Merge pull request #1259 from FlowiseAI/feature/VectaraChain
Feature/Add vectara chain
This commit is contained in:
Henry Heng
GitHub
commit
98eddee2a2
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@ -0,0 +1,307 @@
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { VectorDBQAChain } from 'langchain/chains'
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import { Document } from 'langchain/document'
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import { VectaraStore } from 'langchain/vectorstores/vectara'
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import fetch from 'node-fetch'
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class VectaraChain_Chains 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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baseClasses: string[]
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description: string
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inputs: INodeParams[]
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constructor() {
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this.label = 'Vectara QA Chain'
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this.name = 'vectaraQAChain'
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this.version = 1.0
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this.type = 'VectaraQAChain'
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this.icon = 'vectara.png'
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this.category = 'Chains'
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this.description = 'QA chain for Vectara'
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this.baseClasses = [this.type, ...getBaseClasses(VectorDBQAChain)]
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this.inputs = [
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{
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label: 'Vectara Store',
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name: 'vectaraStore',
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type: 'VectorStore'
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},
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{
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label: 'Summarizer Prompt Name',
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name: 'summarizerPromptName',
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description:
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'Summarize the results fetched from Vectara. Read <a target="_blank" href="https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer">more</a>',
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type: 'options',
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options: [
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{
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label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
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name: 'vectara-summary-ext-v1.2.0'
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},
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{
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label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
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name: 'vectara-experimental-summary-ext-2023-10-23-small',
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description: 'In beta, available to both Growth and Scale Vectara users'
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},
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{
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label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
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name: 'vectara-summary-ext-v1.3.0',
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description: 'Only available to paying Scale Vectara users'
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},
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{
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label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
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name: 'vectara-experimental-summary-ext-2023-10-23-med',
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description: 'In beta, only available to paying Scale Vectara users'
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}
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],
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default: 'vectara-summary-ext-v1.2.0'
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},
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{
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label: 'Response Language',
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name: 'responseLang',
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description:
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'Return the response in specific language. If not selected, Vectara will automatically detects the language. Read <a target="_blank" href="https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-response-languages">more</a>',
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type: 'options',
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options: [
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{
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label: 'English',
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name: 'eng'
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},
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{
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label: 'German',
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name: 'deu'
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},
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{
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label: 'French',
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name: 'fra'
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},
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{
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label: 'Chinese',
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name: 'zho'
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},
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{
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label: 'Korean',
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name: 'kor'
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},
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{
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label: 'Arabic',
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name: 'ara'
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},
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{
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label: 'Russian',
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name: 'rus'
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},
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{
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label: 'Thai',
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name: 'tha'
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},
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{
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label: 'Dutch',
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name: 'nld'
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},
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{
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label: 'Italian',
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name: 'ita'
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},
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{
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label: 'Portuguese',
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name: 'por'
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},
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{
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label: 'Spanish',
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name: 'spa'
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},
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{
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label: 'Japanese',
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name: 'jpn'
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},
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{
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label: 'Polish',
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name: 'pol'
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},
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{
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label: 'Turkish',
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name: 'tur'
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},
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{
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label: 'Vietnamese',
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name: 'vie'
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},
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{
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label: 'Indonesian',
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name: 'ind'
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},
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{
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label: 'Czech',
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name: 'ces'
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},
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{
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label: 'Ukrainian',
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name: 'ukr'
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},
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{
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label: 'Greek',
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name: 'ell'
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},
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{
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label: 'Hebrew',
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name: 'heb'
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},
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{
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label: 'Farsi/Persian',
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name: 'fas'
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},
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{
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label: 'Hindi',
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name: 'hin'
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},
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{
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label: 'Urdu',
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name: 'urd'
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},
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{
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label: 'Swedish',
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name: 'swe'
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},
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{
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label: 'Bengali',
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name: 'ben'
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},
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{
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label: 'Malay',
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name: 'msa'
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},
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{
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label: 'Romanian',
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name: 'ron'
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}
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],
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optional: true,
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default: 'eng'
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},
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{
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label: 'Max Summarized Results',
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name: 'maxSummarizedResults',
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description: 'Maximum results used to build the summarized response',
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type: 'number',
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default: 7
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}
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]
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}
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async init(): Promise<any> {
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return null
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}
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async run(nodeData: INodeData, input: string): Promise<object> {
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const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
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const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto'
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const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
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const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string
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const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7
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const topK = (vectorStore as any)?.k ?? 10
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const headers = await vectorStore.getJsonHeader()
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const vectaraFilter = (vectorStore as any).vectaraFilter ?? {}
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const corpusId: number[] = (vectorStore as any).corpusId ?? []
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const customerId = (vectorStore as any).customerId ?? ''
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const corpusKeys = corpusId.map((corpusId) => ({
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customerId,
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corpusId,
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metadataFilter: vectaraFilter?.filter ?? '',
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lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
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}))
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const data = {
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query: [
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{
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query: input,
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start: 0,
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numResults: topK,
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contextConfig: {
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sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
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sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
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},
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corpusKey: corpusKeys,
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summary: [
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{
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summarizerPromptName,
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responseLang,
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maxSummarizedResults
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}
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]
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}
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]
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}
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try {
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const response = await fetch(`https://api.vectara.io/v1/query`, {
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method: 'POST',
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headers: headers?.headers,
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body: JSON.stringify(data)
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})
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if (response.status !== 200) {
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throw new Error(`Vectara API returned status code ${response.status}`)
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}
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const result = await response.json()
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const responses = result.responseSet[0].response
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const documents = result.responseSet[0].document
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let summarizedText = ''
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for (let i = 0; i < responses.length; i += 1) {
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const responseMetadata = responses[i].metadata
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const documentMetadata = documents[responses[i].documentIndex].metadata
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const combinedMetadata: Record<string, unknown> = {}
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responseMetadata.forEach((item: { name: string; value: unknown }) => {
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combinedMetadata[item.name] = item.value
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})
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documentMetadata.forEach((item: { name: string; value: unknown }) => {
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combinedMetadata[item.name] = item.value
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})
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responses[i].metadata = combinedMetadata
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}
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const summaryStatus = result.responseSet[0].summary[0].status
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if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
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throw new Error(
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`BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.`
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)
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}
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if (
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summaryStatus.length > 0 &&
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summaryStatus[0].code === 'NOT_FOUND' &&
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summaryStatus[0].statusDetail === 'Failed to retrieve summarizer.'
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) {
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throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`)
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}
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summarizedText = result.responseSet[0].summary[0]?.text
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const sourceDocuments: Document[] = responses.map(
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(response: { text: string; metadata: Record<string, unknown>; score: number }) =>
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new Document({
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pageContent: response.text,
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metadata: response.metadata
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})
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)
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return { text: summarizedText, sourceDocuments: sourceDocuments }
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} catch (error) {
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throw new Error(error)
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}
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}
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}
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module.exports = { nodeClass: VectaraChain_Chains }
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@ -842,7 +842,7 @@ export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNod
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let isValidChainOrAgent = false
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if (endingNodeData.category === 'Chains') {
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// Chains that are not available to stream
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const blacklistChains = ['openApiChain']
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const blacklistChains = ['openApiChain', 'vectaraQAChain']
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isValidChainOrAgent = !blacklistChains.includes(endingNodeData.name)
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} else if (endingNodeData.category === 'Agents') {
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// Agent that are available to stream
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@ -699,7 +699,10 @@ const ViewMessagesDialog = ({ show, dialogProps, onCancel }) => {
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{message.sourceDocuments && (
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<div style={{ display: 'block', flexDirection: 'row', width: '100%' }}>
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{removeDuplicateURL(message).map((source, index) => {
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const URL = isValidURL(source.metadata.source)
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const URL =
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source.metadata && source.metadata.source
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? isValidURL(source.metadata.source)
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: undefined
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return (
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<Chip
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size='small'
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@ -423,12 +423,16 @@ export const removeDuplicateURL = (message) => {
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if (!message.sourceDocuments) return newSourceDocuments
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message.sourceDocuments.forEach((source) => {
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if (source.metadata && source.metadata.source) {
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if (isValidURL(source.metadata.source) && !visitedURLs.includes(source.metadata.source)) {
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visitedURLs.push(source.metadata.source)
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newSourceDocuments.push(source)
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} else if (!isValidURL(source.metadata.source)) {
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newSourceDocuments.push(source)
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}
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} else {
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newSourceDocuments.push(source)
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}
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})
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return newSourceDocuments
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}
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@ -379,7 +379,10 @@ export const ChatMessage = ({ open, chatflowid, isDialog }) => {
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{message.sourceDocuments && (
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<div style={{ display: 'block', flexDirection: 'row', width: '100%' }}>
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{removeDuplicateURL(message).map((source, index) => {
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const URL = isValidURL(source.metadata.source)
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const URL =
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source.metadata && source.metadata.source
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? isValidURL(source.metadata.source)
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: undefined
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return (
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<Chip
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size='small'
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