diff --git a/packages/components/nodes/agents/BabyAGI/BabyAGI.ts b/packages/components/nodes/agents/BabyAGI/BabyAGI.ts
index 303c231ec..e31f31c6e 100644
--- a/packages/components/nodes/agents/BabyAGI/BabyAGI.ts
+++ b/packages/components/nodes/agents/BabyAGI/BabyAGI.ts
@@ -1,7 +1,7 @@
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { BabyAGI } from './core'
import { BaseChatModel } from 'langchain/chat_models/base'
-import { VectorStore } from 'langchain/vectorstores'
+import { VectorStore } from 'langchain/vectorstores/base'
class BabyAGI_Agents implements INode {
label: string
diff --git a/packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts b/packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts
index 6c9447d3b..13de17dc4 100644
--- a/packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts
+++ b/packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts
@@ -2,7 +2,7 @@ import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Inter
import { getBaseClasses } from '../../../src/utils'
import { VectorDBQAChain } from 'langchain/chains'
import { BaseLanguageModel } from 'langchain/base_language'
-import { VectorStore } from 'langchain/vectorstores'
+import { VectorStore } from 'langchain/vectorstores/base'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
class VectorDBQAChain_Chains implements INode {
diff --git a/packages/components/nodes/vectorstores/Vectara_Existing/Vectara_Existing.ts b/packages/components/nodes/vectorstores/Vectara/Vectara_Existing.ts
similarity index 99%
rename from packages/components/nodes/vectorstores/Vectara_Existing/Vectara_Existing.ts
rename to packages/components/nodes/vectorstores/Vectara/Vectara_Existing.ts
index 3ef04f079..122fcbd29 100644
--- a/packages/components/nodes/vectorstores/Vectara_Existing/Vectara_Existing.ts
+++ b/packages/components/nodes/vectorstores/Vectara/Vectara_Existing.ts
@@ -92,7 +92,7 @@ class VectaraExisting_VectorStores implements INode {
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 corpusId = getCredentialParam('corpusID', credentialData, nodeData).split(',')
const vectaraMetadataFilter = nodeData.inputs?.filter as string
const sentencesBefore = nodeData.inputs?.sentencesBefore as number
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 000000000..56cfbcffe
--- /dev/null
+++ b/packages/components/nodes/vectorstores/Vectara/Vectara_Upload.ts
@@ -0,0 +1,176 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile } from 'langchain/vectorstores/vectara'
+
+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',
+ description:
+ 'File to upload to Vectara. Supported file types: https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes',
+ type: 'file'
+ },
+ {
+ label: 'Vectara Metadata Filter',
+ name: 'filter',
+ description:
+ 'Filter to apply to Vectara metadata. Refer to the documentation 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 {
+ 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).split(',')
+
+ 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 vectaraFiles: VectaraFile[] = []
+ for (const file of files) {
+ const splitDataURI = file.split(',')
+ splitDataURI.pop()
+ const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
+ const blob = new Blob([bf])
+ vectaraFiles.push({ blob: blob, fileName: getFileName(file) })
+ }
+
+ const vectorStore = new VectaraStore(vectaraArgs)
+ await vectorStore.addFiles(vectaraFiles)
+
+ if (output === 'retriever') {
+ const retriever = vectorStore.asRetriever(k, vectaraFilter)
+ return retriever
+ } else if (output === 'vectorStore') {
+ ;(vectorStore as any).k = k
+ return vectorStore
+ }
+ return vectorStore
+ }
+}
+
+const getFileName = (fileBase64: string) => {
+ let fileNames = []
+ if (fileBase64.startsWith('[') && fileBase64.endsWith(']')) {
+ const files = JSON.parse(fileBase64)
+ for (const file of files) {
+ const splitDataURI = file.split(',')
+ const filename = splitDataURI[splitDataURI.length - 1].split(':')[1]
+ fileNames.push(filename)
+ }
+ return fileNames.join(', ')
+ } else {
+ const splitDataURI = fileBase64.split(',')
+ const filename = splitDataURI[splitDataURI.length - 1].split(':')[1]
+ return filename
+ }
+}
+
+module.exports = { nodeClass: VectaraUpload_VectorStores }
diff --git a/packages/components/nodes/vectorstores/Vectara_Upsert/Vectara_Upsert.ts b/packages/components/nodes/vectorstores/Vectara/Vectara_Upsert.ts
similarity index 99%
rename from packages/components/nodes/vectorstores/Vectara_Upsert/Vectara_Upsert.ts
rename to packages/components/nodes/vectorstores/Vectara/Vectara_Upsert.ts
index 51fb67ed5..9918fff26 100644
--- a/packages/components/nodes/vectorstores/Vectara_Upsert/Vectara_Upsert.ts
+++ b/packages/components/nodes/vectorstores/Vectara/Vectara_Upsert.ts
@@ -101,7 +101,7 @@ class VectaraUpsert_VectorStores implements INode {
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 corpusId = getCredentialParam('corpusID', credentialData, nodeData).split(',')
const docs = nodeData.inputs?.document as Document[]
const embeddings = {} as Embeddings
diff --git a/packages/components/nodes/vectorstores/Vectara_Existing/vectara.png b/packages/components/nodes/vectorstores/Vectara/vectara.png
similarity index 100%
rename from packages/components/nodes/vectorstores/Vectara_Existing/vectara.png
rename to packages/components/nodes/vectorstores/Vectara/vectara.png
diff --git a/packages/components/nodes/vectorstores/Vectara_Upsert/vectara.png b/packages/components/nodes/vectorstores/Vectara_Upsert/vectara.png
deleted file mode 100644
index a13a34e6b..000000000
Binary files a/packages/components/nodes/vectorstores/Vectara_Upsert/vectara.png and /dev/null differ
diff --git a/packages/components/package.json b/packages/components/package.json
index 6e522a5d6..4b3a8ba4a 100644
--- a/packages/components/package.json
+++ b/packages/components/package.json
@@ -42,7 +42,7 @@
"google-auth-library": "^9.0.0",
"graphql": "^16.6.0",
"html-to-text": "^9.0.5",
- "langchain": "^0.0.145",
+ "langchain": "^0.0.147",
"langfuse-langchain": "^1.0.14-alpha.0",
"langsmith": "^0.0.32",
"linkifyjs": "^4.1.1",
diff --git a/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json b/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json
index 784ad2406..47cfef872 100644
--- a/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json
+++ b/packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json
@@ -1,11 +1,125 @@
{
- "description": "A simple LLM chain that uses Vectara to enable conversations with uploaded documents",
+ "description": "A simple LLM chain that uses Vectara to enable conversations with uploaded files",
"nodes": [
+ {
+ "width": 300,
+ "height": 524,
+ "id": "vectaraUpload_0",
+ "position": { "x": 219.0098475967174, "y": 189.74396248534583 },
+ "type": "customNode",
+ "data": {
+ "id": "vectaraUpload_0",
+ "label": "Vectara Upload File",
+ "version": 1,
+ "name": "vectaraUpload",
+ "type": "Vectara",
+ "baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
+ "category": "Vector Stores",
+ "description": "Upload files to Vectara",
+ "inputParams": [
+ {
+ "label": "Connect Credential",
+ "name": "credential",
+ "type": "credential",
+ "credentialNames": ["vectaraApi"],
+ "id": "vectaraUpload_0-input-credential-credential"
+ },
+ {
+ "label": "File",
+ "name": "file",
+ "description": "File to upload to Vectara. Supported file types: https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes",
+ "type": "file",
+ "id": "vectaraUpload_0-input-file-file"
+ },
+ {
+ "label": "Vectara Metadata Filter",
+ "name": "filter",
+ "description": "Filter to apply to Vectara metadata. Refer to the documentation on how to use Vectara filters with Flowise.",
+ "type": "string",
+ "additionalParams": true,
+ "optional": true,
+ "id": "vectaraUpload_0-input-filter-string"
+ },
+ {
+ "label": "Sentences Before",
+ "name": "sentencesBefore",
+ "description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
+ "type": "number",
+ "additionalParams": true,
+ "optional": true,
+ "id": "vectaraUpload_0-input-sentencesBefore-number"
+ },
+ {
+ "label": "Sentences After",
+ "name": "sentencesAfter",
+ "description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
+ "type": "number",
+ "additionalParams": true,
+ "optional": true,
+ "id": "vectaraUpload_0-input-sentencesAfter-number"
+ },
+ {
+ "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,
+ "id": "vectaraUpload_0-input-lambda-number"
+ },
+ {
+ "label": "Top K",
+ "name": "topK",
+ "description": "Number of top results to fetch. Defaults to 4",
+ "placeholder": "4",
+ "type": "number",
+ "additionalParams": true,
+ "optional": true,
+ "id": "vectaraUpload_0-input-topK-number"
+ }
+ ],
+ "inputAnchors": [],
+ "inputs": {
+ "filter": "",
+ "sentencesBefore": "",
+ "sentencesAfter": "",
+ "lambda": "",
+ "topK": ""
+ },
+ "outputAnchors": [
+ {
+ "name": "output",
+ "label": "Output",
+ "type": "options",
+ "options": [
+ {
+ "id": "vectaraUpload_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
+ "name": "retriever",
+ "label": "Vectara Retriever",
+ "type": "Vectara | VectorStoreRetriever | BaseRetriever"
+ },
+ {
+ "id": "vectaraUpload_0-output-vectorStore-Vectara|VectorStore",
+ "name": "vectorStore",
+ "label": "Vectara Vector Store",
+ "type": "Vectara | VectorStore"
+ }
+ ],
+ "default": "retriever"
+ }
+ ],
+ "outputs": { "output": "retriever" },
+ "selected": false
+ },
+ "selected": false,
+ "positionAbsolute": { "x": 219.0098475967174, "y": 189.74396248534583 },
+ "dragging": false
+ },
{
"width": 300,
"height": 525,
"id": "chatOpenAI_0",
- "position": { "x": 514.1088940275924, "y": 199.574479681537 },
+ "position": { "x": 669.6533996522251, "y": 177.86181519287192 },
"type": "customNode",
"data": {
"id": "chatOpenAI_0",
@@ -13,7 +127,7 @@
"version": 1,
"name": "chatOpenAI",
"type": "ChatOpenAI",
- "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
+ "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
"category": "Chat Models",
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
"inputParams": [
@@ -36,7 +150,10 @@
{ "label": "gpt-3.5-turbo", "name": "gpt-3.5-turbo" },
{ "label": "gpt-3.5-turbo-0613", "name": "gpt-3.5-turbo-0613" },
{ "label": "gpt-3.5-turbo-16k", "name": "gpt-3.5-turbo-16k" },
- { "label": "gpt-3.5-turbo-16k-0613", "name": "gpt-3.5-turbo-16k-0613" }
+ {
+ "label": "gpt-3.5-turbo-16k-0613",
+ "name": "gpt-3.5-turbo-16k-0613"
+ }
],
"default": "gpt-3.5-turbo",
"optional": true,
@@ -103,6 +220,14 @@
"optional": true,
"additionalParams": true,
"id": "chatOpenAI_0-input-basepath-string"
+ },
+ {
+ "label": "BaseOptions",
+ "name": "baseOptions",
+ "type": "json",
+ "optional": true,
+ "additionalParams": true,
+ "id": "chatOpenAI_0-input-baseOptions-json"
}
],
"inputAnchors": [],
@@ -114,28 +239,29 @@
"frequencyPenalty": "",
"presencePenalty": "",
"timeout": "",
- "basepath": ""
+ "basepath": "",
+ "baseOptions": ""
},
"outputAnchors": [
{
- "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
+ "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"name": "chatOpenAI",
"label": "ChatOpenAI",
- "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
+ "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
}
],
"outputs": {},
"selected": false
},
"selected": false,
- "positionAbsolute": { "x": 514.1088940275924, "y": 199.574479681537 },
+ "positionAbsolute": { "x": 669.6533996522251, "y": 177.86181519287192 },
"dragging": false
},
{
"width": 300,
"height": 481,
"id": "conversationalRetrievalQAChain_0",
- "position": { "x": 900.4793407261002, "y": 205.9476004518217 },
+ "position": { "x": 1135.5490908971935, "y": 201.62146241822506 },
"type": "customNode",
"data": {
"id": "conversationalRetrievalQAChain_0",
@@ -143,7 +269,7 @@
"version": 1,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
- "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
+ "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -214,234 +340,45 @@
],
"inputs": {
"model": "{{chatOpenAI_0.data.instance}}",
- "vectorStoreRetriever": "{{vectaraUpsert_0.data.instance}}",
+ "vectorStoreRetriever": "{{vectaraUpload_0.data.instance}}",
"memory": "",
- "returnSourceDocuments": "",
+ "returnSourceDocuments": true,
"systemMessagePrompt": "",
"chainOption": ""
},
"outputAnchors": [
{
- "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
+ "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
- "type": "ConversationalRetrievalQAChain | BaseChain"
+ "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
"selected": false
},
"selected": false,
- "positionAbsolute": { "x": 900.4793407261002, "y": 205.9476004518217 },
- "dragging": false
- },
- {
- "width": 300,
- "height": 509,
- "id": "pdfFile_0",
- "position": { "x": -210.44158723479913, "y": 236.6627524951051 },
- "type": "customNode",
- "data": {
- "id": "pdfFile_0",
- "label": "Pdf File",
- "version": 1,
- "name": "pdfFile",
- "type": "Document",
- "baseClasses": ["Document"],
- "category": "Document Loaders",
- "description": "Load data from PDF files",
- "inputParams": [
- { "label": "Pdf File", "name": "pdfFile", "type": "file", "fileType": ".pdf", "id": "pdfFile_0-input-pdfFile-file" },
- {
- "label": "Usage",
- "name": "usage",
- "type": "options",
- "options": [
- { "label": "One document per page", "name": "perPage" },
- { "label": "One document per file", "name": "perFile" }
- ],
- "default": "perPage",
- "id": "pdfFile_0-input-usage-options"
- },
- {
- "label": "Use Legacy Build",
- "name": "legacyBuild",
- "type": "boolean",
- "optional": true,
- "additionalParams": true,
- "id": "pdfFile_0-input-legacyBuild-boolean"
- },
- {
- "label": "Metadata",
- "name": "metadata",
- "type": "json",
- "optional": true,
- "additionalParams": true,
- "id": "pdfFile_0-input-metadata-json"
- }
- ],
- "inputAnchors": [
- {
- "label": "Text Splitter",
- "name": "textSplitter",
- "type": "TextSplitter",
- "optional": true,
- "id": "pdfFile_0-input-textSplitter-TextSplitter"
- }
- ],
- "inputs": { "textSplitter": "", "usage": "perPage", "legacyBuild": "", "metadata": "" },
- "outputAnchors": [
- { "id": "pdfFile_0-output-pdfFile-Document", "name": "pdfFile", "label": "Document", "type": "Document" }
- ],
- "outputs": {},
- "selected": false
- },
- "selected": false,
- "positionAbsolute": { "x": -210.44158723479913, "y": 236.6627524951051 },
- "dragging": false
- },
- {
- "width": 300,
- "height": 408,
- "id": "vectaraUpsert_0",
- "position": { "x": 172.06946164914868, "y": 373.11406233089934 },
- "type": "customNode",
- "data": {
- "id": "vectaraUpsert_0",
- "label": "Vectara Upsert Document",
- "version": 1,
- "name": "vectaraUpsert",
- "type": "Vectara",
- "baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
- "category": "Vector Stores",
- "description": "Upsert documents to Vectara",
- "inputParams": [
- {
- "label": "Connect Credential",
- "name": "credential",
- "type": "credential",
- "credentialNames": ["vectaraApi"],
- "id": "vectaraUpsert_0-input-credential-credential"
- },
- {
- "label": "Vectara Metadata Filter",
- "name": "filter",
- "description": "Filter to apply to Vectara metadata. Refer to the documentation on how to use Vectara filters with Flowise.",
- "type": "string",
- "additionalParams": true,
- "optional": true,
- "id": "vectaraUpsert_0-input-filter-string"
- },
- {
- "label": "Sentences Before",
- "name": "sentencesBefore",
- "description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
- "type": "number",
- "additionalParams": true,
- "optional": true,
- "id": "vectaraUpsert_0-input-sentencesBefore-number"
- },
- {
- "label": "Sentences After",
- "name": "sentencesAfter",
- "description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
- "type": "number",
- "additionalParams": true,
- "optional": true,
- "id": "vectaraUpsert_0-input-sentencesAfter-number"
- },
- {
- "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,
- "id": "vectaraUpsert_0-input-lambda-number"
- },
- {
- "label": "Top K",
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- "optional": true,
- "id": "vectaraUpsert_0-input-topK-number"
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- "type": "Document",
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- }
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- "inputs": {
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- "filter": "",
- "sentencesBefore": "",
- "sentencesAfter": "",
- "lambda": "",
- "topK": ""
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- {
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- "label": "Vectara Vector Store",
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- "selected": false
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- "selected": false
+ "dragging": false,
+ "positionAbsolute": { "x": 1135.5490908971935, "y": 201.62146241822506 }
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],
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+ "data": { "label": "" }
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