121 lines
4.9 KiB
TypeScript
121 lines
4.9 KiB
TypeScript
import { BaseLanguageModel } from '@langchain/core/language_models/base'
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import { MultiRetrievalQAChain } from 'langchain/chains'
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import { ICommonObject, INode, INodeData, INodeParams, IServerSideEventStreamer, VectorStoreRetriever } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
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import { formatResponse } from '../../outputparsers/OutputParserHelpers'
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class MultiRetrievalQAChain_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 = 'Multi Retrieval QA Chain'
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this.name = 'multiRetrievalQAChain'
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this.version = 2.0
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this.type = 'MultiRetrievalQAChain'
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this.icon = 'qa.svg'
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this.category = 'Chains'
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this.description = 'QA Chain that automatically picks an appropriate vector store from multiple retrievers'
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this.baseClasses = [this.type, ...getBaseClasses(MultiRetrievalQAChain)]
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this.inputs = [
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{
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label: 'Language Model',
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name: 'model',
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type: 'BaseLanguageModel'
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},
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{
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label: 'Vector Store Retriever',
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name: 'vectorStoreRetriever',
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type: 'VectorStoreRetriever',
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list: true
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},
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{
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label: 'Return Source Documents',
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name: 'returnSourceDocuments',
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type: 'boolean',
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optional: true
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},
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{
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label: 'Input Moderation',
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description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
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name: 'inputModeration',
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type: 'Moderation',
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optional: true,
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list: true
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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 model = nodeData.inputs?.model as BaseLanguageModel
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as VectorStoreRetriever[]
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const retrieverNames = []
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const retrieverDescriptions = []
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const retrievers = []
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for (const vs of vectorStoreRetriever) {
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retrieverNames.push(vs.name)
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retrieverDescriptions.push(vs.description)
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retrievers.push(vs.vectorStore.asRetriever((vs.vectorStore as any).k ?? 4))
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}
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const chain = MultiRetrievalQAChain.fromLLMAndRetrievers(model, {
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retrieverNames,
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retrieverDescriptions,
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retrievers,
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retrievalQAChainOpts: { verbose: process.env.DEBUG === 'true' ? true : false, returnSourceDocuments }
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})
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return chain
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
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const chain = nodeData.instance as MultiRetrievalQAChain
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const moderations = nodeData.inputs?.inputModeration as Moderation[]
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const shouldStreamResponse = options.shouldStreamResponse
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const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
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const chatId = options.chatId
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if (moderations && moderations.length > 0) {
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try {
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// Use the output of the moderation chain as input for the Multi Retrieval QA Chain
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input = await checkInputs(moderations, input)
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} catch (e) {
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await new Promise((resolve) => setTimeout(resolve, 500))
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if (options.shouldStreamResponse) {
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streamResponse(options.sseStreamer, options.chatId, e.message)
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}
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return formatResponse(e.message)
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}
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}
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const obj = { input }
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const loggerHandler = new ConsoleCallbackHandler(options.logger, options?.orgId)
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const callbacks = await additionalCallbacks(nodeData, options)
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if (shouldStreamResponse) {
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const handler = new CustomChainHandler(sseStreamer, chatId, 2, returnSourceDocuments)
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const res = await chain.call(obj, [loggerHandler, handler, ...callbacks])
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if (res.text && res.sourceDocuments) return res
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return res?.text
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} else {
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const res = await chain.call(obj, [loggerHandler, ...callbacks])
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if (res.text && res.sourceDocuments) return res
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return res?.text
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}
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}
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}
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module.exports = { nodeClass: MultiRetrievalQAChain_Chains }
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