Feat/aws kendra vector search (#5088)

* Add AWS Kendra vector store integration

* Fix import paths in Kendra vector store

* Add AWS Kendra dependencies to package.json

* Update pnpm-lock.yaml with AWS Kendra dependencies

* Fix linter warnings in Kendra vector store

* Fix code formatting in Kendra vector store

* Update pnpm-lock.yaml

---------

Co-authored-by: Henry Heng <[email protected]>
Co-authored-by: Henry <[email protected]>

Author: anatolyburtsev

packages/components/nodes/vectorstores/Kendra/Kendra.ts

@@ -0,0 +1,293 @@
+import { flatten } from 'lodash'
+import { AmazonKendraRetriever } from '@langchain/aws'
+import { KendraClient, BatchPutDocumentCommand, BatchDeleteDocumentCommand } from '@aws-sdk/client-kendra'
+import { Document } from '@langchain/core/documents'
+import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
+import { FLOWISE_CHATID, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { howToUseFileUpload } from '../VectorStoreUtils'
+import { MODEL_TYPE, getRegions } from '../../../src/modelLoader'
+
+class Kendra_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    badge: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'AWS Kendra'
+        this.name = 'kendra'
+        this.version = 1.0
+        this.type = 'Kendra'
+        this.icon = 'kendra.svg'
+        this.category = 'Vector Stores'
+        this.description = `Use AWS Kendra's intelligent search service for document retrieval and semantic search`
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'AWS Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['awsApi'],
+            optional: true
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Region',
+                name: 'region',
+                type: 'asyncOptions',
+                loadMethod: 'listRegions',
+                default: 'us-east-1'
+            },
+            {
+                label: 'Kendra Index ID',
+                name: 'indexId',
+                type: 'string',
+                placeholder: 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx',
+                description: 'The ID of your AWS Kendra index'
+            },
+            {
+                label: 'File Upload',
+                name: 'fileUpload',
+                description: 'Allow file upload on the chat',
+                hint: {
+                    label: 'How to use',
+                    value: howToUseFileUpload
+                },
+                type: 'boolean',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 10',
+                placeholder: '10',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Attribute Filter',
+                name: 'attributeFilter',
+                description: 'Optional filter to apply when retrieving documents',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+        // Note: Kendra doesn't support MMR search, but keeping the structure consistent
+        this.outputs = [
+            {
+                label: 'Kendra Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Kendra Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, 'BaseRetriever']
+            }
+        ]
+    }
+
+    loadMethods = {
+        async listRegions(): Promise<INodeOptionsValue[]> {
+            return await getRegions(MODEL_TYPE.CHAT, 'awsChatBedrock')
+        }
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData, options: ICommonObject): Promise<Partial<IndexingResult>> {
+            const indexId = nodeData.inputs?.indexId as string
+            const region = nodeData.inputs?.region as string
+            const docs = nodeData.inputs?.document as Document[]
+            const isFileUploadEnabled = nodeData.inputs?.fileUpload as boolean
+
+            const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+            let clientConfig: any = { region }
+
+            if (credentialData && Object.keys(credentialData).length !== 0) {
+                const accessKeyId = getCredentialParam('awsKey', credentialData, nodeData)
+                const secretAccessKey = getCredentialParam('awsSecret', credentialData, nodeData)
+                const sessionToken = getCredentialParam('awsSession', credentialData, nodeData)
+
+                if (accessKeyId && secretAccessKey) {
+                    clientConfig.credentials = {
+                        accessKeyId,
+                        secretAccessKey,
+                        ...(sessionToken && { sessionToken })
+                    }
+                }
+            }
+
+            const client = new KendraClient(clientConfig)
+
+            const flattenDocs = docs && docs.length ? flatten(docs) : []
+            const finalDocs = []
+            const kendraDocuments = []
+
+            for (let i = 0; i < flattenDocs.length; i += 1) {
+                if (flattenDocs[i] && flattenDocs[i].pageContent) {
+                    if (isFileUploadEnabled && options.chatId) {
+                        flattenDocs[i].metadata = { ...flattenDocs[i].metadata, [FLOWISE_CHATID]: options.chatId }
+                    }
+                    finalDocs.push(new Document(flattenDocs[i]))
+
+                    // Prepare document for Kendra
+                    const docId = `doc_${Date.now()}_${i}`
+                    const docTitle = flattenDocs[i].metadata?.title || flattenDocs[i].metadata?.source || `Document ${i + 1}`
+
+                    kendraDocuments.push({
+                        Id: docId,
+                        Title: docTitle,
+                        Blob: new Uint8Array(Buffer.from(flattenDocs[i].pageContent, 'utf-8')),
+                        ContentType: 'PLAIN_TEXT' as any
+                    })
+                }
+            }
+
+            try {
+                if (kendraDocuments.length > 0) {
+                    // Kendra has a limit of 10 documents per batch
+                    const batchSize = 10
+                    for (let i = 0; i < kendraDocuments.length; i += batchSize) {
+                        const batch = kendraDocuments.slice(i, i + batchSize)
+                        const command = new BatchPutDocumentCommand({
+                            IndexId: indexId,
+                            Documents: batch
+                        })
+
+                        const response = await client.send(command)
+
+                        if (response.FailedDocuments && response.FailedDocuments.length > 0) {
+                            console.error('Failed documents:', response.FailedDocuments)
+                            throw new Error(`Failed to index some documents: ${JSON.stringify(response.FailedDocuments)}`)
+                        }
+                    }
+                }
+
+                return { numAdded: finalDocs.length, addedDocs: finalDocs }
+            } catch (error) {
+                throw new Error(`Failed to index documents to Kendra: ${error}`)
+            }
+        },
+
+        async delete(nodeData: INodeData, ids: string[], options: ICommonObject): Promise<void> {
+            const indexId = nodeData.inputs?.indexId as string
+            const region = nodeData.inputs?.region as string
+
+            const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+            let clientConfig: any = { region }
+
+            if (credentialData && Object.keys(credentialData).length !== 0) {
+                const accessKeyId = getCredentialParam('awsKey', credentialData, nodeData)
+                const secretAccessKey = getCredentialParam('awsSecret', credentialData, nodeData)
+                const sessionToken = getCredentialParam('awsSession', credentialData, nodeData)
+
+                if (accessKeyId && secretAccessKey) {
+                    clientConfig.credentials = {
+                        accessKeyId,
+                        secretAccessKey,
+                        ...(sessionToken && { sessionToken })
+                    }
+                }
+            }
+
+            const client = new KendraClient(clientConfig)
+
+            try {
+                // Kendra has a limit of 10 documents per batch delete
+                const batchSize = 10
+                for (let i = 0; i < ids.length; i += batchSize) {
+                    const batch = ids.slice(i, i + batchSize)
+                    const command = new BatchDeleteDocumentCommand({
+                        IndexId: indexId,
+                        DocumentIdList: batch
+                    })
+                    await client.send(command)
+                }
+            } catch (error) {
+                throw new Error(`Failed to delete documents from Kendra: ${error}`)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const indexId = nodeData.inputs?.indexId as string
+        const region = nodeData.inputs?.region as string
+        const topK = nodeData.inputs?.topK as string
+        const attributeFilter = nodeData.inputs?.attributeFilter
+        const isFileUploadEnabled = nodeData.inputs?.fileUpload as boolean
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        let clientOptions: any = {}
+
+        if (credentialData && Object.keys(credentialData).length !== 0) {
+            clientOptions.credentials = {
+                accessKeyId: getCredentialParam('awsKey', credentialData, nodeData),
+                secretAccessKey: getCredentialParam('awsSecret', credentialData, nodeData),
+                sessionToken: getCredentialParam('awsSession', credentialData, nodeData)
+            }
+        }
+
+        let filter = undefined
+        if (attributeFilter) {
+            filter = typeof attributeFilter === 'object' ? attributeFilter : JSON.parse(attributeFilter)
+        }
+
+        // Add chat-specific filtering if file upload is enabled
+        if (isFileUploadEnabled && options.chatId) {
+            if (!filter) {
+                filter = {}
+            }
+            filter.OrAllFilters = [
+                ...(filter.OrAllFilters || []),
+                {
+                    EqualsTo: {
+                        Key: FLOWISE_CHATID,
+                        Value: {
+                            StringValue: options.chatId
+                        }
+                    }
+                }
+            ]
+        }
+
+        const retriever = new AmazonKendraRetriever({
+            topK: topK ? parseInt(topK) : 10,
+            indexId,
+            region,
+            attributeFilter: filter,
+            clientOptions
+        })
+
+        const output = nodeData.outputs?.output as string
+
+        if (output === 'retriever') {
+            return retriever
+        } else if (output === 'vectorStore') {
+            // Kendra doesn't have a traditional vector store interface,
+            // but we can return the retriever with additional properties
+            ;(retriever as any).k = topK ? parseInt(topK) : 10
+            ;(retriever as any).filter = filter
+            return retriever
+        }
+    }
+}
+
+module.exports = { nodeClass: Kendra_VectorStores }

packages/components/nodes/vectorstores/Kendra/kendra.svg

@@ -25,6 +25,7 @@
         "@arizeai/openinference-instrumentation-langchain": "^2.0.0",
         "@aws-sdk/client-bedrock-runtime": "3.422.0",
         "@aws-sdk/client-dynamodb": "^3.360.0",
+        "@aws-sdk/client-kendra": "^3.750.0",
         "@aws-sdk/client-s3": "^3.844.0",
         "@aws-sdk/client-secrets-manager": "^3.699.0",
         "@aws-sdk/client-sns": "^3.699.0",