Merge branch 'main' into FEATURE/Vision

# Conflicts:
#	packages/components/nodes/chains/ConversationChain/ConversationChain.ts
#	packages/server/src/index.ts
#	packages/server/src/utils/index.ts

.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

@@ -123,6 +123,8 @@ Flowise support different environment variables to configure your instance. You
 | Variable                    | Description                                                                  | Type                                             | Default                             |
 | --------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------ | ----------------------------------- |
 | PORT                        | The HTTP port Flowise runs on                                                | Number                                           | 3000                                |
+| CORS_ORIGINS                | The allowed origins for all cross-origin HTTP calls                          | String                                           |                                     |
+| IFRAME_ORIGINS              | The allowed origins for iframe src embedding                                 | String                                           |                                     |
 | FLOWISE_USERNAME            | Username to login                                                            | String                                           |                                     |
 | FLOWISE_PASSWORD            | Password to login                                                            | String                                           |                                     |
 | DEBUG                       | Print logs from components                                                   | Boolean                                          |                                     |
@@ -138,9 +140,11 @@ Flowise support different environment variables to configure your instance. You
 | DATABASE_USER               | Database username (When DATABASE_TYPE is not sqlite)                         | String                                           |                                     |
 | DATABASE_PASSWORD           | Database password (When DATABASE_TYPE is not sqlite)                         | String                                           |                                     |
 | DATABASE_NAME               | Database name (When DATABASE_TYPE is not sqlite)                             | String                                           |                                     |
+| DATABASE_SSL_KEY_BASE64     | Database SSL client cert in base64 (takes priority over DATABASE_SSL)        | Boolean                                          | false                               |
 | 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

@@ -4,6 +4,9 @@ APIKEY_PATH=/root/.flowise
 SECRETKEY_PATH=/root/.flowise
 LOG_PATH=/root/.flowise/logs
 
+# CORS_ORIGINS="*"
+# IFRAME_ORIGINS="*"
+
 # NUMBER_OF_PROXIES= 1
 
 # DATABASE_TYPE=postgres
@@ -13,6 +16,7 @@ LOG_PATH=/root/.flowise/logs
 # DATABASE_USER=""
 # DATABASE_PASSWORD=""
 # DATABASE_SSL=true
+# DATABASE_SSL_KEY_BASE64=<Self signed certificate in BASE64>
 
 # FLOWISE_USERNAME=user
 # FLOWISE_PASSWORD=1234
@@ -25,4 +29,6 @@ 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

@@ -6,6 +6,8 @@ services:
         restart: always
         environment:
             - PORT=${PORT}
+            - CORS_ORIGINS=${CORS_ORIGINS}
+            - IFRAME_ORIGINS=${IFRAME_ORIGINS}
             - FLOWISE_USERNAME=${FLOWISE_USERNAME}
             - FLOWISE_PASSWORD=${FLOWISE_PASSWORD}
             - DEBUG=${DEBUG}
@@ -17,11 +19,13 @@ services:
             - DATABASE_USER=${DATABASE_USER}
             - DATABASE_PASSWORD=${DATABASE_PASSWORD}
             - DATABASE_SSL=${DATABASE_SSL}
+            - DATABASE_SSL_KEY_BASE64=${DATABASE_SSL_KEY_BASE64}
             - APIKEY_PATH=${APIKEY_PATH}
             - SECRETKEY_PATH=${SECRETKEY_PATH}
             - 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": "1.4.9",
+    "version": "1.4.12",
     "private": true,
     "homepage": "https://flowiseai.com",
     "workspaces": [
@@ -48,7 +48,7 @@
         "pretty-quick": "^3.1.3",
         "rimraf": "^3.0.2",
         "run-script-os": "^1.1.6",
-        "turbo": "1.7.4",
+        "turbo": "^1.7.4",
         "typescript": "^4.8.4"
     },
     "engines": {

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/agents/ConversationalAgent/ConversationalAgent.ts

@@ -9,7 +9,7 @@ import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams }
 import { AgentExecutor } from '../../../src/agents'
 import { ChatConversationalAgent } from 'langchain/agents'
 import { renderTemplate } from '@langchain/core/prompts'
-import { injectChainNodeData } from '../../../src/MultiModalUtils'
+import { injectChainNodeData } from '../../../src/multiModalUtils'
 
 const DEFAULT_PREFIX = `Assistant is a large language model trained by OpenAI.
 

packages/components/nodes/agents/MRKLAgentChat/MRKLAgentChat.ts

@@ -5,7 +5,7 @@ import { Tool } from 'langchain/tools'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { flatten } from 'lodash'
 import { additionalCallbacks } from '../../../src/handler'
-import { injectChainNodeData } from '../../../src/MultiModalUtils'
+import { injectChainNodeData } from '../../../src/multiModalUtils'
 
 class MRKLAgentChat_Agents implements INode {
     label: string

packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts

@@ -64,7 +64,7 @@ class OpenAIFunctionAgent_Agents implements INode {
         return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
     }
 
-    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
         const memory = nodeData.inputs?.memory as FlowiseMemory
         const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
 
@@ -72,12 +72,20 @@ class OpenAIFunctionAgent_Agents implements INode {
         const callbacks = await additionalCallbacks(nodeData, options)
 
         let res: ChainValues = {}
+        let sourceDocuments: ICommonObject[] = []
 
         if (options.socketIO && options.socketIOClientId) {
             const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
             res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
+            if (res.sourceDocuments) {
+                options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
+                sourceDocuments = res.sourceDocuments
+            }
         } else {
             res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
+            if (res.sourceDocuments) {
+                sourceDocuments = res.sourceDocuments
+            }
         }
 
         await memory.addChatMessages(
@@ -94,7 +102,7 @@ class OpenAIFunctionAgent_Agents implements INode {
             this.sessionId
         )
 
-        return res?.output
+        return sourceDocuments.length ? { text: res?.output, sourceDocuments: flatten(sourceDocuments) } : res?.output
     }
 }
 

packages/components/nodes/cache/RedisCache/RedisCache.ts

@@ -1,9 +1,46 @@
 import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
 import { RedisCache as LangchainRedisCache } from 'langchain/cache/ioredis'
-import { Redis } from 'ioredis'
+import { Redis, RedisOptions } from 'ioredis'
+import { isEqual } from 'lodash'
 import { Generation, ChatGeneration, StoredGeneration, mapStoredMessageToChatMessage } from 'langchain/schema'
 import hash from 'object-hash'
 
+let redisClientSingleton: Redis
+let redisClientOption: RedisOptions
+let redisClientUrl: string
+
+const getRedisClientbyOption = (option: RedisOptions) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    } else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
+const getRedisClientbyUrl = (url: string) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    } else if (redisClientSingleton && url !== redisClientUrl) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
 class RedisCache implements INode {
     label: string
     name: string
@@ -60,7 +97,7 @@ class RedisCache implements INode {
 
             const tlsOptions = sslEnabled === true ? { tls: { rejectUnauthorized: false } } : {}
 
-            client = new Redis({
+            client = getRedisClientbyOption({
                 port: portStr ? parseInt(portStr) : 6379,
                 host,
                 username,
@@ -68,7 +105,7 @@ class RedisCache implements INode {
                 ...tlsOptions
             })
         } else {
-            client = new Redis(redisUrl)
+            client = getRedisClientbyUrl(redisUrl)
         }
 
         const redisClient = new LangchainRedisCache(client)
@@ -94,7 +131,7 @@ class RedisCache implements INode {
             for (let i = 0; i < value.length; i += 1) {
                 const key = getCacheKey(prompt, llmKey, String(i))
                 if (ttl) {
-                    await client.set(key, JSON.stringify(serializeGeneration(value[i])), 'EX', parseInt(ttl, 10))
+                    await client.set(key, JSON.stringify(serializeGeneration(value[i])), 'PX', parseInt(ttl, 10))
                 } else {
                     await client.set(key, JSON.stringify(serializeGeneration(value[i])))
                 }

packages/components/nodes/cache/RedisCache/RedisEmbeddingsCache.ts

@@ -1,9 +1,46 @@
 import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
-import { Redis } from 'ioredis'
+import { Redis, RedisOptions } from 'ioredis'
+import { isEqual } from 'lodash'
 import { CacheBackedEmbeddings } from 'langchain/embeddings/cache_backed'
 import { RedisByteStore } from 'langchain/storage/ioredis'
 import { Embeddings } from 'langchain/embeddings/base'
 
+let redisClientSingleton: Redis
+let redisClientOption: RedisOptions
+let redisClientUrl: string
+
+const getRedisClientbyOption = (option: RedisOptions) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    } else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
+const getRedisClientbyUrl = (url: string) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    } else if (redisClientSingleton && url !== redisClientUrl) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
 class RedisEmbeddingsCache implements INode {
     label: string
     name: string
@@ -75,7 +112,7 @@ class RedisEmbeddingsCache implements INode {
 
             const tlsOptions = sslEnabled === true ? { tls: { rejectUnauthorized: false } } : {}
 
-            client = new Redis({
+            client = getRedisClientbyOption({
                 port: portStr ? parseInt(portStr) : 6379,
                 host,
                 username,
@@ -83,7 +120,7 @@ class RedisEmbeddingsCache implements INode {
                 ...tlsOptions
             })
         } else {
-            client = new Redis(redisUrl)
+            client = getRedisClientbyUrl(redisUrl)
         }
 
         ttl ??= '3600'

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

@@ -1,14 +1,15 @@
 import { FlowiseMemory, ICommonObject, 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 { injectChainNodeData } from '../../../src/MultiModalUtils'
+import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
+import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
+import { formatResponse } from '../../outputparsers/OutputParserHelpers'
+import { injectChainNodeData } from '../../../src/multiModalUtils'
 
 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'
@@ -28,7 +29,7 @@ class ConversationChain_Chains implements INode {
     constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversation Chain'
         this.name = 'conversationChain'
-        this.version = 1.0
+        this.version = 3.0
         this.type = 'ConversationChain'
         this.icon = 'conv.svg'
         this.category = 'Chains'
@@ -45,6 +46,14 @@ 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',
@@ -53,15 +62,25 @@ 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: 'Input Moderation',
+                description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
+                name: 'inputModeration',
+                type: 'Moderation',
+                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
@@ -72,22 +91,40 @@ class ConversationChain_Chains implements INode {
         return chain
     }
 
-    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
         const memory = nodeData.inputs?.memory
         injectChainNodeData(nodeData, options)
 
         const chain = prepareChain(nodeData, options, this.sessionId)
+        const moderations = nodeData.inputs?.inputModeration as Moderation[]
+
+        if (moderations && moderations.length > 0) {
+            try {
+                // Use the output of the moderation chain as input for the LLM chain
+                input = await checkInputs(moderations, input)
+            } catch (e) {
+                await new Promise((resolve) => setTimeout(resolve, 500))
+                streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
+                return formatResponse(e.message)
+            }
+        }
 
         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(
@@ -108,36 +145,33 @@ class ConversationChain_Chains implements INode {
     }
 }
 
-const prepareChatPrompt = (nodeData: INodeData, options: ICommonObject) => {
+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}`
-
-    //TODO, this should not be any[], what interface should it be?
-    let promptMessages: any[] = [
-        SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
+    const chatPrompt = ChatPromptTemplate.fromMessages([
+        SystemMessagePromptTemplate.fromTemplate(prompt ? prompt : systemMessage),
         new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
         HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
-    ]
-    const chatPrompt = ChatPromptTemplate.fromMessages(promptMessages)
+    ])
 
     return chatPrompt
 }
@@ -148,15 +182,31 @@ const prepareChain = (nodeData: INodeData, options: ICommonObject, sessionId?: s
     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, options),
+        prepareChatPrompt(nodeData),
         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

@@ -8,7 +8,7 @@ import { formatResponse, injectOutputParser } from '../../outputparsers/OutputPa
 import { BaseLLMOutputParser } from 'langchain/schema/output_parser'
 import { OutputFixingParser } from 'langchain/output_parsers'
 import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
-import { injectChainNodeData } from '../../../src/MultiModalUtils'
+import { injectChainNodeData } from '../../../src/multiModalUtils'
 
 class LLMChain_Chains implements INode {
     label: string
@@ -83,7 +83,7 @@ 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) {
@@ -108,17 +108,25 @@ class LLMChain_Chains implements INode {
                 verbose: process.env.DEBUG === 'true'
             })
             const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
+            injectChainNodeData(nodeData, options)
+            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/chains/OpenAIMultiModalChain/OpenAIMultiModalChain.ts

@@ -1,340 +0,0 @@
-import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, handleEscapeCharacters } from '../../../src/utils'
-import { OpenAIMultiModalChainInput, VLLMChain } from './VLLMChain'
-import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
-import { formatResponse } from '../../outputparsers/OutputParserHelpers'
-import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
-
-class OpenAIMultiModalChain_Chains implements INode {
-    label: string
-    name: string
-    version: number
-    type: string
-    icon: string
-    badge: string
-    category: string
-    baseClasses: string[]
-    description: string
-    inputs: INodeParams[]
-    outputs: INodeOutputsValue[]
-    credential: INodeParams
-
-    constructor() {
-        this.label = 'Open AI MultiModal Chain'
-        this.name = 'openAIMultiModalChain'
-        this.version = 1.0
-        this.type = 'OpenAIMultiModalChain'
-        this.icon = 'chain.svg'
-        this.category = 'Chains'
-        this.badge = 'BETA'
-        this.description = 'Chain to query against Image and Audio Input.'
-        this.baseClasses = [this.type, ...getBaseClasses(VLLMChain)]
-        this.credential = {
-            label: 'Connect Credential',
-            name: 'credential',
-            type: 'credential',
-            credentialNames: ['openAIApi']
-        }
-        this.inputs = [
-            {
-                label: 'Prompt',
-                name: 'prompt',
-                type: 'BasePromptTemplate',
-                optional: true
-            },
-            {
-                label: 'Input Moderation',
-                description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
-                name: 'inputModeration',
-                type: 'Moderation',
-                optional: true,
-                list: true
-            },
-            {
-                label: 'Model Name',
-                name: 'modelName',
-                type: 'options',
-                options: [
-                    {
-                        label: 'gpt-4-vision-preview',
-                        name: 'gpt-4-vision-preview'
-                    }
-                ],
-                default: 'gpt-4-vision-preview'
-            },
-            {
-                label: 'Speech to Text',
-                name: 'speechToText',
-                type: 'boolean',
-                optional: true
-            },
-            // TODO: only show when speechToText is true
-            {
-                label: 'Speech to Text Method',
-                description: 'How to turn audio into text',
-                name: 'speechToTextMode',
-                type: 'options',
-                options: [
-                    {
-                        label: 'Transcriptions',
-                        name: 'transcriptions',
-                        description:
-                            'Transcribe audio into whatever language the audio is in. Default method when Speech to Text is turned on.'
-                    },
-                    {
-                        label: 'Translations',
-                        name: 'translations',
-                        description: 'Translate and transcribe the audio into english.'
-                    }
-                ],
-                optional: false,
-                default: 'transcriptions',
-                additionalParams: true
-            },
-            {
-                label: 'Image Resolution',
-                description: 'This parameter controls the resolution in which the model views the image.',
-                name: 'imageResolution',
-                type: 'options',
-                options: [
-                    {
-                        label: 'Low',
-                        name: 'low'
-                    },
-                    {
-                        label: 'High',
-                        name: 'high'
-                    },
-                    {
-                        label: 'Auto',
-                        name: 'auto'
-                    }
-                ],
-                default: 'low',
-                optional: false,
-                additionalParams: true
-            },
-            {
-                label: 'Temperature',
-                name: 'temperature',
-                type: 'number',
-                step: 0.1,
-                default: 0.9,
-                optional: true,
-                additionalParams: true
-            },
-            {
-                label: 'Top Probability',
-                name: 'topP',
-                type: 'number',
-                step: 0.1,
-                optional: true,
-                additionalParams: true
-            },
-            {
-                label: 'Max Tokens',
-                name: 'maxTokens',
-                type: 'number',
-                step: 1,
-                optional: true,
-                additionalParams: true
-            },
-            {
-                label: 'Accepted Upload Types',
-                name: 'allowedUploadTypes',
-                type: 'string',
-                default: 'image/gif;image/jpeg;image/png;image/webp;audio/mpeg;audio/x-wav;audio/mp4',
-                hidden: true
-            },
-            {
-                label: 'Maximum Upload Size (MB)',
-                name: 'maxUploadSize',
-                type: 'number',
-                default: '5',
-                hidden: true
-            }
-        ]
-        this.outputs = [
-            {
-                label: 'Open AI MultiModal Chain',
-                name: 'openAIMultiModalChain',
-                baseClasses: [this.type, ...getBaseClasses(VLLMChain)]
-            },
-            {
-                label: 'Output Prediction',
-                name: 'outputPrediction',
-                baseClasses: ['string', 'json']
-            }
-        ]
-    }
-
-    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
-        const prompt = nodeData.inputs?.prompt
-        const output = nodeData.outputs?.output as string
-        const imageResolution = nodeData.inputs?.imageResolution
-        const promptValues = prompt.promptValues as ICommonObject
-        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
-        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
-        const temperature = nodeData.inputs?.temperature as string
-        const modelName = nodeData.inputs?.modelName as string
-        const maxTokens = nodeData.inputs?.maxTokens as string
-        const topP = nodeData.inputs?.topP as string
-        const speechToText = nodeData.inputs?.speechToText as boolean
-
-        const fields: OpenAIMultiModalChainInput = {
-            openAIApiKey: openAIApiKey,
-            imageResolution: imageResolution,
-            verbose: process.env.DEBUG === 'true',
-            uploads: options.uploads,
-            modelName: modelName
-        }
-        if (temperature) fields.temperature = parseFloat(temperature)
-        if (maxTokens) fields.maxTokens = parseInt(maxTokens, 10)
-        if (topP) fields.topP = parseFloat(topP)
-        if (speechToText) {
-            const speechToTextMode = nodeData.inputs?.speechToTextMode ?? 'transcriptions'
-            if (speechToTextMode) fields.speechToTextMode = speechToTextMode
-        }
-
-        if (output === this.name) {
-            const chain = new VLLMChain({
-                ...fields,
-                prompt: prompt
-            })
-            return chain
-        } else if (output === 'outputPrediction') {
-            const chain = new VLLMChain({
-                ...fields
-            })
-            const inputVariables: string[] = prompt.inputVariables as string[] // ["product"]
-            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)
-            /**
-             * 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)
-        }
-    }
-
-    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
-        const prompt = nodeData.inputs?.prompt
-        const inputVariables: string[] = prompt.inputVariables as string[] // ["product"]
-        const chain = nodeData.instance as VLLMChain
-        let promptValues: ICommonObject | undefined = nodeData.inputs?.prompt.promptValues as ICommonObject
-        const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
-        // eslint-disable-next-line no-console
-        console.log('\x1b[93m\x1b[1m\n*****FINAL RESULT*****\n\x1b[0m\x1b[0m')
-        // eslint-disable-next-line no-console
-        console.log(res)
-        return res
-    }
-}
-
-const runPrediction = async (
-    inputVariables: string[],
-    chain: VLLMChain,
-    input: string,
-    promptValuesRaw: ICommonObject | undefined,
-    options: ICommonObject,
-    nodeData: INodeData
-) => {
-    const loggerHandler = new ConsoleCallbackHandler(options.logger)
-    const callbacks = await additionalCallbacks(nodeData, options)
-
-    const isStreaming = options.socketIO && options.socketIOClientId
-    const socketIO = isStreaming ? options.socketIO : undefined
-    const socketIOClientId = isStreaming ? options.socketIOClientId : ''
-    const moderations = nodeData.inputs?.inputModeration as Moderation[]
-    const speechToText = nodeData.inputs?.speechToText as boolean
-
-    if (options?.uploads) {
-        if (options.uploads.length === 1 && input.length === 0) {
-            if (speechToText) {
-                //special case, text input is empty, but we have an upload (recorded audio)
-                const convertedText = await chain.processAudioWithWisper(options.uploads[0], undefined)
-                //so we use the upload as input
-                input = convertedText
-            }
-            // do not send the audio file to the model
-        } else {
-            chain.uploads = options.uploads
-        }
-    }
-
-    if (moderations && moderations.length > 0) {
-        try {
-            // Use the output of the moderation chain as input for the LLM chain
-            input = await checkInputs(moderations, input)
-        } catch (e) {
-            await new Promise((resolve) => setTimeout(resolve, 500))
-            streamResponse(isStreaming, e.message, socketIO, socketIOClientId)
-            return formatResponse(e.message)
-        }
-    }
-
-    /**
-     * Apply string transformation to reverse converted special chars:
-     * FROM: { "value": "hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?" }
-     * TO: { "value": "hello i am ben\n\n\thow are you?" }
-     */
-    const promptValues = handleEscapeCharacters(promptValuesRaw, true)
-    if (promptValues && inputVariables.length > 0) {
-        let seen: string[] = []
-
-        for (const variable of inputVariables) {
-            seen.push(variable)
-            if (promptValues[variable]) {
-                chain.inputKey = variable
-                seen.pop()
-            }
-        }
-
-        if (seen.length === 0) {
-            // All inputVariables have fixed values specified
-            const options = { ...promptValues }
-            if (isStreaming) {
-                const handler = new CustomChainHandler(socketIO, socketIOClientId)
-                const res = await chain.call(options, [loggerHandler, handler, ...callbacks])
-                return formatResponse(res?.text)
-            } else {
-                const res = await chain.call(options, [loggerHandler, ...callbacks])
-                return formatResponse(res?.text)
-            }
-        } else if (seen.length === 1) {
-            // If one inputVariable is not specify, use input (user's question) as value
-            const lastValue = seen.pop()
-            if (!lastValue) throw new Error('Please provide Prompt Values')
-            chain.inputKey = lastValue as string
-            const options = {
-                ...promptValues,
-                [lastValue]: input
-            }
-            if (isStreaming) {
-                const handler = new CustomChainHandler(socketIO, socketIOClientId)
-                const res = await chain.call(options, [loggerHandler, handler, ...callbacks])
-                return formatResponse(res?.text)
-            } else {
-                const res = await chain.call(options, [loggerHandler, ...callbacks])
-                return formatResponse(res?.text)
-            }
-        } else {
-            throw new Error(`Please provide Prompt Values for: ${seen.join(', ')}`)
-        }
-    } else {
-        if (isStreaming) {
-            const handler = new CustomChainHandler(socketIO, socketIOClientId)
-            const res = await chain.run(input, [loggerHandler, handler, ...callbacks])
-            return formatResponse(res)
-        } else {
-            const res = await chain.run(input, [loggerHandler, ...callbacks])
-            return formatResponse(res)
-        }
-    }
-}
-
-module.exports = { nodeClass: OpenAIMultiModalChain_Chains }

packages/components/nodes/chains/OpenAIMultiModalChain/VLLMChain.ts

@@ -1,216 +0,0 @@
-import { OpenAI as OpenAIClient, ClientOptions, OpenAI } from 'openai'
-import { BaseChain, ChainInputs } from 'langchain/chains'
-import { ChainValues } from 'langchain/schema'
-import { BasePromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate } from 'langchain/prompts'
-import path from 'path'
-import { getUserHome } from '../../../src/utils'
-import fs from 'fs'
-import { ChatCompletionContentPart, ChatCompletionMessageParam } from 'openai/src/resources/chat/completions'
-import ChatCompletionCreateParamsNonStreaming = OpenAI.ChatCompletionCreateParamsNonStreaming
-import { IFileUpload } from '../../../src'
-
-/**
- * Interface for the input parameters of the OpenAIVisionChain class.
- */
-export interface OpenAIMultiModalChainInput extends ChainInputs {
-    openAIApiKey?: string
-    openAIOrganization?: string
-    throwError?: boolean
-    prompt?: BasePromptTemplate
-    configuration?: ClientOptions
-    uploads?: IFileUpload[]
-    imageResolution?: 'auto' | 'low' | 'high'
-    temperature?: number
-    modelName?: string
-    maxTokens?: number
-    topP?: number
-    speechToTextMode?: string
-}
-
-/**
- * Class representing a chain for generating text from an image using the OpenAI
- * Vision API. It extends the BaseChain class and implements the
- * OpenAIVisionChainInput interface.
- */
-export class VLLMChain extends BaseChain implements OpenAIMultiModalChainInput {
-    static lc_name() {
-        return 'VLLMChain'
-    }
-    prompt: BasePromptTemplate | undefined
-
-    inputKey = 'input'
-    outputKey = 'text'
-    uploads?: IFileUpload[]
-    imageResolution: 'auto' | 'low' | 'high'
-    openAIApiKey?: string
-    openAIOrganization?: string
-    clientConfig: ClientOptions
-    client: OpenAIClient
-    throwError: boolean
-    temperature?: number
-    modelName?: string
-    maxTokens?: number
-    topP?: number
-
-    speechToTextMode?: any
-
-    constructor(fields: OpenAIMultiModalChainInput) {
-        super(fields)
-        this.throwError = fields?.throwError ?? false
-        this.imageResolution = fields?.imageResolution ?? 'low'
-        this.openAIApiKey = fields?.openAIApiKey
-        this.prompt = fields?.prompt
-        this.temperature = fields?.temperature
-        this.modelName = fields?.modelName
-        this.maxTokens = fields?.maxTokens
-        this.topP = fields?.topP
-        this.uploads = fields?.uploads ?? []
-        this.speechToTextMode = fields?.speechToTextMode ?? {}
-        if (!this.openAIApiKey) {
-            throw new Error('OpenAI API key not found')
-        }
-
-        this.openAIOrganization = fields?.openAIOrganization
-
-        this.clientConfig = {
-            ...fields?.configuration,
-            apiKey: this.openAIApiKey,
-            organization: this.openAIOrganization
-        }
-
-        this.client = new OpenAIClient(this.clientConfig)
-    }
-
-    async _call(values: ChainValues): Promise<ChainValues> {
-        const userInput = values[this.inputKey]
-
-        const vRequest: ChatCompletionCreateParamsNonStreaming = {
-            model: 'gpt-4-vision-preview',
-            temperature: this.temperature,
-            top_p: this.topP,
-            messages: []
-        }
-        if (this.maxTokens) vRequest.max_tokens = this.maxTokens
-        else vRequest.max_tokens = 1024
-
-        const chatMessages: ChatCompletionContentPart[] = []
-        const userRole: ChatCompletionMessageParam = { role: 'user', content: [] }
-        chatMessages.push({
-            type: 'text',
-            text: userInput
-        })
-        if (this.speechToTextMode && this.uploads && this.uploads.length > 0) {
-            const audioUploads = this.getAudioUploads(this.uploads)
-            for (const upload of audioUploads) {
-                await this.processAudioWithWisper(upload, chatMessages)
-            }
-        }
-        if (this.uploads && this.uploads.length > 0) {
-            const imageUploads = this.getImageUploads(this.uploads)
-            for (const upload of imageUploads) {
-                let bf = upload.data
-                if (upload.type == 'stored-file') {
-                    const filePath = path.join(getUserHome(), '.flowise', 'gptvision', upload.data, upload.name)
-
-                    // as the image is stored in the server, read the file and convert it to base64
-                    const contents = fs.readFileSync(filePath)
-                    bf = 'data:' + upload.mime + ';base64,' + contents.toString('base64')
-                }
-                chatMessages.push({
-                    type: 'image_url',
-                    image_url: {
-                        url: bf,
-                        detail: this.imageResolution
-                    }
-                })
-            }
-        }
-        userRole.content = chatMessages
-        vRequest.messages.push(userRole)
-        if (this.prompt && this.prompt instanceof ChatPromptTemplate) {
-            let chatPrompt = this.prompt as ChatPromptTemplate
-            chatPrompt.promptMessages.forEach((message: any) => {
-                if (message instanceof SystemMessagePromptTemplate) {
-                    vRequest.messages.push({
-                        role: 'system',
-                        content: (message.prompt as any).template
-                    })
-                } else if (message instanceof HumanMessagePromptTemplate) {
-                    vRequest.messages.push({
-                        role: 'user',
-                        content: (message.prompt as any).template
-                    })
-                }
-            })
-        }
-
-        let response
-        try {
-            response = await this.client.chat.completions.create(vRequest)
-        } catch (error) {
-            if (error instanceof Error) {
-                throw error
-            } else {
-                throw new Error(error as string)
-            }
-        }
-        const output = response.choices[0]
-        return {
-            [this.outputKey]: output.message.content
-        }
-    }
-
-    public async processAudioWithWisper(upload: IFileUpload, chatMessages: ChatCompletionContentPart[] | undefined): Promise<string> {
-        const filePath = path.join(getUserHome(), '.flowise', 'gptvision', upload.data, upload.name)
-
-        // as the image is stored in the server, read the file and convert it to base64
-        const audio_file = fs.createReadStream(filePath)
-        if (this.speechToTextMode === 'transcriptions') {
-            const transcription = await this.client.audio.transcriptions.create({
-                file: audio_file,
-                model: 'whisper-1'
-            })
-            if (chatMessages) {
-                chatMessages.push({
-                    type: 'text',
-                    text: transcription.text
-                })
-            }
-            return transcription.text
-        } else if (this.speechToTextMode === 'translations') {
-            const translation = await this.client.audio.translations.create({
-                file: audio_file,
-                model: 'whisper-1'
-            })
-            if (chatMessages) {
-                chatMessages.push({
-                    type: 'text',
-                    text: translation.text
-                })
-            }
-            return translation.text
-        }
-        //should never get here
-        return ''
-    }
-
-    getAudioUploads = (urls: any[]) => {
-        return urls.filter((url: any) => url.mime.startsWith('audio/'))
-    }
-
-    getImageUploads = (urls: any[]) => {
-        return urls.filter((url: any) => url.mime.startsWith('image/'))
-    }
-
-    _chainType() {
-        return 'vision_chain'
-    }
-
-    get inputKeys() {
-        return this.prompt?.inputVariables ?? [this.inputKey]
-    }
-
-    get outputKeys(): string[] {
-        return [this.outputKey]
-    }
-}

packages/components/nodes/chains/OpenAIMultiModalChain/chain.svg

@@ -1,6 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" 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>
-   <path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
-   <path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
-   <path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
-</svg>

packages/components/nodes/chatmodels/ChatGoogleGenerativeAI/ChatGoogleGenerativeAI.ts

@@ -1,7 +1,9 @@
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { convertMultiOptionsToStringArray, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { BaseCache } from 'langchain/schema'
-import { ChatGoogleGenerativeAI } from '@langchain/google-genai'
+import { ChatGoogleGenerativeAI, GoogleGenerativeAIChatInput } from '@langchain/google-genai'
+import { HarmBlockThreshold, HarmCategory } from '@google/generative-ai'
+import type { SafetySetting } from '@google/generative-ai'
 
 class GoogleGenerativeAI_ChatModels implements INode {
     label: string
@@ -74,6 +76,73 @@ class GoogleGenerativeAI_ChatModels implements INode {
                 step: 0.1,
                 optional: true,
                 additionalParams: true
+            },
+            {
+                label: 'Top Next Highest Probability Tokens',
+                name: 'topK',
+                type: 'number',
+                description: `Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive`,
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Harm Category',
+                name: 'harmCategory',
+                type: 'multiOptions',
+                description:
+                    'Refer to <a target="_blank" href="https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes#safety_attribute_definitions">official guide</a> on how to use Harm Category',
+                options: [
+                    {
+                        label: 'Dangerous',
+                        name: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT
+                    },
+                    {
+                        label: 'Harassment',
+                        name: HarmCategory.HARM_CATEGORY_HARASSMENT
+                    },
+                    {
+                        label: 'Hate Speech',
+                        name: HarmCategory.HARM_CATEGORY_HATE_SPEECH
+                    },
+                    {
+                        label: 'Sexually Explicit',
+                        name: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Harm Block Threshold',
+                name: 'harmBlockThreshold',
+                type: 'multiOptions',
+                description:
+                    'Refer to <a target="_blank" href="https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes#safety_setting_thresholds">official guide</a> on how to use Harm Block Threshold',
+                options: [
+                    {
+                        label: 'Low and Above',
+                        name: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE
+                    },
+                    {
+                        label: 'Medium and Above',
+                        name: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE
+                    },
+                    {
+                        label: 'None',
+                        name: HarmBlockThreshold.BLOCK_NONE
+                    },
+                    {
+                        label: 'Only High',
+                        name: HarmBlockThreshold.BLOCK_ONLY_HIGH
+                    },
+                    {
+                        label: 'Threshold Unspecified',
+                        name: HarmBlockThreshold.HARM_BLOCK_THRESHOLD_UNSPECIFIED
+                    }
+                ],
+                optional: true,
+                additionalParams: true
             }
         ]
     }
@@ -86,9 +155,12 @@ class GoogleGenerativeAI_ChatModels implements INode {
         const modelName = nodeData.inputs?.modelName as string
         const maxOutputTokens = nodeData.inputs?.maxOutputTokens as string
         const topP = nodeData.inputs?.topP as string
+        const topK = nodeData.inputs?.topK as string
+        const harmCategory = nodeData.inputs?.harmCategory as string
+        const harmBlockThreshold = nodeData.inputs?.harmBlockThreshold as string
         const cache = nodeData.inputs?.cache as BaseCache
 
-        const obj = {
+        const obj: Partial<GoogleGenerativeAIChatInput> = {
             apiKey: apiKey,
             modelName: modelName,
             maxOutputTokens: 2048
@@ -98,8 +170,23 @@ class GoogleGenerativeAI_ChatModels implements INode {
 
         const model = new ChatGoogleGenerativeAI(obj)
         if (topP) model.topP = parseFloat(topP)
+        if (topK) model.topK = parseFloat(topK)
         if (cache) model.cache = cache
         if (temperature) model.temperature = parseFloat(temperature)
+
+        // Safety Settings
+        let harmCategories: string[] = convertMultiOptionsToStringArray(harmCategory)
+        let harmBlockThresholds: string[] = convertMultiOptionsToStringArray(harmBlockThreshold)
+        if (harmCategories.length != harmBlockThresholds.length)
+            throw new Error(`Harm Category & Harm Block Threshold are not the same length`)
+        const safetySettings: SafetySetting[] = harmCategories.map((harmCategory, index) => {
+            return {
+                category: harmCategory as HarmCategory,
+                threshold: harmBlockThresholds[index] as HarmBlockThreshold
+            }
+        })
+        if (safetySettings.length > 0) model.safetySettings = safetySettings
+
         return model
     }
 }

packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI.ts

@@ -1,9 +1,9 @@
-import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, IMultiModalOption, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
-import { ChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
+import { ChatOpenAI as LangchainChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
 import { BaseCache } from 'langchain/schema'
 import { BaseLLMParams } from 'langchain/llms/base'
-import { FlowiseChatOpenAI } from './FlowiseChatOpenAI'
+import { ChatOpenAI } from './FlowiseChatOpenAI'
 
 class ChatOpenAI_ChatModels implements INode {
     label: string
@@ -20,12 +20,12 @@ class ChatOpenAI_ChatModels implements INode {
     constructor() {
         this.label = 'ChatOpenAI'
         this.name = 'chatOpenAI'
-        this.version = 3.0
+        this.version = 4.0
         this.type = 'ChatOpenAI'
         this.icon = 'openai.svg'
         this.category = 'Chat Models'
         this.description = 'Wrapper around OpenAI large language models that use the Chat endpoint'
-        this.baseClasses = [this.type, ...getBaseClasses(ChatOpenAI)]
+        this.baseClasses = [this.type, ...getBaseClasses(LangchainChatOpenAI)]
         this.credential = {
             label: 'Connect Credential',
             name: 'credential',
@@ -48,6 +48,14 @@ class ChatOpenAI_ChatModels implements INode {
                         label: 'gpt-4',
                         name: 'gpt-4'
                     },
+                    {
+                        label: 'gpt-4-turbo-preview',
+                        name: 'gpt-4-turbo-preview'
+                    },
+                    {
+                        label: 'gpt-4-0125-preview',
+                        name: 'gpt-4-0125-preview'
+                    },
                     {
                         label: 'gpt-4-1106-preview',
                         name: 'gpt-4-1106-preview'
@@ -72,6 +80,10 @@ class ChatOpenAI_ChatModels implements INode {
                         label: 'gpt-3.5-turbo',
                         name: 'gpt-3.5-turbo'
                     },
+                    {
+                        label: 'gpt-3.5-turbo-0125',
+                        name: 'gpt-3.5-turbo-0125'
+                    },
                     {
                         label: 'gpt-3.5-turbo-1106',
                         name: 'gpt-3.5-turbo-1106'
@@ -158,7 +170,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Allow Image Uploads',
                 name: 'allowImageUploads',
                 type: 'boolean',
-                description: 'Enabling this option, would default the model to gpt-4-vision-preview',
+                description: 'Automatically uses gpt-4-vision-preview when image is being uploaded from chat',
                 default: false,
                 optional: true
             },
@@ -231,16 +243,19 @@ class ChatOpenAI_ChatModels implements INode {
                 throw new Error("Invalid JSON in the ChatOpenAI's BaseOptions: " + exception)
             }
         }
-        const model = new FlowiseChatOpenAI(obj, {
+
+        const model = new ChatOpenAI(obj, {
             baseURL: basePath,
             baseOptions: parsedBaseOptions
         })
 
-        const multiModal = {
-            allowImageUploads: allowImageUploads ?? false,
-            imageResolution
+        const multiModalOption: IMultiModalOption = {
+            image: {
+                allowImageUploads: allowImageUploads ?? false,
+                imageResolution
+            }
         }
-        model.multiModal = multiModal
+        model.multiModalOption = multiModalOption
         return model
     }
 }

packages/components/nodes/chatmodels/ChatOpenAI/FlowiseChatOpenAI.ts

@@ -1,4 +1,4 @@
-import { ChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
+import { ChatOpenAI as LangchainChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
 import { BaseChatModelParams } from 'langchain/chat_models/base'
 import type { ClientOptions } from 'openai'
 import type { LegacyOpenAIInput } from '@langchain/openai/dist/types'
@@ -6,47 +6,59 @@ import { BaseLanguageModelInput } from 'langchain/base_language'
 import { ChatOpenAICallOptions } from '@langchain/openai/dist/chat_models'
 import { BaseMessageChunk, BaseMessageLike, HumanMessage, LLMResult } from 'langchain/schema'
 import { Callbacks } from '@langchain/core/callbacks/manager'
-import { ICommonObject, INodeData } from '../../../src'
-import { addImagesToMessages } from '../../../src/MultiModalUtils'
+import { ICommonObject, IMultiModalOption, INodeData } from '../../../src'
+import { addImagesToMessages } from '../../../src/multiModalUtils'
 
-export class FlowiseChatOpenAI extends ChatOpenAI {
-    multiModal: {}
+export class ChatOpenAI extends LangchainChatOpenAI {
     //TODO: Should be class variables and not static
     public static chainNodeData: INodeData
     public static chainNodeOptions: ICommonObject
+    configuredModel: string
+    configuredMaxToken?: number
+    multiModalOption?: IMultiModalOption
 
     constructor(
-        fields?: Partial<OpenAIChatInput> & BaseChatModelParams & { openAIApiKey?: string },
+        fields?: Partial<OpenAIChatInput> & BaseChatModelParams & { openAIApiKey?: string; multiModalOption?: IMultiModalOption },
         /** @deprecated */
         configuration?: ClientOptions & LegacyOpenAIInput
     ) {
         super(fields)
+        this.multiModalOption = fields?.multiModalOption
+        this.configuredModel = fields?.modelName ?? 'gpt-3.5-turbo'
+        this.configuredMaxToken = fields?.maxTokens
     }
 
     async invoke(input: BaseLanguageModelInput, options?: ChatOpenAICallOptions): Promise<BaseMessageChunk> {
-        //input.messages
         return super.invoke(input, options)
     }
 
     async generate(messages: BaseMessageLike[][], options?: string[] | ChatOpenAICallOptions, callbacks?: Callbacks): Promise<LLMResult> {
-        //messages
         await this.injectMultiModalMessages(messages)
         return super.generate(messages, options, callbacks)
     }
 
     private async injectMultiModalMessages(messages: BaseMessageLike[][]) {
-        const nodeData = FlowiseChatOpenAI.chainNodeData
-        const optionsData = FlowiseChatOpenAI.chainNodeOptions
-        const messageContent = addImagesToMessages(nodeData, optionsData)
+        const nodeData = ChatOpenAI.chainNodeData
+        const optionsData = ChatOpenAI.chainNodeOptions
+        const messageContent = addImagesToMessages(nodeData, optionsData, this.multiModalOption)
         if (messageContent?.length) {
             if (messages[0].length > 0 && messages[0][messages[0].length - 1] instanceof HumanMessage) {
                 const lastMessage = messages[0].pop()
                 if (lastMessage instanceof HumanMessage) {
                     lastMessage.content = messageContent
+
+                    // Change model to gpt-4-vision
                     this.modelName = 'gpt-4-vision-preview'
+
+                    // Change default max token to higher when using gpt-4-vision
+                    this.maxTokens = 1024
                 }
                 messages[0].push(lastMessage as HumanMessage)
             }
+        } else {
+            // revert to previous values if image upload is empty
+            this.modelName = this.configuredModel
+            this.maxTokens = this.configuredMaxToken
         }
     }
 }

packages/components/nodes/documentloaders/Airtable/Airtable.ts

@@ -20,7 +20,7 @@ class Airtable_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Airtable'
         this.name = 'airtable'
-        this.version = 2.0
+        this.version = 3.0
         this.type = 'Document'
         this.icon = 'airtable.svg'
         this.category = 'Document Loaders'
@@ -64,10 +64,21 @@ class Airtable_DocumentLoaders implements INode {
                     'If your view URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, viw9UrP77Id0CE4ee is the view id',
                 optional: true
             },
+            {
+                label: 'Include Only Fields',
+                name: 'fields',
+                type: 'string',
+                placeholder: 'Name, Assignee, fld1u0qUz0SoOQ9Gg, fldew39v6LBN5CjUl',
+                optional: true,
+                additionalParams: true,
+                description:
+                    'Comma-separated list of field names or IDs to include. If empty, then ALL fields are used. Use field IDs if field names contain commas.'
+            },
             {
                 label: 'Return All',
                 name: 'returnAll',
                 type: 'boolean',
+                optional: true,
                 default: true,
                 additionalParams: true,
                 description: 'If all results should be returned or only up to a given limit'
@@ -76,9 +87,10 @@ class Airtable_DocumentLoaders implements INode {
                 label: 'Limit',
                 name: 'limit',
                 type: 'number',
+                optional: true,
                 default: 100,
                 additionalParams: true,
-                description: 'Number of results to return'
+                description: 'Number of results to return. Ignored when Return All is enabled.'
             },
             {
                 label: 'Metadata',
@@ -93,6 +105,8 @@ class Airtable_DocumentLoaders implements INode {
         const baseId = nodeData.inputs?.baseId as string
         const tableId = nodeData.inputs?.tableId as string
         const viewId = nodeData.inputs?.viewId as string
+        const fieldsInput = nodeData.inputs?.fields as string
+        const fields = fieldsInput ? fieldsInput.split(',').map((field) => field.trim()) : []
         const returnAll = nodeData.inputs?.returnAll as boolean
         const limit = nodeData.inputs?.limit as string
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
@@ -105,6 +119,7 @@ class Airtable_DocumentLoaders implements INode {
             baseId,
             tableId,
             viewId,
+            fields,
             returnAll,
             accessToken,
             limit: limit ? parseInt(limit, 10) : 100
@@ -112,6 +127,10 @@ class Airtable_DocumentLoaders implements INode {
 
         const loader = new AirtableLoader(airtableOptions)
 
+        if (!baseId || !tableId) {
+            throw new Error('Base ID and Table ID must be provided.')
+        }
+
         let docs = []
 
         if (textSplitter) {
@@ -145,10 +164,18 @@ interface AirtableLoaderParams {
     tableId: string
     accessToken: string
     viewId?: string
+    fields?: string[]
     limit?: number
     returnAll?: boolean
 }
 
+interface AirtableLoaderRequest {
+    maxRecords?: number
+    view: string | undefined
+    fields?: string[]
+    offset?: string
+}
+
 interface AirtableLoaderResponse {
     records: AirtableLoaderPage[]
     offset?: string
@@ -167,17 +194,20 @@ class AirtableLoader extends BaseDocumentLoader {
 
     public readonly viewId?: string
 
+    public readonly fields: string[]
+
     public readonly accessToken: string
 
     public readonly limit: number
 
     public readonly returnAll: boolean
 
-    constructor({ baseId, tableId, viewId, accessToken, limit = 100, returnAll = false }: AirtableLoaderParams) {
+    constructor({ baseId, tableId, viewId, fields = [], accessToken, limit = 100, returnAll = false }: AirtableLoaderParams) {
         super()
         this.baseId = baseId
         this.tableId = tableId
         this.viewId = viewId
+        this.fields = fields
         this.accessToken = accessToken
         this.limit = limit
         this.returnAll = returnAll
@@ -190,17 +220,21 @@ class AirtableLoader extends BaseDocumentLoader {
         return this.loadLimit()
     }
 
-    protected async fetchAirtableData(url: string, params: ICommonObject): Promise<AirtableLoaderResponse> {
+    protected async fetchAirtableData(url: string, data: AirtableLoaderRequest): Promise<AirtableLoaderResponse> {
         try {
             const headers = {
                 Authorization: `Bearer ${this.accessToken}`,
                 'Content-Type': 'application/json',
                 Accept: 'application/json'
             }
-            const response = await axios.get(url, { params, headers })
+            const response = await axios.post(url, data, { headers })
             return response.data
         } catch (error) {
-            throw new Error(`Failed to fetch ${url} from Airtable: ${error}`)
+            if (axios.isAxiosError(error)) {
+                throw new Error(`Failed to fetch ${url} from Airtable: ${error.message}, status: ${error.response?.status}`)
+            } else {
+                throw new Error(`Failed to fetch ${url} from Airtable: ${error}`)
+            }
         }
     }
 
@@ -218,24 +252,53 @@ class AirtableLoader extends BaseDocumentLoader {
     }
 
     private async loadLimit(): Promise<Document[]> {
-        const params = { maxRecords: this.limit, view: this.viewId }
-        const data = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}`, params)
-        if (data.records.length === 0) {
-            return []
+        let data: AirtableLoaderRequest = {
+            maxRecords: this.limit,
+            view: this.viewId
         }
-        return data.records.map((page) => this.createDocumentFromPage(page))
+
+        if (this.fields.length > 0) {
+            data.fields = this.fields
+        }
+
+        let response: AirtableLoaderResponse
+        let returnPages: AirtableLoaderPage[] = []
+
+        // Paginate if the user specifies a limit > 100 (like 200) but not return all.
+        do {
+            response = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}/listRecords`, data)
+            returnPages.push(...response.records)
+            data.offset = response.offset
+
+            // Stop if we have fetched enough records
+            if (returnPages.length >= this.limit) break
+        } while (response.offset !== undefined)
+
+        // Truncate array to the limit if necessary
+        if (returnPages.length > this.limit) {
+            returnPages.length = this.limit
+        }
+
+        return returnPages.map((page) => this.createDocumentFromPage(page))
     }
 
     private async loadAll(): Promise<Document[]> {
-        const params: ICommonObject = { pageSize: 100, view: this.viewId }
-        let data: AirtableLoaderResponse
+        let data: AirtableLoaderRequest = {
+            view: this.viewId
+        }
+
+        if (this.fields.length > 0) {
+            data.fields = this.fields
+        }
+
+        let response: AirtableLoaderResponse
         let returnPages: AirtableLoaderPage[] = []
 
         do {
-            data = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}`, params)
-            returnPages.push.apply(returnPages, data.records)
-            params.offset = data.offset
-        } while (data.offset !== undefined)
+            response = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}/listRecords`, data)
+            returnPages.push(...response.records)
+            data.offset = response.offset
+        } while (response.offset !== undefined)
         return returnPages.map((page) => this.createDocumentFromPage(page))
     }
 }

packages/components/nodes/documentloaders/Cheerio/Cheerio.ts

@@ -1,4 +1,4 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { CheerioWebBaseLoader, WebBaseLoaderParams } from 'langchain/document_loaders/web/cheerio'
 import { test } from 'linkifyjs'
@@ -63,6 +63,7 @@ class Cheerio_DocumentLoaders implements INode {
                 name: 'limit',
                 type: 'number',
                 optional: true,
+                default: '10',
                 additionalParams: true,
                 description:
                     'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
@@ -86,11 +87,12 @@ class Cheerio_DocumentLoaders implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const metadata = nodeData.inputs?.metadata
         const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
-        let limit = nodeData.inputs?.limit as string
+        const selectedLinks = nodeData.inputs?.selectedLinks as string[]
+        let limit = parseInt(nodeData.inputs?.limit as string)
 
         let url = nodeData.inputs?.url as string
         url = url.trim()
@@ -117,23 +119,33 @@ class Cheerio_DocumentLoaders implements INode {
                 }
                 return docs
             } catch (err) {
-                if (process.env.DEBUG === 'true') console.error(`error in CheerioWebBaseLoader: ${err.message}, on page: ${url}`)
+                if (process.env.DEBUG === 'true') options.logger.error(`error in CheerioWebBaseLoader: ${err.message}, on page: ${url}`)
             }
         }
 
         let docs = []
         if (relativeLinksMethod) {
-            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = '10'
-            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
+            if (!limit) limit = 10
+            else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
-                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
-            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+                selectedLinks && selectedLinks.length > 0
+                    ? selectedLinks.slice(0, limit)
+                    : relativeLinksMethod === 'webCrawl'
+                    ? await webCrawl(url, limit)
+                    : await xmlScrape(url, limit)
+            if (process.env.DEBUG === 'true') options.logger.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
             if (!pages || pages.length === 0) throw new Error('No relative links found')
             for (const page of pages) {
                 docs.push(...(await cheerioLoader(page)))
             }
-            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
+            if (process.env.DEBUG === 'true') options.logger.info(`Finish ${relativeLinksMethod}`)
+        } else if (selectedLinks && selectedLinks.length > 0) {
+            if (process.env.DEBUG === 'true')
+                options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
+            for (const page of selectedLinks) {
+                docs.push(...(await cheerioLoader(page)))
+            }
         } else {
             docs = await cheerioLoader(url)
         }

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/documentloaders/Playwright/Playwright.ts

@@ -1,4 +1,4 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { Browser, Page, PlaywrightWebBaseLoader, PlaywrightWebBaseLoaderOptions } from 'langchain/document_loaders/web/playwright'
 import { test } from 'linkifyjs'
@@ -61,6 +61,7 @@ class Playwright_DocumentLoaders implements INode {
                 name: 'limit',
                 type: 'number',
                 optional: true,
+                default: '10',
                 additionalParams: true,
                 description:
                     'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
@@ -114,11 +115,12 @@ class Playwright_DocumentLoaders implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const metadata = nodeData.inputs?.metadata
         const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
-        let limit = nodeData.inputs?.limit as string
+        const selectedLinks = nodeData.inputs?.selectedLinks as string[]
+        let limit = parseInt(nodeData.inputs?.limit as string)
         let waitUntilGoToOption = nodeData.inputs?.waitUntilGoToOption as 'load' | 'domcontentloaded' | 'networkidle' | 'commit' | undefined
         let waitForSelector = nodeData.inputs?.waitForSelector as string
 
@@ -158,23 +160,33 @@ class Playwright_DocumentLoaders implements INode {
                 }
                 return docs
             } catch (err) {
-                if (process.env.DEBUG === 'true') console.error(`error in PlaywrightWebBaseLoader: ${err.message}, on page: ${url}`)
+                if (process.env.DEBUG === 'true') options.logger.error(`error in PlaywrightWebBaseLoader: ${err.message}, on page: ${url}`)
             }
         }
 
         let docs = []
         if (relativeLinksMethod) {
-            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = '10'
-            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
+            if (!limit) limit = 10
+            else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
-                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
-            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+                selectedLinks && selectedLinks.length > 0
+                    ? selectedLinks.slice(0, limit)
+                    : relativeLinksMethod === 'webCrawl'
+                    ? await webCrawl(url, limit)
+                    : await xmlScrape(url, limit)
+            if (process.env.DEBUG === 'true') options.logger.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
             if (!pages || pages.length === 0) throw new Error('No relative links found')
             for (const page of pages) {
                 docs.push(...(await playwrightLoader(page)))
             }
-            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
+            if (process.env.DEBUG === 'true') options.logger.info(`Finish ${relativeLinksMethod}`)
+        } else if (selectedLinks && selectedLinks.length > 0) {
+            if (process.env.DEBUG === 'true')
+                options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
+            for (const page of selectedLinks) {
+                docs.push(...(await playwrightLoader(page)))
+            }
         } else {
             docs = await playwrightLoader(url)
         }

packages/components/nodes/documentloaders/Puppeteer/Puppeteer.ts

@@ -1,4 +1,4 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { Browser, Page, PuppeteerWebBaseLoader, PuppeteerWebBaseLoaderOptions } from 'langchain/document_loaders/web/puppeteer'
 import { test } from 'linkifyjs'
@@ -62,6 +62,7 @@ class Puppeteer_DocumentLoaders implements INode {
                 name: 'limit',
                 type: 'number',
                 optional: true,
+                default: '10',
                 additionalParams: true,
                 description:
                     'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
@@ -115,11 +116,12 @@ class Puppeteer_DocumentLoaders implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const metadata = nodeData.inputs?.metadata
         const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
-        let limit = nodeData.inputs?.limit as string
+        const selectedLinks = nodeData.inputs?.selectedLinks as string[]
+        let limit = parseInt(nodeData.inputs?.limit as string)
         let waitUntilGoToOption = nodeData.inputs?.waitUntilGoToOption as PuppeteerLifeCycleEvent
         let waitForSelector = nodeData.inputs?.waitForSelector as string
 
@@ -159,23 +161,33 @@ class Puppeteer_DocumentLoaders implements INode {
                 }
                 return docs
             } catch (err) {
-                if (process.env.DEBUG === 'true') console.error(`error in PuppeteerWebBaseLoader: ${err.message}, on page: ${url}`)
+                if (process.env.DEBUG === 'true') options.logger.error(`error in PuppeteerWebBaseLoader: ${err.message}, on page: ${url}`)
             }
         }
 
         let docs = []
         if (relativeLinksMethod) {
-            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = '10'
-            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
+            if (!limit) limit = 10
+            else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
-                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
-            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+                selectedLinks && selectedLinks.length > 0
+                    ? selectedLinks.slice(0, limit)
+                    : relativeLinksMethod === 'webCrawl'
+                    ? await webCrawl(url, limit)
+                    : await xmlScrape(url, limit)
+            if (process.env.DEBUG === 'true') options.logger.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
             if (!pages || pages.length === 0) throw new Error('No relative links found')
             for (const page of pages) {
                 docs.push(...(await puppeteerLoader(page)))
             }
-            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
+            if (process.env.DEBUG === 'true') options.logger.info(`Finish ${relativeLinksMethod}`)
+        } else if (selectedLinks && selectedLinks.length > 0) {
+            if (process.env.DEBUG === 'true')
+                options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
+            for (const page of selectedLinks) {
+                docs.push(...(await puppeteerLoader(page)))
+            }
         } else {
             docs = await puppeteerLoader(url)
         }

packages/components/nodes/documentloaders/VectorStoreToDocument/VectorStoreToDocument.ts

@@ -51,7 +51,7 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: this.baseClasses
+                baseClasses: [...this.baseClasses, 'json']
             },
             {
                 label: 'Text',

packages/components/nodes/embeddings/AzureOpenAIEmbedding/AzureOpenAIEmbedding.ts

@@ -35,7 +35,7 @@ class AzureOpenAIEmbedding_Embeddings implements INode {
                 label: 'Batch Size',
                 name: 'batchSize',
                 type: 'number',
-                default: '1',
+                default: '100',
                 optional: true,
                 additionalParams: true
             },

packages/components/nodes/embeddings/OpenAIEmbedding/OpenAIEmbedding.ts

@@ -17,7 +17,7 @@ class OpenAIEmbedding_Embeddings implements INode {
     constructor() {
         this.label = 'OpenAI Embeddings'
         this.name = 'openAIEmbeddings'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'OpenAIEmbeddings'
         this.icon = 'openai.svg'
         this.category = 'Embeddings'
@@ -30,6 +30,27 @@ class OpenAIEmbedding_Embeddings implements INode {
             credentialNames: ['openAIApi']
         }
         this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'text-embedding-3-large',
+                        name: 'text-embedding-3-large'
+                    },
+                    {
+                        label: 'text-embedding-3-small',
+                        name: 'text-embedding-3-small'
+                    },
+                    {
+                        label: 'text-embedding-ada-002',
+                        name: 'text-embedding-ada-002'
+                    }
+                ],
+                default: 'text-embedding-ada-002',
+                optional: true
+            },
             {
                 label: 'Strip New Lines',
                 name: 'stripNewLines',
@@ -66,12 +87,14 @@ class OpenAIEmbedding_Embeddings implements INode {
         const batchSize = nodeData.inputs?.batchSize as string
         const timeout = nodeData.inputs?.timeout as string
         const basePath = nodeData.inputs?.basepath as string
+        const modelName = nodeData.inputs?.modelName as string
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
 
         const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
-            openAIApiKey
+            openAIApiKey,
+            modelName
         }
 
         if (stripNewLines) obj.stripNewLines = stripNewLines

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/memory/MongoDBMemory/MongoDBMemory.ts

@@ -5,6 +5,24 @@ import { mapStoredMessageToChatMessage, AIMessage, HumanMessage, BaseMessage } f
 import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
 
+let mongoClientSingleton: MongoClient
+let mongoUrl: string
+
+const getMongoClient = async (newMongoUrl: string) => {
+    if (!mongoClientSingleton) {
+        // if client doesn't exists
+        mongoClientSingleton = new MongoClient(newMongoUrl)
+        mongoUrl = newMongoUrl
+        return mongoClientSingleton
+    } else if (mongoClientSingleton && newMongoUrl !== mongoUrl) {
+        // if client exists but url changed
+        mongoClientSingleton.close()
+        mongoClientSingleton = new MongoClient(newMongoUrl)
+        mongoUrl = newMongoUrl
+        return mongoClientSingleton
+    }
+    return mongoClientSingleton
+}
 class MongoDB_Memory implements INode {
     label: string
     name: string
@@ -79,9 +97,7 @@ const initializeMongoDB = async (nodeData: INodeData, options: ICommonObject): P
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
 
-    const client = new MongoClient(mongoDBConnectUrl)
-    await client.connect()
-
+    const client = await getMongoClient(mongoDBConnectUrl)
     const collection = client.db(databaseName).collection(collectionName)
 
     const mongoDBChatMessageHistory = new MongoDBChatMessageHistory({

packages/components/nodes/memory/RedisBackedChatMemory/RedisBackedChatMemory.ts

@@ -1,10 +1,47 @@
-import { Redis } from 'ioredis'
+import { Redis, RedisOptions } from 'ioredis'
+import { isEqual } from 'lodash'
 import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { RedisChatMessageHistory, RedisChatMessageHistoryInput } from 'langchain/stores/message/ioredis'
 import { mapStoredMessageToChatMessage, BaseMessage, AIMessage, HumanMessage } from 'langchain/schema'
 import { INode, INodeData, INodeParams, ICommonObject, MessageType, IMessage, MemoryMethods, FlowiseMemory } from '../../../src/Interface'
 import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 
+let redisClientSingleton: Redis
+let redisClientOption: RedisOptions
+let redisClientUrl: string
+
+const getRedisClientbyOption = (option: RedisOptions) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    } else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(option)
+        redisClientOption = option
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
+const getRedisClientbyUrl = (url: string) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    } else if (redisClientSingleton && url !== redisClientUrl) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = new Redis(url)
+        redisClientUrl = url
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
 class RedisBackedChatMemory_Memory implements INode {
     label: string
     name: string
@@ -95,7 +132,7 @@ const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Prom
 
         const tlsOptions = sslEnabled === true ? { tls: { rejectUnauthorized: false } } : {}
 
-        client = new Redis({
+        client = getRedisClientbyOption({
             port: portStr ? parseInt(portStr) : 6379,
             host,
             username,
@@ -103,7 +140,7 @@ const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Prom
             ...tlsOptions
         })
     } else {
-        client = new Redis(redisUrl)
+        client = getRedisClientbyUrl(redisUrl)
     }
 
     let obj: RedisChatMessageHistoryInput = {
@@ -120,24 +157,6 @@ const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Prom
 
     const redisChatMessageHistory = new RedisChatMessageHistory(obj)
 
-    /*redisChatMessageHistory.getMessages = async (): Promise<BaseMessage[]> => {
-        const rawStoredMessages = await client.lrange((redisChatMessageHistory as any).sessionId, windowSize ? -windowSize : 0, -1)
-        const orderedMessages = rawStoredMessages.reverse().map((message) => JSON.parse(message))
-        return orderedMessages.map(mapStoredMessageToChatMessage)
-    }
-
-    redisChatMessageHistory.addMessage = async (message: BaseMessage): Promise<void> => {
-        const messageToAdd = [message].map((msg) => msg.toDict())
-        await client.lpush((redisChatMessageHistory as any).sessionId, JSON.stringify(messageToAdd[0]))
-        if (sessionTTL) {
-            await client.expire((redisChatMessageHistory as any).sessionId, sessionTTL)
-        }
-    }
-
-    redisChatMessageHistory.clear = async (): Promise<void> => {
-        await client.del((redisChatMessageHistory as any).sessionId)
-    }*/
-
     const memory = new BufferMemoryExtended({
         memoryKey: memoryKey ?? 'chat_history',
         chatHistory: redisChatMessageHistory,

packages/components/nodes/outputparsers/CustomListOutputParser/CustomListOutputParser.ts

@@ -29,16 +29,17 @@ class CustomListOutputParser implements INode {
                 label: 'Length',
                 name: 'length',
                 type: 'number',
-                default: 5,
                 step: 1,
-                description: 'Number of values to return'
+                description: 'Number of values to return',
+                optional: true
             },
             {
                 label: 'Separator',
                 name: 'separator',
                 type: 'string',
                 description: 'Separator between values',
-                default: ','
+                default: ',',
+                optional: true
             },
             {
                 label: 'Autofix',
@@ -54,10 +55,11 @@ class CustomListOutputParser implements INode {
         const separator = nodeData.inputs?.separator as string
         const lengthStr = nodeData.inputs?.length as string
         const autoFix = nodeData.inputs?.autofixParser as boolean
-        let length = 5
-        if (lengthStr) length = parseInt(lengthStr, 10)
 
-        const parser = new LangchainCustomListOutputParser({ length: length, separator: separator })
+        const parser = new LangchainCustomListOutputParser({
+            length: lengthStr ? parseInt(lengthStr, 10) : undefined,
+            separator: separator
+        })
         Object.defineProperty(parser, 'autoFix', {
             enumerable: true,
             configurable: true,

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.splice(0, this.k)
+        } 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/tools/RetrieverTool/RetrieverTool.ts

@@ -1,8 +1,11 @@
 import { INode, INodeData, INodeParams } from '../../../src/Interface'
 import { getBaseClasses } from '../../../src/utils'
 import { DynamicTool } from 'langchain/tools'
-import { createRetrieverTool } from 'langchain/agents/toolkits'
+import { DynamicStructuredTool } from '@langchain/core/tools'
+import { CallbackManagerForToolRun } from '@langchain/core/callbacks/manager'
 import { BaseRetriever } from 'langchain/schema/retriever'
+import { z } from 'zod'
+import { SOURCE_DOCUMENTS_PREFIX } from '../../../src/agents'
 
 class Retriever_Tools implements INode {
     label: string
@@ -19,7 +22,7 @@ class Retriever_Tools implements INode {
     constructor() {
         this.label = 'Retriever Tool'
         this.name = 'retrieverTool'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'RetrieverTool'
         this.icon = 'retrievertool.svg'
         this.category = 'Tools'
@@ -44,6 +47,12 @@ class Retriever_Tools implements INode {
                 label: 'Retriever',
                 name: 'retriever',
                 type: 'BaseRetriever'
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
             }
         ]
     }
@@ -52,12 +61,25 @@ class Retriever_Tools implements INode {
         const name = nodeData.inputs?.name as string
         const description = nodeData.inputs?.description as string
         const retriever = nodeData.inputs?.retriever as BaseRetriever
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
 
-        const tool = createRetrieverTool(retriever, {
+        const input = {
             name,
             description
+        }
+
+        const func = async ({ input }: { input: string }, runManager?: CallbackManagerForToolRun) => {
+            const docs = await retriever.getRelevantDocuments(input, runManager?.getChild('retriever'))
+            const content = docs.map((doc) => doc.pageContent).join('\n\n')
+            const sourceDocuments = JSON.stringify(docs)
+            return returnSourceDocuments ? content + SOURCE_DOCUMENTS_PREFIX + sourceDocuments : content
+        }
+
+        const schema = z.object({
+            input: z.string().describe('query to look up in retriever')
         })
 
+        const tool = new DynamicStructuredTool({ ...input, func, schema })
         return tool
     }
 }

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
@@ -51,13 +52,31 @@ class CustomFunction_Utilities implements INode {
                 label: 'Output',
                 name: 'output',
                 baseClasses: ['string', 'number', 'boolean', 'json', 'array']
+            },
+            {
+                label: 'Ending Node',
+                name: 'EndingNode',
+                baseClasses: [this.type]
             }
         ]
     }
 
-    async init(nodeData: INodeData, input: string): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
+        const isEndingNode = nodeData?.outputs?.output === 'EndingNode'
+        if (isEndingNode && !options.isRun) return // prevent running both init and run twice
+
         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) {
@@ -69,29 +88,30 @@ class CustomFunction_Utilities implements INode {
             }
         }
 
-        let sandbox: any = { $input: input }
-
-        if (Object.keys(inputVars).length) {
-            for (const item in inputVars) {
-                sandbox[`$${item}`] = inputVars[item]
+        // 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
+                }
             }
         }
 
-        const defaultAllowBuiltInDep = [
-            'assert',
-            'buffer',
-            'crypto',
-            'events',
-            'http',
-            'https',
-            'net',
-            'path',
-            'querystring',
-            'timers',
-            'tls',
-            'url',
-            'zlib'
-        ]
+        let sandbox: any = { $input: input }
+        sandbox['$vars'] = prepareSandboxVars(variables)
+        sandbox['$flow'] = flow
+
+        if (Object.keys(inputVars).length) {
+            for (const item in inputVars) {
+                let value = inputVars[item]
+                if (typeof value === 'string') {
+                    value = handleEscapeCharacters(value, true)
+                }
+                sandbox[`$${item}`] = value
+            }
+        }
 
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))
@@ -111,7 +131,8 @@ class CustomFunction_Utilities implements INode {
         const vm = new NodeVM(nodeVMOptions)
         try {
             const response = await vm.run(`module.exports = async function() {${javascriptFunction}}()`, __dirname)
-            if (typeof response === 'string') {
+
+            if (typeof response === 'string' && !isEndingNode) {
                 return handleEscapeCharacters(response, false)
             }
             return response
@@ -119,6 +140,10 @@ class CustomFunction_Utilities implements INode {
             throw new Error(e)
         }
     }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        return await this.init(nodeData, input, { ...options, isRun: true })
+    }
 }
 
 module.exports = { nodeClass: CustomFunction_Utilities }

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/utilities/StickyNote/StickyNote.ts

@@ -0,0 +1,40 @@
+import { INode, INodeParams } from '../../../src/Interface'
+
+class StickyNote implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Sticky Note'
+        this.name = 'stickyNote'
+        this.version = 1.0
+        this.type = 'StickyNote'
+        this.icon = 'stickyNote.svg'
+        this.category = 'Utilities'
+        this.description = 'Add a sticky note'
+        this.inputs = [
+            {
+                label: '',
+                name: 'note',
+                type: 'string',
+                rows: 1,
+                placeholder: 'Type something here',
+                optional: true
+            }
+        ]
+        this.baseClasses = [this.type]
+    }
+
+    async init(): Promise<any> {
+        return new StickyNote()
+    }
+}
+
+module.exports = { nodeClass: StickyNote }

packages/components/nodes/utilities/StickyNote/stickyNote.svg

@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+     stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+    <path d="M15.5 3H5a2 2 0 0 0-2 2v14c0 1.1.9 2 2 2h14a2 2 0 0 0 2-2V8.5L15.5 3Z"/>
+    <path d="M15 3v6h6"/>
+</svg>

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/Qdrant/Qdrant.ts

@@ -194,7 +194,7 @@ class Qdrant_VectorStores implements INode {
         const qdrantVectorDimension = nodeData.inputs?.qdrantVectorDimension
         const output = nodeData.outputs?.output as string
         const topK = nodeData.inputs?.topK as string
-        let queryFilter = nodeData.inputs?.queryFilter
+        let queryFilter = nodeData.inputs?.qdrantFilter
 
         const k = topK ? parseFloat(topK) : 4
 

packages/components/nodes/vectorstores/Qdrant/Qdrant_Existing.ts

@@ -135,7 +135,7 @@ class Qdrant_Existing_VectorStores implements INode {
         const qdrantVectorDimension = nodeData.inputs?.qdrantVectorDimension
         const output = nodeData.outputs?.output as string
         const topK = nodeData.inputs?.topK as string
-        let queryFilter = nodeData.inputs?.queryFilter
+        let queryFilter = nodeData.inputs?.qdrantFilter
 
         const k = topK ? parseFloat(topK) : 4
 

packages/components/nodes/vectorstores/Redis/Redis.ts

@@ -1,5 +1,5 @@
-import { flatten } from 'lodash'
-import { createClient, SearchOptions } from 'redis'
+import { flatten, isEqual } from 'lodash'
+import { createClient, SearchOptions, RedisClientOptions } from 'redis'
 import { Embeddings } from 'langchain/embeddings/base'
 import { RedisVectorStore, RedisVectorStoreConfig } from 'langchain/vectorstores/redis'
 import { Document } from 'langchain/document'
@@ -7,6 +7,27 @@ import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { escapeAllStrings, escapeSpecialChars, unEscapeSpecialChars } from './utils'
 
+let redisClientSingleton: ReturnType<typeof createClient>
+let redisClientOption: RedisClientOptions
+
+const getRedisClient = async (option: RedisClientOptions) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = createClient(option)
+        await redisClientSingleton.connect()
+        redisClientOption = option
+        return redisClientSingleton
+    } else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = createClient(option)
+        await redisClientSingleton.connect()
+        redisClientOption = option
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
+
 class Redis_VectorStores implements INode {
     label: string
     name: string
@@ -149,8 +170,7 @@ class Redis_VectorStores implements INode {
             }
 
             try {
-                const redisClient = createClient({ url: redisUrl })
-                await redisClient.connect()
+                const redisClient = await getRedisClient({ url: redisUrl })
 
                 const storeConfig: RedisVectorStoreConfig = {
                     redisClient: redisClient,
@@ -210,8 +230,7 @@ class Redis_VectorStores implements INode {
             redisUrl = 'redis://' + username + ':' + password + '@' + host + ':' + portStr
         }
 
-        const redisClient = createClient({ url: redisUrl })
-        await redisClient.connect()
+        const redisClient = await getRedisClient({ url: redisUrl })
 
         const storeConfig: RedisVectorStoreConfig = {
             redisClient: redisClient,

packages/components/nodes/vectorstores/Redis/RedisSearchBase.ts

@@ -7,13 +7,34 @@ import {
     INodeOutputsValue,
     INodeParams
 } from '../../../src'
-
 import { Embeddings } from 'langchain/embeddings/base'
 import { VectorStore } from 'langchain/vectorstores/base'
 import { Document } from 'langchain/document'
-import { createClient, SearchOptions } from 'redis'
+import { createClient, SearchOptions, RedisClientOptions } from 'redis'
 import { RedisVectorStore } from 'langchain/vectorstores/redis'
 import { escapeSpecialChars, unEscapeSpecialChars } from './utils'
+import { isEqual } from 'lodash'
+
+let redisClientSingleton: ReturnType<typeof createClient>
+let redisClientOption: RedisClientOptions
+
+const getRedisClient = async (option: RedisClientOptions) => {
+    if (!redisClientSingleton) {
+        // if client doesn't exists
+        redisClientSingleton = createClient(option)
+        await redisClientSingleton.connect()
+        redisClientOption = option
+        return redisClientSingleton
+    } else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
+        // if client exists but option changed
+        redisClientSingleton.quit()
+        redisClientSingleton = createClient(option)
+        await redisClientSingleton.connect()
+        redisClientOption = option
+        return redisClientSingleton
+    }
+    return redisClientSingleton
+}
 
 export abstract class RedisSearchBase {
     label: string
@@ -141,8 +162,7 @@ export abstract class RedisSearchBase {
             redisUrl = 'redis://' + username + ':' + password + '@' + host + ':' + portStr
         }
 
-        this.redisClient = createClient({ url: redisUrl })
-        await this.redisClient.connect()
+        this.redisClient = await getRedisClient({ url: redisUrl })
 
         const vectorStore = await this.constructVectorStore(embeddings, indexName, replaceIndex, docs)
         if (!contentKey || contentKey === '') contentKey = 'content'

packages/components/nodes/vectorstores/Redis/Redis_Existing.ts

@@ -3,7 +3,6 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { VectorStore } from 'langchain/vectorstores/base'
 import { RedisVectorStore, RedisVectorStoreConfig } from 'langchain/vectorstores/redis'
 import { Document } from 'langchain/document'
-
 import { RedisSearchBase } from './RedisSearchBase'
 
 class RedisExisting_VectorStores extends RedisSearchBase implements INode {

packages/components/nodes/vectorstores/Redis/Redis_Upsert.ts

@@ -1,7 +1,6 @@
 import { ICommonObject, INode, INodeData } from '../../../src/Interface'
 import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
-
 import { flatten } from 'lodash'
 import { RedisSearchBase } from './RedisSearchBase'
 import { VectorStore } from 'langchain/vectorstores/base'

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": "1.5.0",
+    "version": "1.5.3",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -26,18 +26,19 @@
         "@gomomento/sdk": "^1.51.1",
         "@gomomento/sdk-core": "^1.51.1",
         "@google-ai/generativelanguage": "^0.2.1",
+        "@google/generative-ai": "^0.1.3",
         "@huggingface/inference": "^2.6.1",
         "@langchain/community": "^0.0.16",
         "@langchain/google-genai": "^0.0.6",
         "@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",
         "@types/jsdom": "^21.1.1",
-        "@upstash/redis": "^1.22.1",
+        "@upstash/redis": "1.22.1",
         "@zilliz/milvus2-sdk-node": "^2.2.24",
         "apify-client": "^2.7.1",
         "assemblyai": "^4.2.2",

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
 }
@@ -90,7 +96,7 @@ export interface INodeProperties {
     type: string
     icon: string
     version: number
-    category: string
+    category: string // TODO: use enum instead of string
     baseClasses: string[]
     description?: string
     filePath?: string
@@ -139,6 +145,18 @@ export interface IUsedTool {
     toolOutput: string | object
 }
 
+export interface IFileUpload {
+    data?: string
+    type: string
+    name: string
+    mime: string
+}
+
+export interface IMultiModalOption {
+    image?: Record<string, any>
+    audio?: Record<string, any>
+}
+
 /**
  * Classes
  */
@@ -234,10 +252,3 @@ export abstract class FlowiseSummaryMemory extends ConversationSummaryMemory imp
     abstract addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId?: string): Promise<void>
     abstract clearChatMessages(overrideSessionId?: string): Promise<void>
 }
-
-export interface IFileUpload {
-    data: string
-    type: string
-    name: string
-    mime: string
-}

packages/components/src/MultiModalUtils.ts

@@ -1,54 +1,57 @@
-import { ICommonObject, INodeData } from './Interface'
+import { ICommonObject, IFileUpload, IMultiModalOption, INodeData } from './Interface'
 import { BaseChatModel } from 'langchain/chat_models/base'
-import { ChatOpenAI } from 'langchain/chat_models/openai'
+import { ChatOpenAI as LangchainChatOpenAI } from 'langchain/chat_models/openai'
 import path from 'path'
-import { getUserHome } from './utils'
+import { getStoragePath } from './utils'
 import fs from 'fs'
 import { MessageContent } from '@langchain/core/dist/messages'
-import { FlowiseChatOpenAI } from '../nodes/chatmodels/ChatOpenAI/FlowiseChatOpenAI'
+import { ChatOpenAI } from '../nodes/chatmodels/ChatOpenAI/FlowiseChatOpenAI'
 
 export const injectChainNodeData = (nodeData: INodeData, options: ICommonObject) => {
     let model = nodeData.inputs?.model as BaseChatModel
 
-    if (model instanceof FlowiseChatOpenAI) {
+    if (model instanceof ChatOpenAI) {
         // TODO: this should not be static, need to figure out how to pass the nodeData and options to the invoke method
-        FlowiseChatOpenAI.chainNodeOptions = options
-        FlowiseChatOpenAI.chainNodeData = nodeData
+        ChatOpenAI.chainNodeOptions = options
+        ChatOpenAI.chainNodeData = nodeData
     }
 }
 
-export const addImagesToMessages = (nodeData: INodeData, options: ICommonObject): MessageContent => {
+export const addImagesToMessages = (nodeData: INodeData, options: ICommonObject, multiModalOption?: IMultiModalOption): MessageContent => {
     const imageContent: MessageContent = []
-    let model = nodeData.inputs?.model as BaseChatModel
-    if (model instanceof ChatOpenAI && (model as any).multiModal) {
-        if (options?.uploads && options?.uploads.length > 0) {
+    let model = nodeData.inputs?.model
+
+    if (model instanceof LangchainChatOpenAI && multiModalOption) {
+        // Image Uploaded
+        if (multiModalOption.image && multiModalOption.image.allowImageUploads && options?.uploads && options?.uploads.length > 0) {
             const imageUploads = getImageUploads(options.uploads)
             for (const upload of imageUploads) {
                 let bf = upload.data
                 if (upload.type == 'stored-file') {
-                    const filePath = path.join(getUserHome(), '.flowise', 'gptvision', upload.data, upload.name)
+                    const filePath = path.join(getStoragePath(), options.chatflowid, options.chatId, upload.name)
 
                     // as the image is stored in the server, read the file and convert it to base64
                     const contents = fs.readFileSync(filePath)
                     bf = 'data:' + upload.mime + ';base64,' + contents.toString('base64')
+
+                    imageContent.push({
+                        type: 'image_url',
+                        image_url: {
+                            url: bf,
+                            detail: multiModalOption.image.imageResolution ?? 'low'
+                        }
+                    })
                 }
-                imageContent.push({
-                    type: 'image_url',
-                    image_url: {
-                        url: bf,
-                        detail: 'low'
-                    }
-                })
             }
         }
     }
     return imageContent
 }
 
-export const getAudioUploads = (uploads: any[]) => {
-    return uploads.filter((url: any) => url.mime.startsWith('audio/'))
+export const getAudioUploads = (uploads: IFileUpload[]) => {
+    return uploads.filter((upload: IFileUpload) => upload.mime.startsWith('audio/'))
 }
 
-export const getImageUploads = (uploads: any[]) => {
-    return uploads.filter((url: any) => url.mime.startsWith('image/'))
+export const getImageUploads = (uploads: IFileUpload[]) => {
+    return uploads.filter((upload: IFileUpload) => upload.mime.startsWith('image/'))
 }

packages/components/src/agents.ts

@@ -1,5 +1,6 @@
+import { flatten } from 'lodash'
 import { AgentExecutorInput, BaseSingleActionAgent, BaseMultiActionAgent, RunnableAgent, StoppingMethod } from 'langchain/agents'
-import { ChainValues, AgentStep, AgentFinish, AgentAction, BaseMessage, FunctionMessage, AIMessage } from 'langchain/schema'
+import { ChainValues, AgentStep, AgentAction, BaseMessage, FunctionMessage, AIMessage } from 'langchain/schema'
 import { OutputParserException } from 'langchain/schema/output_parser'
 import { CallbackManager, CallbackManagerForChainRun, Callbacks } from 'langchain/callbacks'
 import { ToolInputParsingException, Tool } from '@langchain/core/tools'
@@ -7,6 +8,11 @@ import { Runnable } from 'langchain/schema/runnable'
 import { BaseChain, SerializedLLMChain } from 'langchain/chains'
 import { Serializable } from '@langchain/core/load/serializable'
 
+export const SOURCE_DOCUMENTS_PREFIX = '\n\n----FLOWISE_SOURCE_DOCUMENTS----\n\n'
+type AgentFinish = {
+    returnValues: Record<string, any>
+    log: string
+}
 type AgentExecutorOutput = ChainValues
 
 interface AgentExecutorIteratorInput {
@@ -315,10 +321,12 @@ export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
 
         const steps: AgentStep[] = []
         let iterations = 0
+        let sourceDocuments: Array<Document> = []
 
         const getOutput = async (finishStep: AgentFinish): Promise<AgentExecutorOutput> => {
             const { returnValues } = finishStep
             const additional = await this.agent.prepareForOutput(returnValues, steps)
+            if (sourceDocuments.length) additional.sourceDocuments = flatten(sourceDocuments)
 
             if (this.returnIntermediateSteps) {
                 return { ...returnValues, intermediateSteps: steps, ...additional }
@@ -406,6 +414,17 @@ export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
                             return { action, observation: observation ?? '' }
                         }
                     }
+                    if (observation?.includes(SOURCE_DOCUMENTS_PREFIX)) {
+                        const observationArray = observation.split(SOURCE_DOCUMENTS_PREFIX)
+                        observation = observationArray[0]
+                        const docs = observationArray[1]
+                        try {
+                            const parsedDocs = JSON.parse(docs)
+                            sourceDocuments.push(parsedDocs)
+                        } catch (e) {
+                            console.error('Error parsing source documents from tool')
+                        }
+                    }
                     return { action, observation: observation ?? '' }
                 })
             )
@@ -500,6 +519,10 @@ export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
                         chatId: this.chatId,
                         input: this.input
                     })
+                    if (observation?.includes(SOURCE_DOCUMENTS_PREFIX)) {
+                        const observationArray = observation.split(SOURCE_DOCUMENTS_PREFIX)
+                        observation = observationArray[0]
+                    }
                 } catch (e) {
                     if (e instanceof ToolInputParsingException) {
                         if (this.handleParsingErrors === true) {

packages/components/src/speechToText.ts

@@ -1,17 +1,16 @@
-import { ICommonObject } from './Interface'
-import { getCredentialData, getUserHome } from './utils'
+import { ICommonObject, IFileUpload } from './Interface'
+import { getCredentialData, getStoragePath } from './utils'
 import { type ClientOptions, OpenAIClient } from '@langchain/openai'
 import fs from 'fs'
 import path from 'path'
 import { AssemblyAI } from 'assemblyai'
 
-export const convertSpeechToText = async (upload: any, speechToTextConfig: any, options: ICommonObject) => {
+export const convertSpeechToText = async (upload: IFileUpload, speechToTextConfig: ICommonObject, options: ICommonObject) => {
     if (speechToTextConfig) {
         const credentialId = speechToTextConfig.credentialId as string
         const credentialData = await getCredentialData(credentialId ?? '', options)
-        const filePath = path.join(getUserHome(), '.flowise', 'gptvision', upload.data, upload.name)
+        const filePath = path.join(getStoragePath(), options.chatflowid, options.chatId, upload.name)
 
-        // as the image is stored in the server, read the file and convert it to base64
         const audio_file = fs.createReadStream(filePath)
 
         if (speechToTextConfig.name === 'openAIWhisper') {

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
  *
@@ -673,3 +689,79 @@ export const convertBaseMessagetoIMessage = (messages: BaseMessage[]): IMessage[
     }
     return formatmessages
 }
+
+/**
+ * Convert MultiOptions String to String Array
+ * @param {string} inputString
+ * @returns {string[]}
+ */
+export const convertMultiOptionsToStringArray = (inputString: string): string[] => {
+    let ArrayString: string[] = []
+    try {
+        ArrayString = JSON.parse(inputString)
+    } catch (e) {
+        ArrayString = []
+    }
+    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
+}
+
+/**
+ * Prepare storage path
+ */
+export const getStoragePath = (): string => {
+    return process.env.BLOB_STORAGE_PATH ? path.join(process.env.BLOB_STORAGE_PATH) : path.join(getUserHome(), '.flowise', 'storage')
+}

packages/server/.env.example

@@ -1,4 +1,6 @@
 PORT=3000
+# CORS_ORIGINS="*"
+# IFRAME_ORIGINS="*"
 # DATABASE_PATH=/your_database_path/.flowise
 # APIKEY_PATH=/your_api_key_path/.flowise
 # SECRETKEY_PATH=/your_api_key_path/.flowise
@@ -13,6 +15,7 @@ PORT=3000
 # DATABASE_USER=""
 # DATABASE_PASSWORD=""
 # DATABASE_SSL=true
+# DATABASE_SSL_KEY_BASE64=<Self signed certificate in BASE64>
 
 # FLOWISE_USERNAME=user
 # FLOWISE_PASSWORD=1234
@@ -26,3 +29,5 @@ 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/API Agent OpenAI.json

@@ -88,7 +88,7 @@
             "data": {
                 "id": "chatOpenAI_1",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -111,6 +111,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -127,6 +143,10 @@
                                 "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"
@@ -407,7 +427,7 @@
             "data": {
                 "id": "chatOpenAI_2",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -430,6 +450,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -446,6 +482,10 @@
                                 "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"

packages/server/marketplaces/chatflows/API Agent.json

@@ -396,7 +396,7 @@
             "data": {
                 "id": "chatOpenAI_2",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -419,6 +419,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -435,6 +451,10 @@
                                 "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"
@@ -567,7 +587,7 @@
             "data": {
                 "id": "chatOpenAI_1",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -590,6 +610,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -606,6 +642,10 @@
                                 "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"
@@ -738,7 +778,7 @@
             "data": {
                 "id": "chatOpenAI_3",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -761,6 +801,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -777,6 +833,10 @@
                                 "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"

packages/server/marketplaces/chatflows/Antonym.json

@@ -175,7 +175,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -198,6 +198,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -214,6 +230,10 @@
                                 "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"
@@ -381,13 +401,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "prompt": "{{fewShotPromptTemplate_1.data.instance}}",
                     "outputParser": "",
-                    "chainName": ""
+                    "chainName": "",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/AutoGPT.json

@@ -251,7 +251,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -274,6 +274,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -290,6 +306,10 @@
                                 "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"
@@ -422,7 +442,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -436,6 +456,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -474,7 +516,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -506,12 +549,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +595,45 @@
                         "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 +658,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/BabyAGI.json

@@ -77,7 +77,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -91,6 +91,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -129,7 +151,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -161,12 +184,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +230,45 @@
                         "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 +293,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -279,7 +344,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -302,6 +367,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

packages/server/marketplaces/chatflows/CSV Agent.json

@@ -70,7 +70,7 @@
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
                 "name": "chatOpenAI",
-                "version": 2,
+                "version": 3,
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
                 "category": "Chat Models",
@@ -92,6 +92,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -108,6 +124,10 @@
                                 "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"

packages/server/marketplaces/chatflows/Chat with a Podcast.json

@@ -194,7 +194,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -217,6 +217,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -233,6 +249,10 @@
                                 "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"
@@ -440,7 +460,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -454,6 +474,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -492,7 +534,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/ChatGPTPlugin.json

@@ -215,7 +215,7 @@
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
                 "name": "chatOpenAI",
-                "version": 2,
+                "version": 3,
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
                 "category": "Chat Models",
@@ -237,6 +237,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -253,6 +269,10 @@
                                 "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"

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": 3,
                 "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 +104,36 @@
                         "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,
+                        "id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
                         "optional": true,
                         "list": true,
-                        "id": "conversationChain_0-input-document-Document"
+                        "id": "conversationChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
+                    "inputModeration": "",
                     "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 +141,27 @@
             },
             "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 +237,7 @@
                                 "name": "claude-instant-v1.1-100k"
                             }
                         ],
-                        "default": "claude-v1",
+                        "default": "claude-2",
                         "optional": true,
                         "id": "chatAnthropic_0-input-modelName-options"
                     },
@@ -234,6 +245,7 @@
                         "label": "Temperature",
                         "name": "temperature",
                         "type": "number",
+                        "step": 0.1,
                         "default": 0.9,
                         "optional": true,
                         "id": "chatAnthropic_0-input-temperature-number"
@@ -242,6 +254,7 @@
                         "label": "Max Tokens",
                         "name": "maxTokensToSample",
                         "type": "number",
+                        "step": 1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-maxTokensToSample-number"
@@ -250,6 +263,7 @@
                         "label": "Top P",
                         "name": "topP",
                         "type": "number",
+                        "step": 0.1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-topP-number"
@@ -258,6 +272,7 @@
                         "label": "Top K",
                         "name": "topK",
                         "type": "number",
+                        "step": 0.1,
                         "optional": true,
                         "additionalParams": true,
                         "id": "chatAnthropic_0-input-topK-number"
@@ -273,6 +288,7 @@
                     }
                 ],
                 "inputs": {
+                    "cache": "",
                     "modelName": "claude-2.1",
                     "temperature": 0.9,
                     "maxTokensToSample": "",
@@ -281,10 +297,10 @@
                 },
                 "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 +308,106 @@
             },
             "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 +415,7 @@
                         "type": "json",
                         "optional": true,
                         "additionalParams": true,
-                        "id": "pdfFile_0-input-metadata-json"
+                        "id": "plainText_0-input-metadata-json"
                     }
                 ],
                 "inputAnchors": [
@@ -363,30 +424,45 @@
                         "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 +474,31 @@
             "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 Agent.json

@@ -156,7 +156,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -179,6 +179,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -195,6 +211,10 @@
                                 "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"

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

@@ -14,7 +14,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -28,6 +28,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -66,7 +88,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -194,6 +217,13 @@
                         "rows": 3,
                         "placeholder": "Searches and returns documents regarding the state-of-the-union.",
                         "id": "retrieverTool_0-input-description-string"
+                    },
+                    {
+                        "label": "Return Source Documents",
+                        "name": "returnSourceDocuments",
+                        "type": "boolean",
+                        "optional": true,
+                        "id": "retrieverTool_0-input-returnSourceDocuments-boolean"
                     }
                 ],
                 "inputAnchors": [
@@ -207,7 +237,8 @@
                 "inputs": {
                     "name": "search_website",
                     "description": "Searches and return documents regarding Jane - a culinary institution that offers top quality coffee, pastries, breakfast, lunch, and a variety of baked goods. They have multiple locations, including Jane on Fillmore, Jane on Larkin, Jane the Bakery, Toy Boat By Jane, and Little Jane on Grant. They emphasize healthy eating with a focus on flavor and quality ingredients. They bake everything in-house and work with local suppliers to source ingredients directly from farmers. They also offer catering services and delivery options.",
-                    "retriever": "{{pinecone_0.data.instance}}"
+                    "retriever": "{{pinecone_0.data.instance}}",
+                    "returnSourceDocuments": true
                 },
                 "outputAnchors": [
                     {
@@ -296,12 +327,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +373,45 @@
                         "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 +436,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -414,7 +487,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -437,6 +510,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

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

@@ -14,7 +14,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -28,6 +28,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -66,7 +88,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -346,7 +369,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -369,6 +392,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"
@@ -536,12 +567,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +613,45 @@
                         "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 +676,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Flowise Docs QnA.json

@@ -376,7 +376,7 @@
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
                 "name": "chatOpenAI",
-                "version": 2,
+                "version": 3,
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
                 "category": "Chat Models",
@@ -398,6 +398,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -414,6 +430,10 @@
                                 "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"
@@ -547,7 +567,7 @@
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
                 "name": "openAIEmbeddings",
-                "version": 1,
+                "version": 2,
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
                 "category": "Embeddings",
@@ -560,6 +580,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -598,7 +640,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/HuggingFace LLM Chain.json

@@ -234,13 +234,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{huggingFaceInference_LLMs_0.data.instance}}",
                     "prompt": "{{promptTemplate_0.data.instance}}",
                     "outputParser": "",
-                    "chainName": ""
+                    "chainName": "",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/IfElse.json

@@ -489,13 +489,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{openAI_1.data.instance}}",
                     "prompt": "{{promptTemplate_0.data.instance}}",
                     "outputParser": "",
-                    "chainName": "FirstChain"
+                    "chainName": "FirstChain",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -578,13 +588,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_1-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_1-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{openAI_2.data.instance}}",
                     "prompt": "{{promptTemplate_1.data.instance}}",
                     "outputParser": "",
-                    "chainName": "LastChain"
+                    "chainName": "LastChain",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -742,8 +762,8 @@
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "prompt": "{{promptTemplate_2.data.instance}}",
                     "outputParser": "",
-                    "inputModeration": "",
-                    "chainName": "FallbackChain"
+                    "chainName": "FallbackChain",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -888,7 +908,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -911,6 +931,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

packages/server/marketplaces/chatflows/Image Generation.json

@@ -289,13 +289,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{replicate_0.data.instance}}",
                     "prompt": "{{promptTemplate_0.data.instance}}",
                     "outputParser": "",
-                    "chainName": ""
+                    "chainName": "",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -378,13 +388,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_1-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_1-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "prompt": "{{promptTemplate_1.data.instance}}",
                     "outputParser": "",
-                    "chainName": ""
+                    "chainName": "",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -432,7 +452,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -455,6 +475,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

packages/server/marketplaces/chatflows/Input Moderation.json

@@ -164,7 +164,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -187,6 +187,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

packages/server/marketplaces/chatflows/List Output Parser.json

@@ -49,13 +49,23 @@
                         "type": "BaseLLMOutputParser",
                         "optional": true,
                         "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "prompt": "{{promptTemplate_0.data.instance}}",
                     "outputParser": "{{csvOutputParser_0.data.instance}}",
-                    "chainName": ""
+                    "chainName": "",
+                    "inputModeration": ""
                 },
                 "outputAnchors": [
                     {
@@ -213,7 +223,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -236,6 +246,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -252,6 +278,10 @@
                                 "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"

packages/server/marketplaces/chatflows/Long Term Memory.json

@@ -112,7 +112,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -126,6 +126,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -164,7 +186,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -483,7 +506,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -506,6 +529,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

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

@@ -346,7 +346,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -360,6 +360,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -398,7 +420,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -430,7 +453,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -453,6 +476,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"
@@ -620,12 +651,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +697,45 @@
                         "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 +760,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "{\"id\":{\"$in\":[\"doc1\",\"doc2\"]}}",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Multi Prompt Chain.json

@@ -278,7 +278,7 @@
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
                 "name": "chatOpenAI",
-                "version": 2,
+                "version": 3,
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
                 "category": "Chat Models",
@@ -300,6 +300,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -316,6 +332,10 @@
                                 "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"

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

@@ -281,7 +281,7 @@
             "data": {
                 "id": "openAIEmbeddings_0",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -295,6 +295,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_0-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_0-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -333,7 +355,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -365,7 +388,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -388,6 +411,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"
@@ -555,12 +586,12 @@
             "data": {
                 "id": "pinecone_0",
                 "label": "Pinecone",
-                "version": 1,
+                "version": 2,
                 "name": "pinecone",
                 "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 +632,45 @@
                         "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 +695,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -840,6 +913,45 @@
                         "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 +977,10 @@
                     "tableName": "",
                     "queryName": "",
                     "supabaseMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Multiple VectorDB.json

@@ -271,7 +271,7 @@
             "data": {
                 "id": "openAIEmbeddings_1",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -285,6 +285,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_1-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_1-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -323,7 +345,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -355,7 +378,7 @@
             "data": {
                 "id": "openAIEmbeddings_2",
                 "label": "OpenAI Embeddings",
-                "version": 1,
+                "version": 2,
                 "name": "openAIEmbeddings",
                 "type": "OpenAIEmbeddings",
                 "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
@@ -369,6 +392,28 @@
                         "credentialNames": ["openAIApi"],
                         "id": "openAIEmbeddings_2-input-credential-credential"
                     },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbeddings_2-input-modelName-options"
+                    },
                     {
                         "label": "Strip New Lines",
                         "name": "stripNewLines",
@@ -407,7 +452,8 @@
                     "stripNewLines": "",
                     "batchSize": "",
                     "timeout": "",
-                    "basepath": ""
+                    "basepath": "",
+                    "modelName": "text-embedding-ada-002"
                 },
                 "outputAnchors": [
                     {
@@ -439,7 +485,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -462,6 +508,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"
@@ -949,7 +1003,7 @@
             "data": {
                 "id": "chatOpenAI_1",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -972,6 +1026,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"
@@ -1139,7 +1201,7 @@
             "data": {
                 "id": "chatOpenAI_2",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
@@ -1162,6 +1224,14 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
                             {
                                 "label": "gpt-4-1106-preview",
                                 "name": "gpt-4-1106-preview"

packages/server/marketplaces/chatflows/OpenAI Agent.json

@@ -279,7 +279,7 @@
             "data": {
                 "id": "chatOpenAI_0",
                 "label": "ChatOpenAI",
-                "version": 2,
+                "version": 3,
                 "name": "chatOpenAI",
                 "type": "ChatOpenAI",
                 "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
@@ -302,6 +302,22 @@
                                 "label": "gpt-4",
                                 "name": "gpt-4"
                             },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "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"
@@ -318,6 +334,10 @@
                                 "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"