Support for ElasticSearch as a vector store
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vinodkiran
parent
ef5bc230b9
commit
f108c62acf
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import { INodeParams, INodeCredential } from '../src/Interface'
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class ElectricsearchAPI implements INodeCredential {
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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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inputs: INodeParams[]
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constructor() {
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this.label = 'Elasticsearch API'
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this.name = 'elasticsearchApi'
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this.version = 1.0
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this.description =
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'Refer to <a target="_blank" href="https://www.elastic.co/guide/en/kibana/current/api-keys.html">official guide</a> on how to get an API Key from ElasticSearch'
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this.inputs = [
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{
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label: 'Elasticsearch Endpoint',
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name: 'endpoint',
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type: 'string'
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},
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{
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label: 'Elasticsearch API ID',
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name: 'apiKey',
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type: 'password'
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}
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]
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}
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}
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module.exports = { credClass: ElectricsearchAPI }
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import { INodeParams, INodeCredential } from '../src/Interface'
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class ElasticSearchUserPassword implements INodeCredential {
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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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inputs: INodeParams[]
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constructor() {
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this.label = 'ElasticSearch User Password'
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this.name = 'elasticSearchUserPassword'
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this.version = 1.0
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this.description =
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'Refer to <a target="_blank" href="https://www.elastic.co/guide/en/kibana/current/tutorial-secure-access-to-kibana.html">official guide</a> on how to get User Password from ElasticSearch'
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this.inputs = [
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{
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label: 'ElasticSearch User',
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name: 'elasticSearchUser',
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type: 'string'
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},
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{
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label: 'ElasticSearch Password',
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name: 'elasticSearchPassword',
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type: 'password'
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}
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]
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}
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}
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module.exports = { credClass: ElasticSearchUserPassword }
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { Embeddings } from 'langchain/embeddings/base'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src'
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import { Client, ClientOptions } from '@elastic/elasticsearch'
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import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
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class ElasicsearchExisting_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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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 Load Existing Index'
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this.name = 'ElasticsearchIndex'
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this.version = 1.0
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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.description = 'Load existing index from Elasticsearch (i.e: Document has been upserted)'
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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: '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: '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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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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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 apiKey = getCredentialParam('apiKey', 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 k = topK ? parseFloat(topK) : 4
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const output = nodeData.outputs?.output as string
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// eslint-disable-next-line no-console
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console.log('EndPoint:: ' + endPoint + ', APIKey:: ' + apiKey + ', Index:: ' + indexName)
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const elasticSearchClientOptions: ClientOptions = {
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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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const elasticSearchClientArgs: ElasticClientArgs = {
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client: new Client(elasticSearchClientOptions),
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indexName: indexName
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}
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const vectorStore = await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
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// eslint-disable-next-line no-console
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console.log('vectorStore ::' + vectorStore._vectorstoreType())
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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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module.exports = { nodeClass: ElasicsearchExisting_VectorStores }
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@ -0,0 +1,165 @@
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { Embeddings } from 'langchain/embeddings/base'
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import { Document } from 'langchain/document'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src'
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import { Client, ClientOptions } from '@elastic/elasticsearch'
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import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
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import { flatten } from 'lodash'
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class ElasicsearchUpsert_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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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 Upsert Document'
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this.name = 'ElasticsearchUpsert'
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this.version = 1.0
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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.description = 'Upsert documents to Elasticsearch'
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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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},
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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: '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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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 apiKey = getCredentialParam('apiKey', credentialData, nodeData)
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const docs = nodeData.inputs?.document as Document[]
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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 k = topK ? parseFloat(topK) : 4
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const output = nodeData.outputs?.output as string
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const similarityMeasure = nodeData.inputs?.similarityMeasure as string
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// eslint-disable-next-line no-console
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console.log('EndPoint:: ' + endPoint + ', APIKey:: ' + apiKey + ', Index:: ' + indexName)
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const elasticSearchClientOptions: ClientOptions = {
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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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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 elasticSearchClientArgs: ElasticClientArgs = {
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client: new Client(elasticSearchClientOptions),
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indexName: indexName,
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vectorSearchOptions: vectorSearchOptions
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}
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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 ElasticVectorSearch.fromDocuments(finalDocs, embeddings, elasticSearchClientArgs)
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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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module.exports = { nodeClass: ElasicsearchUpsert_VectorStores }
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@ -19,6 +19,7 @@
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"@aws-sdk/client-dynamodb": "^3.360.0",
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"@dqbd/tiktoken": "^1.0.7",
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"@getzep/zep-js": "^0.6.3",
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"@elastic/elasticsearch": "^8.9.0",
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"@google-ai/generativelanguage": "^0.2.1",
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"@huggingface/inference": "^2.6.1",
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"@notionhq/client": "^2.2.8",
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