Add Vectara vectorstore components
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { VectaraStore, VectaraLibArgs, VectaraFilter } from 'langchain/vectorstores/vectara'
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class VectaraExisting_VectorStores implements INode {
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label: string
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name: string
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Vectara Load Existing Index'
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this.name = 'vectaraExistingIndex'
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this.type = 'Vectara'
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this.icon = 'vectara.png'
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this.category = 'Vector Stores'
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this.description = 'Load existing index from Vectara (i.e: Document has been upserted)'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.inputs = [
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{
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label: 'Vectara Customer ID',
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name: 'customerID',
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type: 'string'
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},
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{
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label: 'Vectara Corpus ID',
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name: 'corpusID',
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type: 'string'
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},
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{
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label: 'Vectara API Key',
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name: 'apiKey',
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type: 'password'
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},
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{
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label: 'Vectara Metadata Filter',
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name: 'filter',
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type: 'json',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Lambda',
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name: 'lambda',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Defaults to 4',
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placeholder: '4',
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type: 'number',
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additionalParams: true,
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Vectara Retriever',
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name: 'retriever',
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baseClasses: this.baseClasses
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},
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{
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label: 'Vectara Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(VectaraStore)]
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const customerId = nodeData.inputs?.customerID as number
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const corpusId = nodeData.inputs?.corpusID as number
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const apiKey = nodeData.inputs?.apiKey as string
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const vectaraMetadatafilter = nodeData.inputs?.filter as VectaraFilter
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const lambda = nodeData.inputs?.lambda as number
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseInt(topK, 10) : 4
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const vectaraArgs: VectaraLibArgs = {
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apiKey: apiKey,
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customerId: customerId,
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corpusId: corpusId
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}
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const vectaraFilter: VectaraFilter = {}
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if (vectaraMetadatafilter) {
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const metadatafilter = typeof vectaraMetadatafilter === 'object' ? vectaraMetadatafilter : JSON.parse(vectaraMetadatafilter)
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vectaraFilter.filter = metadatafilter
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}
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if (lambda) vectaraFilter.lambda = lambda
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const vectorStore = new VectaraStore(vectaraArgs)
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(k, vectaraFilter)
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return retriever
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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return vectorStore
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}
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return vectorStore
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}
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}
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module.exports = { nodeClass: VectaraExisting_VectorStores }
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@ -0,0 +1,132 @@
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { Embeddings } from 'langchain/embeddings/base'
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import { getBaseClasses } from '../../../src/utils'
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import { VectaraStore, VectaraLibArgs, VectaraFilter } from 'langchain/vectorstores/vectara'
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import { Document } from 'langchain/document'
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import { flatten } from 'lodash'
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class VectaraExisting_VectorStores implements INode {
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label: string
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name: string
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Vectara Upsert Document'
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this.name = 'vectaraExisting'
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this.type = 'Vectara'
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this.icon = 'vectara.png'
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this.category = 'Vector Stores'
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this.description = 'Upsert documents to Vectara'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.inputs = [
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{
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label: 'Vectara Customer ID',
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name: 'customerID',
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type: 'string'
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},
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{
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label: 'Vectara Corpus ID',
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name: 'corpusID',
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type: 'string'
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},
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{
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label: 'Vectara API Key',
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name: 'apiKey',
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type: 'password'
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},
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{
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label: 'Document',
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name: 'document',
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type: 'Document',
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list: true
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},
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{
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label: 'Filter',
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name: 'filter',
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type: 'json',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Lambda',
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name: 'lambda',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Defaults to 4',
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placeholder: '4',
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type: 'number',
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additionalParams: true,
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Vectara Retriever',
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name: 'retriever',
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baseClasses: this.baseClasses
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},
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{
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label: 'Vectara Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(VectaraStore)]
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const customerId = nodeData.inputs?.customerID as number
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const corpusId = nodeData.inputs?.corpusID as number
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const apiKey = nodeData.inputs?.apiKey as string
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const docs = nodeData.inputs?.document as Document[]
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const embeddings = {} as Embeddings
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const vectaraMetadatafilter = nodeData.inputs?.filter as VectaraFilter
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const lambda = nodeData.inputs?.lambda as number
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseInt(topK, 10) : 4
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const vectaraArgs: VectaraLibArgs = {
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apiKey: apiKey,
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customerId: customerId,
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corpusId: corpusId
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}
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const vectaraFilter: VectaraFilter = {}
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if (vectaraMetadatafilter) {
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const metadatafilter = typeof vectaraMetadatafilter === 'object' ? vectaraMetadatafilter : JSON.parse(vectaraMetadatafilter)
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vectaraFilter.filter = metadatafilter
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}
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if (lambda) vectaraFilter.lambda = lambda
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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const vectorStore = await VectaraStore.fromDocuments(finalDocs, embeddings, vectaraArgs)
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(k, vectaraFilter)
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return retriever
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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return vectorStore
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}
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return vectorStore
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}
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}
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module.exports = { nodeClass: VectaraExisting_VectorStores }
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