diff --git a/packages/components/nodes/vectorstores/Vectara/Vectara_Upload.ts b/packages/components/nodes/vectorstores/Vectara/Vectara_Upload.ts
new file mode 100644
index 00000000..a7a2d799
--- /dev/null
+++ b/packages/components/nodes/vectorstores/Vectara/Vectara_Upload.ts
@@ -0,0 +1,161 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { Embeddings } from 'langchain/embeddings/base'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig } from 'langchain/vectorstores/vectara'
+import { Document } from 'langchain/document'
+import { flatten } from 'lodash'
+
+class VectaraUpload_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Vectara Upload File'
+        this.name = 'vectaraUpload'
+        this.version = 1.0
+        this.type = 'Vectara'
+        this.icon = 'vectara.png'
+        this.category = 'Vector Stores'
+        this.description = 'Upload files to Vectara'
+        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['vectaraApi']
+        }
+        this.inputs = [
+            {
+                label: 'File',
+                name: 'file',
+                type: 'file'
+            },
+            {
+                label: 'Vectara Metadata Filter',
+                name: 'filter',
+                description:
+                    'Filter to apply to Vectara metadata. Refer to the <a target="_blank" href="https://docs.flowiseai.com/vector-stores/vectara">documentation</a> on how to use Vectara filters with Flowise.',
+                type: 'string',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Sentences Before',
+                name: 'sentencesBefore',
+                description: 'Number of sentences to fetch before the matched sentence. Defaults to 2.',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Sentences After',
+                name: 'sentencesAfter',
+                description: 'Number of sentences to fetch after the matched sentence. Defaults to 2.',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Lambda',
+                name: 'lambda',
+                description:
+                    'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Defaults to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Vectara Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Vectara Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(VectaraStore)]
+            }
+        ]
+    }
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
+        const customerId = getCredentialParam('customerID', credentialData, nodeData)
+        const corpusId = getCredentialParam('corpusID', credentialData, nodeData)
+
+        const fileBase64 = nodeData.inputs?.file
+        const vectaraMetadataFilter = nodeData.inputs?.filter as string
+        const sentencesBefore = nodeData.inputs?.sentencesBefore as number
+        const sentencesAfter = nodeData.inputs?.sentencesAfter as number
+        const lambda = nodeData.inputs?.lambda as number
+        const output = nodeData.outputs?.output as string
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseInt(topK, 10) : 4
+
+        const vectaraArgs: VectaraLibArgs = {
+            apiKey: apiKey,
+            customerId: customerId,
+            corpusId: corpusId
+        }
+
+        const vectaraFilter: VectaraFilter = {}
+        if (vectaraMetadataFilter) vectaraFilter.filter = vectaraMetadataFilter
+        if (lambda) vectaraFilter.lambda = lambda
+
+        const vectaraContextConfig: VectaraContextConfig = {}
+        if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
+        if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
+        vectaraFilter.contextConfig = vectaraContextConfig
+
+        let files: string[] = []
+
+        if (fileBase64.startsWith('[') && fileBase64.endsWith(']')) {
+            files = JSON.parse(fileBase64)
+        } else {
+            files = [fileBase64]
+        }
+
+        const blobs: Blob[] = []
+        for (const file of files) {
+            const splitDataURI = file.split(',')
+            splitDataURI.pop()
+            const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
+            const blob = new Blob([bf])
+            blobs.push(blob)
+        }
+
+        const vectorStore = new VectaraStore(vectaraArgs)
+        const res = await vectorStore.addFiles(blobs)
+        files = []
+
+        if (output === 'retriever') {
+            const retriever = vectorStore.asRetriever(k, vectaraFilter)
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+}
+
+module.exports = { nodeClass: VectaraUpload_VectorStores }