/core/caches'
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import { ICommonObject, IMultiModalOption, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { FlowiseChatLmStudio } from './FlowiseChatLmStudio'
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class ChatLmStudio_ChatModels implements INode {
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label: string
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name: string
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version: number
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type: string
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icon: string
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category: string
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description: string
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baseClasses: string[]
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credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'Chat LMStudio'
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this.name = 'chatLmStudio'
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this.version = 3.0
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this.type = 'ChatLmStudio'
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this.icon = 'lmstudio.png'
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this.category = 'Chat Models'
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this.description = 'Use local LLMs using LmStudio'
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this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(LangchainChatLmStudio)]
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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credentialNames: ['lmStudioApi'],
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optional: true
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}
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this.inputs = [
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{
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label: 'Cache',
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name: 'cache',
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type: 'BaseCache',
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optional: true
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},
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{
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label: 'Base URL',
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name: 'baseURL',
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type: 'string',
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placeholder: 'http://localhost:1234/v1'
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},
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'string',
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placeholder: 'gpt4all-lora-quantized.bin'
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},
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{
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label: 'Temperature',
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name: 'temperature',
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type: 'number',
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step: 0.1,
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default: 0.9,
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optional: true
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},
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{
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label: 'Allow Image Uploads',
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name: 'allowImageUploads',
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type: 'boolean',
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description:
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'Allow image input. Refer to the <a href="https://docs.flowiseai.com/using-flowise/uploads#image" target="_blank">docs</a> for more details.',
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default: false,
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optional: true
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},
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{
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label: 'Streaming',
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name: 'streaming',
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type: 'boolean',
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default: true,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Max Tokens',
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name: 'maxTokens',
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type: 'number',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Top Probability',
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name: 'topP',
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type: 'number',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Timeout',
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name: 'timeout',
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type: 'number',
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step: 1,
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optional: true,
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additionalParams: true
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}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const temperature = nodeData.inputs?.temperature as string
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const modelName = nodeData.inputs?.modelName as string
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const maxTokens = nodeData.inputs?.maxTokens as string
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const topP = nodeData.inputs?.topP as string
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const timeout = nodeData.inputs?.timeout as string
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const baseURL = nodeData.inputs?.baseURL as string
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const streaming = nodeData.inputs?.streaming as boolean
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const allowImageUploads = nodeData.inputs?.allowImageUploads as boolean
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const lmStudioApiKey = getCredentialParam('lmStudioApiKey', credentialData, nodeData)
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const cache = nodeData.inputs?.cache as BaseCache
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const obj: ChatLmStudioFields = {
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temperature: parseFloat(temperature),
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modelName,
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streaming: streaming ?? true,
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configuration: {
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baseURL,
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apiKey: lmStudioApiKey
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}
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}
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if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
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if (topP) obj.topP = parseFloat(topP)
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if (timeout) obj.timeout = parseInt(timeout, 10)
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if (cache) obj.cache = cache
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const multiModalOption: IMultiModalOption = {
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image: {
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allowImageUploads: allowImageUploads ?? false
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}
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}
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const model = new FlowiseChatLmStudio(nodeData.id, obj)
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model.setMultiModalOption(multiModalOption)
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return model
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}
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}
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module.exports = { nodeClass: ChatLmStudio_ChatModels }

‎packages/components/nodes/chatmodels/ChatLmStudio/FlowiseChatLmStudio.ts‎

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import { ChatOpenAI as LangchainChatLmStudio, ChatOpenAIFields as ChatLmStudioFields } from '@langchain/openai'
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import { IMultiModalOption, IVisionChatModal } from '../../../src'
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export class FlowiseChatLmStudio extends LangchainChatLmStudio implements IVisionChatModal {
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configuredModel: string
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configuredMaxToken?: number
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multiModalOption: IMultiModalOption
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builtInTools: Record<string, any>[] = []
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id: string
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constructor(id: string, fields?: ChatLmStudioFields) {
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super(fields)
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this.id = id
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this.configuredModel = fields?.modelName ?? ''
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this.configuredMaxToken = fields?.maxTokens
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}
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revertToOriginalModel(): void {
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this.model = this.configuredModel
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this.maxTokens = this.configuredMaxToken
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}
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setMultiModalOption(multiModalOption: IMultiModalOption): void {
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this.multiModalOption = multiModalOption
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}
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setVisionModel(): void {
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// pass
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}
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addBuiltInTools(builtInTool: Record<string, any>): void {
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this.builtInTools.push(builtInTool)
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}
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}

‎packages/components/nodes/chatmodels/ChatLmStudio/lmstudio.png‎

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‎packages/components/nodes/embeddings/LMStudioAIEmbedding/LmStudioEmbedding.ts‎

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import {
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ClientOptions,
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OpenAIEmbeddings as LmStudioEmbeddings,
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OpenAIEmbeddingsParams as LmStudioEmbeddingsParams
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} from '@langchain/openai'
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getCredentialData, getCredentialParam } from '../../../src/utils'
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class LmStudioEmbedding_Embeddings implements INode {
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label: string
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name: string
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version: number
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type: string
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icon: string
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category: string
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description: string
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baseClasses: string[]
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credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'LMStudio Embeddings'
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this.name = 'lmStudioEmbeddings'
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this.version = 1.0
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this.type = 'LmStudio Embeddings'
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this.icon = 'lmstudio.png'
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this.category = 'Embeddings'
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this.description = 'Use local embeddings from LMStudio'
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this.baseClasses = [this.type, 'Embeddings']
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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credentialNames: ['lmStudioApi'],
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optional: true
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}
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this.inputs = [
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{
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label: 'Base URL',
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name: 'baseURL',
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type: 'string',
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placeholder: 'http://localhost:1234/v1'
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},
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'string',
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placeholder: 'text-embedding-ada-002'
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}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const modelName = nodeData.inputs?.modelName as string
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const baseURL = nodeData.inputs?.baseURL as string
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const lmStudioApiKey = getCredentialParam('lmStudioApiKey', credentialData, nodeData)
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const obj: Partial<LmStudioEmbeddingsParams> & { configuration?: ClientOptions } = {
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modelName,
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configuration: {
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apiKey: lmStudioApiKey,
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baseURL
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}
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}
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const model = new LmStudioEmbeddings(obj)
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return model
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}
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}
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module.exports = { nodeClass: LmStudioEmbedding_Embeddings }

‎packages/components/nodes/embeddings/LMStudioAIEmbedding/lmstudio.png‎

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