342 lines
12 KiB
TypeScript
342 lines
12 KiB
TypeScript
import { flatten } from 'lodash'
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import { Client, ClientOptions } from '@elastic/elasticsearch'
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import { Document } from '@langchain/core/documents'
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import { Embeddings } from '@langchain/core/embeddings'
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import { ElasticClientArgs, ElasticVectorSearch } from '@langchain/community/vectorstores/elasticsearch'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { index } from '../../../src/indexing'
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class Elasticsearch_VectorStores implements INode {
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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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constructor() {
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this.label = 'Elasticsearch'
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this.name = 'elasticsearch'
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this.version = 2.0
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this.description =
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'Upsert embedded data and perform similarity search upon query using Elasticsearch, a distributed search and analytics engine'
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this.type = 'Elasticsearch'
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this.icon = 'elasticsearch.png'
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this.category = 'Vector Stores'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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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: ['elasticsearchApi', 'elasticSearchUserPassword']
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}
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this.inputs = [
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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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optional: true
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},
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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: 'Record Manager',
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name: 'recordManager',
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type: 'RecordManager',
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description: 'Keep track of the record to prevent duplication',
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optional: true
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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: '<INDEX_NAME>',
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type: 'string'
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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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label: 'Similarity',
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name: 'similarity',
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description: 'Similarity measure used in Elasticsearch.',
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type: 'options',
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default: 'l2_norm',
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options: [
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{
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label: 'l2_norm',
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name: 'l2_norm'
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},
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{
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label: 'dot_product',
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name: 'dot_product'
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},
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{
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label: 'cosine',
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name: 'cosine'
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}
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],
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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: 'Elasticsearch 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: 'Elasticsearch Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(ElasticVectorSearch)]
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}
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]
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}
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//@ts-ignore
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vectorStoreMethods = {
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async upsert(nodeData: INodeData, options: ICommonObject): Promise<Partial<IndexingResult>> {
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
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const cloudId = getCredentialParam('cloudId', credentialData, nodeData)
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const indexName = nodeData.inputs?.indexName as string
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const similarityMeasure = nodeData.inputs?.similarityMeasure as string
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const recordManager = nodeData.inputs?.recordManager
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const docs = nodeData.inputs?.document as Document[]
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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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if (flattenDocs[i] && flattenDocs[i].pageContent) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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}
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// The following code is a workaround for a bug (Langchain Issue #1589) in the underlying library.
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// Store does not support object in metadata and fail silently
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finalDocs.forEach((d) => {
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delete d.metadata.pdf
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delete d.metadata.loc
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})
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// end of workaround
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const { elasticClient, elasticSearchClientArgs } = prepareClientArgs(
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endPoint,
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cloudId,
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credentialData,
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nodeData,
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similarityMeasure,
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indexName
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)
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const vectorStore = new ElasticVectorSearch(embeddings, elasticSearchClientArgs)
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try {
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if (recordManager) {
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const vectorStore = await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
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await recordManager.createSchema()
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const res = await index({
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docsSource: finalDocs,
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recordManager,
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vectorStore,
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options: {
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cleanup: recordManager?.cleanup,
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sourceIdKey: recordManager?.sourceIdKey ?? 'source',
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vectorStoreName: indexName
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}
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})
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await elasticClient.close()
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return res
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} else {
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await vectorStore.addDocuments(finalDocs)
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await elasticClient.close()
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return { numAdded: finalDocs.length, addedDocs: finalDocs }
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}
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} catch (e) {
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throw new Error(e)
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}
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},
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async delete(nodeData: INodeData, ids: string[], options: ICommonObject): Promise<void> {
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const indexName = nodeData.inputs?.indexName as string
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const similarityMeasure = nodeData.inputs?.similarityMeasure as string
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const recordManager = nodeData.inputs?.recordManager
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
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const cloudId = getCredentialParam('cloudId', credentialData, nodeData)
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const { elasticClient, elasticSearchClientArgs } = prepareClientArgs(
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endPoint,
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cloudId,
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credentialData,
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nodeData,
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similarityMeasure,
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indexName
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)
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const vectorStore = new ElasticVectorSearch(embeddings, elasticSearchClientArgs)
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try {
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if (recordManager) {
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const vectorStoreName = indexName
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await recordManager.createSchema()
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;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
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const filterKeys: ICommonObject = {}
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if (options.docId) {
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filterKeys.docId = options.docId
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}
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const keys: string[] = await recordManager.listKeys(filterKeys)
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await vectorStore.delete({ ids: keys })
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await recordManager.deleteKeys(keys)
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await elasticClient.close()
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} else {
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await vectorStore.delete({ ids })
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await elasticClient.close()
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}
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} catch (e) {
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throw new Error(e)
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}
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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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
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const cloudId = getCredentialParam('cloudId', credentialData, nodeData)
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const indexName = nodeData.inputs?.indexName 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 similarityMeasure = nodeData.inputs?.similarityMeasure 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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const { elasticClient, elasticSearchClientArgs } = prepareClientArgs(
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endPoint,
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cloudId,
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credentialData,
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nodeData,
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similarityMeasure,
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indexName
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)
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const vectorStore = await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
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const originalSimilaritySearchVectorWithScore = vectorStore.similaritySearchVectorWithScore
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vectorStore.similaritySearchVectorWithScore = async (query: number[], k: number, filter?: any) => {
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const results = await originalSimilaritySearchVectorWithScore.call(vectorStore, query, k, filter)
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await elasticClient.close()
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return results
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}
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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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const prepareConnectionOptions = (
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endPoint: string | undefined,
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cloudId: string | undefined,
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credentialData: ICommonObject,
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nodeData: INodeData
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) => {
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let elasticSearchClientOptions: ClientOptions = {}
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if (endPoint) {
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let apiKey = getCredentialParam('apiKey', credentialData, nodeData)
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elasticSearchClientOptions = {
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node: endPoint,
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auth: {
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apiKey: apiKey
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}
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}
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} else if (cloudId) {
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let username = getCredentialParam('username', credentialData, nodeData)
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let password = getCredentialParam('password', credentialData, nodeData)
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if (cloudId.startsWith('http')) {
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elasticSearchClientOptions = {
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node: cloudId,
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auth: {
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username: username,
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password: password
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},
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tls: {
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rejectUnauthorized: false
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}
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}
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} else {
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elasticSearchClientOptions = {
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cloud: {
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id: cloudId
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},
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auth: {
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username: username,
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password: password
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}
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}
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}
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}
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return elasticSearchClientOptions
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}
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const prepareClientArgs = (
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endPoint: string | undefined,
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cloudId: string | undefined,
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credentialData: ICommonObject,
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nodeData: INodeData,
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similarityMeasure: string,
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indexName: string
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) => {
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let elasticSearchClientOptions = prepareConnectionOptions(endPoint, cloudId, credentialData, nodeData)
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let vectorSearchOptions = {}
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switch (similarityMeasure) {
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case 'dot_product':
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vectorSearchOptions = {
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similarity: 'dot_product'
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}
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break
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case 'cosine':
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vectorSearchOptions = {
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similarity: 'cosine'
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}
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break
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default:
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vectorSearchOptions = {
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similarity: 'l2_norm'
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}
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}
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const elasticClient = new Client(elasticSearchClientOptions)
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const elasticSearchClientArgs: ElasticClientArgs = {
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client: elasticClient,
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indexName: indexName,
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vectorSearchOptions: vectorSearchOptions
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}
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return {
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elasticClient,
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elasticSearchClientArgs
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}
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}
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module.exports = { nodeClass: Elasticsearch_VectorStores }
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