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https://github.com/farcasclaudiu/Flowise.git
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add mongodb atlas
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@@ -0,0 +1,147 @@
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import {
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getBaseClasses,
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getCredentialData,
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getCredentialParam,
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ICommonObject,
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INodeData,
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INodeOutputsValue,
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INodeParams
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} from '../../../src'
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import { Embeddings } from 'langchain/embeddings/base'
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import { VectorStore } from 'langchain/vectorstores/base'
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import { Document } from 'langchain/document'
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import { MongoDBAtlasVectorSearch } from 'langchain/vectorstores/mongodb_atlas'
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import { Collection, MongoClient } from 'mongodb'
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export abstract class MongoDBSearchBase {
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label: string
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name: string
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version: number
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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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badge: string
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baseClasses: string[]
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inputs: INodeParams[]
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credential: INodeParams
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outputs: INodeOutputsValue[]
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mongoClient: MongoClient
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protected constructor() {
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this.type = 'MongoDB Atlas'
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this.icon = 'mongodb.png'
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this.category = 'Vector Stores'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.badge = 'DEPRECATING'
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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: ['mongoDBUrlApi']
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}
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this.inputs = [
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{
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label: 'Embeddings',
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name: 'embeddings',
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type: 'Embeddings'
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},
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{
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label: 'Database',
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name: 'databaseName',
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placeholder: '<DB_NAME>',
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type: 'string'
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},
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{
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label: 'Collection Name',
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name: 'collectionName',
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placeholder: '<COLLECTION_NAME>',
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type: 'string'
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},
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{
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label: 'Index Name',
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name: 'indexName',
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placeholder: '<VECTOR_INDEX_NAME>',
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type: 'string'
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},
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{
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label: 'Content Field',
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name: 'textKey',
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description: 'Name of the field (column) that contains the actual content',
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type: 'string',
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default: 'text',
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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: 'Embedded Field',
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name: 'embeddingKey',
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description: 'Name of the field (column) that contains the Embedding',
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type: 'string',
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default: 'embedding',
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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. Default 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: 'MongoDB 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: 'MongoDB Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(MongoDBAtlasVectorSearch)]
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}
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]
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}
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abstract constructVectorStore(
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embeddings: Embeddings,
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collection: Collection,
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indexName: string,
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textKey: string,
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embeddingKey: string,
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docs: Document<Record<string, any>>[] | undefined
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): Promise<VectorStore>
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async init(nodeData: INodeData, _: string, options: ICommonObject, docs: Document<Record<string, any>>[] | undefined): Promise<any> {
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const databaseName = nodeData.inputs?.databaseName as string
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const collectionName = nodeData.inputs?.collectionName as string
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const indexName = nodeData.inputs?.indexName as string
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let textKey = nodeData.inputs?.textKey as string
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let embeddingKey = nodeData.inputs?.embeddingKey as string
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseFloat(topK) : 4
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const output = nodeData.outputs?.output as string
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let mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
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this.mongoClient = new MongoClient(mongoDBConnectUrl)
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const collection = this.mongoClient.db(databaseName).collection(collectionName)
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if (!textKey || textKey === '') textKey = 'text'
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if (!embeddingKey || embeddingKey === '') embeddingKey = 'embedding'
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const vectorStore = await this.constructVectorStore(embeddings, collection, indexName, textKey, embeddingKey, docs)
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if (output === 'retriever') {
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return vectorStore.asRetriever(k)
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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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