mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-22 09:01:09 +03:00
Vinod Kiran
788d40f26b
* fixes * test json * test json * externalize model list * externalize model list * correcting awsChatBedRock Name * lint fixes * externalize models * externalize chat models * externalize chat models * externalize llm models * externalize llm models * lint fixes * addition of models for ChatMistral * updating version numbers for all imapacted nodes. * marketplace template updates * update embeddings, marketplaces, add chatCohere * config changes for new env variable * removal of local models.json * update models json file --------- Co-authored-by: Henry <hzj94@hotmail.com>
107 lines
3.6 KiB
TypeScript
107 lines
3.6 KiB
TypeScript
import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from '@langchain/openai'
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import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { MODEL_TYPE, getModels } from '../../../src/modelLoader'
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class OpenAIEmbedding_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 = 'OpenAI Embeddings'
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this.name = 'openAIEmbeddings'
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this.version = 3.0
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this.type = 'OpenAIEmbeddings'
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this.icon = 'openai.svg'
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this.category = 'Embeddings'
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this.description = 'OpenAI API to generate embeddings for a given text'
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this.baseClasses = [this.type, ...getBaseClasses(OpenAIEmbeddings)]
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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: ['openAIApi']
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}
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this.inputs = [
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'asyncOptions',
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loadMethod: 'listModels',
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default: 'text-embedding-ada-002'
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},
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{
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label: 'Strip New Lines',
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name: 'stripNewLines',
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type: 'boolean',
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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: 'Batch Size',
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name: 'batchSize',
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type: 'number',
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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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optional: true,
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additionalParams: true
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},
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{
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label: 'BasePath',
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name: 'basepath',
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type: 'string',
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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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//@ts-ignore
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loadMethods = {
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async listModels(): Promise<INodeOptionsValue[]> {
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return await getModels(MODEL_TYPE.EMBEDDING, 'openAIEmbeddings')
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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 stripNewLines = nodeData.inputs?.stripNewLines as boolean
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const batchSize = nodeData.inputs?.batchSize as string
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const timeout = nodeData.inputs?.timeout as string
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const basePath = nodeData.inputs?.basepath as string
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const modelName = nodeData.inputs?.modelName as string
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if (nodeData.inputs?.credentialId) {
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nodeData.credential = nodeData.inputs?.credentialId
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}
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
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const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
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openAIApiKey,
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modelName
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}
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if (stripNewLines) obj.stripNewLines = stripNewLines
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if (batchSize) obj.batchSize = parseInt(batchSize, 10)
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if (timeout) obj.timeout = parseInt(timeout, 10)
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const model = new OpenAIEmbeddings(obj, { basePath })
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return model
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}
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}
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module.exports = { nodeClass: OpenAIEmbedding_Embeddings }
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