Merge branch 'main' of github.com:0xi4o/Flowise into feature/scrapped-links

.github/workflows/autoSyncMergedPullRequest.yml

@@ -0,0 +1,33 @@
+name: autoSyncMergedPullRequest
+on:
+  pull_request_target:
+    types:
+      - closed
+    branches: [ "main" ]
+jobs:
+  autoSyncMergedPullRequest:
+    if: github.event.pull_request.merged == true
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+      - name: Show PR info
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
+      - name: Repository Dispatch
+        uses: peter-evans/repository-dispatch@v2
+        with:
+          token: ${{ secrets.AUTOSYNC_TOKEN }}
+          repository: ${{ secrets.AUTOSYNC_CH_URL }}
+          event-type: ${{ secrets.AUTOSYNC_PR_EVENT_TYPE }}
+          client-payload: >-
+            {
+              "ref": "${{ github.ref }}",
+              "prNumber": "${{ github.event.pull_request.number }}",
+              "prTitle": "${{ github.event.pull_request.title }}",
+              "prDescription": "${{ github.event.pull_request.description }}",
+              "sha": "${{ github.sha }}"
+            }

.github/workflows/autoSyncSingleCommit.yml

@@ -0,0 +1,36 @@
+name: autoSyncSingleCommit
+on:
+  push:
+    branches:
+      - main
+jobs:
+  doNotAutoSyncSingleCommit:
+    if: github.event.commits[1] != null
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: IGNORE autoSyncSingleCommit
+        run: |
+          echo This single commit has came from a merged commit. We will ignore it. This case is handled in autoSyncMergedPullRequest workflow for merge commits comming from merged pull requests only! Beware, the regular merge commits are not handled by any workflow for the moment.
+  autoSyncSingleCommit:
+    if: github.event.commits[1] == null
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: autoSyncSingleCommit
+        env:
+          GITHUB_CONTEXT: ${{ toJSON(github) }}
+        run: |
+          echo Autosync a single commit with id: ${{ github.sha }} from openSource main branch towards cloud hosted version.
+      - name: Repository Dispatch
+        uses: peter-evans/repository-dispatch@v2
+        with:
+          token: ${{ secrets.AUTOSYNC_TOKEN }}
+          repository: ${{ secrets.AUTOSYNC_CH_URL }}
+          event-type: ${{ secrets.AUTOSYNC_SC_EVENT_TYPE }}
+          client-payload: >-
+            {
+              "ref": "${{ github.ref }}",
+              "sha": "${{ github.sha }}",
+              "commitMessage": "${{ github.event.commits[0].message }}"
+            }

CONTRIBUTING-ZH.md

@@ -138,6 +138,7 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
 | DATABASE_NAME               | 数据库名称（当 DATABASE_TYPE 不是 sqlite 时）          | 字符串                                          |                                     |
 | SECRETKEY_PATH              | 保存加密密钥（用于加密/解密凭据）的位置                | 字符串                                          | `your-path/Flowise/packages/server` |
 | FLOWISE_SECRETKEY_OVERWRITE | 加密密钥用于替代存储在 SECRETKEY_PATH 中的密钥         | 字符串                                          |
+| DISABLE_FLOWISE_TELEMETRY   | 关闭遥测                                               | 字符串                                          |
 
 您也可以在使用 `npx` 时指定环境变量。例如：
 

CONTRIBUTING.md

@@ -141,6 +141,7 @@ Flowise support different environment variables to configure your instance. You
 | DATABASE_SSL                | Database connection overssl (When DATABASE_TYPE is postgre)                  | Boolean                                          | false                               |
 | SECRETKEY_PATH              | Location where encryption key (used to encrypt/decrypt credentials) is saved | String                                           | `your-path/Flowise/packages/server` |
 | FLOWISE_SECRETKEY_OVERWRITE | Encryption key to be used instead of the key stored in SECRETKEY_PATH        | String                                           |
+| DISABLE_FLOWISE_TELEMETRY   | Turn off telemetry                                                           | Boolean                                          |
 
 You can also specify the env variables when using `npx`. For example:
 

artillery-load-test.yml

@@ -33,4 +33,4 @@ scenarios:
 #     Seconds
 # Total Users = 2 + 3 + 3 = 8
 # Each making 1 HTTP call
-# Over a duration of 3 seconds
+# Over a durations of 3 seconds

docker/.env.example

@@ -25,4 +25,6 @@ LOG_PATH=/root/.flowise/logs
 # LANGCHAIN_TRACING_V2=true
 # LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
 # LANGCHAIN_API_KEY=your_api_key
-# LANGCHAIN_PROJECT=your_project
+# LANGCHAIN_PROJECT=your_project
+
+# DISABLE_FLOWISE_TELEMETRY=true

docker/docker-compose.yml

@@ -22,6 +22,7 @@ services:
             - FLOWISE_SECRETKEY_OVERWRITE=${FLOWISE_SECRETKEY_OVERWRITE}
             - LOG_LEVEL=${LOG_LEVEL}
             - LOG_PATH=${LOG_PATH}
+            - DISABLE_FLOWISE_TELEMETRY=${DISABLE_FLOWISE_TELEMETRY}
         ports:
             - '${PORT}:${PORT}'
         volumes:

package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.10",
+    "version": "1.4.11",
     "private": true,
     "homepage": "https://flowiseai.com",
     "workspaces": [

packages/components/credentials/PineconeApi.credential.ts

@@ -16,11 +16,6 @@ class PineconeApi implements INodeCredential {
                 label: 'Pinecone Api Key',
                 name: 'pineconeApiKey',
                 type: 'password'
-            },
-            {
-                label: 'Pinecone Environment',
-                name: 'pineconeEnv',
-                type: 'string'
             }
         ]
     }

packages/components/nodes/chains/ConversationChain/ConversationChain.ts

@@ -1,13 +1,12 @@
 import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { ConversationChain } from 'langchain/chains'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
 import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts'
 import { BaseChatModel } from 'langchain/chat_models/base'
 import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
-import { flatten } from 'lodash'
-import { Document } from 'langchain/document'
 import { RunnableSequence } from 'langchain/schema/runnable'
 import { StringOutputParser } from 'langchain/schema/output_parser'
+import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
 
 let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
 const inputKey = 'input'
@@ -27,7 +26,7 @@ class ConversationChain_Chains implements INode {
     constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversation Chain'
         this.name = 'conversationChain'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'ConversationChain'
         this.icon = 'conv.svg'
         this.category = 'Chains'
@@ -44,6 +43,14 @@ class ConversationChain_Chains implements INode {
                 name: 'memory',
                 type: 'BaseMemory'
             },
+            {
+                label: 'Chat Prompt Template',
+                name: 'chatPromptTemplate',
+                type: 'ChatPromptTemplate',
+                description: 'Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable',
+                optional: true
+            },
+            /* Deprecated
             {
                 label: 'Document',
                 name: 'document',
@@ -52,15 +59,17 @@ class ConversationChain_Chains implements INode {
                     'Include whole document into the context window, if you get maximum context length error, please use model with higher context window like Claude 100k, or gpt4 32k',
                 optional: true,
                 list: true
-            },
+            },*/
             {
                 label: 'System Message',
                 name: 'systemMessagePrompt',
                 type: 'string',
                 rows: 4,
+                description: 'If Chat Prompt Template is provided, this will be ignored',
                 additionalParams: true,
                 optional: true,
-                placeholder: 'You are a helpful assistant that write codes'
+                default: systemMessage,
+                placeholder: systemMessage
             }
         ]
         this.sessionId = fields?.sessionId
@@ -76,15 +85,21 @@ class ConversationChain_Chains implements INode {
         const chain = prepareChain(nodeData, this.sessionId, options.chatHistory)
 
         const loggerHandler = new ConsoleCallbackHandler(options.logger)
-        const callbacks = await additionalCallbacks(nodeData, options)
+        const additionalCallback = await additionalCallbacks(nodeData, options)
 
         let res = ''
+        let callbacks = [loggerHandler, ...additionalCallback]
+
+        if (process.env.DEBUG === 'true') {
+            callbacks.push(new LCConsoleCallbackHandler())
+        }
 
         if (options.socketIO && options.socketIOClientId) {
             const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
-            res = await chain.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
+            callbacks.push(handler)
+            res = await chain.invoke({ input }, { callbacks })
         } else {
-            res = await chain.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
+            res = await chain.invoke({ input }, { callbacks })
         }
 
         await memory.addChatMessages(
@@ -108,28 +123,27 @@ class ConversationChain_Chains implements INode {
 const prepareChatPrompt = (nodeData: INodeData) => {
     const memory = nodeData.inputs?.memory as FlowiseMemory
     const prompt = nodeData.inputs?.systemMessagePrompt as string
-    const docs = nodeData.inputs?.document as Document[]
+    const chatPromptTemplate = nodeData.inputs?.chatPromptTemplate as ChatPromptTemplate
 
-    const flattenDocs = docs && docs.length ? flatten(docs) : []
-    const finalDocs = []
-    for (let i = 0; i < flattenDocs.length; i += 1) {
-        if (flattenDocs[i] && flattenDocs[i].pageContent) {
-            finalDocs.push(new Document(flattenDocs[i]))
+    if (chatPromptTemplate && chatPromptTemplate.promptMessages.length) {
+        const sysPrompt = chatPromptTemplate.promptMessages[0]
+        const humanPrompt = chatPromptTemplate.promptMessages[chatPromptTemplate.promptMessages.length - 1]
+        const chatPrompt = ChatPromptTemplate.fromMessages([
+            sysPrompt,
+            new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
+            humanPrompt
+        ])
+
+        if ((chatPromptTemplate as any).promptValues) {
+            // @ts-ignore
+            chatPrompt.promptValues = (chatPromptTemplate as any).promptValues
         }
+
+        return chatPrompt
     }
 
-    let finalText = ''
-    for (let i = 0; i < finalDocs.length; i += 1) {
-        finalText += finalDocs[i].pageContent
-    }
-
-    const replaceChar: string[] = ['{', '}']
-    for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
-
-    if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
-
     const chatPrompt = ChatPromptTemplate.fromMessages([
-        SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
+        SystemMessagePromptTemplate.fromTemplate(prompt ? prompt : systemMessage),
         new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
         HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
     ])
@@ -142,15 +156,31 @@ const prepareChain = (nodeData: INodeData, sessionId?: string, chatHistory: IMes
     const memory = nodeData.inputs?.memory as FlowiseMemory
     const memoryKey = memory.memoryKey ?? 'chat_history'
 
+    const chatPrompt = prepareChatPrompt(nodeData)
+    let promptVariables = {}
+    const promptValuesRaw = (chatPrompt as any).promptValues
+    if (promptValuesRaw) {
+        const promptValues = handleEscapeCharacters(promptValuesRaw, true)
+        for (const val in promptValues) {
+            promptVariables = {
+                ...promptVariables,
+                [val]: () => {
+                    return promptValues[val]
+                }
+            }
+        }
+    }
+
     const conversationChain = RunnableSequence.from([
         {
             [inputKey]: (input: { input: string }) => input.input,
             [memoryKey]: async () => {
                 const history = await memory.getChatMessages(sessionId, true, chatHistory)
                 return history
-            }
+            },
+            ...promptVariables
         },
-        prepareChatPrompt(nodeData),
+        chatPrompt,
         model,
         new StringOutputParser()
     ])

packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts

@@ -13,6 +13,7 @@ import { applyPatch } from 'fast-json-patch'
 import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
 import { ConsoleCallbackHandler, additionalCallbacks } from '../../../src/handler'
 import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
+import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
 
 type RetrievalChainInput = {
     chat_history: string
@@ -176,11 +177,17 @@ class ConversationalRetrievalQAChain_Chains implements INode {
         const history = ((await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]) ?? []
 
         const loggerHandler = new ConsoleCallbackHandler(options.logger)
-        const callbacks = await additionalCallbacks(nodeData, options)
+        const additionalCallback = await additionalCallbacks(nodeData, options)
+
+        let callbacks = [loggerHandler, ...additionalCallback]
+
+        if (process.env.DEBUG === 'true') {
+            callbacks.push(new LCConsoleCallbackHandler())
+        }
 
         const stream = answerChain.streamLog(
             { question: input, chat_history: history },
-            { callbacks: [loggerHandler, ...callbacks] },
+            { callbacks },
             {
                 includeNames: [sourceRunnableName]
             }

packages/components/nodes/chains/LLMChain/LLMChain.ts

@@ -82,7 +82,7 @@ class LLMChain_Chains implements INode {
         const model = nodeData.inputs?.model as BaseLanguageModel
         const prompt = nodeData.inputs?.prompt
         const output = nodeData.outputs?.output as string
-        const promptValues = prompt.promptValues as ICommonObject
+        let promptValues: ICommonObject | undefined = nodeData.inputs?.prompt.promptValues as ICommonObject
         const llmOutputParser = nodeData.inputs?.outputParser as BaseOutputParser
         this.outputParser = llmOutputParser
         if (llmOutputParser) {
@@ -107,17 +107,24 @@ class LLMChain_Chains implements INode {
                 verbose: process.env.DEBUG === 'true'
             })
             const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
+            promptValues = injectOutputParser(this.outputParser, chain, promptValues)
             const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
             // eslint-disable-next-line no-console
             console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
             // eslint-disable-next-line no-console
             console.log(res)
+
+            let finalRes = res
+            if (this.outputParser && typeof res === 'object' && Object.prototype.hasOwnProperty.call(res, 'json')) {
+                finalRes = (res as ICommonObject).json
+            }
+
             /**
              * Apply string transformation to convert special chars:
              * FROM: hello i am ben\n\n\thow are you?
              * TO: hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?
              */
-            return handleEscapeCharacters(res, false)
+            return handleEscapeCharacters(finalRes, false)
         }
     }
 

packages/components/nodes/documentloaders/Figma/Figma.ts

@@ -1,6 +1,7 @@
 import { getCredentialData, getCredentialParam } from '../../../src'
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { FigmaFileLoader, FigmaLoaderParams } from 'langchain/document_loaders/web/figma'
+import { TextSplitter } from 'langchain/text_splitter'
 
 class Figma_DocumentLoaders implements INode {
     label: string
@@ -71,6 +72,8 @@ class Figma_DocumentLoaders implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const nodeIds = (nodeData.inputs?.nodeIds as string)?.trim().split(',') || []
         const fileKey = nodeData.inputs?.fileKey as string
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
@@ -82,7 +85,21 @@ class Figma_DocumentLoaders implements INode {
         }
 
         const loader = new FigmaFileLoader(figmaOptions)
-        const docs = await loader.load()
+
+        const docs = textSplitter ? await loader.loadAndSplit() : await loader.load()
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            return docs.map((doc) => {
+                return {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+            })
+        }
 
         return docs
     }

packages/components/nodes/llms/Azure OpenAI/AzureOpenAI.ts

@@ -18,7 +18,7 @@ class AzureOpenAI_LLMs implements INode {
     constructor() {
         this.label = 'Azure OpenAI'
         this.name = 'azureOpenAI'
-        this.version = 2.0
+        this.version = 2.1
         this.type = 'AzureOpenAI'
         this.icon = 'Azure.svg'
         this.category = 'LLMs'
@@ -89,6 +89,14 @@ class AzureOpenAI_LLMs implements INode {
                     {
                         label: 'gpt-35-turbo',
                         name: 'gpt-35-turbo'
+                    },
+                    {
+                        label: 'gpt-4',
+                        name: 'gpt-4'
+                    },
+                    {
+                        label: 'gpt-4-32k',
+                        name: 'gpt-4-32k'
                     }
                 ],
                 default: 'text-davinci-003',

packages/components/nodes/retrievers/CohereRerankRetriever/Cohere.svg

@@ -0,0 +1 @@
+<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M11.776 18.304c.64 0 1.92-.032 3.712-.768 2.08-.864 6.176-2.4 9.152-4 2.08-1.12 2.976-2.592 2.976-4.576 0-2.72-2.208-4.96-4.96-4.96h-11.52A7.143 7.143 0 0 0 4 11.136c0 3.936 3.008 7.168 7.776 7.168Z" fill="#39594D"/><path fill-rule="evenodd" clip-rule="evenodd" d="M13.728 23.2c0-1.92 1.152-3.68 2.944-4.416l3.616-1.504C23.968 15.776 28 18.464 28 22.432A5.572 5.572 0 0 1 22.432 28h-3.936c-2.624 0-4.768-2.144-4.768-4.8Z" fill="#D18EE2"/><path d="M8.128 19.232A4.138 4.138 0 0 0 4 23.36v.544C4 26.144 5.856 28 8.128 28a4.138 4.138 0 0 0 4.128-4.128v-.544c-.032-2.24-1.856-4.096-4.128-4.096Z" fill="#FF7759"/></svg>

packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerank.ts

@@ -0,0 +1,55 @@
+import { Callbacks } from 'langchain/callbacks'
+import { Document } from 'langchain/document'
+import { BaseDocumentCompressor } from 'langchain/retrievers/document_compressors'
+import axios from 'axios'
+export class CohereRerank extends BaseDocumentCompressor {
+    private cohereAPIKey: any
+    private COHERE_API_URL = 'https://api.cohere.ai/v1/rerank'
+    private readonly model: string
+    private readonly k: number
+    private readonly maxChunksPerDoc: number
+    constructor(cohereAPIKey: string, model: string, k: number, maxChunksPerDoc: number) {
+        super()
+        this.cohereAPIKey = cohereAPIKey
+        this.model = model
+        this.k = k
+        this.maxChunksPerDoc = maxChunksPerDoc
+    }
+    async compressDocuments(
+        documents: Document<Record<string, any>>[],
+        query: string,
+        _?: Callbacks | undefined
+    ): Promise<Document<Record<string, any>>[]> {
+        // avoid empty api call
+        if (documents.length === 0) {
+            return []
+        }
+        const config = {
+            headers: {
+                Authorization: `Bearer ${this.cohereAPIKey}`,
+                'Content-Type': 'application/json',
+                Accept: 'application/json'
+            }
+        }
+        const data = {
+            model: this.model,
+            topN: this.k,
+            max_chunks_per_doc: this.maxChunksPerDoc,
+            query: query,
+            return_documents: false,
+            documents: documents.map((doc) => doc.pageContent)
+        }
+        try {
+            let returnedDocs = await axios.post(this.COHERE_API_URL, data, config)
+            const finalResults: Document<Record<string, any>>[] = []
+            returnedDocs.data.results.forEach((result: any) => {
+                const doc = documents[result.index]
+                doc.metadata.relevance_score = result.relevance_score
+                finalResults.push(doc)
+            })
+            return finalResults
+        } catch (error) {
+            return documents
+        }
+    }
+}

packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerankRetriever.ts

@@ -0,0 +1,142 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
+import { getCredentialData, getCredentialParam, handleEscapeCharacters } from '../../../src'
+import { CohereRerank } from './CohereRerank'
+import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+
+class CohereRerankRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    badge: string
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Cohere Rerank Retriever'
+        this.name = 'cohereRerankRetriever'
+        this.version = 1.0
+        this.type = 'Cohere Rerank Retriever'
+        this.icon = 'Cohere.svg'
+        this.category = 'Retrievers'
+        this.badge = 'NEW'
+        this.description = 'Cohere Rerank indexes the documents from most to least semantically relevant to the query.'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['cohereApi']
+        }
+        this.inputs = [
+            {
+                label: 'Vector Store Retriever',
+                name: 'baseRetriever',
+                type: 'VectorStoreRetriever'
+            },
+            {
+                label: 'Model Name',
+                name: 'model',
+                type: 'options',
+                options: [
+                    {
+                        label: 'rerank-english-v2.0',
+                        name: 'rerank-english-v2.0'
+                    },
+                    {
+                        label: 'rerank-multilingual-v2.0',
+                        name: 'rerank-multilingual-v2.0'
+                    }
+                ],
+                default: 'rerank-english-v2.0',
+                optional: true
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Max Chunks Per Doc',
+                name: 'maxChunksPerDoc',
+                description: 'The maximum number of chunks to produce internally from a document. Default to 10',
+                placeholder: '10',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Cohere Rerank Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
+        const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const model = nodeData.inputs?.model as string
+        const query = nodeData.inputs?.query as string
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const cohereApiKey = getCredentialParam('cohereApiKey', credentialData, nodeData)
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
+        const maxChunksPerDoc = nodeData.inputs?.maxChunksPerDoc as string
+        const max_chunks_per_doc = maxChunksPerDoc ? parseFloat(maxChunksPerDoc) : 10
+        const output = nodeData.outputs?.output as string
+
+        const cohereCompressor = new CohereRerank(cohereApiKey, model, k, max_chunks_per_doc)
+
+        const retriever = new ContextualCompressionRetriever({
+            baseCompressor: cohereCompressor,
+            baseRetriever: baseRetriever
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: CohereRerankRetriever_Retrievers }

packages/components/nodes/retrievers/EmbeddingsFilterRetriever/EmbeddingsFilterRetriever.ts

@@ -0,0 +1,133 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { Embeddings } from 'langchain/embeddings/base'
+import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
+import { EmbeddingsFilter } from 'langchain/retrievers/document_compressors/embeddings_filter'
+import { handleEscapeCharacters } from '../../../src/utils'
+
+class EmbeddingsFilterRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    badge: string
+
+    constructor() {
+        this.label = 'Embeddings Filter Retriever'
+        this.name = 'embeddingsFilterRetriever'
+        this.version = 1.0
+        this.type = 'EmbeddingsFilterRetriever'
+        this.icon = 'compressionRetriever.svg'
+        this.category = 'Retrievers'
+        this.badge = 'NEW'
+        this.description = 'A document compressor that uses embeddings to drop documents unrelated to the query'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Vector Store Retriever',
+                name: 'baseRetriever',
+                type: 'VectorStoreRetriever'
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
+            {
+                label: 'Similarity Threshold',
+                name: 'similarityThreshold',
+                description:
+                    'Threshold for determining when two documents are similar enough to be considered redundant. Must be specified if `k` is not set',
+                type: 'number',
+                default: 0.8,
+                step: 0.1,
+                optional: true
+            },
+            {
+                label: 'K',
+                name: 'k',
+                description:
+                    'The number of relevant documents to return. Can be explicitly set to undefined, in which case similarity_threshold must be specified. Defaults to 20',
+                type: 'number',
+                default: 20,
+                step: 1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Embeddings Filter Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const query = nodeData.inputs?.query as string
+        const similarityThreshold = nodeData.inputs?.similarityThreshold as string
+        const k = nodeData.inputs?.k as string
+        const output = nodeData.outputs?.output as string
+
+        if (k === undefined && similarityThreshold === undefined) {
+            throw new Error(`Must specify one of "k" or "similarity_threshold".`)
+        }
+
+        const similarityThresholdNumber = similarityThreshold ? parseFloat(similarityThreshold) : 0.8
+        const kNumber = k ? parseFloat(k) : undefined
+
+        const baseCompressor = new EmbeddingsFilter({
+            embeddings: embeddings,
+            similarityThreshold: similarityThresholdNumber,
+            k: kNumber
+        })
+
+        const retriever = new ContextualCompressionRetriever({
+            baseCompressor,
+            baseRetriever: baseRetriever
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: EmbeddingsFilterRetriever_Retrievers }

packages/components/nodes/retrievers/EmbeddingsFilterRetriever/compressionRetriever.svg

@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+   <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+   <path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M4 20l14 0" />
+</svg>

packages/components/nodes/retrievers/HydeRetriever/HydeRetriever.ts

@@ -1,8 +1,9 @@
 import { VectorStore } from 'langchain/vectorstores/base'
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { HydeRetriever, HydeRetrieverOptions, PromptKey } from 'langchain/retrievers/hyde'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { PromptTemplate } from 'langchain/prompts'
+import { handleEscapeCharacters } from '../../../src/utils'
 
 class HydeRetriever_Retrievers implements INode {
     label: string
@@ -14,11 +15,12 @@ class HydeRetriever_Retrievers implements INode {
     category: string
     baseClasses: string[]
     inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
 
     constructor() {
-        this.label = 'Hyde Retriever'
+        this.label = 'HyDE Retriever'
         this.name = 'HydeRetriever'
-        this.version = 2.0
+        this.version = 3.0
         this.type = 'HydeRetriever'
         this.icon = 'hyderetriever.svg'
         this.category = 'Retrievers'
@@ -35,6 +37,14 @@ class HydeRetriever_Retrievers implements INode {
                 name: 'vectorStore',
                 type: 'VectorStore'
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Select Defined Prompt',
                 name: 'promptKey',
@@ -121,15 +131,34 @@ Passage:`
                 optional: true
             }
         ]
+        this.outputs = [
+            {
+                label: 'HyDE Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string): Promise<any> {
         const llm = nodeData.inputs?.model as BaseLanguageModel
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
         const promptKey = nodeData.inputs?.promptKey as PromptKey
         const customPrompt = nodeData.inputs?.customPrompt as string
+        const query = nodeData.inputs?.query as string
         const topK = nodeData.inputs?.topK as string
         const k = topK ? parseFloat(topK) : 4
+        const output = nodeData.outputs?.output as string
 
         const obj: HydeRetrieverOptions<any> = {
             llm,
@@ -141,6 +170,19 @@ Passage:`
         else if (promptKey) obj.promptTemplate = promptKey
 
         const retriever = new HydeRetriever(obj)
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
         return retriever
     }
 }

packages/components/nodes/retrievers/LLMFilterRetriever/LLMFilterCompressionRetriever.ts

@@ -0,0 +1,100 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { LLMChainExtractor } from 'langchain/retrievers/document_compressors/chain_extract'
+import { handleEscapeCharacters } from '../../../src/utils'
+
+class LLMFilterCompressionRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    badge: string
+
+    constructor() {
+        this.label = 'LLM Filter Retriever'
+        this.name = 'llmFilterRetriever'
+        this.version = 1.0
+        this.type = 'LLMFilterRetriever'
+        this.icon = 'llmFilterRetriever.svg'
+        this.category = 'Retrievers'
+        this.badge = 'NEW'
+        this.description =
+            'Iterate over the initially returned documents and extract, from each, only the content that is relevant to the query'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Vector Store Retriever',
+                name: 'baseRetriever',
+                type: 'VectorStoreRetriever'
+            },
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'LLM Filter Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const query = nodeData.inputs?.query as string
+        const output = nodeData.outputs?.output as string
+
+        if (!model) throw new Error('There must be a LLM model connected to LLM Filter Retriever')
+
+        const retriever = new ContextualCompressionRetriever({
+            baseCompressor: LLMChainExtractor.fromLLM(model),
+            baseRetriever: baseRetriever
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: LLMFilterCompressionRetriever_Retrievers }

packages/components/nodes/retrievers/LLMFilterRetriever/llmFilterRetriever.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-check" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M11.18 20.274l-2.18 .726v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414v3" /><path d="M15 19l2 2l4 -4" /></svg>

packages/components/nodes/retrievers/RRFRetriever/RRFRetriever.ts

@@ -0,0 +1,136 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { ReciprocalRankFusion } from './ReciprocalRankFusion'
+import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+import { handleEscapeCharacters } from '../../../src/utils'
+
+class RRFRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    badge: string
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Reciprocal Rank Fusion Retriever'
+        this.name = 'RRFRetriever'
+        this.version = 1.0
+        this.type = 'RRFRetriever'
+        this.badge = 'NEW'
+        this.icon = 'rrfRetriever.svg'
+        this.category = 'Retrievers'
+        this.description = 'Reciprocal Rank Fusion to re-rank search results by multiple query generation.'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Vector Store Retriever',
+                name: 'baseRetriever',
+                type: 'VectorStoreRetriever'
+            },
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
+            {
+                label: 'Query Count',
+                name: 'queryCount',
+                description: 'Number of synthetic queries to generate. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                default: 4,
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
+                placeholder: '0',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Constant',
+                name: 'c',
+                description:
+                    'A constant added to the rank, controlling the balance between the importance of high-ranked items and the consideration given to lower-ranked items.\n' +
+                    'Default is 60',
+                placeholder: '60',
+                type: 'number',
+                default: 60,
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Reciprocal Rank Fusion Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const llm = nodeData.inputs?.model as BaseLanguageModel
+        const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const query = nodeData.inputs?.query as string
+        const queryCount = nodeData.inputs?.queryCount as string
+        const q = queryCount ? parseFloat(queryCount) : 4
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
+        const constantC = nodeData.inputs?.c as string
+        const c = topK ? parseFloat(constantC) : 60
+        const output = nodeData.outputs?.output as string
+
+        const ragFusion = new ReciprocalRankFusion(llm, baseRetriever as VectorStoreRetriever, q, k, c)
+        const retriever = new ContextualCompressionRetriever({
+            baseCompressor: ragFusion,
+            baseRetriever: baseRetriever
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
+    }
+}
+
+module.exports = { nodeClass: RRFRetriever_Retrievers }

packages/components/nodes/retrievers/RRFRetriever/ReciprocalRankFusion.ts

@@ -0,0 +1,96 @@
+import { BaseDocumentCompressor } from 'langchain/retrievers/document_compressors'
+import { Document } from 'langchain/document'
+import { Callbacks } from 'langchain/callbacks'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate } from 'langchain/prompts'
+import { LLMChain } from 'langchain/chains'
+import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+
+export class ReciprocalRankFusion extends BaseDocumentCompressor {
+    private readonly llm: BaseLanguageModel
+    private readonly queryCount: number
+    private readonly topK: number
+    private readonly c: number
+    private baseRetriever: VectorStoreRetriever
+    constructor(llm: BaseLanguageModel, baseRetriever: VectorStoreRetriever, queryCount: number, topK: number, c: number) {
+        super()
+        this.queryCount = queryCount
+        this.llm = llm
+        this.baseRetriever = baseRetriever
+        this.topK = topK
+        this.c = c
+    }
+    async compressDocuments(
+        documents: Document<Record<string, any>>[],
+        query: string,
+        _?: Callbacks | undefined
+    ): Promise<Document<Record<string, any>>[]> {
+        // avoid empty api call
+        if (documents.length === 0) {
+            return []
+        }
+        const chatPrompt = ChatPromptTemplate.fromMessages([
+            SystemMessagePromptTemplate.fromTemplate(
+                'You are a helpful assistant that generates multiple search queries based on a single input query.'
+            ),
+            HumanMessagePromptTemplate.fromTemplate(
+                'Generate multiple search queries related to: {input}. Provide these alternative questions separated by newlines, do not add any numbers.'
+            ),
+            HumanMessagePromptTemplate.fromTemplate('OUTPUT (' + this.queryCount + ' queries):')
+        ])
+        const llmChain = new LLMChain({
+            llm: this.llm,
+            prompt: chatPrompt
+        })
+        const multipleQueries = await llmChain.call({ input: query })
+        const queries = []
+        queries.push(query)
+        multipleQueries.text.split('\n').map((q: string) => {
+            queries.push(q)
+        })
+        const docList: Document<Record<string, any>>[][] = []
+        for (let i = 0; i < queries.length; i++) {
+            const resultOne = await this.baseRetriever.vectorStore.similaritySearch(queries[i], 5)
+            const docs: any[] = []
+            resultOne.forEach((doc) => {
+                docs.push(doc)
+            })
+            docList.push(docs)
+        }
+
+        return this.reciprocalRankFunction(docList, this.c)
+    }
+
+    reciprocalRankFunction(docList: Document<Record<string, any>>[][], k: number): Document<Record<string, any>>[] {
+        docList.forEach((docs: Document<Record<string, any>>[]) => {
+            docs.forEach((doc: any, index: number) => {
+                let rank = index + 1
+                if (doc.metadata.relevancy_score) {
+                    doc.metadata.relevancy_score += 1 / (rank + k)
+                } else {
+                    doc.metadata.relevancy_score = 1 / (rank + k)
+                }
+            })
+        })
+        const scoreArray: any[] = []
+        docList.forEach((docs: Document<Record<string, any>>[]) => {
+            docs.forEach((doc: any) => {
+                scoreArray.push(doc.metadata.relevancy_score)
+            })
+        })
+        scoreArray.sort((a, b) => b - a)
+        const rerankedDocuments: Document<Record<string, any>>[] = []
+        const seenScores: any[] = []
+        scoreArray.forEach((score) => {
+            docList.forEach((docs) => {
+                docs.forEach((doc: any) => {
+                    if (doc.metadata.relevancy_score === score && seenScores.indexOf(score) === -1) {
+                        rerankedDocuments.push(doc)
+                        seenScores.push(doc.metadata.relevancy_score)
+                    }
+                })
+            })
+        })
+        return rerankedDocuments.splice(0, this.topK)
+    }
+}

packages/components/nodes/retrievers/RRFRetriever/rrfRetriever.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-math-x-divide-y-2" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M3 21l18 -18" /><path d="M15 14l3 4.5" /><path d="M21 14l-4.5 7" /><path d="M3 4l6 6" /><path d="M3 10l6 -6" /></svg>

packages/components/nodes/retrievers/SimilarityThresholdRetriever/SimilarityThresholdRetriever.ts

@@ -18,7 +18,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
     constructor() {
         this.label = 'Similarity Score Threshold Retriever'
         this.name = 'similarityThresholdRetriever'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'SimilarityThresholdRetriever'
         this.icon = 'similaritythreshold.svg'
         this.category = 'Retrievers'
@@ -30,6 +30,14 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 name: 'vectorStore',
                 type: 'VectorStore'
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Minimum Similarity Score (%)',
                 name: 'minSimilarityScore',
@@ -44,7 +52,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 description: `The maximum number of results to fetch`,
                 type: 'number',
                 default: 20,
-                step: 1
+                step: 1,
+                additionalParams: true
             },
             {
                 label: 'K Increment',
@@ -52,7 +61,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
                 type: 'number',
                 default: 2,
-                step: 1
+                step: 1,
+                additionalParams: true
             }
         ]
         this.outputs = [
@@ -77,6 +87,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
     async init(nodeData: INodeData, input: string): Promise<any> {
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
         const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
+        const query = nodeData.inputs?.query as string
         const maxK = nodeData.inputs?.maxK as string
         const kIncrement = nodeData.inputs?.kIncrement as string
 
@@ -89,11 +100,11 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
         })
 
         if (output === 'retriever') return retriever
-        else if (output === 'document') return await retriever.getRelevantDocuments(input)
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
         else if (output === 'text') {
             let finaltext = ''
 
-            const docs = await retriever.getRelevantDocuments(input)
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
 
             for (const doc of docs) finaltext += `${doc.pageContent}\n`
 

packages/components/nodes/tools/CustomTool/CustomTool.ts

@@ -1,5 +1,5 @@
 import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
-import { convertSchemaToZod, getBaseClasses } from '../../../src/utils'
+import { convertSchemaToZod, getBaseClasses, getVars } from '../../../src/utils'
 import { DynamicStructuredTool } from './core'
 import { z } from 'zod'
 import { DataSource } from 'typeorm'
@@ -81,23 +81,7 @@ class CustomTool_Tools implements INode {
             }
             if (customToolFunc) obj.code = customToolFunc
 
-            const variables = await appDataSource.getRepository(databaseEntities['Variable']).find()
-
-            // override variables defined in overrideConfig
-            // nodeData.inputs.variables is an Object, check each property and override the variable
-            if (nodeData?.inputs?.vars) {
-                for (const propertyName of Object.getOwnPropertyNames(nodeData.inputs.vars)) {
-                    const foundVar = variables.find((v) => v.name === propertyName)
-                    if (foundVar) {
-                        // even if the variable was defined as runtime, we override it with static value
-                        foundVar.type = 'static'
-                        foundVar.value = nodeData.inputs.vars[propertyName]
-                    } else {
-                        // add it the variables, if not found locally in the db
-                        variables.push({ name: propertyName, type: 'static', value: nodeData.inputs.vars[propertyName] })
-                    }
-                }
-            }
+            const variables = await getVars(appDataSource, databaseEntities, nodeData)
 
             const flow = { chatflowId: options.chatflowid }
 

packages/components/nodes/tools/CustomTool/core.ts

@@ -1,6 +1,6 @@
 import { z } from 'zod'
 import { NodeVM } from 'vm2'
-import { availableDependencies } from '../../../src/utils'
+import { availableDependencies, defaultAllowBuiltInDep, prepareSandboxVars } from '../../../src/utils'
 import { RunnableConfig } from '@langchain/core/runnables'
 import { StructuredTool, ToolParams } from '@langchain/core/tools'
 import { CallbackManagerForToolRun, Callbacks, CallbackManager, parseCallbackConfigArg } from '@langchain/core/callbacks/manager'
@@ -112,48 +112,13 @@ export class DynamicStructuredTool<
             }
         }
 
-        // inject variables
-        let vars = {}
-        if (this.variables) {
-            for (const item of this.variables) {
-                let value = item.value
-
-                // read from .env file
-                if (item.type === 'runtime') {
-                    value = process.env[item.name]
-                }
-
-                Object.defineProperty(vars, item.name, {
-                    enumerable: true,
-                    configurable: true,
-                    writable: true,
-                    value: value
-                })
-            }
-        }
-        sandbox['$vars'] = vars
+        sandbox['$vars'] = prepareSandboxVars(this.variables)
 
         // inject flow properties
         if (this.flowObj) {
             sandbox['$flow'] = { ...this.flowObj, ...flowConfig }
         }
 
-        const defaultAllowBuiltInDep = [
-            'assert',
-            'buffer',
-            'crypto',
-            'events',
-            'http',
-            'https',
-            'net',
-            'path',
-            'querystring',
-            'timers',
-            'tls',
-            'url',
-            'zlib'
-        ]
-
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))
             : defaultAllowBuiltInDep

packages/components/nodes/utilities/CustomFunction/CustomFunction.ts

@@ -1,6 +1,7 @@
-import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { NodeVM } from 'vm2'
-import { availableDependencies, handleEscapeCharacters } from '../../../src/utils'
+import { DataSource } from 'typeorm'
+import { availableDependencies, defaultAllowBuiltInDep, getVars, handleEscapeCharacters, prepareSandboxVars } from '../../../src/utils'
 
 class CustomFunction_Utilities implements INode {
     label: string
@@ -55,9 +56,19 @@ class CustomFunction_Utilities implements INode {
         ]
     }
 
-    async init(nodeData: INodeData, input: string): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const javascriptFunction = nodeData.inputs?.javascriptFunction as string
         const functionInputVariablesRaw = nodeData.inputs?.functionInputVariables
+        const appDataSource = options.appDataSource as DataSource
+        const databaseEntities = options.databaseEntities as IDatabaseEntity
+
+        const variables = await getVars(appDataSource, databaseEntities, nodeData)
+        const flow = {
+            chatflowId: options.chatflowid,
+            sessionId: options.sessionId,
+            chatId: options.chatId,
+            input
+        }
 
         let inputVars: ICommonObject = {}
         if (functionInputVariablesRaw) {
@@ -69,7 +80,20 @@ class CustomFunction_Utilities implements INode {
             }
         }
 
+        // Some values might be a stringified JSON, parse it
+        for (const key in inputVars) {
+            if (typeof inputVars[key] === 'string' && inputVars[key].startsWith('{') && inputVars[key].endsWith('}')) {
+                try {
+                    inputVars[key] = JSON.parse(inputVars[key])
+                } catch (e) {
+                    continue
+                }
+            }
+        }
+
         let sandbox: any = { $input: input }
+        sandbox['$vars'] = prepareSandboxVars(variables)
+        sandbox['$flow'] = flow
 
         if (Object.keys(inputVars).length) {
             for (const item in inputVars) {
@@ -81,22 +105,6 @@ class CustomFunction_Utilities implements INode {
             }
         }
 
-        const defaultAllowBuiltInDep = [
-            'assert',
-            'buffer',
-            'crypto',
-            'events',
-            'http',
-            'https',
-            'net',
-            'path',
-            'querystring',
-            'timers',
-            'tls',
-            'url',
-            'zlib'
-        ]
-
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))
             : defaultAllowBuiltInDep

packages/components/nodes/utilities/IfElseFunction/IfElseFunction.ts

@@ -1,6 +1,7 @@
-import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { NodeVM } from 'vm2'
-import { availableDependencies } from '../../../src/utils'
+import { DataSource } from 'typeorm'
+import { availableDependencies, defaultAllowBuiltInDep, getVars, handleEscapeCharacters, prepareSandboxVars } from '../../../src/utils'
 
 class IfElseFunction_Utilities implements INode {
     label: string
@@ -73,10 +74,20 @@ class IfElseFunction_Utilities implements INode {
         ]
     }
 
-    async init(nodeData: INodeData, input: string): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const ifFunction = nodeData.inputs?.ifFunction as string
         const elseFunction = nodeData.inputs?.elseFunction as string
         const functionInputVariablesRaw = nodeData.inputs?.functionInputVariables
+        const appDataSource = options.appDataSource as DataSource
+        const databaseEntities = options.databaseEntities as IDatabaseEntity
+
+        const variables = await getVars(appDataSource, databaseEntities, nodeData)
+        const flow = {
+            chatflowId: options.chatflowid,
+            sessionId: options.sessionId,
+            chatId: options.chatId,
+            input
+        }
 
         let inputVars: ICommonObject = {}
         if (functionInputVariablesRaw) {
@@ -84,34 +95,35 @@ class IfElseFunction_Utilities implements INode {
                 inputVars =
                     typeof functionInputVariablesRaw === 'object' ? functionInputVariablesRaw : JSON.parse(functionInputVariablesRaw)
             } catch (exception) {
-                throw new Error("Invalid JSON in the PromptTemplate's promptValues: " + exception)
+                throw new Error("Invalid JSON in the IfElse's Input Variables: " + exception)
+            }
+        }
+
+        // Some values might be a stringified JSON, parse it
+        for (const key in inputVars) {
+            if (typeof inputVars[key] === 'string' && inputVars[key].startsWith('{') && inputVars[key].endsWith('}')) {
+                try {
+                    inputVars[key] = JSON.parse(inputVars[key])
+                } catch (e) {
+                    continue
+                }
             }
         }
 
         let sandbox: any = { $input: input }
+        sandbox['$vars'] = prepareSandboxVars(variables)
+        sandbox['$flow'] = flow
 
         if (Object.keys(inputVars).length) {
             for (const item in inputVars) {
-                sandbox[`$${item}`] = inputVars[item]
+                let value = inputVars[item]
+                if (typeof value === 'string') {
+                    value = handleEscapeCharacters(value, true)
+                }
+                sandbox[`$${item}`] = value
             }
         }
 
-        const defaultAllowBuiltInDep = [
-            'assert',
-            'buffer',
-            'crypto',
-            'events',
-            'http',
-            'https',
-            'net',
-            'path',
-            'querystring',
-            'timers',
-            'tls',
-            'url',
-            'zlib'
-        ]
-
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))
             : defaultAllowBuiltInDep

packages/components/nodes/vectorstores/Astra/Astra.ts

@@ -4,6 +4,7 @@ import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData } from '../../../src/utils'
 import { AstraDBVectorStore, AstraLibArgs } from '@langchain/community/vectorstores/astradb'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Astra_VectorStores implements INode {
     label: string
@@ -26,7 +27,7 @@ class Astra_VectorStores implements INode {
         this.type = 'Astra'
         this.icon = 'astra.svg'
         this.category = 'Vector Stores'
-        this.description = `Upsert embedded data and perform similarity search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`
+        this.description = `Upsert embedded data and perform similarity or mmr search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -74,6 +75,7 @@ class Astra_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Astra Retriever',
@@ -139,9 +141,6 @@ class Astra_VectorStores implements INode {
         const embeddings = nodeData.inputs?.embeddings as Embeddings
         const vectorDimension = nodeData.inputs?.vectorDimension as number
         const similarityMetric = nodeData.inputs?.similarityMetric as 'cosine' | 'euclidean' | 'dot_product' | undefined
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
 
@@ -176,14 +175,7 @@ class Astra_VectorStores implements INode {
 
         const vectorStore = await AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig)
 
-        if (output === 'retriever') {
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 

packages/components/nodes/vectorstores/MongoDBAtlas/MongoDBAtlas.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class MongoDBAtlas_VectorStores implements INode {
     label: string
@@ -24,7 +25,7 @@ class MongoDBAtlas_VectorStores implements INode {
         this.label = 'MongoDB Atlas'
         this.name = 'mongoDBAtlas'
         this.version = 1.0
-        this.description = `Upsert embedded data and perform similarity search upon query using MongoDB Atlas, a managed cloud mongodb database`
+        this.description = `Upsert embedded data and perform similarity or mmr search upon query using MongoDB Atlas, a managed cloud mongodb database`
         this.type = 'MongoDB Atlas'
         this.icon = 'mongodb.svg'
         this.category = 'Vector Stores'
@@ -95,6 +96,7 @@ class MongoDBAtlas_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'MongoDB Retriever',
@@ -162,9 +164,6 @@ class MongoDBAtlas_VectorStores implements INode {
         let textKey = nodeData.inputs?.textKey as string
         let embeddingKey = nodeData.inputs?.embeddingKey as string
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
-        const output = nodeData.outputs?.output as string
 
         let mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
 
@@ -181,13 +180,7 @@ class MongoDBAtlas_VectorStores implements INode {
             embeddingKey
         })
 
-        if (output === 'retriever') {
-            return vectorStore.asRetriever(k)
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 

packages/components/nodes/vectorstores/Pinecone/Pinecone.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Pinecone_VectorStores implements INode {
     label: string
@@ -23,11 +24,11 @@ class Pinecone_VectorStores implements INode {
     constructor() {
         this.label = 'Pinecone'
         this.name = 'pinecone'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Pinecone'
         this.icon = 'pinecone.svg'
         this.category = 'Vector Stores'
-        this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
+        this.description = `Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database`
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -79,6 +80,7 @@ class Pinecone_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Pinecone Retriever',
@@ -103,11 +105,9 @@ class Pinecone_VectorStores implements INode {
 
             const credentialData = await getCredentialData(nodeData.credential ?? '', options)
             const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
-            const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
 
             const client = new Pinecone({
-                apiKey: pineconeApiKey,
-                environment: pineconeEnv
+                apiKey: pineconeApiKey
             })
 
             const pineconeIndex = client.Index(index)
@@ -140,17 +140,12 @@ class Pinecone_VectorStores implements INode {
         const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
         const docs = nodeData.inputs?.document as Document[]
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
-        const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
 
         const client = new Pinecone({
-            apiKey: pineconeApiKey,
-            environment: pineconeEnv
+            apiKey: pineconeApiKey
         })
 
         const pineconeIndex = client.Index(index)
@@ -175,14 +170,7 @@ class Pinecone_VectorStores implements INode {
 
         const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 

packages/components/nodes/vectorstores/Pinecone/Pinecone_Existing.ts

@@ -95,11 +95,9 @@ class Pinecone_Existing_VectorStores implements INode {
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
-        const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
 
         const client = new Pinecone({
-            apiKey: pineconeApiKey,
-            environment: pineconeEnv
+            apiKey: pineconeApiKey
         })
 
         const pineconeIndex = client.Index(index)

packages/components/nodes/vectorstores/Pinecone/Pinecone_Upsert.ts

@@ -96,11 +96,9 @@ class PineconeUpsert_VectorStores implements INode {
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
-        const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
 
         const client = new Pinecone({
-            apiKey: pineconeApiKey,
-            environment: pineconeEnv
+            apiKey: pineconeApiKey
         })
 
         const pineconeIndex = client.Index(index)

packages/components/nodes/vectorstores/Supabase/Supabase.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { SupabaseLibArgs, SupabaseVectorStore } from 'langchain/vectorstores/supabase'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Supabase_VectorStores implements INode {
     label: string
@@ -23,11 +24,11 @@ class Supabase_VectorStores implements INode {
     constructor() {
         this.label = 'Supabase'
         this.name = 'supabase'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Supabase'
         this.icon = 'supabase.svg'
         this.category = 'Vector Stores'
-        this.description = 'Upsert embedded data and perform similarity search upon query using Supabase via pgvector extension'
+        this.description = 'Upsert embedded data and perform similarity or mmr search upon query using Supabase via pgvector extension'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -81,6 +82,7 @@ class Supabase_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Supabase Retriever',
@@ -135,9 +137,6 @@ class Supabase_VectorStores implements INode {
         const queryName = nodeData.inputs?.queryName as string
         const embeddings = nodeData.inputs?.embeddings as Embeddings
         const supabaseMetadataFilter = nodeData.inputs?.supabaseMetadataFilter
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const supabaseApiKey = getCredentialParam('supabaseApiKey', credentialData, nodeData)
@@ -157,14 +156,7 @@ class Supabase_VectorStores implements INode {
 
         const vectorStore = await SupabaseVectorStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 

packages/components/nodes/vectorstores/VectorStoreUtils.ts

@@ -0,0 +1,75 @@
+import { INodeData } from '../../src'
+
+export const resolveVectorStoreOrRetriever = (nodeData: INodeData, vectorStore: any) => {
+    const output = nodeData.outputs?.output as string
+    const searchType = nodeData.outputs?.searchType as string
+    const topK = nodeData.inputs?.topK as string
+    const k = topK ? parseFloat(topK) : 4
+
+    if (output === 'retriever') {
+        if ('mmr' === searchType) {
+            const fetchK = nodeData.inputs?.fetchK as string
+            const lambda = nodeData.inputs?.lambda as string
+            const f = fetchK ? parseInt(fetchK) : 20
+            const l = lambda ? parseFloat(lambda) : 0.5
+            return vectorStore.asRetriever({
+                searchType: 'mmr',
+                k: k,
+                searchKwargs: {
+                    fetchK: f,
+                    lambda: l
+                }
+            })
+        } else {
+            // "searchType" is "similarity"
+            return vectorStore.asRetriever(k)
+        }
+    } else if (output === 'vectorStore') {
+        ;(vectorStore as any).k = k
+        return vectorStore
+    }
+}
+
+export const addMMRInputParams = (inputs: any[]) => {
+    const mmrInputParams = [
+        {
+            label: 'Search Type',
+            name: 'searchType',
+            type: 'options',
+            default: 'similarity',
+            options: [
+                {
+                    label: 'Similarity',
+                    name: 'similarity'
+                },
+                {
+                    label: 'Max Marginal Relevance',
+                    name: 'mmr'
+                }
+            ],
+            additionalParams: true,
+            optional: true
+        },
+        {
+            label: 'Fetch K (for MMR Search)',
+            name: 'fetchK',
+            description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
+            placeholder: '20',
+            type: 'number',
+            additionalParams: true,
+            optional: true
+        },
+        {
+            label: 'Lambda (for MMR Search)',
+            name: 'lambda',
+            description:
+                'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
+            placeholder: '0.5',
+            type: 'number',
+            additionalParams: true,
+            optional: true
+        }
+    ]
+
+    inputs.push(...mmrInputParams)
+}

packages/components/nodes/vectorstores/Weaviate/Weaviate.ts

@@ -5,6 +5,7 @@ import { Document } from 'langchain/document'
 import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Weaviate_VectorStores implements INode {
     label: string
@@ -23,12 +24,12 @@ class Weaviate_VectorStores implements INode {
     constructor() {
         this.label = 'Weaviate'
         this.name = 'weaviate'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Weaviate'
         this.icon = 'weaviate.png'
         this.category = 'Vector Stores'
         this.description =
-            'Upsert embedded data and perform similarity search upon query using Weaviate, a scalable open-source vector database'
+            'Upsert embedded data and perform similarity or mmr search using Weaviate, a scalable open-source vector database'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -107,6 +108,7 @@ class Weaviate_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Weaviate Retriever',
@@ -174,9 +176,6 @@ class Weaviate_VectorStores implements INode {
         const weaviateTextKey = nodeData.inputs?.weaviateTextKey as string
         const weaviateMetadataKeys = nodeData.inputs?.weaviateMetadataKeys as string
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const weaviateApiKey = getCredentialParam('weaviateApiKey', credentialData, nodeData)
@@ -199,14 +198,7 @@ class Weaviate_VectorStores implements INode {
 
         const vectorStore = await WeaviateStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 

packages/components/nodes/vectorstores/Zep/Zep.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Zep_VectorStores implements INode {
     label: string
@@ -23,12 +24,12 @@ class Zep_VectorStores implements INode {
     constructor() {
         this.label = 'Zep'
         this.name = 'zep'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Zep'
         this.icon = 'zep.svg'
         this.category = 'Vector Stores'
         this.description =
-            'Upsert embedded data and perform similarity search upon query using Zep, a fast and scalable building block for LLM apps'
+            'Upsert embedded data and perform similarity or mmr search upon query using Zep, a fast and scalable building block for LLM apps'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -88,6 +89,7 @@ class Zep_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Zep Retriever',
@@ -144,9 +146,6 @@ class Zep_VectorStores implements INode {
         const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter
         const dimension = nodeData.inputs?.dimension as number
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
@@ -165,14 +164,7 @@ class Zep_VectorStores implements INode {
 
         const vectorStore = await ZepExistingVS.fromExistingIndex(embeddings, zepConfig)
 
-        if (output === 'retriever') {
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
     }
 }
 
@@ -210,7 +202,7 @@ class ZepExistingVS extends ZepVectorStore {
         this.args = args
     }
 
-    async initalizeCollection(args: IZepConfig & Partial<ZepFilter>) {
+    async initializeCollection(args: IZepConfig & Partial<ZepFilter>) {
         this.client = await ZepClient.init(args.apiUrl, args.apiKey)
         try {
             this.collection = await this.client.document.getCollection(args.collectionName)
@@ -259,7 +251,7 @@ class ZepExistingVS extends ZepVectorStore {
         const newfilter = {
             where: { and: ANDFilters }
         }
-        await this.initalizeCollection(this.args!).catch((err) => {
+        await this.initializeCollection(this.args!).catch((err) => {
             console.error('Error initializing collection:', err)
             throw err
         })

packages/components/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-components",
-    "version": "1.5.1",
+    "version": "1.5.2",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -33,7 +33,7 @@
         "@langchain/mistralai": "^0.0.6",
         "@notionhq/client": "^2.2.8",
         "@opensearch-project/opensearch": "^1.2.0",
-        "@pinecone-database/pinecone": "^1.1.1",
+        "@pinecone-database/pinecone": "^2.0.1",
         "@qdrant/js-client-rest": "^1.2.2",
         "@supabase/supabase-js": "^2.29.0",
         "@types/js-yaml": "^4.0.5",

packages/components/src/Interface.ts

@@ -29,6 +29,12 @@ export interface ICommonObject {
     [key: string]: any | CommonType | ICommonObject | CommonType[] | ICommonObject[]
 }
 
+export interface IVariable {
+    name: string
+    value: string
+    type: string
+}
+
 export type IDatabaseEntity = {
     [key: string]: any
 }

packages/components/src/utils.ts

@@ -5,7 +5,7 @@ import * as path from 'path'
 import { JSDOM } from 'jsdom'
 import { z } from 'zod'
 import { DataSource } from 'typeorm'
-import { ICommonObject, IDatabaseEntity, IMessage, INodeData } from './Interface'
+import { ICommonObject, IDatabaseEntity, IMessage, INodeData, IVariable } from './Interface'
 import { AES, enc } from 'crypto-js'
 import { ChatMessageHistory } from 'langchain/memory'
 import { AIMessage, HumanMessage, BaseMessage } from 'langchain/schema'
@@ -70,6 +70,22 @@ export const availableDependencies = [
     'weaviate-ts-client'
 ]
 
+export const defaultAllowBuiltInDep = [
+    'assert',
+    'buffer',
+    'crypto',
+    'events',
+    'http',
+    'https',
+    'net',
+    'path',
+    'querystring',
+    'timers',
+    'tls',
+    'url',
+    'zlib'
+]
+
 /**
  * Get base classes of components
  *
@@ -688,3 +704,57 @@ export const convertMultiOptionsToStringArray = (inputString: string): string[]
     }
     return ArrayString
 }
+
+/**
+ * Get variables
+ * @param {DataSource} appDataSource
+ * @param {IDatabaseEntity} databaseEntities
+ * @param {INodeData} nodeData
+ */
+export const getVars = async (appDataSource: DataSource, databaseEntities: IDatabaseEntity, nodeData: INodeData) => {
+    const variables = ((await appDataSource.getRepository(databaseEntities['Variable']).find()) as IVariable[]) ?? []
+
+    // override variables defined in overrideConfig
+    // nodeData.inputs.variables is an Object, check each property and override the variable
+    if (nodeData?.inputs?.vars) {
+        for (const propertyName of Object.getOwnPropertyNames(nodeData.inputs.vars)) {
+            const foundVar = variables.find((v) => v.name === propertyName)
+            if (foundVar) {
+                // even if the variable was defined as runtime, we override it with static value
+                foundVar.type = 'static'
+                foundVar.value = nodeData.inputs.vars[propertyName]
+            } else {
+                // add it the variables, if not found locally in the db
+                variables.push({ name: propertyName, type: 'static', value: nodeData.inputs.vars[propertyName] })
+            }
+        }
+    }
+
+    return variables
+}
+
+/**
+ * Prepare sandbox variables
+ * @param {IVariable[]} variables
+ */
+export const prepareSandboxVars = (variables: IVariable[]) => {
+    let vars = {}
+    if (variables) {
+        for (const item of variables) {
+            let value = item.value
+
+            // read from .env file
+            if (item.type === 'runtime') {
+                value = process.env[item.name] ?? ''
+            }
+
+            Object.defineProperty(vars, item.name, {
+                enumerable: true,
+                configurable: true,
+                writable: true,
+                value: value
+            })
+        }
+    }
+    return vars
+}

packages/server/.env.example

@@ -26,3 +26,5 @@ PORT=3000
 # LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
 # LANGCHAIN_API_KEY=your_api_key
 # LANGCHAIN_PROJECT=your_project
+
+# DISABLE_FLOWISE_TELEMETRY=true

packages/server/marketplaces/chatflows/AutoGPT.json

@@ -511,7 +511,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -552,6 +552,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -576,7 +615,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/BabyAGI.json

@@ -166,7 +166,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -207,6 +207,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -231,7 +270,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Claude LLM.json

@@ -6,15 +6,15 @@
             "height": 376,
             "id": "bufferMemory_0",
             "position": {
-                "x": 451.4449437285705,
-                "y": 118.30026803362762
+                "x": 240.5161028076149,
+                "y": 165.35849026339048
             },
             "type": "customNode",
             "data": {
                 "id": "bufferMemory_0",
                 "label": "Buffer Memory",
-                "name": "bufferMemory",
                 "version": 1,
+                "name": "bufferMemory",
                 "type": "BufferMemory",
                 "baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
                 "category": "Memory",
@@ -53,8 +53,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 451.4449437285705,
-                "y": 118.30026803362762
+                "x": 240.5161028076149,
+                "y": 165.35849026339048
             },
             "dragging": false
         },
@@ -63,17 +63,17 @@
             "height": 383,
             "id": "conversationChain_0",
             "position": {
-                "x": 1176.1569322079652,
-                "y": 303.56879146735974
+                "x": 958.9887390513221,
+                "y": 318.8734467468765
             },
             "type": "customNode",
             "data": {
                 "id": "conversationChain_0",
                 "label": "Conversation Chain",
+                "version": 2,
                 "name": "conversationChain",
-                "version": 1,
                 "type": "ConversationChain",
-                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain"],
+                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Chat models specific conversational chain with memory",
                 "inputParams": [
@@ -82,9 +82,11 @@
                         "name": "systemMessagePrompt",
                         "type": "string",
                         "rows": 4,
+                        "description": "If Chat Prompt Template is provided, this will be ignored",
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "You are a helpful assistant that write codes",
+                        "default": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
+                        "placeholder": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
                         "id": "conversationChain_0-input-systemMessagePrompt-string"
                     }
                 ],
@@ -102,27 +104,26 @@
                         "id": "conversationChain_0-input-memory-BaseMemory"
                     },
                     {
-                        "label": "Document",
-                        "name": "document",
-                        "type": "Document",
-                        "description": "Include whole document into the context window",
+                        "label": "Chat Prompt Template",
+                        "name": "chatPromptTemplate",
+                        "type": "ChatPromptTemplate",
+                        "description": "Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable",
                         "optional": true,
-                        "list": true,
-                        "id": "conversationChain_0-input-document-Document"
+                        "id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatAnthropic_0.data.instance}}",
                     "memory": "{{bufferMemory_0.data.instance}}",
-                    "document": ["{{pdfFile_0.data.instance}}"],
-                    "systemMessagePrompt": ""
+                    "chatPromptTemplate": "{{chatPromptTemplate_0.data.instance}}",
+                    "systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain",
+                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain|Runnable",
                         "name": "conversationChain",
                         "label": "ConversationChain",
-                        "type": "ConversationChain | LLMChain | BaseChain"
+                        "type": "ConversationChain | LLMChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -130,27 +131,27 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 1176.1569322079652,
-                "y": 303.56879146735974
+                "x": 958.9887390513221,
+                "y": 318.8734467468765
             },
             "dragging": false
         },
         {
             "width": 300,
-            "height": 523,
+            "height": 574,
             "id": "chatAnthropic_0",
             "position": {
-                "x": 800.5525382783799,
-                "y": -130.7988221837009
+                "x": 585.3308245972187,
+                "y": -116.32789506560908
             },
             "type": "customNode",
             "data": {
                 "id": "chatAnthropic_0",
                 "label": "ChatAnthropic",
-                "name": "chatAnthropic",
                 "version": 3,
+                "name": "chatAnthropic",
                 "type": "ChatAnthropic",
-                "baseClasses": ["ChatAnthropic", "BaseChatModel", "BaseLanguageModel"],
+                "baseClasses": ["ChatAnthropic", "BaseChatModel", "BaseLanguageModel", "Runnable"],
                 "category": "Chat Models",
                 "description": "Wrapper around ChatAnthropic large language models that use the Chat endpoint",
                 "inputParams": [
@@ -226,7 +227,7 @@
                                 "name": "claude-instant-v1.1-100k"
                             }
                         ],
-                        "default": "claude-v1",
+                        "default": "claude-2",
                         "optional": true,
                         "id": "chatAnthropic_0-input-modelName-options"
                     },
@@ -234,6 +235,7 @@
                         "label": "Temperature",
                         "name": "temperature",
                         "type": "number",
+                        "step": 0.1,
                         "default": 0.9,
                         "optional": true,
                         "id": "chatAnthropic_0-input-temperature-number"
@@ -242,6 +244,7 @@
                         "label": "Max Tokens",
                         "name": "maxTokensToSample",
                         "type": "number",
+                        "step": 1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-maxTokensToSample-number"
@@ -250,6 +253,7 @@
                         "label": "Top P",
                         "name": "topP",
                         "type": "number",
+                        "step": 0.1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-topP-number"
@@ -258,6 +262,7 @@
                         "label": "Top K",
                         "name": "topK",
                         "type": "number",
+                        "step": 0.1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-topK-number"
@@ -273,6 +278,7 @@
                     }
                 ],
                 "inputs": {
+                    "cache": "",
                     "modelName": "claude-2.1",
                     "temperature": 0.9,
                     "maxTokensToSample": "",
@@ -281,10 +287,10 @@
                 },
                 "outputAnchors": [
                     {
-                        "id": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel",
+                        "id": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable",
                         "name": "chatAnthropic",
                         "label": "ChatAnthropic",
-                        "type": "ChatAnthropic | BaseChatModel | BaseLanguageModel"
+                        "type": "ChatAnthropic | BaseChatModel | BaseLanguageModel | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -292,61 +298,106 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 800.5525382783799,
-                "y": -130.7988221837009
+                "x": 585.3308245972187,
+                "y": -116.32789506560908
             },
             "dragging": false
         },
         {
             "width": 300,
-            "height": 507,
-            "id": "pdfFile_0",
+            "height": 688,
+            "id": "chatPromptTemplate_0",
             "position": {
-                "x": 94.16886576108482,
-                "y": 37.12056504707391
+                "x": -106.44189698270114,
+                "y": 20.133956087516538
             },
             "type": "customNode",
             "data": {
-                "id": "pdfFile_0",
-                "label": "Pdf File",
-                "name": "pdfFile",
+                "id": "chatPromptTemplate_0",
+                "label": "Chat Prompt Template",
                 "version": 1,
+                "name": "chatPromptTemplate",
+                "type": "ChatPromptTemplate",
+                "baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate", "Runnable"],
+                "category": "Prompts",
+                "description": "Schema to represent a chat prompt",
+                "inputParams": [
+                    {
+                        "label": "System Message",
+                        "name": "systemMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "placeholder": "You are a helpful assistant that translates {input_language} to {output_language}.",
+                        "id": "chatPromptTemplate_0-input-systemMessagePrompt-string"
+                    },
+                    {
+                        "label": "Human Message",
+                        "name": "humanMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "placeholder": "{text}",
+                        "id": "chatPromptTemplate_0-input-humanMessagePrompt-string"
+                    },
+                    {
+                        "label": "Format Prompt Values",
+                        "name": "promptValues",
+                        "type": "json",
+                        "optional": true,
+                        "acceptVariable": true,
+                        "list": true,
+                        "id": "chatPromptTemplate_0-input-promptValues-json"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\nThe AI has the following context:\n{context}",
+                    "humanMessagePrompt": "{input}",
+                    "promptValues": "{\"context\":\"{{plainText_0.data.instance}}\",\"input\":\"{{question}}\"}"
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
+                        "name": "chatPromptTemplate",
+                        "label": "ChatPromptTemplate",
+                        "type": "ChatPromptTemplate | BaseChatPromptTemplate | BasePromptTemplate | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": -106.44189698270114,
+                "y": 20.133956087516538
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 485,
+            "id": "plainText_0",
+            "position": {
+                "x": -487.7511991135089,
+                "y": 77.83838996645807
+            },
+            "type": "customNode",
+            "data": {
+                "id": "plainText_0",
+                "label": "Plain Text",
+                "version": 2,
+                "name": "plainText",
                 "type": "Document",
                 "baseClasses": ["Document"],
                 "category": "Document Loaders",
-                "description": "Load data from PDF files",
+                "description": "Load data from plain text",
                 "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": "Text",
+                        "name": "text",
+                        "type": "string",
+                        "rows": 4,
+                        "placeholder": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua...",
+                        "id": "plainText_0-input-text-string"
                     },
                     {
                         "label": "Metadata",
@@ -354,7 +405,7 @@
                         "type": "json",
                         "optional": true,
                         "additionalParams": true,
-                        "id": "pdfFile_0-input-metadata-json"
+                        "id": "plainText_0-input-metadata-json"
                     }
                 ],
                 "inputAnchors": [
@@ -363,30 +414,45 @@
                         "name": "textSplitter",
                         "type": "TextSplitter",
                         "optional": true,
-                        "id": "pdfFile_0-input-textSplitter-TextSplitter"
+                        "id": "plainText_0-input-textSplitter-TextSplitter"
                     }
                 ],
                 "inputs": {
+                    "text": "Welcome to Skyworld Hotel, where your dreams take flight and your stay soars to new heights. Nestled amidst breathtaking cityscape views, our upscale establishment offers an unparalleled blend of luxury and comfort. Our rooms are elegantly appointed, featuring modern amenities and plush furnishings to ensure your relaxation.\n\nIndulge in culinary delights at our rooftop restaurant, offering a gastronomic journey with panoramic vistas. Skyworld Hotel boasts state-of-the-art conference facilities, perfect for business travelers, and an inviting spa for relaxation seekers. Our attentive staff is dedicated to ensuring your every need is met, making your stay memorable.\n\nCentrally located, we offer easy access to local attractions, making us an ideal choice for both leisure and business travelers. Experience the world of hospitality like never before at Skyworld Hotel.",
                     "textSplitter": "",
-                    "usage": "perPage",
-                    "legacyBuild": "",
                     "metadata": ""
                 },
                 "outputAnchors": [
                     {
-                        "id": "pdfFile_0-output-pdfFile-Document",
-                        "name": "pdfFile",
-                        "label": "Document",
-                        "type": "Document"
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "plainText_0-output-document-Document",
+                                "name": "document",
+                                "label": "Document",
+                                "type": "Document"
+                            },
+                            {
+                                "id": "plainText_0-output-text-string|json",
+                                "name": "text",
+                                "label": "Text",
+                                "type": "string | json"
+                            }
+                        ],
+                        "default": "document"
                     }
                 ],
-                "outputs": {},
+                "outputs": {
+                    "output": "text"
+                },
                 "selected": false
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 94.16886576108482,
-                "y": 37.12056504707391
+                "x": -487.7511991135089,
+                "y": 77.83838996645807
             },
             "dragging": false
         }
@@ -398,32 +464,31 @@
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-memory-BaseMemory",
             "type": "buttonedge",
-            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory",
-            "data": {
-                "label": ""
-            }
+            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory"
         },
         {
             "source": "chatAnthropic_0",
-            "sourceHandle": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel",
+            "sourceHandle": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatAnthropic_0-chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel-conversationChain_0-conversationChain_0-input-model-BaseChatModel",
-            "data": {
-                "label": ""
-            }
+            "id": "chatAnthropic_0-chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable-conversationChain_0-conversationChain_0-input-model-BaseChatModel"
         },
         {
-            "source": "pdfFile_0",
-            "sourceHandle": "pdfFile_0-output-pdfFile-Document",
-            "target": "conversationChain_0",
-            "targetHandle": "conversationChain_0-input-document-Document",
+            "source": "plainText_0",
+            "sourceHandle": "plainText_0-output-text-string|json",
+            "target": "chatPromptTemplate_0",
+            "targetHandle": "chatPromptTemplate_0-input-promptValues-json",
             "type": "buttonedge",
-            "id": "pdfFile_0-pdfFile_0-output-pdfFile-Document-conversationChain_0-conversationChain_0-input-document-Document",
-            "data": {
-                "label": ""
-            }
+            "id": "plainText_0-plainText_0-output-text-string|json-chatPromptTemplate_0-chatPromptTemplate_0-input-promptValues-json"
+        },
+        {
+            "source": "chatPromptTemplate_0",
+            "sourceHandle": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
+            "target": "conversationChain_0",
+            "targetHandle": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate",
+            "type": "buttonedge",
+            "id": "chatPromptTemplate_0-chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable-conversationChain_0-conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
         }
     ]
 }

packages/server/marketplaces/chatflows/Conversational Retrieval Agent.json

@@ -301,7 +301,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -342,6 +342,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -366,7 +405,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json

@@ -541,7 +541,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -582,6 +582,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -606,7 +645,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Metadata Filter.json

@@ -625,7 +625,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -666,6 +666,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -690,7 +729,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "{\"id\":{\"$in\":[\"doc1\",\"doc2\"]}}",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Multi Retrieval QA Chain.json

@@ -560,7 +560,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -601,6 +601,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -625,7 +664,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -840,6 +882,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "supabase_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -865,7 +946,10 @@
                     "tableName": "",
                     "queryName": "",
                     "supabaseMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Simple Conversation Chain.json

@@ -2,20 +2,210 @@
     "description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
     "badge": "POPULAR",
     "nodes": [
+        {
+            "width": 300,
+            "height": 574,
+            "id": "chatOpenAI_0",
+            "position": {
+                "x": 579.0877964395976,
+                "y": -138.68792413227874
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatOpenAI_0",
+                "label": "ChatOpenAI",
+                "version": 2,
+                "name": "chatOpenAI",
+                "type": "ChatOpenAI",
+                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
+                "category": "Chat Models",
+                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "chatOpenAI_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "gpt-4",
+                                "name": "gpt-4"
+                            },
+                            {
+                                "label": "gpt-4-1106-preview",
+                                "name": "gpt-4-1106-preview"
+                            },
+                            {
+                                "label": "gpt-4-vision-preview",
+                                "name": "gpt-4-vision-preview"
+                            },
+                            {
+                                "label": "gpt-4-0613",
+                                "name": "gpt-4-0613"
+                            },
+                            {
+                                "label": "gpt-4-32k",
+                                "name": "gpt-4-32k"
+                            },
+                            {
+                                "label": "gpt-4-32k-0613",
+                                "name": "gpt-4-32k-0613"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo",
+                                "name": "gpt-3.5-turbo"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-1106",
+                                "name": "gpt-3.5-turbo-1106"
+                            },
+                            {
+                                "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"
+                            }
+                        ],
+                        "default": "gpt-3.5-turbo",
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokens",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-maxTokens-number"
+                    },
+                    {
+                        "label": "Top Probability",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-topP-number"
+                    },
+                    {
+                        "label": "Frequency Penalty",
+                        "name": "frequencyPenalty",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
+                    },
+                    {
+                        "label": "Presence Penalty",
+                        "name": "presencePenalty",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-presencePenalty-number"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-timeout-number"
+                    },
+                    {
+                        "label": "BasePath",
+                        "name": "basepath",
+                        "type": "string",
+                        "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": [
+                    {
+                        "label": "Cache",
+                        "name": "cache",
+                        "type": "BaseCache",
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-cache-BaseCache"
+                    }
+                ],
+                "inputs": {
+                    "cache": "",
+                    "modelName": "gpt-3.5-turbo-16k",
+                    "temperature": 0.9,
+                    "maxTokens": "",
+                    "topP": "",
+                    "frequencyPenalty": "",
+                    "presencePenalty": "",
+                    "timeout": "",
+                    "basepath": "",
+                    "baseOptions": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
+                        "name": "chatOpenAI",
+                        "label": "ChatOpenAI",
+                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 579.0877964395976,
+                "y": -138.68792413227874
+            },
+            "dragging": false
+        },
         {
             "width": 300,
             "height": 376,
             "id": "bufferMemory_0",
             "position": {
-                "x": 753.4300788823234,
-                "y": 479.5336426526603
+                "x": 220.30240896145915,
+                "y": 351.61324070296877
             },
             "type": "customNode",
             "data": {
                 "id": "bufferMemory_0",
                 "label": "Buffer Memory",
-                "name": "bufferMemory",
                 "version": 1,
+                "name": "bufferMemory",
                 "type": "BufferMemory",
                 "baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
                 "category": "Memory",
@@ -54,179 +244,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 753.4300788823234,
-                "y": 479.5336426526603
-            },
-            "dragging": false
-        },
-        {
-            "width": 300,
-            "height": 523,
-            "id": "chatOpenAI_0",
-            "position": {
-                "x": 754.8942497823595,
-                "y": -140
-            },
-            "type": "customNode",
-            "data": {
-                "id": "chatOpenAI_0",
-                "label": "ChatOpenAI",
-                "name": "chatOpenAI",
-                "version": 2,
-                "type": "ChatOpenAI",
-                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
-                "category": "Chat Models",
-                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
-                "inputParams": [
-                    {
-                        "label": "Connect Credential",
-                        "name": "credential",
-                        "type": "credential",
-                        "credentialNames": ["openAIApi"],
-                        "id": "chatOpenAI_0-input-credential-credential"
-                    },
-                    {
-                        "label": "Model Name",
-                        "name": "modelName",
-                        "type": "options",
-                        "options": [
-                            {
-                                "label": "gpt-4",
-                                "name": "gpt-4"
-                            },
-                            {
-                                "label": "gpt-4-0613",
-                                "name": "gpt-4-0613"
-                            },
-                            {
-                                "label": "gpt-4-32k",
-                                "name": "gpt-4-32k"
-                            },
-                            {
-                                "label": "gpt-4-32k-0613",
-                                "name": "gpt-4-32k-0613"
-                            },
-                            {
-                                "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"
-                            }
-                        ],
-                        "default": "gpt-3.5-turbo",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-modelName-options"
-                    },
-                    {
-                        "label": "Temperature",
-                        "name": "temperature",
-                        "type": "number",
-                        "default": 0.9,
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-temperature-number"
-                    },
-                    {
-                        "label": "Max Tokens",
-                        "name": "maxTokens",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-maxTokens-number"
-                    },
-                    {
-                        "label": "Top Probability",
-                        "name": "topP",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-topP-number"
-                    },
-                    {
-                        "label": "Frequency Penalty",
-                        "name": "frequencyPenalty",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
-                    },
-                    {
-                        "label": "Presence Penalty",
-                        "name": "presencePenalty",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-presencePenalty-number"
-                    },
-                    {
-                        "label": "Timeout",
-                        "name": "timeout",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-timeout-number"
-                    },
-                    {
-                        "label": "BasePath",
-                        "name": "basepath",
-                        "type": "string",
-                        "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": [
-                    {
-                        "label": "Cache",
-                        "name": "cache",
-                        "type": "BaseCache",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-cache-BaseCache"
-                    }
-                ],
-                "inputs": {
-                    "modelName": "gpt-3.5-turbo",
-                    "temperature": 0.9,
-                    "maxTokens": "",
-                    "topP": "",
-                    "frequencyPenalty": "",
-                    "presencePenalty": "",
-                    "timeout": "",
-                    "basepath": "",
-                    "baseOptions": ""
-                },
-                "outputAnchors": [
-                    {
-                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
-                        "name": "chatOpenAI",
-                        "label": "ChatOpenAI",
-                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
-                    }
-                ],
-                "outputs": {},
-                "selected": false
-            },
-            "selected": false,
-            "positionAbsolute": {
-                "x": 754.8942497823595,
-                "y": -140
+                "x": 220.30240896145915,
+                "y": 351.61324070296877
             },
             "dragging": false
         },
@@ -235,17 +254,17 @@
             "height": 383,
             "id": "conversationChain_0",
             "position": {
-                "x": 1174.6496397666272,
-                "y": 311.1052536740497
+                "x": 958.9887390513221,
+                "y": 318.8734467468765
             },
             "type": "customNode",
             "data": {
                 "id": "conversationChain_0",
                 "label": "Conversation Chain",
+                "version": 2,
                 "name": "conversationChain",
-                "version": 1,
                 "type": "ConversationChain",
-                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain"],
+                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Chat models specific conversational chain with memory",
                 "inputParams": [
@@ -254,9 +273,11 @@
                         "name": "systemMessagePrompt",
                         "type": "string",
                         "rows": 4,
+                        "description": "If Chat Prompt Template is provided, this will be ignored",
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "You are a helpful assistant that write codes",
+                        "default": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
+                        "placeholder": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
                         "id": "conversationChain_0-input-systemMessagePrompt-string"
                     }
                 ],
@@ -274,27 +295,26 @@
                         "id": "conversationChain_0-input-memory-BaseMemory"
                     },
                     {
-                        "label": "Document",
-                        "name": "document",
-                        "type": "Document",
-                        "description": "Include whole document into the context window",
+                        "label": "Chat Prompt Template",
+                        "name": "chatPromptTemplate",
+                        "type": "ChatPromptTemplate",
+                        "description": "Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable",
                         "optional": true,
-                        "list": true,
-                        "id": "conversationChain_0-input-document-Document"
+                        "id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "memory": "{{bufferMemory_0.data.instance}}",
-                    "document": "",
-                    "systemMessagePrompt": ""
+                    "chatPromptTemplate": "",
+                    "systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain",
+                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain|Runnable",
                         "name": "conversationChain",
                         "label": "ConversationChain",
-                        "type": "ConversationChain | LLMChain | BaseChain"
+                        "type": "ConversationChain | LLMChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -302,8 +322,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 1174.6496397666272,
-                "y": 311.1052536740497
+                "x": 958.9887390513221,
+                "y": 318.8734467468765
             },
             "dragging": false
         }
@@ -311,14 +331,11 @@
     "edges": [
         {
             "source": "chatOpenAI_0",
-            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
+            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationChain_0-conversationChain_0-input-model-BaseChatModel",
-            "data": {
-                "label": ""
-            }
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationChain_0-conversationChain_0-input-model-BaseChatModel"
         },
         {
             "source": "bufferMemory_0",
@@ -326,10 +343,7 @@
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-memory-BaseMemory",
             "type": "buttonedge",
-            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory",
-            "data": {
-                "label": ""
-            }
+            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory"
         }
     ]
 }

packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json

@@ -1,461 +0,0 @@
-{
-    "description": "A simple LLM chain that uses Vectara to enable conversations with uploaded files",
-    "nodes": [
-        {
-            "width": 300,
-            "height": 574,
-            "id": "chatOpenAI_0",
-            "position": {
-                "x": 581.1784360612766,
-                "y": -229.3906666911439
-            },
-            "type": "customNode",
-            "data": {
-                "id": "chatOpenAI_0",
-                "label": "ChatOpenAI",
-                "version": 2,
-                "name": "chatOpenAI",
-                "type": "ChatOpenAI",
-                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
-                "category": "Chat Models",
-                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
-                "inputParams": [
-                    {
-                        "label": "Connect Credential",
-                        "name": "credential",
-                        "type": "credential",
-                        "credentialNames": ["openAIApi"],
-                        "id": "chatOpenAI_0-input-credential-credential"
-                    },
-                    {
-                        "label": "Model Name",
-                        "name": "modelName",
-                        "type": "options",
-                        "options": [
-                            {
-                                "label": "gpt-4",
-                                "name": "gpt-4"
-                            },
-                            {
-                                "label": "gpt-4-0613",
-                                "name": "gpt-4-0613"
-                            },
-                            {
-                                "label": "gpt-4-32k",
-                                "name": "gpt-4-32k"
-                            },
-                            {
-                                "label": "gpt-4-32k-0613",
-                                "name": "gpt-4-32k-0613"
-                            },
-                            {
-                                "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"
-                            }
-                        ],
-                        "default": "gpt-3.5-turbo",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-modelName-options"
-                    },
-                    {
-                        "label": "Temperature",
-                        "name": "temperature",
-                        "type": "number",
-                        "step": 0.1,
-                        "default": 0.9,
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-temperature-number"
-                    },
-                    {
-                        "label": "Max Tokens",
-                        "name": "maxTokens",
-                        "type": "number",
-                        "step": 1,
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-maxTokens-number"
-                    },
-                    {
-                        "label": "Top Probability",
-                        "name": "topP",
-                        "type": "number",
-                        "step": 0.1,
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-topP-number"
-                    },
-                    {
-                        "label": "Frequency Penalty",
-                        "name": "frequencyPenalty",
-                        "type": "number",
-                        "step": 0.1,
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
-                    },
-                    {
-                        "label": "Presence Penalty",
-                        "name": "presencePenalty",
-                        "type": "number",
-                        "step": 0.1,
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-presencePenalty-number"
-                    },
-                    {
-                        "label": "Timeout",
-                        "name": "timeout",
-                        "type": "number",
-                        "step": 1,
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-timeout-number"
-                    },
-                    {
-                        "label": "BasePath",
-                        "name": "basepath",
-                        "type": "string",
-                        "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": [
-                    {
-                        "label": "Cache",
-                        "name": "cache",
-                        "type": "BaseCache",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-cache-BaseCache"
-                    }
-                ],
-                "inputs": {
-                    "modelName": "gpt-3.5-turbo",
-                    "temperature": "0.6",
-                    "maxTokens": "",
-                    "topP": "",
-                    "frequencyPenalty": "",
-                    "presencePenalty": "",
-                    "timeout": "",
-                    "basepath": "",
-                    "baseOptions": ""
-                },
-                "outputAnchors": [
-                    {
-                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
-                        "name": "chatOpenAI",
-                        "label": "ChatOpenAI",
-                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
-                    }
-                ],
-                "outputs": {},
-                "selected": false
-            },
-            "selected": false,
-            "positionAbsolute": {
-                "x": 581.1784360612766,
-                "y": -229.3906666911439
-            },
-            "dragging": false
-        },
-        {
-            "width": 300,
-            "height": 480,
-            "id": "conversationalRetrievalQAChain_0",
-            "position": {
-                "x": 979.9713511176517,
-                "y": 200.09513217589273
-            },
-            "type": "customNode",
-            "data": {
-                "id": "conversationalRetrievalQAChain_0",
-                "label": "Conversational Retrieval QA Chain",
-                "version": 2,
-                "name": "conversationalRetrievalQAChain",
-                "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
-                "category": "Chains",
-                "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
-                "inputParams": [
-                    {
-                        "label": "Return Source Documents",
-                        "name": "returnSourceDocuments",
-                        "type": "boolean",
-                        "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
-                    },
-                    {
-                        "label": "Rephrase Prompt",
-                        "name": "rephrasePrompt",
-                        "type": "string",
-                        "description": "Using previous chat history, rephrase question into a standalone question",
-                        "warning": "Prompt must include input variables: {chat_history} and {question}",
-                        "rows": 4,
-                        "additionalParams": true,
-                        "optional": true,
-                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
-                    },
-                    {
-                        "label": "Response Prompt",
-                        "name": "responsePrompt",
-                        "type": "string",
-                        "description": "Taking the rephrased question, search for answer from the provided context",
-                        "warning": "Prompt must include input variable: {context}",
-                        "rows": 4,
-                        "additionalParams": true,
-                        "optional": true,
-                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
-                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
-                    }
-                ],
-                "inputAnchors": [
-                    {
-                        "label": "Chat Model",
-                        "name": "model",
-                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
-                    },
-                    {
-                        "label": "Vector Store Retriever",
-                        "name": "vectorStoreRetriever",
-                        "type": "BaseRetriever",
-                        "id": "conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever"
-                    },
-                    {
-                        "label": "Memory",
-                        "name": "memory",
-                        "type": "BaseMemory",
-                        "optional": true,
-                        "description": "If left empty, a default BufferMemory will be used",
-                        "id": "conversationalRetrievalQAChain_0-input-memory-BaseMemory"
-                    }
-                ],
-                "inputs": {
-                    "model": "{{chatOpenAI_0.data.instance}}",
-                    "vectorStoreRetriever": "{{vectara_0.data.instance}}",
-                    "memory": "",
-                    "returnSourceDocuments": true,
-                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
-                },
-                "outputAnchors": [
-                    {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
-                        "name": "conversationalRetrievalQAChain",
-                        "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
-                    }
-                ],
-                "outputs": {},
-                "selected": false
-            },
-            "selected": false,
-            "dragging": false,
-            "positionAbsolute": {
-                "x": 979.9713511176517,
-                "y": 200.09513217589273
-            }
-        },
-        {
-            "width": 300,
-            "height": 535,
-            "id": "vectara_0",
-            "position": {
-                "x": 199.28476672510158,
-                "y": 177.63260741741112
-            },
-            "type": "customNode",
-            "data": {
-                "id": "vectara_0",
-                "label": "Vectara",
-                "version": 1,
-                "name": "vectara",
-                "type": "Vectara",
-                "baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
-                "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Vectara, a LLM-powered search-as-a-service",
-                "inputParams": [
-                    {
-                        "label": "Connect Credential",
-                        "name": "credential",
-                        "type": "credential",
-                        "credentialNames": ["vectaraApi"],
-                        "id": "vectara_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",
-                        "optional": true,
-                        "id": "vectara_0-input-file-file"
-                    },
-                    {
-                        "label": "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,
-                        "id": "vectara_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": "vectara_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": "vectara_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": "vectara_0-input-lambda-number"
-                    },
-                    {
-                        "label": "Top K",
-                        "name": "topK",
-                        "description": "Number of top results to fetch. Defaults to 5",
-                        "placeholder": "5",
-                        "type": "number",
-                        "additionalParams": true,
-                        "optional": true,
-                        "id": "vectara_0-input-topK-number"
-                    },
-                    {
-                        "label": "MMR K",
-                        "name": "mmrK",
-                        "description": "The number of results to rerank if MMR is enabled.",
-                        "placeholder": "50",
-                        "type": "number",
-                        "additionalParams": true,
-                        "optional": true,
-                        "id": "vectara_0-input-mmrK-number"
-                    },
-                    {
-                        "label": "MMR Diversity Bias",
-                        "name": "mmrDiversityBias",
-                        "step": 0.1,
-                        "description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).",
-                        "placeholder": "0.0",
-                        "type": "number",
-                        "additionalParams": true,
-                        "optional": true,
-                        "id": "vectara_0-input-mmrDiversityBias-number"
-                    }
-                ],
-                "inputAnchors": [
-                    {
-                        "label": "Document",
-                        "name": "document",
-                        "type": "Document",
-                        "list": true,
-                        "optional": true,
-                        "id": "vectara_0-input-document-Document"
-                    }
-                ],
-                "inputs": {
-                    "document": "",
-                    "filter": "",
-                    "sentencesBefore": "",
-                    "sentencesAfter": "",
-                    "lambda": "",
-                    "topK": "",
-                    "mmrK": "",
-                    "mmrDiversityBias": ""
-                },
-                "outputAnchors": [
-                    {
-                        "name": "output",
-                        "label": "Output",
-                        "type": "options",
-                        "options": [
-                            {
-                                "id": "vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
-                                "name": "retriever",
-                                "label": "Vectara Retriever",
-                                "type": "Vectara | VectorStoreRetriever | BaseRetriever"
-                            },
-                            {
-                                "id": "vectara_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": 199.28476672510158,
-                "y": 177.63260741741112
-            },
-            "dragging": false
-        }
-    ],
-    "edges": [
-        {
-            "source": "chatOpenAI_0",
-            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
-            "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
-            "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
-            "data": {
-                "label": ""
-            }
-        },
-        {
-            "source": "vectara_0",
-            "sourceHandle": "vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
-            "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
-            "type": "buttonedge",
-            "id": "vectara_0-vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
-            "data": {
-                "label": ""
-            }
-        }
-    ]
-}

packages/server/marketplaces/chatflows/Vectara RAG Chain.json

@@ -0,0 +1,385 @@
+{
+    "nodes": [
+        {
+            "width": 300,
+            "height": 520,
+            "id": "vectaraQAChain_0",
+            "position": {
+                "x": 740.28434119739,
+                "y": 164.93261446841598
+            },
+            "type": "customNode",
+            "data": {
+                "id": "vectaraQAChain_0",
+                "label": "Vectara QA Chain",
+                "version": 1,
+                "name": "vectaraQAChain",
+                "type": "VectaraQAChain",
+                "baseClasses": ["VectaraQAChain", "BaseChain", "Runnable"],
+                "category": "Chains",
+                "description": "QA chain for Vectara",
+                "inputParams": [
+                    {
+                        "label": "Summarizer Prompt Name",
+                        "name": "summarizerPromptName",
+                        "description": "Summarize the results fetched from Vectara. Read <a target=\"_blank\" href=\"https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer\">more</a>",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)",
+                                "name": "vectara-summary-ext-v1.2.0"
+                            },
+                            {
+                                "label": "vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)",
+                                "name": "vectara-experimental-summary-ext-2023-10-23-small",
+                                "description": "In beta, available to both Growth and Scale Vectara users"
+                            },
+                            {
+                                "label": "vectara-summary-ext-v1.3.0 (gpt-4.0)",
+                                "name": "vectara-summary-ext-v1.3.0",
+                                "description": "Only available to paying Scale Vectara users"
+                            },
+                            {
+                                "label": "vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)",
+                                "name": "vectara-experimental-summary-ext-2023-10-23-med",
+                                "description": "In beta, only available to paying Scale Vectara users"
+                            }
+                        ],
+                        "default": "vectara-summary-ext-v1.2.0",
+                        "id": "vectaraQAChain_0-input-summarizerPromptName-options"
+                    },
+                    {
+                        "label": "Response Language",
+                        "name": "responseLang",
+                        "description": "Return the response in specific language. If not selected, Vectara will automatically detects the language. Read <a target=\"_blank\" href=\"https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-response-languages\">more</a>",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "English",
+                                "name": "eng"
+                            },
+                            {
+                                "label": "German",
+                                "name": "deu"
+                            },
+                            {
+                                "label": "French",
+                                "name": "fra"
+                            },
+                            {
+                                "label": "Chinese",
+                                "name": "zho"
+                            },
+                            {
+                                "label": "Korean",
+                                "name": "kor"
+                            },
+                            {
+                                "label": "Arabic",
+                                "name": "ara"
+                            },
+                            {
+                                "label": "Russian",
+                                "name": "rus"
+                            },
+                            {
+                                "label": "Thai",
+                                "name": "tha"
+                            },
+                            {
+                                "label": "Dutch",
+                                "name": "nld"
+                            },
+                            {
+                                "label": "Italian",
+                                "name": "ita"
+                            },
+                            {
+                                "label": "Portuguese",
+                                "name": "por"
+                            },
+                            {
+                                "label": "Spanish",
+                                "name": "spa"
+                            },
+                            {
+                                "label": "Japanese",
+                                "name": "jpn"
+                            },
+                            {
+                                "label": "Polish",
+                                "name": "pol"
+                            },
+                            {
+                                "label": "Turkish",
+                                "name": "tur"
+                            },
+                            {
+                                "label": "Vietnamese",
+                                "name": "vie"
+                            },
+                            {
+                                "label": "Indonesian",
+                                "name": "ind"
+                            },
+                            {
+                                "label": "Czech",
+                                "name": "ces"
+                            },
+                            {
+                                "label": "Ukrainian",
+                                "name": "ukr"
+                            },
+                            {
+                                "label": "Greek",
+                                "name": "ell"
+                            },
+                            {
+                                "label": "Hebrew",
+                                "name": "heb"
+                            },
+                            {
+                                "label": "Farsi/Persian",
+                                "name": "fas"
+                            },
+                            {
+                                "label": "Hindi",
+                                "name": "hin"
+                            },
+                            {
+                                "label": "Urdu",
+                                "name": "urd"
+                            },
+                            {
+                                "label": "Swedish",
+                                "name": "swe"
+                            },
+                            {
+                                "label": "Bengali",
+                                "name": "ben"
+                            },
+                            {
+                                "label": "Malay",
+                                "name": "msa"
+                            },
+                            {
+                                "label": "Romanian",
+                                "name": "ron"
+                            }
+                        ],
+                        "optional": true,
+                        "default": "eng",
+                        "id": "vectaraQAChain_0-input-responseLang-options"
+                    },
+                    {
+                        "label": "Max Summarized Results",
+                        "name": "maxSummarizedResults",
+                        "description": "Maximum results used to build the summarized response",
+                        "type": "number",
+                        "default": 7,
+                        "id": "vectaraQAChain_0-input-maxSummarizedResults-number"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Vectara Store",
+                        "name": "vectaraStore",
+                        "type": "VectorStore",
+                        "id": "vectaraQAChain_0-input-vectaraStore-VectorStore"
+                    }
+                ],
+                "inputs": {
+                    "vectaraStore": "{{vectara_1.data.instance}}",
+                    "summarizerPromptName": "vectara-experimental-summary-ext-2023-10-23-small",
+                    "responseLang": "eng",
+                    "maxSummarizedResults": 7
+                },
+                "outputAnchors": [
+                    {
+                        "id": "vectaraQAChain_0-output-vectaraQAChain-VectaraQAChain|BaseChain|Runnable",
+                        "name": "vectaraQAChain",
+                        "label": "VectaraQAChain",
+                        "type": "VectaraQAChain | BaseChain | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 740.28434119739,
+                "y": 164.93261446841598
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 536,
+            "id": "vectara_1",
+            "position": {
+                "x": 139.43135627266395,
+                "y": 189.3685569634871
+            },
+            "type": "customNode",
+            "data": {
+                "id": "vectara_1",
+                "label": "Vectara",
+                "version": 2,
+                "name": "vectara",
+                "type": "Vectara",
+                "baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
+                "category": "Vector Stores",
+                "description": "Upsert embedded data and perform similarity search upon query using Vectara, a LLM-powered search-as-a-service",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["vectaraApi"],
+                        "id": "vectara_1-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",
+                        "optional": true,
+                        "id": "vectara_1-input-file-file"
+                    },
+                    {
+                        "label": "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,
+                        "id": "vectara_1-input-filter-string"
+                    },
+                    {
+                        "label": "Sentences Before",
+                        "name": "sentencesBefore",
+                        "description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
+                        "type": "number",
+                        "default": 2,
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-sentencesBefore-number"
+                    },
+                    {
+                        "label": "Sentences After",
+                        "name": "sentencesAfter",
+                        "description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
+                        "type": "number",
+                        "default": 2,
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-sentencesAfter-number"
+                    },
+                    {
+                        "label": "Lambda",
+                        "name": "lambda",
+                        "description": "Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).",
+                        "default": 0,
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-lambda-number"
+                    },
+                    {
+                        "label": "Top K",
+                        "name": "topK",
+                        "description": "Number of top results to fetch. Defaults to 5",
+                        "placeholder": "5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-topK-number"
+                    },
+                    {
+                        "label": "MMR K",
+                        "name": "mmrK",
+                        "description": "Number of top results to fetch for MMR. Defaults to 50",
+                        "placeholder": "50",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-mmrK-number"
+                    },
+                    {
+                        "label": "MMR diversity bias",
+                        "name": "mmrDiversityBias",
+                        "step": 0.1,
+                        "description": "The diversity bias to use for MMR. This is a value between 0.0 and 1.0Values closer to 1.0 optimize for the most diverse results.Defaults to 0 (MMR disabled)",
+                        "placeholder": "0.0",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_1-input-mmrDiversityBias-number"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Document",
+                        "name": "document",
+                        "type": "Document",
+                        "list": true,
+                        "optional": true,
+                        "id": "vectara_1-input-document-Document"
+                    }
+                ],
+                "inputs": {
+                    "document": "",
+                    "filter": "",
+                    "sentencesBefore": 2,
+                    "sentencesAfter": 2,
+                    "lambda": "",
+                    "topK": "",
+                    "mmrK": "",
+                    "mmrDiversityBias": ""
+                },
+                "outputAnchors": [
+                    {
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "vectara_1-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
+                                "name": "retriever",
+                                "label": "Vectara Retriever",
+                                "type": "Vectara | VectorStoreRetriever | BaseRetriever"
+                            },
+                            {
+                                "id": "vectara_1-output-vectorStore-Vectara|VectorStore",
+                                "name": "vectorStore",
+                                "label": "Vectara Vector Store",
+                                "type": "Vectara | VectorStore"
+                            }
+                        ],
+                        "default": "retriever"
+                    }
+                ],
+                "outputs": {
+                    "output": "vectorStore"
+                },
+                "selected": false
+            },
+            "positionAbsolute": {
+                "x": 139.43135627266395,
+                "y": 189.3685569634871
+            },
+            "selected": false,
+            "dragging": false
+        }
+    ],
+    "edges": [
+        {
+            "source": "vectara_1",
+            "sourceHandle": "vectara_1-output-vectorStore-Vectara|VectorStore",
+            "target": "vectaraQAChain_0",
+            "targetHandle": "vectaraQAChain_0-input-vectaraStore-VectorStore",
+            "type": "buttonedge",
+            "id": "vectara_1-vectara_1-output-vectorStore-Vectara|VectorStore-vectaraQAChain_0-vectaraQAChain_0-input-vectaraStore-VectorStore"
+        }
+    ]
+}

packages/server/marketplaces/chatflows/WebPage QnA.json

@@ -643,7 +643,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -684,6 +684,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -708,7 +747,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.10",
+    "version": "1.4.11",
     "description": "Flowiseai Server",
     "main": "dist/index",
     "types": "dist/index.d.ts",
@@ -60,6 +60,7 @@
         "multer": "^1.4.5-lts.1",
         "mysql": "^2.18.1",
         "pg": "^8.11.1",
+        "posthog-node": "^3.5.0",
         "reflect-metadata": "^0.1.13",
         "sanitize-html": "^2.11.0",
         "socket.io": "^4.6.1",

packages/server/src/commands/start.ts

@@ -39,7 +39,8 @@ export default class Start extends Command {
         LANGCHAIN_TRACING_V2: Flags.string(),
         LANGCHAIN_ENDPOINT: Flags.string(),
         LANGCHAIN_API_KEY: Flags.string(),
-        LANGCHAIN_PROJECT: Flags.string()
+        LANGCHAIN_PROJECT: Flags.string(),
+        DISABLE_FLOWISE_TELEMETRY: Flags.string()
     }
 
     async stopProcess() {
@@ -113,6 +114,9 @@ export default class Start extends Command {
         if (flags.LANGCHAIN_API_KEY) process.env.LANGCHAIN_API_KEY = flags.LANGCHAIN_API_KEY
         if (flags.LANGCHAIN_PROJECT) process.env.LANGCHAIN_PROJECT = flags.LANGCHAIN_PROJECT
 
+        // Telemetry
+        if (flags.DISABLE_FLOWISE_TELEMETRY) process.env.DISABLE_FLOWISE_TELEMETRY = flags.DISABLE_FLOWISE_TELEMETRY
+
         await (async () => {
             try {
                 logger.info('Starting Flowise...')

packages/server/src/index.ts

@@ -45,7 +45,9 @@ import {
     getSessionChatHistory,
     getAllConnectedNodes,
     clearSessionMemory,
-    findMemoryNode
+    findMemoryNode,
+    getTelemetryFlowObj,
+    getAppVersion
 } from './utils'
 import { cloneDeep, omit, uniqWith, isEqual } from 'lodash'
 import { getDataSource } from './DataSource'
@@ -64,6 +66,7 @@ import { sanitizeMiddleware } from './utils/XSS'
 import axios from 'axios'
 import { Client } from 'langchainhub'
 import { parsePrompt } from './utils/hub'
+import { Telemetry } from './utils/telemetry'
 import { Variable } from './database/entities/Variable'
 
 export class App {
@@ -71,6 +74,7 @@ export class App {
     nodesPool: NodesPool
     chatflowPool: ChatflowPool
     cachePool: CachePool
+    telemetry: Telemetry
     AppDataSource = getDataSource()
 
     constructor() {
@@ -105,6 +109,9 @@ export class App {
 
                 // Initialize cache pool
                 this.cachePool = new CachePool()
+
+                // Initialize telemetry
+                this.telemetry = new Telemetry()
             })
             .catch((err) => {
                 logger.error('❌ [server]: Error during Data Source initialization:', err)
@@ -294,7 +301,13 @@ export class App {
                     const nodeModule = await import(nodeInstanceFilePath)
                     const newNodeInstance = new nodeModule.nodeClass()
 
-                    const returnData = await newNodeInstance.init(nodeData)
+                    const options: ICommonObject = {
+                        appDataSource: this.AppDataSource,
+                        databaseEntities,
+                        logger
+                    }
+
+                    const returnData = await newNodeInstance.init(nodeData, '', options)
                     const result = typeof returnData === 'string' ? handleEscapeCharacters(returnData, true) : returnData
 
                     return res.json(result)
@@ -382,6 +395,12 @@ export class App {
             const chatflow = this.AppDataSource.getRepository(ChatFlow).create(newChatFlow)
             const results = await this.AppDataSource.getRepository(ChatFlow).save(chatflow)
 
+            await this.telemetry.sendTelemetry('chatflow_created', {
+                version: await getAppVersion(),
+                chatlowId: results.id,
+                flowGraph: getTelemetryFlowObj(JSON.parse(results.flowData)?.nodes, JSON.parse(results.flowData)?.edges)
+            })
+
             return res.json(results)
         })
 
@@ -668,6 +687,12 @@ export class App {
             const tool = this.AppDataSource.getRepository(Tool).create(newTool)
             const results = await this.AppDataSource.getRepository(Tool).save(tool)
 
+            await this.telemetry.sendTelemetry('tool_created', {
+                version: await getAppVersion(),
+                toolId: results.id,
+                toolName: results.name
+            })
+
             return res.json(results)
         })
 
@@ -874,6 +899,11 @@ export class App {
             const assistant = this.AppDataSource.getRepository(Assistant).create(newAssistant)
             const results = await this.AppDataSource.getRepository(Assistant).save(assistant)
 
+            await this.telemetry.sendTelemetry('assistant_created', {
+                version: await getAppVersion(),
+                assistantId: results.id
+            })
+
             return res.json(results)
         })
 
@@ -1461,6 +1491,11 @@ export class App {
             let chatId = incomingInput.chatId ?? ''
             let isUpsert = true
 
+            // Get session ID
+            const memoryNode = findMemoryNode(nodes, edges)
+            let sessionId = undefined
+            if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
+
             const vsNodes = nodes.filter(
                 (node) =>
                     node.data.category === 'Vector Stores' &&
@@ -1498,6 +1533,7 @@ export class App {
                 incomingInput.question,
                 chatHistory,
                 chatId,
+                sessionId ?? '',
                 chatflowid,
                 this.AppDataSource,
                 incomingInput?.overrideConfig,
@@ -1509,6 +1545,15 @@ export class App {
             const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.data.id))
 
             this.chatflowPool.add(chatflowid, undefined, startingNodes, incomingInput?.overrideConfig)
+
+            await this.telemetry.sendTelemetry('vector_upserted', {
+                version: await getAppVersion(),
+                chatlowId: chatflowid,
+                type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
+                flowGraph: getTelemetryFlowObj(nodes, edges),
+                stopNodeId
+            })
+
             return res.status(201).send('Successfully Upserted')
         } catch (e: any) {
             logger.error('[server]: Error:', e)
@@ -1575,6 +1620,12 @@ export class App {
             const nodes = parsedFlowData.nodes
             const edges = parsedFlowData.edges
 
+            // Get session ID
+            const memoryNode = findMemoryNode(nodes, edges)
+            const memoryType = memoryNode?.data.label
+            let sessionId = undefined
+            if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
+
             /*   Reuse the flow without having to rebuild (to avoid duplicated upsert, recomputation, reinitialization of memory) when all these conditions met:
              * - Node Data already exists in pool
              * - Still in sync (i.e the flow has not been modified since)
@@ -1684,6 +1735,7 @@ export class App {
                     incomingInput.question,
                     chatHistory,
                     chatId,
+                    sessionId ?? '',
                     chatflowid,
                     this.AppDataSource,
                     incomingInput?.overrideConfig,
@@ -1713,12 +1765,6 @@ export class App {
 
             logger.debug(`[server]: Running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
 
-            const memoryNode = findMemoryNode(nodes, edges)
-            const memoryType = memoryNode?.data.label
-
-            let sessionId = undefined
-            if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
-
             const nodeInstanceFilePath = this.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string
             const nodeModule = await import(nodeInstanceFilePath)
             const nodeInstance = new nodeModule.nodeClass({ sessionId })
@@ -1781,12 +1827,22 @@ export class App {
             if (result?.sourceDocuments) apiMessage.sourceDocuments = JSON.stringify(result.sourceDocuments)
             if (result?.usedTools) apiMessage.usedTools = JSON.stringify(result.usedTools)
             if (result?.fileAnnotations) apiMessage.fileAnnotations = JSON.stringify(result.fileAnnotations)
-            await this.addChatMessage(apiMessage)
+            const chatMessage = await this.addChatMessage(apiMessage)
+            result.chatMessageId = chatMessage.id
 
             logger.debug(`[server]: Finished running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
+            await this.telemetry.sendTelemetry('prediction_sent', {
+                version: await getAppVersion(),
+                chatlowId: chatflowid,
+                chatId,
+                type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
+                flowGraph: getTelemetryFlowObj(nodes, edges)
+            })
 
-            // Only return ChatId when its Internal OR incoming input has ChatId, to avoid confusion when calling API
-            if (incomingInput.chatId || isInternal) result.chatId = chatId
+            // Prepare response
+            result.chatId = chatId
+            if (sessionId) result.sessionId = sessionId
+            if (memoryType) result.memoryType = memoryType
 
             return res.json(result)
         } catch (e: any) {
@@ -1798,6 +1854,7 @@ export class App {
     async stopApp() {
         try {
             const removePromises: any[] = []
+            removePromises.push(this.telemetry.flush())
             await Promise.all(removePromises)
         } catch (e) {
             logger.error(`❌[server]: Flowise Server shut down error: ${e}`)

packages/server/src/utils/index.ts

@@ -273,6 +273,7 @@ export const buildLangchain = async (
     question: string,
     chatHistory: IMessage[],
     chatId: string,
+    sessionId: string,
     chatflowid: string,
     appDataSource: DataSource,
     overrideConfig?: ICommonObject,
@@ -317,6 +318,7 @@ export const buildLangchain = async (
                 logger.debug(`[server]: Upserting ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
                 await newNodeInstance.vectorStoreMethods!['upsert']!.call(newNodeInstance, reactFlowNodeData, {
                     chatId,
+                    sessionId,
                     chatflowid,
                     chatHistory,
                     logger,
@@ -331,6 +333,7 @@ export const buildLangchain = async (
                 logger.debug(`[server]: Initializing ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
                 let outputResult = await newNodeInstance.init(reactFlowNodeData, question, {
                     chatId,
+                    sessionId,
                     chatflowid,
                     chatHistory,
                     logger,
@@ -1079,3 +1082,60 @@ export const getAllValuesFromJson = (obj: any): any[] => {
     extractValues(obj)
     return values
 }
+
+/**
+ * Get only essential flow data items for telemetry
+ * @param {IReactFlowNode[]} nodes
+ * @param {IReactFlowEdge[]} edges
+ */
+export const getTelemetryFlowObj = (nodes: IReactFlowNode[], edges: IReactFlowEdge[]) => {
+    const nodeData = nodes.map((node) => node.id)
+    const edgeData = edges.map((edge) => ({ source: edge.source, target: edge.target }))
+    return { nodes: nodeData, edges: edgeData }
+}
+
+/**
+ * Get user settings file
+ * TODO: move env variables to settings json file, easier configuration
+ */
+export const getUserSettingsFilePath = () => {
+    if (process.env.SECRETKEY_PATH) return path.join(process.env.SECRETKEY_PATH, 'settings.json')
+    const checkPaths = [path.join(getUserHome(), '.flowise', 'settings.json')]
+    for (const checkPath of checkPaths) {
+        if (fs.existsSync(checkPath)) {
+            return checkPath
+        }
+    }
+    return ''
+}
+
+/**
+ * Get app current version
+ */
+export const getAppVersion = async () => {
+    const getPackageJsonPath = (): string => {
+        const checkPaths = [
+            path.join(__dirname, '..', 'package.json'),
+            path.join(__dirname, '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', '..', '..', 'package.json')
+        ]
+        for (const checkPath of checkPaths) {
+            if (fs.existsSync(checkPath)) {
+                return checkPath
+            }
+        }
+        return ''
+    }
+
+    const packagejsonPath = getPackageJsonPath()
+    if (!packagejsonPath) return ''
+    try {
+        const content = await fs.promises.readFile(packagejsonPath, 'utf8')
+        const parsedContent = JSON.parse(content)
+        return parsedContent.version
+    } catch (error) {
+        return ''
+    }
+}

packages/server/src/utils/telemetry.ts

@@ -0,0 +1,52 @@
+import { v4 as uuidv4 } from 'uuid'
+import { PostHog } from 'posthog-node'
+import path from 'path'
+import fs from 'fs'
+import { getUserHome, getUserSettingsFilePath } from '.'
+
+export class Telemetry {
+    postHog?: PostHog
+
+    constructor() {
+        if (process.env.DISABLE_FLOWISE_TELEMETRY !== 'true') {
+            this.postHog = new PostHog('phc_jEDuFYnOnuXsws986TLWzuisbRjwFqTl9JL8tDMgqme')
+        } else {
+            this.postHog = undefined
+        }
+    }
+
+    async id(): Promise<string> {
+        try {
+            const settingsContent = await fs.promises.readFile(getUserSettingsFilePath(), 'utf8')
+            const settings = JSON.parse(settingsContent)
+            return settings.instanceId
+        } catch (error) {
+            const instanceId = uuidv4()
+            const settings = {
+                instanceId
+            }
+            const defaultLocation = process.env.SECRETKEY_PATH
+                ? path.join(process.env.SECRETKEY_PATH, 'settings.json')
+                : path.join(getUserHome(), '.flowise', 'settings.json')
+            await fs.promises.writeFile(defaultLocation, JSON.stringify(settings, null, 2))
+            return instanceId
+        }
+    }
+
+    async sendTelemetry(event: string, properties = {}): Promise<void> {
+        if (this.postHog) {
+            const distinctId = await this.id()
+            this.postHog.capture({
+                event,
+                distinctId,
+                properties
+            })
+        }
+    }
+
+    async flush(): Promise<void> {
+        if (this.postHog) {
+            await this.postHog.shutdownAsync()
+        }
+    }
+}

packages/ui/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-ui",
-    "version": "1.4.7",
+    "version": "1.4.8",
     "license": "SEE LICENSE IN LICENSE.md",
     "homepage": "https://flowiseai.com",
     "author": {