Support for ElasticSearch as a vector store

packages/components/credentials/ElasticsearchAPI.credential.ts

@@ -0,0 +1,31 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class ElectricsearchAPI implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Elasticsearch API'
+        this.name = 'elasticsearchApi'
+        this.version = 1.0
+        this.description =
+            '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'
+        this.inputs = [
+            {
+                label: 'Elasticsearch Endpoint',
+                name: 'endpoint',
+                type: 'string'
+            },
+            {
+                label: 'Elasticsearch API ID',
+                name: 'apiKey',
+                type: 'password'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: ElectricsearchAPI }

packages/components/credentials/ElectricsearchUserPassword.credential.ts

@@ -0,0 +1,36 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class ElasticSearchUserPassword implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ElasticSearch User Password'
+        this.name = 'elasticSearchUserPassword'
+        this.version = 1.0
+        this.description =
+            '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'
+        this.inputs = [
+            {
+                label: 'Cloud ID',
+                name: 'cloudId',
+                type: 'string'
+            },
+            {
+                label: 'ElasticSearch User',
+                name: 'username',
+                type: 'string'
+            },
+            {
+                label: 'ElasticSearch Password',
+                name: 'password',
+                type: 'password'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: ElasticSearchUserPassword }

packages/components/nodes/vectorstores/Elasticsearch/ElasticSearchBase.ts

@@ -0,0 +1,193 @@
+import {
+    getBaseClasses,
+    getCredentialData,
+    getCredentialParam,
+    ICommonObject,
+    INodeData,
+    INodeOutputsValue,
+    INodeParams
+} from '../../../src'
+import { Client, ClientOptions } from '@elastic/elasticsearch'
+import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
+import { Embeddings } from 'langchain/embeddings/base'
+import { VectorStore } from 'langchain/vectorstores/base'
+import { Document } from 'langchain/document'
+
+export abstract class ElasticSearchBase {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    protected constructor() {
+        this.type = 'Elasticsearch'
+        this.icon = 'elasticsearch.png'
+        this.category = 'Vector Stores'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['elasticsearchApi', 'elasticSearchUserPassword']
+        }
+        this.inputs = [
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Index Name',
+                name: 'indexName',
+                placeholder: '<INDEX_NAME>',
+                type: 'string'
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Similarity',
+                name: 'similarity',
+                description: 'Similarity measure used in Elasticsearch.',
+                type: 'options',
+                default: 'l2_norm',
+                options: [
+                    {
+                        label: 'l2_norm',
+                        name: 'l2_norm'
+                    },
+                    {
+                        label: 'dot_product',
+                        name: 'dot_product'
+                    },
+                    {
+                        label: 'cosine',
+                        name: 'cosine'
+                    }
+                ],
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Elasticsearch Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Elasticsearch Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(ElasticVectorSearch)]
+            }
+        ]
+    }
+
+    abstract constructVectorStore(
+        embeddings: Embeddings,
+        elasticSearchClientArgs: ElasticClientArgs,
+        docs: Document<Record<string, any>>[] | undefined
+    ): Promise<VectorStore>
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject, docs: Document<Record<string, any>>[] | undefined): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
+        const cloudId = getCredentialParam('cloudId', credentialData, nodeData)
+        const indexName = nodeData.inputs?.indexName as string
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const topK = nodeData.inputs?.topK as string
+        const similarityMeasure = nodeData.inputs?.similarityMeasure as string
+        const k = topK ? parseFloat(topK) : 4
+        const output = nodeData.outputs?.output as string
+
+        const elasticSearchClientArgs = this.prepareClientArgs(endPoint, cloudId, credentialData, nodeData, similarityMeasure, indexName)
+
+        const vectorStore = await this.constructVectorStore(embeddings, elasticSearchClientArgs, docs)
+
+        if (output === 'retriever') {
+            return vectorStore.asRetriever(k)
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+
+    protected prepareConnectionOptions(
+        endPoint: string | undefined,
+        cloudId: string | undefined,
+        credentialData: ICommonObject,
+        nodeData: INodeData
+    ) {
+        let elasticSearchClientOptions: ClientOptions = {}
+        if (endPoint) {
+            let apiKey = getCredentialParam('apiKey', credentialData, nodeData)
+            elasticSearchClientOptions = {
+                node: endPoint,
+                auth: {
+                    apiKey: apiKey
+                }
+            }
+        } else if (cloudId) {
+            let username = getCredentialParam('username', credentialData, nodeData)
+            let password = getCredentialParam('password', credentialData, nodeData)
+            elasticSearchClientOptions = {
+                cloud: {
+                    id: cloudId
+                },
+                auth: {
+                    username: username,
+                    password: password
+                }
+            }
+        }
+        return elasticSearchClientOptions
+    }
+
+    protected prepareClientArgs(
+        endPoint: string | undefined,
+        cloudId: string | undefined,
+        credentialData: ICommonObject,
+        nodeData: INodeData,
+        similarityMeasure: string,
+        indexName: string
+    ) {
+        let elasticSearchClientOptions = this.prepareConnectionOptions(endPoint, cloudId, credentialData, nodeData)
+        let vectorSearchOptions = {}
+        switch (similarityMeasure) {
+            case 'dot_product':
+                vectorSearchOptions = {
+                    similarity: 'dot_product'
+                }
+                break
+            case 'cosine':
+                vectorSearchOptions = {
+                    similarity: 'cosine'
+                }
+                break
+            default:
+                vectorSearchOptions = {
+                    similarity: 'l2_norm'
+                }
+        }
+        const elasticSearchClientArgs: ElasticClientArgs = {
+            client: new Client(elasticSearchClientOptions),
+            indexName: indexName,
+            vectorSearchOptions: vectorSearchOptions
+        }
+        return elasticSearchClientArgs
+    }
+}

packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch_Existing.ts

@@ -0,0 +1,31 @@
+import { ICommonObject, INode, INodeData } from '../../../src/Interface'
+import { Embeddings } from 'langchain/embeddings/base'
+
+import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
+import { ElasticSearchBase } from './ElasticSearchBase'
+import { VectorStore } from 'langchain/vectorstores/base'
+import { Document } from 'langchain/document'
+
+class ElasicsearchExisting_VectorStores extends ElasticSearchBase implements INode {
+    constructor() {
+        super()
+        this.label = 'Elasticsearch Load Existing Index'
+        this.name = 'ElasticsearchIndex'
+        this.version = 1.0
+        this.description = 'Load existing index from Elasticsearch (i.e: Document has been upserted)'
+    }
+
+    async constructVectorStore(
+        embeddings: Embeddings,
+        elasticSearchClientArgs: ElasticClientArgs,
+        docs: Document<Record<string, any>>[] | undefined
+    ): Promise<VectorStore> {
+        return await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        return super.init(nodeData, _, options, undefined)
+    }
+}
+
+module.exports = { nodeClass: ElasicsearchExisting_VectorStores }

packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch_Upsert.ts

@@ -0,0 +1,55 @@
+import { ICommonObject, INode, INodeData } from '../../../src/Interface'
+import { Embeddings } from 'langchain/embeddings/base'
+import { Document } from 'langchain/document'
+
+import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
+import { flatten } from 'lodash'
+import { ElasticSearchBase } from './ElasticSearchBase'
+import { VectorStore } from 'langchain/vectorstores/base'
+
+class ElasicsearchUpsert_VectorStores extends ElasticSearchBase implements INode {
+    constructor() {
+        super()
+        this.label = 'Elasticsearch Upsert Document'
+        this.name = 'ElasticsearchUpsert'
+        this.version = 1.0
+        this.description = 'Upsert documents to Elasticsearch'
+        this.inputs.unshift({
+            label: 'Document',
+            name: 'document',
+            type: 'Document',
+            list: true
+        })
+    }
+
+    async constructVectorStore(
+        embeddings: Embeddings,
+        elasticSearchClientArgs: ElasticClientArgs,
+        docs: Document<Record<string, any>>[]
+    ): Promise<VectorStore> {
+        const vectorStore = new ElasticVectorSearch(embeddings, elasticSearchClientArgs)
+        await vectorStore.addDocuments(docs)
+        return vectorStore
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const docs = nodeData.inputs?.document as Document[]
+
+        const flattenDocs = docs && docs.length ? flatten(docs) : []
+        const finalDocs = []
+        for (let i = 0; i < flattenDocs.length; i += 1) {
+            finalDocs.push(new Document(flattenDocs[i]))
+        }
+
+        // The following code is a workaround for a bug (Langchain Issue #1589) in the underlying library.
+        // Store does not support object in metadata and fail silently
+        finalDocs.forEach((d) => {
+            delete d.metadata.pdf
+            delete d.metadata.loc
+        })
+        // end of workaround
+        return super.init(nodeData, _, options, flattenDocs)
+    }
+}
+
+module.exports = { nodeClass: ElasicsearchUpsert_VectorStores }

packages/components/package.json

@@ -18,6 +18,7 @@
     "dependencies": {
         "@aws-sdk/client-dynamodb": "^3.360.0",
         "@dqbd/tiktoken": "^1.0.7",
+        "@elastic/elasticsearch": "^8.9.0",
         "@getzep/zep-js": "^0.6.3",
         "@gomomento/sdk": "^1.40.2",
         "@google-ai/generativelanguage": "^0.2.1",