Merge branch 'FlowiseAI:main' into main

.github/workflows/autoSyncMergedPullRequest.yml

@@ -0,0 +1,26 @@
+name: autoSyncMergedPullRequest
+on:
+  pull_request:
+    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": "${{ github.event.pull_request.number }}", "sha": "${{ github.sha }}"}'

.github/workflows/autoSyncSingleCommit.yml

@@ -0,0 +1,31 @@
+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 }}"}'

CONTRIBUTING-ZH.md

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

CONTRIBUTING.md

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

README-ZH.md

@@ -145,25 +145,40 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
 
 ## 🌐 自托管
 
-### [Railway](https://docs.flowiseai.com/deployment/railway)
+在您现有的基础设施中部署自托管的 Flowise，我们支持各种[部署](https://docs.flowiseai.com/configuration/deployment)
 
-[![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
+-   [AWS](https://docs.flowiseai.com/deployment/aws)
+-   [Azure](https://docs.flowiseai.com/deployment/azure)
+-   [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean)
+-   [GCP](https://docs.flowiseai.com/deployment/gcp)
+-   <details>
+      <summary>其他</summary>
 
-### [Render](https://docs.flowiseai.com/deployment/render)
+    -   [Railway](https://docs.flowiseai.com/deployment/railway)
 
-[![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
+        [![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
 
-### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
+    -   [Render](https://docs.flowiseai.com/deployment/render)
 
-<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
+        [![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
 
-### [AWS](https://docs.flowiseai.com/deployment/aws)
+    -   [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
 
-### [Azure](https://docs.flowiseai.com/deployment/azure)
+        <a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
 
-### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean)
+    -   [Elestio](https://elest.io/open-source/flowiseai)
 
-### [GCP](https://docs.flowiseai.com/deployment/gcp)
+        [![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai)
+
+    -   [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
+
+        [![部署到 Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
+
+    -   [RepoCloud](https://repocloud.io/details/?app_id=29)
+
+        [![部署到 RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploy.png)](https://repocloud.io/details/?app_id=29)
+
+      </details>
 
 ## 💻 云托管
 

artillery-load-test.yml

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

docker/.env.example

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

docker/README.md

@@ -1,6 +1,6 @@
 # Flowise Docker Hub Image
 
-Starts Flowise from [DockerHub Image](https://hub.docker.com/repository/docker/flowiseai/flowise/general)
+Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise)
 
 ## Usage
 

docker/docker-compose.yml

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

package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.9",
+    "version": "1.4.10",
     "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/AstraApi.credential.ts

@@ -0,0 +1,34 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class AstraDBApi implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Astra DB API'
+        this.name = 'AstraDBApi'
+        this.version = 1.0
+        this.inputs = [
+            {
+                label: 'Astra DB Collection Name',
+                name: 'collectionName',
+                type: 'string'
+            },
+            {
+                label: 'Astra DB Application Token',
+                name: 'applicationToken',
+                type: 'password'
+            },
+            {
+                label: 'Astra DB Api Endpoint',
+                name: 'dbEndPoint',
+                type: 'string'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: AstraDBApi }

packages/components/credentials/LocalAIApi.credential.ts

@@ -0,0 +1,23 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class LocalAIApi implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'LocalAI API'
+        this.name = 'localAIApi'
+        this.version = 1.0
+        this.inputs = [
+            {
+                label: 'LocalAI Api Key',
+                name: 'localAIApiKey',
+                type: 'password'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: LocalAIApi }

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

@@ -1,11 +1,14 @@
-import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { initializeAgentExecutorWithOptions, AgentExecutor, InitializeAgentExecutorOptions } from 'langchain/agents'
 import { Tool } from 'langchain/tools'
-import { BaseChatMemory } from 'langchain/memory'
-import { getBaseClasses, mapChatHistory } from '../../../src/utils'
 import { BaseChatModel } from 'langchain/chat_models/base'
 import { flatten } from 'lodash'
-import { additionalCallbacks } from '../../../src/handler'
+import { AgentStep, BaseMessage, ChainValues, AIMessage, HumanMessage } from 'langchain/schema'
+import { RunnableSequence } from 'langchain/schema/runnable'
+import { getBaseClasses } from '../../../src/utils'
+import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { AgentExecutor } from '../../../src/agents'
+import { ChatConversationalAgent } from 'langchain/agents'
+import { renderTemplate } from '@langchain/core/prompts'
 
 const DEFAULT_PREFIX = `Assistant is a large language model trained by OpenAI.
 
@@ -15,6 +18,15 @@ Assistant is constantly learning and improving, and its capabilities are constan
 
 Overall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.`
 
+const TEMPLATE_TOOL_RESPONSE = `TOOL RESPONSE:
+---------------------
+{observation}
+
+USER'S INPUT
+--------------------
+
+Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else.`
+
 class ConversationalAgent_Agents implements INode {
     label: string
     name: string
@@ -25,8 +37,9 @@ class ConversationalAgent_Agents implements INode {
     category: string
     baseClasses: string[]
     inputs: INodeParams[]
+    sessionId?: string
 
-    constructor() {
+    constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversational Agent'
         this.name = 'conversationalAgent'
         this.version = 2.0
@@ -43,7 +56,7 @@ class ConversationalAgent_Agents implements INode {
                 list: true
             },
             {
-                label: 'Language Model',
+                label: 'Chat Model',
                 name: 'model',
                 type: 'BaseChatModel'
             },
@@ -62,52 +75,114 @@ class ConversationalAgent_Agents implements INode {
                 additionalParams: true
             }
         ]
+        this.sessionId = fields?.sessionId
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
-        const model = nodeData.inputs?.model as BaseChatModel
+        return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-        let tools = nodeData.inputs?.tools as Tool[]
-        tools = flatten(tools)
-        const memory = nodeData.inputs?.memory as BaseChatMemory
-        const systemMessage = nodeData.inputs?.systemMessage as string
-
-        const obj: InitializeAgentExecutorOptions = {
-            agentType: 'chat-conversational-react-description',
-            verbose: process.env.DEBUG === 'true' ? true : false
-        }
-
-        const agentArgs: any = {}
-        if (systemMessage) {
-            agentArgs.systemMessage = systemMessage
-        }
-
-        if (Object.keys(agentArgs).length) obj.agentArgs = agentArgs
-
-        const executor = await initializeAgentExecutorWithOptions(tools, model, obj)
-        executor.memory = memory
-        return executor
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
-        const executor = nodeData.instance as AgentExecutor
+        const memory = nodeData.inputs?.memory as FlowiseMemory
-        const memory = nodeData.inputs?.memory as BaseChatMemory
+        const executor = await prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-
-        if (options && options.chatHistory) {
-            const chatHistoryClassName = memory.chatHistory.constructor.name
-            // Only replace when its In-Memory
-            if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
-                memory.chatHistory = mapChatHistory(options)
-                executor.memory = memory
-            }
-        }
-
-        ;(executor.memory as any).returnMessages = true // Return true for BaseChatModel
 
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
         const callbacks = await additionalCallbacks(nodeData, options)
 
-        const result = await executor.call({ input }, [...callbacks])
+        let res: ChainValues = {}
-        return result?.output
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
+        } else {
+            res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
+        }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: res?.output,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        return res?.output
     }
 }
 
+const prepareAgent = async (
+    nodeData: INodeData,
+    flowObj: { sessionId?: string; chatId?: string; input?: string },
+    chatHistory: IMessage[] = []
+) => {
+    const model = nodeData.inputs?.model as BaseChatModel
+    let tools = nodeData.inputs?.tools as Tool[]
+    tools = flatten(tools)
+    const memory = nodeData.inputs?.memory as FlowiseMemory
+    const systemMessage = nodeData.inputs?.systemMessage as string
+    const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
+    const inputKey = memory.inputKey ? memory.inputKey : 'input'
+
+    /** Bind a stop token to the model */
+    const modelWithStop = model.bind({
+        stop: ['\nObservation']
+    })
+
+    const outputParser = ChatConversationalAgent.getDefaultOutputParser({
+        llm: model,
+        toolNames: tools.map((tool) => tool.name)
+    })
+
+    const prompt = ChatConversationalAgent.createPrompt(tools, {
+        systemMessage: systemMessage ? systemMessage : DEFAULT_PREFIX,
+        outputParser
+    })
+
+    const runnableAgent = RunnableSequence.from([
+        {
+            [inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
+            agent_scratchpad: async (i: { input: string; steps: AgentStep[] }) => await constructScratchPad(i.steps),
+            [memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
+                const messages = (await memory.getChatMessages(flowObj?.sessionId, true, chatHistory)) as BaseMessage[]
+                return messages ?? []
+            }
+        },
+        prompt,
+        modelWithStop,
+        outputParser
+    ])
+
+    const executor = AgentExecutor.fromAgentAndTools({
+        agent: runnableAgent,
+        tools,
+        sessionId: flowObj?.sessionId,
+        chatId: flowObj?.chatId,
+        input: flowObj?.input,
+        verbose: process.env.DEBUG === 'true' ? true : false
+    })
+
+    return executor
+}
+
+const constructScratchPad = async (steps: AgentStep[]): Promise<BaseMessage[]> => {
+    const thoughts: BaseMessage[] = []
+    for (const step of steps) {
+        thoughts.push(new AIMessage(step.action.log))
+        thoughts.push(
+            new HumanMessage(
+                renderTemplate(TEMPLATE_TOOL_RESPONSE, 'f-string', {
+                    observation: step.observation
+                })
+            )
+        )
+    }
+    return thoughts
+}
+
 module.exports = { nodeClass: ConversationalAgent_Agents }

packages/components/nodes/agents/ConversationalRetrievalAgent/ConversationalRetrievalAgent.ts

@@ -1,9 +1,14 @@
-import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ChainValues, AgentStep, BaseMessage } from 'langchain/schema'
-import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
-import { getBaseClasses, mapChatHistory } from '../../../src/utils'
 import { flatten } from 'lodash'
-import { BaseChatMemory } from 'langchain/memory'
+import { ChatOpenAI } from 'langchain/chat_models/openai'
+import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'
+import { formatToOpenAIFunction } from 'langchain/tools'
+import { RunnableSequence } from 'langchain/schema/runnable'
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
 import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
+import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
+import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
 
 const defaultMessage = `Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`
 
@@ -17,8 +22,9 @@ class ConversationalRetrievalAgent_Agents implements INode {
     category: string
     baseClasses: string[]
     inputs: INodeParams[]
+    sessionId?: string
 
-    constructor() {
+    constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversational Retrieval Agent'
         this.name = 'conversationalRetrievalAgent'
         this.version = 3.0
@@ -54,55 +60,96 @@ class ConversationalRetrievalAgent_Agents implements INode {
                 additionalParams: true
             }
         ]
+        this.sessionId = fields?.sessionId
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
-        const model = nodeData.inputs?.model
+        return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-        const memory = nodeData.inputs?.memory as BaseChatMemory
-        const systemMessage = nodeData.inputs?.systemMessage as string
-
-        let tools = nodeData.inputs?.tools
-        tools = flatten(tools)
-
-        const executor = await initializeAgentExecutorWithOptions(tools, model, {
-            agentType: 'openai-functions',
-            verbose: process.env.DEBUG === 'true' ? true : false,
-            agentArgs: {
-                prefix: systemMessage ?? defaultMessage
-            },
-            returnIntermediateSteps: true
-        })
-        executor.memory = memory
-        return executor
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
-        const executor = nodeData.instance as AgentExecutor
+        const memory = nodeData.inputs?.memory as FlowiseMemory
-
+        const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-        if (executor.memory) {
-            ;(executor.memory as any).memoryKey = 'chat_history'
-            ;(executor.memory as any).outputKey = 'output'
-            ;(executor.memory as any).returnMessages = true
-
-            const chatHistoryClassName = (executor.memory as any).chatHistory.constructor.name
-            // Only replace when its In-Memory
-            if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
-                ;(executor.memory as any).chatHistory = mapChatHistory(options)
-            }
-        }
 
         const loggerHandler = new ConsoleCallbackHandler(options.logger)
         const callbacks = await additionalCallbacks(nodeData, options)
 
+        let res: ChainValues = {}
+
         if (options.socketIO && options.socketIOClientId) {
             const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
-            const result = await executor.call({ input }, [loggerHandler, handler, ...callbacks])
+            res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
-            return result?.output
         } else {
-            const result = await executor.call({ input }, [loggerHandler, ...callbacks])
+            res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
-            return result?.output
         }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: res?.output,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        return res?.output
     }
 }
 
+const prepareAgent = (
+    nodeData: INodeData,
+    flowObj: { sessionId?: string; chatId?: string; input?: string },
+    chatHistory: IMessage[] = []
+) => {
+    const model = nodeData.inputs?.model as ChatOpenAI
+    const memory = nodeData.inputs?.memory as FlowiseMemory
+    const systemMessage = nodeData.inputs?.systemMessage as string
+    let tools = nodeData.inputs?.tools
+    tools = flatten(tools)
+    const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
+    const inputKey = memory.inputKey ? memory.inputKey : 'input'
+
+    const prompt = ChatPromptTemplate.fromMessages([
+        ['ai', systemMessage ? systemMessage : defaultMessage],
+        new MessagesPlaceholder(memoryKey),
+        ['human', `{${inputKey}}`],
+        new MessagesPlaceholder('agent_scratchpad')
+    ])
+
+    const modelWithFunctions = model.bind({
+        functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
+    })
+
+    const runnableAgent = RunnableSequence.from([
+        {
+            [inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
+            agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
+            [memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
+                const messages = (await memory.getChatMessages(flowObj?.sessionId, true, chatHistory)) as BaseMessage[]
+                return messages ?? []
+            }
+        },
+        prompt,
+        modelWithFunctions,
+        new OpenAIFunctionsAgentOutputParser()
+    ])
+
+    const executor = AgentExecutor.fromAgentAndTools({
+        agent: runnableAgent,
+        tools,
+        sessionId: flowObj?.sessionId,
+        chatId: flowObj?.chatId,
+        input: flowObj?.input,
+        returnIntermediateSteps: true,
+        verbose: process.env.DEBUG === 'true' ? true : false
+    })
+
+    return executor
+}
+
 module.exports = { nodeClass: ConversationalRetrievalAgent_Agents }

packages/components/nodes/agents/OpenAIAssistant/OpenAIAssistant.ts

@@ -96,45 +96,51 @@ class OpenAIAssistant_Agents implements INode {
         return null
     }
 
-    //@ts-ignore
+    async clearChatMessages(nodeData: INodeData, options: ICommonObject, sessionIdObj: { type: string; id: string }): Promise<void> {
-    memoryMethods = {
+        const selectedAssistantId = nodeData.inputs?.selectedAssistant as string
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
+        const appDataSource = options.appDataSource as DataSource
-            const selectedAssistantId = nodeData.inputs?.selectedAssistant as string
+        const databaseEntities = options.databaseEntities as IDatabaseEntity
-            const appDataSource = options.appDataSource as DataSource
-            const databaseEntities = options.databaseEntities as IDatabaseEntity
-            let sessionId = nodeData.inputs?.sessionId as string
 
-            const assistant = await appDataSource.getRepository(databaseEntities['Assistant']).findOneBy({
+        const assistant = await appDataSource.getRepository(databaseEntities['Assistant']).findOneBy({
-                id: selectedAssistantId
+            id: selectedAssistantId
+        })
+
+        if (!assistant) {
+            options.logger.error(`Assistant ${selectedAssistantId} not found`)
+            return
+        }
+
+        if (!sessionIdObj) return
+
+        let sessionId = ''
+        if (sessionIdObj.type === 'chatId') {
+            const chatId = sessionIdObj.id
+            const chatmsg = await appDataSource.getRepository(databaseEntities['ChatMessage']).findOneBy({
+                chatId
             })
-
+            if (!chatmsg) {
-            if (!assistant) {
+                options.logger.error(`Chat Message with Chat Id: ${chatId} not found`)
-                options.logger.error(`Assistant ${selectedAssistantId} not found`)
                 return
             }
+            sessionId = chatmsg.sessionId
+        } else if (sessionIdObj.type === 'threadId') {
+            sessionId = sessionIdObj.id
+        }
 
-            if (!sessionId && options.chatId) {
+        const credentialData = await getCredentialData(assistant.credential ?? '', options)
-                const chatmsg = await appDataSource.getRepository(databaseEntities['ChatMessage']).findOneBy({
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
-                    chatId: options.chatId
+        if (!openAIApiKey) {
-                })
+            options.logger.error(`OpenAI ApiKey not found`)
-                if (!chatmsg) {
+            return
-                    options.logger.error(`Chat Message with Chat Id: ${options.chatId} not found`)
+        }
-                    return
-                }
-                sessionId = chatmsg.sessionId
-            }
 
-            const credentialData = await getCredentialData(assistant.credential ?? '', options)
+        const openai = new OpenAI({ apiKey: openAIApiKey })
-            const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
+        options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
-            if (!openAIApiKey) {
+        try {
-                options.logger.error(`OpenAI ApiKey not found`)
-                return
-            }
-
-            const openai = new OpenAI({ apiKey: openAIApiKey })
-            options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
             if (sessionId) await openai.beta.threads.del(sessionId)
             options.logger.info(`Successfully cleared OpenAI Thread ${sessionId}`)
+        } catch (e) {
+            throw new Error(e)
         }
     }
 
@@ -297,7 +303,11 @@ class OpenAIAssistant_Agents implements INode {
                                         options.socketIO.to(options.socketIOClientId).emit('tool', tool.name)
 
                                     try {
-                                        const toolOutput = await tool.call(actions[i].toolInput, undefined, undefined, threadId)
+                                        const toolOutput = await tool.call(actions[i].toolInput, undefined, undefined, {
+                                            sessionId: threadId,
+                                            chatId: options.chatId,
+                                            input
+                                        })
                                         await analyticHandlers.onToolEnd(toolIds, toolOutput)
                                         submitToolOutputs.push({
                                             tool_call_id: actions[i].toolCallId,
@@ -462,6 +472,7 @@ class OpenAIAssistant_Agents implements INode {
             const imageRegex = /<img[^>]*\/>/g
             let llmOutput = returnVal.replace(imageRegex, '')
             llmOutput = llmOutput.replace('<br/>', '')
+
             await analyticHandlers.onLLMEnd(llmIds, llmOutput)
             await analyticHandlers.onChainEnd(parentIds, messageData, true)
 

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

@@ -1,17 +1,14 @@
-import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ChainValues, AgentStep, BaseMessage } from 'langchain/schema'
-import { AgentExecutor as LCAgentExecutor, AgentExecutorInput } from 'langchain/agents'
-import { ChainValues, AgentStep, AgentFinish, AgentAction, BaseMessage, FunctionMessage, AIMessage } from 'langchain/schema'
-import { OutputParserException } from 'langchain/schema/output_parser'
-import { CallbackManagerForChainRun } from 'langchain/callbacks'
-import { formatToOpenAIFunction } from 'langchain/tools'
-import { ToolInputParsingException, Tool } from '@langchain/core/tools'
 import { getBaseClasses } from '../../../src/utils'
 import { flatten } from 'lodash'
 import { RunnableSequence } from 'langchain/schema/runnable'
+import { formatToOpenAIFunction } from 'langchain/tools'
+import { ChatOpenAI } from 'langchain/chat_models/openai'
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
 import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'
-import { ChatOpenAI } from 'langchain/chat_models/openai'
 import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
+import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
 
 class OpenAIFunctionAgent_Agents implements INode {
     label: string
@@ -25,7 +22,7 @@ class OpenAIFunctionAgent_Agents implements INode {
     inputs: INodeParams[]
     sessionId?: string
 
-    constructor(fields: { sessionId?: string }) {
+    constructor(fields?: { sessionId?: string }) {
         this.label = 'OpenAI Function Agent'
         this.name = 'openAIFunctionAgent'
         this.version = 3.0
@@ -33,7 +30,7 @@ class OpenAIFunctionAgent_Agents implements INode {
         this.category = 'Agents'
         this.icon = 'function.svg'
         this.description = `An agent that uses Function Calling to pick the tool and args to call`
-        this.baseClasses = [this.type, ...getBaseClasses(LCAgentExecutor)]
+        this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
         this.inputs = [
             {
                 label: 'Allowed Tools',
@@ -63,19 +60,13 @@ class OpenAIFunctionAgent_Agents implements INode {
         this.sessionId = fields?.sessionId
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
-        const memory = nodeData.inputs?.memory as FlowiseMemory
+        return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-
-        const executor = prepareAgent(nodeData, this.sessionId)
-        if (memory) executor.memory = memory
-
-        return executor
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
         const memory = nodeData.inputs?.memory as FlowiseMemory
-
+        const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
-        const executor = prepareAgent(nodeData, this.sessionId)
 
         const loggerHandler = new ConsoleCallbackHandler(options.logger)
         const callbacks = await additionalCallbacks(nodeData, options)
@@ -107,17 +98,11 @@ class OpenAIFunctionAgent_Agents implements INode {
     }
 }
 
-const formatAgentSteps = (steps: AgentStep[]): BaseMessage[] =>
+const prepareAgent = (
-    steps.flatMap(({ action, observation }) => {
+    nodeData: INodeData,
-        if ('messageLog' in action && action.messageLog !== undefined) {
+    flowObj: { sessionId?: string; chatId?: string; input?: string },
-            const log = action.messageLog as BaseMessage[]
+    chatHistory: IMessage[] = []
-            return log.concat(new FunctionMessage(observation, action.tool))
+) => {
-        } else {
-            return [new AIMessage(action.log)]
-        }
-    })
-
-const prepareAgent = (nodeData: INodeData, sessionId?: string) => {
     const model = nodeData.inputs?.model as ChatOpenAI
     const memory = nodeData.inputs?.memory as FlowiseMemory
     const systemMessage = nodeData.inputs?.systemMessage as string
@@ -127,7 +112,7 @@ const prepareAgent = (nodeData: INodeData, sessionId?: string) => {
     const inputKey = memory.inputKey ? memory.inputKey : 'input'
 
     const prompt = ChatPromptTemplate.fromMessages([
-        ['ai', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
+        ['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
         new MessagesPlaceholder(memoryKey),
         ['human', `{${inputKey}}`],
         new MessagesPlaceholder('agent_scratchpad')
@@ -142,7 +127,7 @@ const prepareAgent = (nodeData: INodeData, sessionId?: string) => {
             [inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
             agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
             [memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
-                const messages = (await memory.getChatMessages(sessionId, true)) as BaseMessage[]
+                const messages = (await memory.getChatMessages(flowObj?.sessionId, true, chatHistory)) as BaseMessage[]
                 return messages ?? []
             }
         },
@@ -154,231 +139,13 @@ const prepareAgent = (nodeData: INodeData, sessionId?: string) => {
     const executor = AgentExecutor.fromAgentAndTools({
         agent: runnableAgent,
         tools,
-        sessionId
+        sessionId: flowObj?.sessionId,
+        chatId: flowObj?.chatId,
+        input: flowObj?.input,
+        verbose: process.env.DEBUG === 'true' ? true : false
     })
 
     return executor
 }
 
-type AgentExecutorOutput = ChainValues
-
-class AgentExecutor extends LCAgentExecutor {
-    sessionId?: string
-
-    static fromAgentAndTools(fields: AgentExecutorInput & { sessionId?: string }): AgentExecutor {
-        const newInstance = new AgentExecutor(fields)
-        if (fields.sessionId) newInstance.sessionId = fields.sessionId
-        return newInstance
-    }
-
-    shouldContinueIteration(iterations: number): boolean {
-        return this.maxIterations === undefined || iterations < this.maxIterations
-    }
-
-    async _call(inputs: ChainValues, runManager?: CallbackManagerForChainRun): Promise<AgentExecutorOutput> {
-        const toolsByName = Object.fromEntries(this.tools.map((t) => [t.name.toLowerCase(), t]))
-
-        const steps: AgentStep[] = []
-        let iterations = 0
-
-        const getOutput = async (finishStep: AgentFinish): Promise<AgentExecutorOutput> => {
-            const { returnValues } = finishStep
-            const additional = await this.agent.prepareForOutput(returnValues, steps)
-
-            if (this.returnIntermediateSteps) {
-                return { ...returnValues, intermediateSteps: steps, ...additional }
-            }
-            await runManager?.handleAgentEnd(finishStep)
-            return { ...returnValues, ...additional }
-        }
-
-        while (this.shouldContinueIteration(iterations)) {
-            let output
-            try {
-                output = await this.agent.plan(steps, inputs, runManager?.getChild())
-            } catch (e) {
-                if (e instanceof OutputParserException) {
-                    let observation
-                    let text = e.message
-                    if (this.handleParsingErrors === true) {
-                        if (e.sendToLLM) {
-                            observation = e.observation
-                            text = e.llmOutput ?? ''
-                        } else {
-                            observation = 'Invalid or incomplete response'
-                        }
-                    } else if (typeof this.handleParsingErrors === 'string') {
-                        observation = this.handleParsingErrors
-                    } else if (typeof this.handleParsingErrors === 'function') {
-                        observation = this.handleParsingErrors(e)
-                    } else {
-                        throw e
-                    }
-                    output = {
-                        tool: '_Exception',
-                        toolInput: observation,
-                        log: text
-                    } as AgentAction
-                } else {
-                    throw e
-                }
-            }
-            // Check if the agent has finished
-            if ('returnValues' in output) {
-                return getOutput(output)
-            }
-
-            let actions: AgentAction[]
-            if (Array.isArray(output)) {
-                actions = output as AgentAction[]
-            } else {
-                actions = [output as AgentAction]
-            }
-
-            const newSteps = await Promise.all(
-                actions.map(async (action) => {
-                    await runManager?.handleAgentAction(action)
-                    const tool = action.tool === '_Exception' ? new ExceptionTool() : toolsByName[action.tool?.toLowerCase()]
-                    let observation
-                    try {
-                        // here we need to override Tool call method to include sessionId as parameter
-                        observation = tool
-                            ? // @ts-ignore
-                              await tool.call(action.toolInput, runManager?.getChild(), undefined, this.sessionId)
-                            : `${action.tool} is not a valid tool, try another one.`
-                    } catch (e) {
-                        if (e instanceof ToolInputParsingException) {
-                            if (this.handleParsingErrors === true) {
-                                observation = 'Invalid or incomplete tool input. Please try again.'
-                            } else if (typeof this.handleParsingErrors === 'string') {
-                                observation = this.handleParsingErrors
-                            } else if (typeof this.handleParsingErrors === 'function') {
-                                observation = this.handleParsingErrors(e)
-                            } else {
-                                throw e
-                            }
-                            observation = await new ExceptionTool().call(observation, runManager?.getChild())
-                            return { action, observation: observation ?? '' }
-                        }
-                    }
-                    return { action, observation: observation ?? '' }
-                })
-            )
-
-            steps.push(...newSteps)
-
-            const lastStep = steps[steps.length - 1]
-            const lastTool = toolsByName[lastStep.action.tool?.toLowerCase()]
-
-            if (lastTool?.returnDirect) {
-                return getOutput({
-                    returnValues: { [this.agent.returnValues[0]]: lastStep.observation },
-                    log: ''
-                })
-            }
-
-            iterations += 1
-        }
-
-        const finish = await this.agent.returnStoppedResponse(this.earlyStoppingMethod, steps, inputs)
-
-        return getOutput(finish)
-    }
-
-    async _takeNextStep(
-        nameToolMap: Record<string, Tool>,
-        inputs: ChainValues,
-        intermediateSteps: AgentStep[],
-        runManager?: CallbackManagerForChainRun
-    ): Promise<AgentFinish | AgentStep[]> {
-        let output
-        try {
-            output = await this.agent.plan(intermediateSteps, inputs, runManager?.getChild())
-        } catch (e) {
-            if (e instanceof OutputParserException) {
-                let observation
-                let text = e.message
-                if (this.handleParsingErrors === true) {
-                    if (e.sendToLLM) {
-                        observation = e.observation
-                        text = e.llmOutput ?? ''
-                    } else {
-                        observation = 'Invalid or incomplete response'
-                    }
-                } else if (typeof this.handleParsingErrors === 'string') {
-                    observation = this.handleParsingErrors
-                } else if (typeof this.handleParsingErrors === 'function') {
-                    observation = this.handleParsingErrors(e)
-                } else {
-                    throw e
-                }
-                output = {
-                    tool: '_Exception',
-                    toolInput: observation,
-                    log: text
-                } as AgentAction
-            } else {
-                throw e
-            }
-        }
-
-        if ('returnValues' in output) {
-            return output
-        }
-
-        let actions: AgentAction[]
-        if (Array.isArray(output)) {
-            actions = output as AgentAction[]
-        } else {
-            actions = [output as AgentAction]
-        }
-
-        const result: AgentStep[] = []
-        for (const agentAction of actions) {
-            let observation = ''
-            if (runManager) {
-                await runManager?.handleAgentAction(agentAction)
-            }
-            if (agentAction.tool in nameToolMap) {
-                const tool = nameToolMap[agentAction.tool]
-                try {
-                    // here we need to override Tool call method to include sessionId as parameter
-                    // @ts-ignore
-                    observation = await tool.call(agentAction.toolInput, runManager?.getChild(), undefined, this.sessionId)
-                } catch (e) {
-                    if (e instanceof ToolInputParsingException) {
-                        if (this.handleParsingErrors === true) {
-                            observation = 'Invalid or incomplete tool input. Please try again.'
-                        } else if (typeof this.handleParsingErrors === 'string') {
-                            observation = this.handleParsingErrors
-                        } else if (typeof this.handleParsingErrors === 'function') {
-                            observation = this.handleParsingErrors(e)
-                        } else {
-                            throw e
-                        }
-                        observation = await new ExceptionTool().call(observation, runManager?.getChild())
-                    }
-                }
-            } else {
-                observation = `${agentAction.tool} is not a valid tool, try another available tool: ${Object.keys(nameToolMap).join(', ')}`
-            }
-            result.push({
-                action: agentAction,
-                observation
-            })
-        }
-        return result
-    }
-}
-
-class ExceptionTool extends Tool {
-    name = '_Exception'
-
-    description = 'Exception tool'
-
-    async _call(query: string) {
-        return query
-    }
-}
-
 module.exports = { nodeClass: OpenAIFunctionAgent_Agents }

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

@@ -1,14 +1,15 @@
-import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { ConversationChain } from 'langchain/chains'
-import { getBaseClasses, mapChatHistory } from '../../../src/utils'
+import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
 import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts'
-import { BufferMemory } from 'langchain/memory'
 import { BaseChatModel } from 'langchain/chat_models/base'
 import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
-import { flatten } from 'lodash'
+import { RunnableSequence } from 'langchain/schema/runnable'
-import { Document } from 'langchain/document'
+import { StringOutputParser } from 'langchain/schema/output_parser'
+import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
 
 let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
+const inputKey = 'input'
 
 class ConversationChain_Chains implements INode {
     label: string
@@ -20,11 +21,12 @@ class ConversationChain_Chains implements INode {
     baseClasses: string[]
     description: string
     inputs: INodeParams[]
+    sessionId?: string
 
-    constructor() {
+    constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversation Chain'
         this.name = 'conversationChain'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'ConversationChain'
         this.icon = 'conv.svg'
         this.category = 'Chains'
@@ -32,7 +34,7 @@ class ConversationChain_Chains implements INode {
         this.baseClasses = [this.type, ...getBaseClasses(ConversationChain)]
         this.inputs = [
             {
-                label: 'Language Model',
+                label: 'Chat Model',
                 name: 'model',
                 type: 'BaseChatModel'
             },
@@ -41,6 +43,14 @@ class ConversationChain_Chains implements INode {
                 name: 'memory',
                 type: 'BaseMemory'
             },
+            {
+                label: 'Chat Prompt Template',
+                name: 'chatPromptTemplate',
+                type: 'ChatPromptTemplate',
+                description: 'Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable',
+                optional: true
+            },
+            /* Deprecated
             {
                 label: 'Document',
                 name: 'document',
@@ -49,87 +59,133 @@ class ConversationChain_Chains implements INode {
                     'Include whole document into the context window, if you get maximum context length error, please use model with higher context window like Claude 100k, or gpt4 32k',
                 optional: true,
                 list: true
-            },
+            },*/
             {
                 label: 'System Message',
                 name: 'systemMessagePrompt',
                 type: 'string',
                 rows: 4,
+                description: 'If Chat Prompt Template is provided, this will be ignored',
                 additionalParams: true,
                 optional: true,
-                placeholder: 'You are a helpful assistant that write codes'
+                default: systemMessage,
+                placeholder: systemMessage
             }
         ]
+        this.sessionId = fields?.sessionId
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
-        const model = nodeData.inputs?.model as BaseChatModel
+        const chain = prepareChain(nodeData, this.sessionId, options.chatHistory)
-        const memory = nodeData.inputs?.memory as BufferMemory
-        const prompt = nodeData.inputs?.systemMessagePrompt as string
-        const docs = nodeData.inputs?.document as Document[]
-
-        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]))
-            }
-        }
-
-        let finalText = ''
-        for (let i = 0; i < finalDocs.length; i += 1) {
-            finalText += finalDocs[i].pageContent
-        }
-
-        const replaceChar: string[] = ['{', '}']
-        for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
-
-        if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
-
-        const obj: any = {
-            llm: model,
-            memory,
-            verbose: process.env.DEBUG === 'true' ? true : false
-        }
-
-        const chatPrompt = ChatPromptTemplate.fromMessages([
-            SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
-            new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
-            HumanMessagePromptTemplate.fromTemplate('{input}')
-        ])
-        obj.prompt = chatPrompt
-
-        const chain = new ConversationChain(obj)
         return chain
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
-        const chain = nodeData.instance as ConversationChain
+        const memory = nodeData.inputs?.memory
-        const memory = nodeData.inputs?.memory as BufferMemory
+        const chain = prepareChain(nodeData, this.sessionId, options.chatHistory)
-        memory.returnMessages = true // Return true for BaseChatModel
-
-        if (options && options.chatHistory) {
-            const chatHistoryClassName = memory.chatHistory.constructor.name
-            // Only replace when its In-Memory
-            if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
-                memory.chatHistory = mapChatHistory(options)
-            }
-        }
-
-        chain.memory = memory
 
         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)
-            const res = await chain.call({ input }, [loggerHandler, handler, ...callbacks])
+            callbacks.push(handler)
-            return res?.response
+            res = await chain.invoke({ input }, { callbacks })
         } else {
-            const res = await chain.call({ input }, [loggerHandler, ...callbacks])
+            res = await chain.invoke({ input }, { callbacks })
-            return res?.response
         }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: res,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        return res
     }
 }
 
+const prepareChatPrompt = (nodeData: INodeData) => {
+    const memory = nodeData.inputs?.memory as FlowiseMemory
+    const prompt = nodeData.inputs?.systemMessagePrompt as string
+    const chatPromptTemplate = nodeData.inputs?.chatPromptTemplate as ChatPromptTemplate
+
+    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
+    }
+
+    const chatPrompt = ChatPromptTemplate.fromMessages([
+        SystemMessagePromptTemplate.fromTemplate(prompt ? prompt : systemMessage),
+        new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
+        HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
+    ])
+
+    return chatPrompt
+}
+
+const prepareChain = (nodeData: INodeData, sessionId?: string, chatHistory: IMessage[] = []) => {
+    const model = nodeData.inputs?.model as BaseChatModel
+    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
+        },
+        chatPrompt,
+        model,
+        new StringOutputParser()
+    ])
+
+    return conversationChain
+}
+
 module.exports = { nodeClass: ConversationChain_Chains }

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

@@ -1,20 +1,26 @@
 import { BaseLanguageModel } from 'langchain/base_language'
-import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ConversationalRetrievalQAChain } from 'langchain/chains'
-import { getBaseClasses, mapChatHistory } from '../../../src/utils'
-import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
 import { BaseRetriever } from 'langchain/schema/retriever'
-import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
+import { BufferMemoryInput } from 'langchain/memory'
 import { PromptTemplate } from 'langchain/prompts'
-import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
+import { QA_TEMPLATE, REPHRASE_TEMPLATE, RESPONSE_TEMPLATE } from './prompts'
-import {
+import { Runnable, RunnableSequence, RunnableMap, RunnableBranch, RunnableLambda } from 'langchain/schema/runnable'
-    default_map_reduce_template,
+import { BaseMessage, HumanMessage, AIMessage } from 'langchain/schema'
-    default_qa_template,
+import { StringOutputParser } from 'langchain/schema/output_parser'
-    qa_template,
+import type { Document } from 'langchain/document'
-    map_reduce_template,
+import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'
-    CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
+import { applyPatch } from 'fast-json-patch'
-    refine_question_template,
+import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
-    refine_template
+import { ConsoleCallbackHandler, additionalCallbacks } from '../../../src/handler'
-} from './prompts'
+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
+    question: string
+}
+
+const sourceRunnableName = 'FindDocs'
 
 class ConversationalRetrievalQAChain_Chains implements INode {
     label: string
@@ -26,11 +32,12 @@ class ConversationalRetrievalQAChain_Chains implements INode {
     baseClasses: string[]
     description: string
     inputs: INodeParams[]
+    sessionId?: string
 
-    constructor() {
+    constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversational Retrieval QA Chain'
         this.name = 'conversationalRetrievalQAChain'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'ConversationalRetrievalQAChain'
         this.icon = 'qa.svg'
         this.category = 'Chains'
@@ -38,9 +45,9 @@ class ConversationalRetrievalQAChain_Chains implements INode {
         this.baseClasses = [this.type, ...getBaseClasses(ConversationalRetrievalQAChain)]
         this.inputs = [
             {
-                label: 'Language Model',
+                label: 'Chat Model',
                 name: 'model',
-                type: 'BaseLanguageModel'
+                type: 'BaseChatModel'
             },
             {
                 label: 'Vector Store Retriever',
@@ -60,6 +67,29 @@ class ConversationalRetrievalQAChain_Chains implements INode {
                 type: 'boolean',
                 optional: true
             },
+            {
+                label: 'Rephrase Prompt',
+                name: 'rephrasePrompt',
+                type: 'string',
+                description: 'Using previous chat history, rephrase question into a standalone question',
+                warning: 'Prompt must include input variables: {chat_history} and {question}',
+                rows: 4,
+                additionalParams: true,
+                optional: true,
+                default: REPHRASE_TEMPLATE
+            },
+            {
+                label: 'Response Prompt',
+                name: 'responsePrompt',
+                type: 'string',
+                description: 'Taking the rephrased question, search for answer from the provided context',
+                warning: 'Prompt must include input variable: {context}',
+                rows: 4,
+                additionalParams: true,
+                optional: true,
+                default: RESPONSE_TEMPLATE
+            }
+            /** Deprecated
             {
                 label: 'System Message',
                 name: 'systemMessagePrompt',
@@ -70,6 +100,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
                 placeholder:
                     'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
             },
+            // TODO: create standalone chains for these 3 modes as they are not compatible with memory
             {
                 label: 'Chain Option',
                 name: 'chainOption',
@@ -95,124 +126,252 @@ class ConversationalRetrievalQAChain_Chains implements INode {
                 additionalParams: true,
                 optional: true
             }
+            */
         ]
+        this.sessionId = fields?.sessionId
     }
 
     async init(nodeData: INodeData): Promise<any> {
         const model = nodeData.inputs?.model as BaseLanguageModel
         const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
         const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
-        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+        const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
-        const chainOption = nodeData.inputs?.chainOption as string
+        const responsePrompt = nodeData.inputs?.responsePrompt as string
-        const externalMemory = nodeData.inputs?.memory
 
-        const obj: any = {
+        let customResponsePrompt = responsePrompt
-            verbose: process.env.DEBUG === 'true' ? true : false,
+        // If the deprecated systemMessagePrompt is still exists
-            questionGeneratorChainOptions: {
+        if (systemMessagePrompt) {
-                template: CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
+            customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
-            }
         }
 
-        if (returnSourceDocuments) obj.returnSourceDocuments = returnSourceDocuments
+        const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
-
+        return answerChain
-        if (chainOption === 'map_reduce') {
-            obj.qaChainOptions = {
-                type: 'map_reduce',
-                combinePrompt: PromptTemplate.fromTemplate(
-                    systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
-                )
-            } as QAChainParams
-        } else if (chainOption === 'refine') {
-            const qprompt = new PromptTemplate({
-                inputVariables: ['context', 'question'],
-                template: refine_question_template(systemMessagePrompt)
-            })
-            const rprompt = new PromptTemplate({
-                inputVariables: ['context', 'question', 'existing_answer'],
-                template: refine_template
-            })
-            obj.qaChainOptions = {
-                type: 'refine',
-                questionPrompt: qprompt,
-                refinePrompt: rprompt
-            } as QAChainParams
-        } else {
-            obj.qaChainOptions = {
-                type: 'stuff',
-                prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
-            } as QAChainParams
-        }
-
-        if (externalMemory) {
-            externalMemory.memoryKey = 'chat_history'
-            externalMemory.inputKey = 'question'
-            externalMemory.outputKey = 'text'
-            externalMemory.returnMessages = true
-            if (chainOption === 'refine') externalMemory.outputKey = 'output_text'
-            obj.memory = externalMemory
-        } else {
-            const fields: BufferMemoryInput = {
-                memoryKey: 'chat_history',
-                inputKey: 'question',
-                outputKey: 'text',
-                returnMessages: true
-            }
-            if (chainOption === 'refine') fields.outputKey = 'output_text'
-            obj.memory = new BufferMemory(fields)
-        }
-
-        const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, obj)
-        return chain
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
-        const chain = nodeData.instance as ConversationalRetrievalQAChain
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const externalMemory = nodeData.inputs?.memory
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
+        const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
+        const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
+        const responsePrompt = nodeData.inputs?.responsePrompt as string
         const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
-        const chainOption = nodeData.inputs?.chainOption as string
 
-        let model = nodeData.inputs?.model
+        let customResponsePrompt = responsePrompt
-
+        // If the deprecated systemMessagePrompt is still exists
-        // Temporary fix: https://github.com/hwchase17/langchainjs/issues/754
+        if (systemMessagePrompt) {
-        model.streaming = false
+            customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
-        chain.questionGeneratorChain.llm = model
-
-        const obj = { question: input }
-
-        if (options && options.chatHistory && chain.memory) {
-            const chatHistoryClassName = (chain.memory as any).chatHistory.constructor.name
-            // Only replace when its In-Memory
-            if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
-                ;(chain.memory as any).chatHistory = mapChatHistory(options)
-            }
         }
 
+        let memory: FlowiseMemory | undefined = externalMemory
+        if (!memory) {
+            memory = new BufferMemory({
+                returnMessages: true,
+                memoryKey: 'chat_history',
+                inputKey: 'input'
+            })
+        }
+
+        const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
+
+        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)
 
-        if (options.socketIO && options.socketIOClientId) {
+        let callbacks = [loggerHandler, ...additionalCallback]
-            const handler = new CustomChainHandler(
+
-                options.socketIO,
+        if (process.env.DEBUG === 'true') {
-                options.socketIOClientId,
+            callbacks.push(new LCConsoleCallbackHandler())
-                chainOption === 'refine' ? 4 : undefined,
-                returnSourceDocuments
-            )
-            const res = await chain.call(obj, [loggerHandler, handler, ...callbacks])
-            if (chainOption === 'refine') {
-                if (res.output_text && res.sourceDocuments) {
-                    return {
-                        text: res.output_text,
-                        sourceDocuments: res.sourceDocuments
-                    }
-                }
-                return res?.output_text
-            }
-            if (res.text && res.sourceDocuments) return res
-            return res?.text
-        } else {
-            const res = await chain.call(obj, [loggerHandler, ...callbacks])
-            if (res.text && res.sourceDocuments) return res
-            return res?.text
         }
+
+        const stream = answerChain.streamLog(
+            { question: input, chat_history: history },
+            { callbacks },
+            {
+                includeNames: [sourceRunnableName]
+            }
+        )
+
+        let streamedResponse: Record<string, any> = {}
+        let sourceDocuments: ICommonObject[] = []
+        let text = ''
+        let isStreamingStarted = false
+        const isStreamingEnabled = options.socketIO && options.socketIOClientId
+
+        for await (const chunk of stream) {
+            streamedResponse = applyPatch(streamedResponse, chunk.ops).newDocument
+
+            if (streamedResponse.final_output) {
+                text = streamedResponse.final_output?.output
+                if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('end')
+                if (Array.isArray(streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output)) {
+                    sourceDocuments = streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output
+                    if (isStreamingEnabled && returnSourceDocuments)
+                        options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
+                }
+            }
+
+            if (
+                Array.isArray(streamedResponse?.streamed_output) &&
+                streamedResponse?.streamed_output.length &&
+                !streamedResponse.final_output
+            ) {
+                const token = streamedResponse.streamed_output[streamedResponse.streamed_output.length - 1]
+
+                if (!isStreamingStarted) {
+                    isStreamingStarted = true
+                    if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('start', token)
+                }
+                if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('token', token)
+            }
+        }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: text,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        if (returnSourceDocuments) return { text, sourceDocuments }
+        else return { text }
+    }
+}
+
+const createRetrieverChain = (llm: BaseLanguageModel, retriever: Runnable, rephrasePrompt: string) => {
+    // Small speed/accuracy optimization: no need to rephrase the first question
+    // since there shouldn't be any meta-references to prior chat history
+    const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(rephrasePrompt)
+    const condenseQuestionChain = RunnableSequence.from([CONDENSE_QUESTION_PROMPT, llm, new StringOutputParser()]).withConfig({
+        runName: 'CondenseQuestion'
+    })
+
+    const hasHistoryCheckFn = RunnableLambda.from((input: RetrievalChainInput) => input.chat_history.length > 0).withConfig({
+        runName: 'HasChatHistoryCheck'
+    })
+
+    const conversationChain = condenseQuestionChain.pipe(retriever).withConfig({
+        runName: 'RetrievalChainWithHistory'
+    })
+
+    const basicRetrievalChain = RunnableLambda.from((input: RetrievalChainInput) => input.question)
+        .withConfig({
+            runName: 'Itemgetter:question'
+        })
+        .pipe(retriever)
+        .withConfig({ runName: 'RetrievalChainWithNoHistory' })
+
+    return RunnableBranch.from([[hasHistoryCheckFn, conversationChain], basicRetrievalChain]).withConfig({ runName: sourceRunnableName })
+}
+
+const formatDocs = (docs: Document[]) => {
+    return docs.map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`).join('\n')
+}
+
+const formatChatHistoryAsString = (history: BaseMessage[]) => {
+    return history.map((message) => `${message._getType()}: ${message.content}`).join('\n')
+}
+
+const serializeHistory = (input: any) => {
+    const chatHistory: IMessage[] = input.chat_history || []
+    const convertedChatHistory = []
+    for (const message of chatHistory) {
+        if (message.type === 'userMessage') {
+            convertedChatHistory.push(new HumanMessage({ content: message.message }))
+        }
+        if (message.type === 'apiMessage') {
+            convertedChatHistory.push(new AIMessage({ content: message.message }))
+        }
+    }
+    return convertedChatHistory
+}
+
+const createChain = (
+    llm: BaseLanguageModel,
+    retriever: Runnable,
+    rephrasePrompt = REPHRASE_TEMPLATE,
+    responsePrompt = RESPONSE_TEMPLATE
+) => {
+    const retrieverChain = createRetrieverChain(llm, retriever, rephrasePrompt)
+
+    const context = RunnableMap.from({
+        context: RunnableSequence.from([
+            ({ question, chat_history }) => ({
+                question,
+                chat_history: formatChatHistoryAsString(chat_history)
+            }),
+            retrieverChain,
+            RunnableLambda.from(formatDocs).withConfig({
+                runName: 'FormatDocumentChunks'
+            })
+        ]),
+        question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
+            runName: 'Itemgetter:question'
+        }),
+        chat_history: RunnableLambda.from((input: RetrievalChainInput) => input.chat_history).withConfig({
+            runName: 'Itemgetter:chat_history'
+        })
+    }).withConfig({ tags: ['RetrieveDocs'] })
+
+    const prompt = ChatPromptTemplate.fromMessages([
+        ['system', responsePrompt],
+        new MessagesPlaceholder('chat_history'),
+        ['human', `{question}`]
+    ])
+
+    const responseSynthesizerChain = RunnableSequence.from([prompt, llm, new StringOutputParser()]).withConfig({
+        tags: ['GenerateResponse']
+    })
+
+    const conversationalQAChain = RunnableSequence.from([
+        {
+            question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
+                runName: 'Itemgetter:question'
+            }),
+            chat_history: RunnableLambda.from(serializeHistory).withConfig({
+                runName: 'SerializeHistory'
+            })
+        },
+        context,
+        responseSynthesizerChain
+    ])
+
+    return conversationalQAChain
+}
+
+class BufferMemory extends FlowiseMemory implements MemoryMethods {
+    constructor(fields: BufferMemoryInput) {
+        super(fields)
+    }
+
+    async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
+        await this.chatHistory.clear()
+
+        for (const msg of prevHistory) {
+            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
+            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
+        }
+
+        const memoryResult = await this.loadMemoryVariables({})
+        const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
+        return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
+    }
+
+    async addChatMessages(): Promise<void> {
+        // adding chat messages will be done on the fly in getChatMessages()
+        return
+    }
+
+    async clearChatMessages(): Promise<void> {
+        await this.clear()
     }
 }
 

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

@@ -1,64 +1,27 @@
-export const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-{context}
-
-Question: {question}
-Helpful Answer:`
-
-export const qa_template = `Use the following pieces of context to answer the question at the end.
-
-{context}
-
-Question: {question}
-Helpful Answer:`
-
-export const default_map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer. 
-If you don't know the answer, just say that you don't know. Don't try to make up an answer.
-
-{summaries}
-
-Question: {question}
-Helpful Answer:`
-
-export const map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer. 
-
-{summaries}
-
-Question: {question}
-Helpful Answer:`
-
-export const refine_question_template = (sysPrompt?: string) => {
-    let returnPrompt = ''
-    if (sysPrompt)
-        returnPrompt = `Context information is below. 
----------------------
-{context}
----------------------
-Given the context information and not prior knowledge, ${sysPrompt}
-Answer the question: {question}.
-Answer:`
-    if (!sysPrompt)
-        returnPrompt = `Context information is below. 
----------------------
-{context}
----------------------
-Given the context information and not prior knowledge, answer the question: {question}.
-Answer:`
-    return returnPrompt
-}
-
-export const refine_template = `The original question is as follows: {question}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer (only if needed) with some more context below.
-------------
-{context}
-------------
-Given the new context, refine the original answer to better answer the question. 
-If you can't find answer from the context, return the original answer.`
-
 export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, answer in the same language as the follow up question. include it in the standalone question.
 
 Chat History:
 {chat_history}
 Follow Up Input: {question}
 Standalone question:`
+
+export const RESPONSE_TEMPLATE = `I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". Using the provided context, answer the user's question to the best of your ability using the resources provided.
+If there is nothing in the context relevant to the question at hand, just say "Hmm, I'm not sure" and stop after that. Refuse to answer any question not about the info. Never break character.
+------------
+{context}
+------------
+REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm not sure". Don't try to make up an answer. Never break character.`
+
+export const QA_TEMPLATE = `Use the following pieces of context to answer the question at the end.
+
+{context}
+
+Question: {question}
+Helpful Answer:`
+
+export const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone Question:`

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

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

packages/components/nodes/chains/VectaraChain/VectaraChain.ts

@@ -69,22 +69,23 @@ class VectaraChain_Chains implements INode {
                 options: [
                     {
                         label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
-                        name: 'vectara-summary-ext-v1.2.0'
+                        name: 'vectara-summary-ext-v1.2.0',
+                        description: 'base summarizer, available to all Vectara users'
                     },
                     {
                         label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
                         name: 'vectara-experimental-summary-ext-2023-10-23-small',
-                        description: 'In beta, available to both Growth and Scale Vectara users'
+                        description: `In beta, available to both Growth and <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
                     },
                     {
                         label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
                         name: 'vectara-summary-ext-v1.3.0',
-                        description: 'Only available to paying Scale Vectara users'
+                        description: 'Only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users'
                     },
                     {
                         label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
                         name: 'vectara-experimental-summary-ext-2023-10-23-med',
-                        description: 'In beta, only available to paying Scale Vectara users'
+                        description: `In beta, only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
                     }
                 ],
                 default: 'vectara-summary-ext-v1.2.0'
@@ -228,7 +229,7 @@ class VectaraChain_Chains implements INode {
 
     async run(nodeData: INodeData, input: string): Promise<object> {
         const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
-        const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto'
+        const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng'
         const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
         const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string
         const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7
@@ -247,17 +248,31 @@ class VectaraChain_Chains implements INode {
             lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
         }))
 
+        // Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker)
+        const mmrRerankerId = 272725718
+        const mmrEnabled = vectaraFilter?.mmrConfig?.enabled
+
         const data = {
             query: [
                 {
                     query: input,
                     start: 0,
-                    numResults: topK,
+                    numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK,
+                    corpusKey: corpusKeys,
                     contextConfig: {
                         sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
                         sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
                     },
-                    corpusKey: corpusKeys,
+                    ...(mmrEnabled
+                        ? {
+                              rerankingConfig: {
+                                  rerankerId: mmrRerankerId,
+                                  mmrConfig: {
+                                      diversityBias: vectaraFilter?.mmrConfig.diversityBias
+                                  }
+                              }
+                          }
+                        : {}),
                     summary: [
                         {
                             summarizerPromptName,
@@ -285,6 +300,14 @@ class VectaraChain_Chains implements INode {
             const documents = result.responseSet[0].document
             let rawSummarizedText = ''
 
+            // remove responses that are not in the topK (in case of MMR)
+            // Note that this does not really matter functionally due to the reorder citations, but it is more efficient
+            const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length
+            if (responses.length > maxResponses) {
+                responses.splice(0, maxResponses)
+            }
+
+            // Add metadata to each text response given its corresponding document metadata
             for (let i = 0; i < responses.length; i += 1) {
                 const responseMetadata = responses[i].metadata
                 const documentMetadata = documents[responses[i].documentIndex].metadata
@@ -301,13 +324,13 @@ class VectaraChain_Chains implements INode {
                 responses[i].metadata = combinedMetadata
             }
 
+            // Create the summarization response
             const summaryStatus = result.responseSet[0].summary[0].status
             if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
                 throw new Error(
                     `BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.`
                 )
             }
-
             if (
                 summaryStatus.length > 0 &&
                 summaryStatus[0].code === 'NOT_FOUND' &&
@@ -316,8 +339,8 @@ class VectaraChain_Chains implements INode {
                 throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`)
             }
 
+            // Reorder citations in summary and create the list of returned source documents
             rawSummarizedText = result.responseSet[0].summary[0]?.text
-
             let summarizedText = reorderCitations(rawSummarizedText)
             let summaryResponses = applyCitationOrder(responses, rawSummarizedText)
 

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/ChatLocalAI/ChatLocalAI.ts

@@ -1,5 +1,5 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIChat } from 'langchain/llms/openai'
 import { OpenAIChatInput } from 'langchain/chat_models/openai'
 import { BaseCache } from 'langchain/schema'
@@ -14,6 +14,7 @@ class ChatLocalAI_ChatModels implements INode {
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
@@ -25,6 +26,13 @@ class ChatLocalAI_ChatModels implements INode {
         this.category = 'Chat Models'
         this.description = 'Use local LLMs like llama.cpp, gpt4all using LocalAI'
         this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(OpenAIChat)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['localAIApi'],
+            optional: true
+        }
         this.inputs = [
             {
                 label: 'Cache',
@@ -79,13 +87,16 @@ class ChatLocalAI_ChatModels implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         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 timeout = nodeData.inputs?.timeout as string
         const basePath = nodeData.inputs?.basePath as string
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData)
+
         const cache = nodeData.inputs?.cache as BaseCache
 
         const obj: Partial<OpenAIChatInput> & BaseLLMParams & { openAIApiKey?: string } = {
@@ -98,6 +109,7 @@ class ChatLocalAI_ChatModels implements INode {
         if (topP) obj.topP = parseFloat(topP)
         if (timeout) obj.timeout = parseInt(timeout, 10)
         if (cache) obj.cache = cache
+        if (localAIApiKey) obj.openAIApiKey = localAIApiKey
 
         const model = new OpenAIChat(obj, { basePath })
 

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/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts

@@ -1,4 +1,5 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai'
 
 class LocalAIEmbedding_Embeddings implements INode {
@@ -10,6 +11,7 @@ class LocalAIEmbedding_Embeddings implements INode {
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
@@ -21,6 +23,13 @@ class LocalAIEmbedding_Embeddings implements INode {
         this.category = 'Embeddings'
         this.description = 'Use local embeddings models like llama.cpp'
         this.baseClasses = [this.type, 'Embeddings']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['localAIApi'],
+            optional: true
+        }
         this.inputs = [
             {
                 label: 'Base Path',
@@ -37,15 +46,20 @@ class LocalAIEmbedding_Embeddings implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const modelName = nodeData.inputs?.modelName as string
         const basePath = nodeData.inputs?.basePath as string
 
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData)
+
         const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
             modelName,
             openAIApiKey: 'sk-'
         }
 
+        if (localAIApiKey) obj.openAIApiKey = localAIApiKey
+
         const model = new OpenAIEmbeddings(obj, { basePath })
 
         return model

packages/components/nodes/memory/BufferMemory/BufferMemory.ts

@@ -1,4 +1,4 @@
-import { FlowiseMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
+import { FlowiseMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
 import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
 import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { BaseMessage } from 'langchain/schema'
@@ -55,36 +55,27 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         super(fields)
     }
 
-    async getChatMessages(_?: string, returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
+    async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
+        await this.chatHistory.clear()
+
+        for (const msg of prevHistory) {
+            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
+            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
+        }
+
         const memoryResult = await this.loadMemoryVariables({})
         const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
         return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
     }
 
-    async addChatMessages(msgArray: { text: string; type: MessageType }[]): Promise<void> {
+    async addChatMessages(): Promise<void> {
-        const input = msgArray.find((msg) => msg.type === 'userMessage')
+        // adding chat messages will be done on the fly in getChatMessages()
-        const output = msgArray.find((msg) => msg.type === 'apiMessage')
+        return
-
-        const inputValues = { [this.inputKey ?? 'input']: input?.text }
-        const outputValues = { output: output?.text }
-
-        await this.saveContext(inputValues, outputValues)
     }
 
     async clearChatMessages(): Promise<void> {
         await this.clear()
     }
-
-    async resumeMessages(messages: IMessage[]): Promise<void> {
-        // Clear existing chatHistory to avoid duplication
-        if (messages.length) await this.clear()
-
-        // Insert into chatHistory
-        for (const msg of messages) {
-            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
-            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
-        }
-    }
 }
 
 module.exports = { nodeClass: BufferMemory_Memory }

packages/components/nodes/memory/BufferWindowMemory/BufferWindowMemory.ts

@@ -1,4 +1,4 @@
-import { FlowiseWindowMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
+import { FlowiseWindowMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
 import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
 import { BufferWindowMemory, BufferWindowMemoryInput } from 'langchain/memory'
 import { BaseMessage } from 'langchain/schema'
@@ -67,36 +67,28 @@ class BufferWindowMemoryExtended extends FlowiseWindowMemory implements MemoryMe
         super(fields)
     }
 
-    async getChatMessages(_?: string, returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
+    async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
+        await this.chatHistory.clear()
+
+        // Insert into chatHistory
+        for (const msg of prevHistory) {
+            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
+            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
+        }
+
         const memoryResult = await this.loadMemoryVariables({})
         const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
         return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
     }
 
-    async addChatMessages(msgArray: { text: string; type: MessageType }[]): Promise<void> {
+    async addChatMessages(): Promise<void> {
-        const input = msgArray.find((msg) => msg.type === 'userMessage')
+        // adding chat messages will be done on the fly in getChatMessages()
-        const output = msgArray.find((msg) => msg.type === 'apiMessage')
+        return
-
-        const inputValues = { [this.inputKey ?? 'input']: input?.text }
-        const outputValues = { output: output?.text }
-
-        await this.saveContext(inputValues, outputValues)
     }
 
     async clearChatMessages(): Promise<void> {
         await this.clear()
     }
-
-    async resumeMessages(messages: IMessage[]): Promise<void> {
-        // Clear existing chatHistory to avoid duplication
-        if (messages.length) await this.clear()
-
-        // Insert into chatHistory
-        for (const msg of messages) {
-            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
-            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
-        }
-    }
 }
 
 module.exports = { nodeClass: BufferWindowMemory_Memory }

packages/components/nodes/memory/ConversationSummaryMemory/ConversationSummaryMemory.ts

@@ -1,4 +1,4 @@
-import { FlowiseSummaryMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
+import { FlowiseSummaryMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
 import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
 import { ConversationSummaryMemory, ConversationSummaryMemoryInput } from 'langchain/memory'
 import { BaseLanguageModel } from 'langchain/base_language'
@@ -66,40 +66,32 @@ class ConversationSummaryMemoryExtended extends FlowiseSummaryMemory implements
         super(fields)
     }
 
-    async getChatMessages(_?: string, returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
+    async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
+        await this.chatHistory.clear()
+        this.buffer = ''
+
+        for (const msg of prevHistory) {
+            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
+            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
+        }
+
+        // Get summary
+        const chatMessages = await this.chatHistory.getMessages()
+        this.buffer = chatMessages.length ? await this.predictNewSummary(chatMessages.slice(-2), this.buffer) : ''
+
         const memoryResult = await this.loadMemoryVariables({})
         const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
         return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
     }
 
-    async addChatMessages(msgArray: { text: string; type: MessageType }[]): Promise<void> {
+    async addChatMessages(): Promise<void> {
-        const input = msgArray.find((msg) => msg.type === 'userMessage')
+        // adding chat messages will be done on the fly in getChatMessages()
-        const output = msgArray.find((msg) => msg.type === 'apiMessage')
+        return
-
-        const inputValues = { [this.inputKey ?? 'input']: input?.text }
-        const outputValues = { output: output?.text }
-
-        await this.saveContext(inputValues, outputValues)
     }
 
     async clearChatMessages(): Promise<void> {
         await this.clear()
     }
-
-    async resumeMessages(messages: IMessage[]): Promise<void> {
-        // Clear existing chatHistory to avoid duplication
-        if (messages.length) await this.clear()
-
-        // Insert into chatHistory
-        for (const msg of messages) {
-            if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
-            else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
-        }
-
-        // Replace buffer
-        const chatMessages = await this.chatHistory.getMessages()
-        this.buffer = await this.predictNewSummary(chatMessages.slice(-2), this.buffer)
-    }
 }
 
 module.exports = { nodeClass: ConversationSummaryMemory_Memory }

packages/components/nodes/memory/DynamoDb/DynamoDb.ts

@@ -12,13 +12,7 @@ import {
 import { DynamoDBChatMessageHistory } from 'langchain/stores/message/dynamodb'
 import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { mapStoredMessageToChatMessage, AIMessage, HumanMessage, StoredMessage, BaseMessage } from 'langchain/schema'
-import {
+import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
-    convertBaseMessagetoIMessage,
-    getBaseClasses,
-    getCredentialData,
-    getCredentialParam,
-    serializeChatHistory
-} from '../../../src/utils'
 import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
 
 class DynamoDb_Memory implements INode {
@@ -70,7 +64,8 @@ class DynamoDb_Memory implements INode {
                 label: 'Session ID',
                 name: 'sessionId',
                 type: 'string',
-                description: 'If not specified, the first CHAT_MESSAGE_ID will be used as sessionId',
+                description:
+                    'If not specified, a random id will be used. Learn <a target="_blank" href="https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat">more</a>',
                 default: '',
                 additionalParams: true,
                 optional: true
@@ -88,25 +83,6 @@ class DynamoDb_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return initalizeDynamoDB(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const dynamodbMemory = await initalizeDynamoDB(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing DynamoDb memory session ${sessionId ? sessionId : chatId}`)
-            await dynamodbMemory.clear()
-            options.logger.info(`Successfully cleared DynamoDb memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const memoryKey = nodeData.inputs?.memoryKey as string
-            const dynamodbMemory = await initalizeDynamoDB(nodeData, options)
-            const key = memoryKey ?? 'chat_history'
-            const memoryResult = await dynamodbMemory.loadMemoryVariables({})
-            return serializeChatHistory(memoryResult[key])
-        }
-    }
 }
 
 const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): Promise<BufferMemory> => {
@@ -114,17 +90,7 @@ const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): P
     const partitionKey = nodeData.inputs?.partitionKey as string
     const region = nodeData.inputs?.region as string
     const memoryKey = nodeData.inputs?.memoryKey as string
-    const chatId = options.chatId
+    const sessionId = nodeData.inputs?.sessionId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const accessKeyId = getCredentialParam('accessKey', credentialData, nodeData)
@@ -150,7 +116,6 @@ const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): P
     const memory = new BufferMemoryExtended({
         memoryKey: memoryKey ?? 'chat_history',
         chatHistory: dynamoDb,
-        isSessionIdUsingChatMessageId,
         sessionId,
         dynamodbClient: client
     })
@@ -158,7 +123,6 @@ const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): P
 }
 
 interface BufferMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
     dynamodbClient: DynamoDBClient
     sessionId: string
 }
@@ -178,7 +142,6 @@ interface DynamoDBSerializedChatMessage {
 }
 
 class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
-    isSessionIdUsingChatMessageId = false
     sessionId = ''
     dynamodbClient: DynamoDBClient
 
@@ -306,10 +269,6 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         await this.dynamodbClient.send(new DeleteItemCommand(params))
         await this.clear()
     }
-
-    async resumeMessages(): Promise<void> {
-        return
-    }
 }
 
 module.exports = { nodeClass: DynamoDb_Memory }

packages/components/nodes/memory/MongoDBMemory/MongoDBMemory.ts

@@ -2,13 +2,7 @@ import { MongoClient, Collection, Document } from 'mongodb'
 import { MongoDBChatMessageHistory } from 'langchain/stores/message/mongodb'
 import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { mapStoredMessageToChatMessage, AIMessage, HumanMessage, BaseMessage } from 'langchain/schema'
-import {
+import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
-    convertBaseMessagetoIMessage,
-    getBaseClasses,
-    getCredentialData,
-    getCredentialParam,
-    serializeChatHistory
-} from '../../../src/utils'
 import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
 
 class MongoDB_Memory implements INode {
@@ -55,7 +49,8 @@ class MongoDB_Memory implements INode {
                 label: 'Session Id',
                 name: 'sessionId',
                 type: 'string',
-                description: 'If not specified, the first CHAT_MESSAGE_ID will be used as sessionId',
+                description:
+                    'If not specified, a random id will be used. Learn <a target="_blank" href="https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat">more</a>',
                 default: '',
                 additionalParams: true,
                 optional: true
@@ -73,42 +68,13 @@ class MongoDB_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return initializeMongoDB(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const mongodbMemory = await initializeMongoDB(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing MongoDB memory session ${sessionId ? sessionId : chatId}`)
-            await mongodbMemory.clear()
-            options.logger.info(`Successfully cleared MongoDB memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const memoryKey = nodeData.inputs?.memoryKey as string
-            const mongodbMemory = await initializeMongoDB(nodeData, options)
-            const key = memoryKey ?? 'chat_history'
-            const memoryResult = await mongodbMemory.loadMemoryVariables({})
-            return serializeChatHistory(memoryResult[key])
-        }
-    }
 }
 
 const initializeMongoDB = async (nodeData: INodeData, options: ICommonObject): Promise<BufferMemory> => {
     const databaseName = nodeData.inputs?.databaseName as string
     const collectionName = nodeData.inputs?.collectionName as string
     const memoryKey = nodeData.inputs?.memoryKey as string
-    const chatId = options?.chatId as string
+    const sessionId = nodeData.inputs?.sessionId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
@@ -149,14 +115,12 @@ const initializeMongoDB = async (nodeData: INodeData, options: ICommonObject): P
     return new BufferMemoryExtended({
         memoryKey: memoryKey ?? 'chat_history',
         chatHistory: mongoDBChatMessageHistory,
-        isSessionIdUsingChatMessageId,
         sessionId,
         collection
     })
 }
 
 interface BufferMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
     collection: Collection<Document>
     sessionId: string
 }
@@ -164,7 +128,6 @@ interface BufferMemoryExtendedInput {
 class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     sessionId = ''
     collection: Collection<Document>
-    isSessionIdUsingChatMessageId? = false
 
     constructor(fields: BufferMemoryInput & BufferMemoryExtendedInput) {
         super(fields)
@@ -221,10 +184,6 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         await this.collection.deleteOne({ sessionId: id })
         await this.clear()
     }
-
-    async resumeMessages(): Promise<void> {
-        return
-    }
 }
 
 module.exports = { nodeClass: MongoDB_Memory }

packages/components/nodes/memory/MotorheadMemory/MotorheadMemory.ts

@@ -1,9 +1,14 @@
 import { IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
 import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { ICommonObject } from '../../../src'
-import { MotorheadMemory, MotorheadMemoryInput, InputValues, MemoryVariables, OutputValues, getBufferString } from 'langchain/memory'
+import { MotorheadMemory, MotorheadMemoryInput, InputValues, OutputValues } from 'langchain/memory'
 import fetch from 'node-fetch'
-import { BaseMessage } from 'langchain/schema'
+import { AIMessage, BaseMessage, ChatMessage, HumanMessage } from 'langchain/schema'
+
+type MotorheadMessage = {
+    content: string
+    role: 'Human' | 'AI'
+}
 
 class MotorMemory_Memory implements INode {
     label: string
@@ -46,7 +51,8 @@ class MotorMemory_Memory implements INode {
                 label: 'Session Id',
                 name: 'sessionId',
                 type: 'string',
-                description: 'If not specified, the first CHAT_MESSAGE_ID will be used as sessionId',
+                description:
+                    'If not specified, a random id will be used. Learn <a target="_blank" href="https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat">more</a>',
                 default: '',
                 additionalParams: true,
                 optional: true
@@ -64,49 +70,19 @@ class MotorMemory_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return initalizeMotorhead(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const motorhead = await initalizeMotorhead(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing Motorhead memory session ${sessionId ? sessionId : chatId}`)
-            await motorhead.clear()
-            options.logger.info(`Successfully cleared Motorhead memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const memoryKey = nodeData.inputs?.memoryKey as string
-            const motorhead = await initalizeMotorhead(nodeData, options)
-            const key = memoryKey ?? 'chat_history'
-            const memoryResult = await motorhead.loadMemoryVariables({})
-            return getBufferString(memoryResult[key])
-        }
-    }
 }
 
 const initalizeMotorhead = async (nodeData: INodeData, options: ICommonObject): Promise<MotorheadMemory> => {
     const memoryKey = nodeData.inputs?.memoryKey as string
     const baseURL = nodeData.inputs?.baseURL as string
-    const chatId = options?.chatId as string
+    const sessionId = nodeData.inputs?.sessionId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
     const clientId = getCredentialParam('clientId', credentialData, nodeData)
 
-    let obj: MotorheadMemoryInput & MotorheadMemoryExtendedInput = {
+    let obj: MotorheadMemoryInput = {
         returnMessages: true,
-        isSessionIdUsingChatMessageId,
         sessionId,
         memoryKey
     }
@@ -132,23 +108,9 @@ const initalizeMotorhead = async (nodeData: INodeData, options: ICommonObject):
     return motorheadMemory
 }
 
-interface MotorheadMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
-}
-
 class MotorheadMemoryExtended extends MotorheadMemory implements MemoryMethods {
-    isSessionIdUsingChatMessageId? = false
+    constructor(fields: MotorheadMemoryInput) {
-
-    constructor(fields: MotorheadMemoryInput & MotorheadMemoryExtendedInput) {
         super(fields)
-        this.isSessionIdUsingChatMessageId = fields.isSessionIdUsingChatMessageId
-    }
-
-    async loadMemoryVariables(values: InputValues, overrideSessionId = ''): Promise<MemoryVariables> {
-        if (overrideSessionId) {
-            this.sessionId = overrideSessionId
-        }
-        return super.loadMemoryVariables({ values })
     }
 
     async saveContext(inputValues: InputValues, outputValues: OutputValues, overrideSessionId = ''): Promise<void> {
@@ -180,9 +142,33 @@ class MotorheadMemoryExtended extends MotorheadMemory implements MemoryMethods {
 
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         const id = overrideSessionId ?? this.sessionId
-        const memoryVariables = await this.loadMemoryVariables({}, id)
+        try {
-        const baseMessages = memoryVariables[this.memoryKey]
+            const resp = await this.caller.call(fetch, `${this.url}/sessions/${id}/memory`, {
-        return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
+                //@ts-ignore
+                signal: this.timeout ? AbortSignal.timeout(this.timeout) : undefined,
+                headers: this._getHeaders() as ICommonObject,
+                method: 'GET'
+            })
+            const data = await resp.json()
+            const rawStoredMessages: MotorheadMessage[] = data?.data?.messages ?? []
+
+            const baseMessages = rawStoredMessages.reverse().map((message) => {
+                const { content, role } = message
+                if (role === 'Human') {
+                    return new HumanMessage(content)
+                } else if (role === 'AI') {
+                    return new AIMessage(content)
+                } else {
+                    // default to generic ChatMessage
+                    return new ChatMessage(content, role)
+                }
+            })
+
+            return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
+        } catch (error) {
+            console.error('Error getting session: ', error)
+            return []
+        }
     }
 
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {

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

@@ -1,15 +1,9 @@
-import { INode, INodeData, INodeParams, ICommonObject, IMessage, MessageType, FlowiseMemory, MemoryMethods } from '../../../src/Interface'
+import { Redis } from 'ioredis'
-import {
-    convertBaseMessagetoIMessage,
-    getBaseClasses,
-    getCredentialData,
-    getCredentialParam,
-    serializeChatHistory
-} from '../../../src/utils'
 import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { RedisChatMessageHistory, RedisChatMessageHistoryInput } from 'langchain/stores/message/ioredis'
 import { mapStoredMessageToChatMessage, BaseMessage, AIMessage, HumanMessage } from 'langchain/schema'
-import { Redis } from 'ioredis'
+import { INode, INodeData, INodeParams, ICommonObject, MessageType, IMessage, MemoryMethods, FlowiseMemory } from '../../../src/Interface'
+import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 
 class RedisBackedChatMemory_Memory implements INode {
     label: string
@@ -44,7 +38,8 @@ class RedisBackedChatMemory_Memory implements INode {
                 label: 'Session Id',
                 name: 'sessionId',
                 type: 'string',
-                description: 'If not specified, the first CHAT_MESSAGE_ID will be used as sessionId',
+                description:
+                    'If not specified, a random id will be used. Learn <a target="_blank" href="https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat">more</a>',
                 default: '',
                 additionalParams: true,
                 optional: true
@@ -78,47 +73,19 @@ class RedisBackedChatMemory_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return await initalizeRedis(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const redis = await initalizeRedis(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing Redis memory session ${sessionId ? sessionId : chatId}`)
-            await redis.clear()
-            options.logger.info(`Successfully cleared Redis memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const memoryKey = nodeData.inputs?.memoryKey as string
-            const redis = await initalizeRedis(nodeData, options)
-            const key = memoryKey ?? 'chat_history'
-            const memoryResult = await redis.loadMemoryVariables({})
-            return serializeChatHistory(memoryResult[key])
-        }
-    }
 }
 
 const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Promise<BufferMemory> => {
     const sessionTTL = nodeData.inputs?.sessionTTL as number
     const memoryKey = nodeData.inputs?.memoryKey as string
+    const sessionId = nodeData.inputs?.sessionId as string
     const windowSize = nodeData.inputs?.windowSize as number
-    const chatId = options?.chatId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const redisUrl = getCredentialParam('redisUrl', credentialData, nodeData)
 
     let client: Redis
+
     if (!redisUrl || redisUrl === '') {
         const username = getCredentialParam('redisCacheUser', credentialData, nodeData)
         const password = getCredentialParam('redisCachePwd', credentialData, nodeData)
@@ -153,7 +120,7 @@ const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Prom
 
     const redisChatMessageHistory = new RedisChatMessageHistory(obj)
 
-    redisChatMessageHistory.getMessages = async (): Promise<BaseMessage[]> => {
+    /*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)
@@ -169,44 +136,45 @@ const initalizeRedis = async (nodeData: INodeData, options: ICommonObject): Prom
 
     redisChatMessageHistory.clear = async (): Promise<void> => {
         await client.del((redisChatMessageHistory as any).sessionId)
-    }
+    }*/
 
     const memory = new BufferMemoryExtended({
         memoryKey: memoryKey ?? 'chat_history',
         chatHistory: redisChatMessageHistory,
-        isSessionIdUsingChatMessageId,
         sessionId,
+        windowSize,
         redisClient: client
     })
+
     return memory
 }
 
 interface BufferMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
     redisClient: Redis
     sessionId: string
+    windowSize?: number
 }
 
 class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
-    isSessionIdUsingChatMessageId? = false
     sessionId = ''
     redisClient: Redis
+    windowSize?: number
 
     constructor(fields: BufferMemoryInput & BufferMemoryExtendedInput) {
         super(fields)
-        this.isSessionIdUsingChatMessageId = fields.isSessionIdUsingChatMessageId
         this.sessionId = fields.sessionId
         this.redisClient = fields.redisClient
+        this.windowSize = fields.windowSize
     }
 
-    async getChatMessages(overrideSessionId = '', returnBaseMessage = false): Promise<IMessage[] | BaseMessage[]> {
+    async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         if (!this.redisClient) return []
 
         const id = overrideSessionId ?? this.sessionId
-        const rawStoredMessages = await this.redisClient.lrange(id, 0, -1)
+        const rawStoredMessages = await this.redisClient.lrange(id, this.windowSize ? this.windowSize * -1 : 0, -1)
         const orderedMessages = rawStoredMessages.reverse().map((message) => JSON.parse(message))
         const baseMessages = orderedMessages.map(mapStoredMessageToChatMessage)
-        return returnBaseMessage ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
+        return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
     }
 
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
@@ -236,10 +204,6 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         await this.redisClient.del(id)
         await this.clear()
     }
-
-    async resumeMessages(): Promise<void> {
-        return
-    }
 }
 
 module.exports = { nodeClass: RedisBackedChatMemory_Memory }

packages/components/nodes/memory/UpstashRedisBackedChatMemory/UpstashRedisBackedChatMemory.ts

@@ -3,13 +3,7 @@ import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
 import { UpstashRedisChatMessageHistory } from 'langchain/stores/message/upstash_redis'
 import { mapStoredMessageToChatMessage, AIMessage, HumanMessage, StoredMessage, BaseMessage } from 'langchain/schema'
 import { FlowiseMemory, IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } from '../../../src/Interface'
-import {
+import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
-    convertBaseMessagetoIMessage,
-    getBaseClasses,
-    getCredentialData,
-    getCredentialParam,
-    serializeChatHistory
-} from '../../../src/utils'
 import { ICommonObject } from '../../../src/Interface'
 
 class UpstashRedisBackedChatMemory_Memory implements INode {
@@ -51,7 +45,8 @@ class UpstashRedisBackedChatMemory_Memory implements INode {
                 label: 'Session Id',
                 name: 'sessionId',
                 type: 'string',
-                description: 'If not specified, the first CHAT_MESSAGE_ID will be used as sessionId',
+                description:
+                    'If not specified, a random id will be used. Learn <a target="_blank" href="https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat">more</a>',
                 default: '',
                 additionalParams: true,
                 optional: true
@@ -70,40 +65,12 @@ class UpstashRedisBackedChatMemory_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return initalizeUpstashRedis(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const redis = await initalizeUpstashRedis(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing Upstash Redis memory session ${sessionId ? sessionId : chatId}`)
-            await redis.clear()
-            options.logger.info(`Successfully cleared Upstash Redis memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const redis = await initalizeUpstashRedis(nodeData, options)
-            const key = 'chat_history'
-            const memoryResult = await redis.loadMemoryVariables({})
-            return serializeChatHistory(memoryResult[key])
-        }
-    }
 }
 
 const initalizeUpstashRedis = async (nodeData: INodeData, options: ICommonObject): Promise<BufferMemory> => {
     const baseURL = nodeData.inputs?.baseURL as string
     const sessionTTL = nodeData.inputs?.sessionTTL as string
-    const chatId = options?.chatId as string
+    const sessionId = nodeData.inputs?.sessionId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const upstashRestToken = getCredentialParam('upstashRestToken', credentialData, nodeData)
@@ -122,7 +89,6 @@ const initalizeUpstashRedis = async (nodeData: INodeData, options: ICommonObject
     const memory = new BufferMemoryExtended({
         memoryKey: 'chat_history',
         chatHistory: redisChatMessageHistory,
-        isSessionIdUsingChatMessageId,
         sessionId,
         redisClient: client
     })
@@ -131,19 +97,16 @@ const initalizeUpstashRedis = async (nodeData: INodeData, options: ICommonObject
 }
 
 interface BufferMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
     redisClient: Redis
     sessionId: string
 }
 
 class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
-    isSessionIdUsingChatMessageId? = false
     sessionId = ''
     redisClient: Redis
 
     constructor(fields: BufferMemoryInput & BufferMemoryExtendedInput) {
         super(fields)
-        this.isSessionIdUsingChatMessageId = fields.isSessionIdUsingChatMessageId
         this.sessionId = fields.sessionId
         this.redisClient = fields.redisClient
     }
@@ -186,10 +149,6 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         await this.redisClient.del(id)
         await this.clear()
     }
-
-    async resumeMessages(): Promise<void> {
-        return
-    }
 }
 
 module.exports = { nodeClass: UpstashRedisBackedChatMemory_Memory }

packages/components/nodes/memory/ZepMemory/ZepMemory.ts

@@ -2,7 +2,7 @@ import { IMessage, INode, INodeData, INodeParams, MemoryMethods, MessageType } f
 import { convertBaseMessagetoIMessage, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { ZepMemory, ZepMemoryInput } from 'langchain/memory/zep'
 import { ICommonObject } from '../../../src'
-import { InputValues, MemoryVariables, OutputValues, getBufferString } from 'langchain/memory'
+import { InputValues, MemoryVariables, OutputValues } from 'langchain/memory'
 import { BaseMessage } from 'langchain/schema'
 
 class ZepMemory_Memory implements INode {
@@ -55,10 +55,9 @@ class ZepMemory_Memory implements INode {
                 label: 'Size',
                 name: 'k',
                 type: 'number',
-                placeholder: '10',
+                default: '10',
                 description: 'Window of size k to surface the last k back-and-forth to use as memory.',
-                additionalParams: true,
+                additionalParams: true
-                optional: true
             },
             {
                 label: 'AI Prefix',
@@ -101,27 +100,6 @@ class ZepMemory_Memory implements INode {
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         return await initalizeZep(nodeData, options)
     }
-
-    //@ts-ignore
-    memoryMethods = {
-        async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
-            const zep = await initalizeZep(nodeData, options)
-            const sessionId = nodeData.inputs?.sessionId as string
-            const chatId = options?.chatId as string
-            options.logger.info(`Clearing Zep memory session ${sessionId ? sessionId : chatId}`)
-            await zep.clear()
-            options.logger.info(`Successfully cleared Zep memory session ${sessionId ? sessionId : chatId}`)
-        },
-        async getChatMessages(nodeData: INodeData, options: ICommonObject): Promise<string> {
-            const memoryKey = nodeData.inputs?.memoryKey as string
-            const aiPrefix = nodeData.inputs?.aiPrefix as string
-            const humanPrefix = nodeData.inputs?.humanPrefix as string
-            const zep = await initalizeZep(nodeData, options)
-            const key = memoryKey ?? 'chat_history'
-            const memoryResult = await zep.loadMemoryVariables({})
-            return getBufferString(memoryResult[key], humanPrefix, aiPrefix)
-        }
-    }
 }
 
 const initalizeZep = async (nodeData: INodeData, options: ICommonObject): Promise<ZepMemory> => {
@@ -131,30 +109,19 @@ const initalizeZep = async (nodeData: INodeData, options: ICommonObject): Promis
     const memoryKey = nodeData.inputs?.memoryKey as string
     const inputKey = nodeData.inputs?.inputKey as string
     const k = nodeData.inputs?.k as string
-    const chatId = options?.chatId as string
+    const sessionId = nodeData.inputs?.sessionId as string
-
-    let isSessionIdUsingChatMessageId = false
-    let sessionId = ''
-
-    if (!nodeData.inputs?.sessionId && chatId) {
-        isSessionIdUsingChatMessageId = true
-        sessionId = chatId
-    } else {
-        sessionId = nodeData.inputs?.sessionId
-    }
 
     const credentialData = await getCredentialData(nodeData.credential ?? '', options)
     const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
 
     const obj: ZepMemoryInput & ZepMemoryExtendedInput = {
         baseURL,
-        sessionId,
         aiPrefix,
         humanPrefix,
         returnMessages: true,
         memoryKey,
         inputKey,
-        isSessionIdUsingChatMessageId,
+        sessionId,
         k: k ? parseInt(k, 10) : undefined
     }
     if (apiKey) obj.apiKey = apiKey
@@ -163,17 +130,14 @@ const initalizeZep = async (nodeData: INodeData, options: ICommonObject): Promis
 }
 
 interface ZepMemoryExtendedInput {
-    isSessionIdUsingChatMessageId: boolean
     k?: number
 }
 
 class ZepMemoryExtended extends ZepMemory implements MemoryMethods {
-    isSessionIdUsingChatMessageId? = false
     lastN?: number
 
     constructor(fields: ZepMemoryInput & ZepMemoryExtendedInput) {
         super(fields)
-        this.isSessionIdUsingChatMessageId = fields.isSessionIdUsingChatMessageId
         this.lastN = fields.k
     }
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,5 +1,5 @@
 import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
-import { convertSchemaToZod, getBaseClasses } from '../../../src/utils'
+import { convertSchemaToZod, getBaseClasses, getVars } from '../../../src/utils'
 import { DynamicStructuredTool } from './core'
 import { z } from 'zod'
 import { DataSource } from 'typeorm'
@@ -60,7 +60,7 @@ class CustomTool_Tools implements INode {
         }
     }
 
-    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const selectedToolId = nodeData.inputs?.selectedTool as string
         const customToolFunc = nodeData.inputs?.customToolFunc as string
 
@@ -81,29 +81,9 @@ class CustomTool_Tools implements INode {
             }
             if (customToolFunc) obj.code = customToolFunc
 
-            const variables = await appDataSource.getRepository(databaseEntities['Variable']).find()
+            const variables = await getVars(appDataSource, databaseEntities, nodeData)
 
-            // override variables defined in overrideConfig
+            const flow = { chatflowId: options.chatflowid }
-            // 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 flow = {
-                chatId: options.chatId, // id is uppercase (I)
-                chatflowId: options.chatflowid, // id is lowercase (i)
-                input
-            }
 
             let dynamicStructuredTool = new DynamicStructuredTool(obj)
             dynamicStructuredTool.setVariables(variables)

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'
@@ -55,7 +55,12 @@ export class DynamicStructuredTool<
         this.schema = fields.schema
     }
 
-    async call(arg: z.output<T>, configArg?: RunnableConfig | Callbacks, tags?: string[], overrideSessionId?: string): Promise<string> {
+    async call(
+        arg: z.output<T>,
+        configArg?: RunnableConfig | Callbacks,
+        tags?: string[],
+        flowConfig?: { sessionId?: string; chatId?: string; input?: string }
+    ): Promise<string> {
         const config = parseCallbackConfigArg(configArg)
         if (config.runName === undefined) {
             config.runName = this.name
@@ -86,7 +91,7 @@ export class DynamicStructuredTool<
         )
         let result
         try {
-            result = await this._call(parsed, runManager, overrideSessionId)
+            result = await this._call(parsed, runManager, flowConfig)
         } catch (e) {
             await runManager?.handleToolError(e)
             throw e
@@ -95,7 +100,11 @@ export class DynamicStructuredTool<
         return result
     }
 
-    protected async _call(arg: z.output<T>, _?: CallbackManagerForToolRun, overrideSessionId?: string): Promise<string> {
+    protected async _call(
+        arg: z.output<T>,
+        _?: CallbackManagerForToolRun,
+        flowConfig?: { sessionId?: string; chatId?: string; input?: string }
+    ): Promise<string> {
         let sandbox: any = {}
         if (typeof arg === 'object' && Object.keys(arg).length) {
             for (const item in arg) {
@@ -103,48 +112,13 @@ export class DynamicStructuredTool<
             }
         }
 
-        // inject variables
+        sandbox['$vars'] = prepareSandboxVars(this.variables)
-        let vars = {}
-        if (this.variables) {
-            for (const item of this.variables) {
-                let value = item.value
-
-                // read from .env file
-                if (item.type === 'runtime') {
-                    value = process.env[item.name]
-                }
-
-                Object.defineProperty(vars, item.name, {
-                    enumerable: true,
-                    configurable: true,
-                    writable: true,
-                    value: value
-                })
-            }
-        }
-        sandbox['$vars'] = vars
 
         // inject flow properties
         if (this.flowObj) {
-            sandbox['$flow'] = { ...this.flowObj, sessionId: overrideSessionId }
+            sandbox['$flow'] = { ...this.flowObj, ...flowConfig }
         }
 
-        const defaultAllowBuiltInDep = [
-            'assert',
-            'buffer',
-            'crypto',
-            'events',
-            'http',
-            'https',
-            'net',
-            'path',
-            'querystring',
-            'timers',
-            'tls',
-            'url',
-            'zlib'
-        ]
-
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))
             : defaultAllowBuiltInDep

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

@@ -1,6 +1,7 @@
-import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { NodeVM } from 'vm2'
-import { availableDependencies, handleEscapeCharacters } from '../../../src/utils'
+import { DataSource } from 'typeorm'
+import { availableDependencies, defaultAllowBuiltInDep, getVars, handleEscapeCharacters, prepareSandboxVars } from '../../../src/utils'
 
 class CustomFunction_Utilities implements INode {
     label: string
@@ -55,9 +56,19 @@ class CustomFunction_Utilities implements INode {
         ]
     }
 
-    async init(nodeData: INodeData, input: string): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const javascriptFunction = nodeData.inputs?.javascriptFunction as string
         const functionInputVariablesRaw = nodeData.inputs?.functionInputVariables
+        const appDataSource = options.appDataSource as DataSource
+        const databaseEntities = options.databaseEntities as IDatabaseEntity
+
+        const variables = await getVars(appDataSource, databaseEntities, nodeData)
+        const flow = {
+            chatflowId: options.chatflowid,
+            sessionId: options.sessionId,
+            chatId: options.chatId,
+            input
+        }
 
         let inputVars: ICommonObject = {}
         if (functionInputVariablesRaw) {
@@ -69,29 +80,30 @@ class CustomFunction_Utilities implements INode {
             }
         }
 
-        let sandbox: any = { $input: input }
+        // Some values might be a stringified JSON, parse it
-
+        for (const key in inputVars) {
-        if (Object.keys(inputVars).length) {
+            if (typeof inputVars[key] === 'string' && inputVars[key].startsWith('{') && inputVars[key].endsWith('}')) {
-            for (const item in inputVars) {
+                try {
-                sandbox[`$${item}`] = inputVars[item]
+                    inputVars[key] = JSON.parse(inputVars[key])
+                } catch (e) {
+                    continue
+                }
             }
         }
 
-        const defaultAllowBuiltInDep = [
+        let sandbox: any = { $input: input }
-            'assert',
+        sandbox['$vars'] = prepareSandboxVars(variables)
-            'buffer',
+        sandbox['$flow'] = flow
-            'crypto',
+
-            'events',
+        if (Object.keys(inputVars).length) {
-            'http',
+            for (const item in inputVars) {
-            'https',
+                let value = inputVars[item]
-            'net',
+                if (typeof value === 'string') {
-            'path',
+                    value = handleEscapeCharacters(value, true)
-            'querystring',
+                }
-            'timers',
+                sandbox[`$${item}`] = value
-            'tls',
+            }
-            'url',
+        }
-            'zlib'
-        ]
 
         const builtinDeps = process.env.TOOL_FUNCTION_BUILTIN_DEP
             ? defaultAllowBuiltInDep.concat(process.env.TOOL_FUNCTION_BUILTIN_DEP.split(','))

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

@@ -0,0 +1,182 @@
+import { flatten } from 'lodash'
+import { Embeddings } from 'langchain/embeddings/base'
+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
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    badge: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Astra'
+        this.name = 'Astra'
+        this.version = 1.0
+        this.type = 'Astra'
+        this.icon = 'astra.svg'
+        this.category = 'Vector Stores'
+        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 = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['AstraDBApi']
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Vector Dimension',
+                name: 'vectorDimension',
+                type: 'number',
+                placeholder: '1536',
+                optional: true,
+                description: 'Dimension used for storing vector embedding'
+            },
+            {
+                label: 'Similarity Metric',
+                name: 'similarityMetric',
+                type: 'string',
+                placeholder: 'cosine',
+                optional: true,
+                description: 'cosine | euclidean | dot_product'
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        addMMRInputParams(this.inputs)
+        this.outputs = [
+            {
+                label: 'Astra Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Astra Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(AstraDBVectorStore)]
+            }
+        ]
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
+            const docs = nodeData.inputs?.document as Document[]
+            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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
+
+            const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
+            if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
+                throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
+            }
+
+            const clientConfig = {
+                token: credentialData?.applicationToken,
+                endpoint: credentialData?.dbEndPoint
+            }
+
+            const astraConfig: AstraLibArgs = {
+                ...clientConfig,
+                collection: credentialData.collectionName ?? 'flowise_test',
+                collectionOptions: {
+                    vector: {
+                        dimension: vectorDimension ?? 1536,
+                        metric: similarityMetric ?? 'cosine'
+                    }
+                }
+            }
+
+            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]))
+                }
+            }
+
+            try {
+                await AstraDBVectorStore.fromDocuments(finalDocs, embeddings, astraConfig)
+            } catch (e) {
+                throw new Error(e)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const docs = nodeData.inputs?.document as Document[]
+        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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
+
+        const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
+        if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
+            throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
+        }
+
+        const clientConfig = {
+            token: credentialData?.applicationToken,
+            endpoint: credentialData?.dbEndPoint
+        }
+
+        const astraConfig: AstraLibArgs = {
+            ...clientConfig,
+            collection: credentialData.collectionName ?? 'flowise_test',
+            collectionOptions: {
+                vector: {
+                    dimension: vectorDimension ?? 1536,
+                    metric: similarityMetric ?? 'cosine'
+                }
+            }
+        }
+
+        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]))
+            }
+        }
+
+        const vectorStore = await AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig)
+
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
+    }
+}
+
+module.exports = { nodeClass: Astra_VectorStores }

packages/components/nodes/vectorstores/Astra/astra.svg

@@ -0,0 +1,12 @@
+<svg width="1200" height="1200" viewBox="0 0 1200 1200" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="1200" height="1200" fill="black"/>
+<g clip-path="url(#clip0_102_1968)">
+<path d="M508.819 464.97H267.001V737.697H508.819L569.566 690.526V512.14L508.819 464.97ZM313.864 512.14H522.703V690.575H313.864V512.14Z" fill="white"/>
+<path d="M917.531 514.121V468H696.425L636.389 514.121V577.447L696.425 623.568H889.124V688.545H648.348V734.667H875.409L935.444 688.545V623.568L875.409 577.447H682.709V514.121H917.531Z" fill="white"/>
+</g>
+<defs>
+<clipPath id="clip0_102_1968">
+<rect width="668.444" height="266.667" fill="white" transform="translate(267 468)"/>
+</clipPath>
+</defs>
+</svg>

packages/components/nodes/vectorstores/Milvus/Milvus.ts

@@ -65,6 +65,14 @@ class Milvus_VectorStores implements INode {
                 name: 'milvusCollection',
                 type: 'string'
             },
+            {
+                label: 'Milvus Text Field',
+                name: 'milvusTextField',
+                type: 'string',
+                placeholder: 'langchain_text',
+                optional: true,
+                additionalParams: true
+            },
             {
                 label: 'Milvus Filter',
                 name: 'milvusFilter',
@@ -150,6 +158,7 @@ class Milvus_VectorStores implements INode {
         const address = nodeData.inputs?.milvusServerUrl as string
         const collectionName = nodeData.inputs?.milvusCollection as string
         const milvusFilter = nodeData.inputs?.milvusFilter as string
+        const textField = nodeData.inputs?.milvusTextField as string
 
         // embeddings
         const embeddings = nodeData.inputs?.embeddings as Embeddings
@@ -169,7 +178,8 @@ class Milvus_VectorStores implements INode {
         // init MilvusLibArgs
         const milVusArgs: MilvusLibArgs = {
             url: address,
-            collectionName: collectionName
+            collectionName: collectionName,
+            textField: textField
         }
 
         if (milvusUser) milVusArgs.username = milvusUser

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 resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            return vectorStore.asRetriever(k)
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return 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',
@@ -106,8 +108,7 @@ class Pinecone_VectorStores implements INode {
             const pineconeEnv = getCredentialParam('pineconeEnv', credentialData, nodeData)
 
             const client = new Pinecone({
-                apiKey: pineconeApiKey,
+                apiKey: pineconeApiKey
-                environment: pineconeEnv
             })
 
             const pineconeIndex = client.Index(index)
@@ -140,17 +141,13 @@ 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,
+            apiKey: pineconeApiKey
-            environment: pineconeEnv
         })
 
         const pineconeIndex = client.Index(index)
@@ -175,14 +172,7 @@ class Pinecone_VectorStores implements INode {
 
         const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return 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,
+            apiKey: pineconeApiKey
-            environment: pineconeEnv
         })
 
         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,
+            apiKey: pineconeApiKey
-            environment: pineconeEnv
         })
 
         const pineconeIndex = client.Index(index)

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

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

packages/components/nodes/vectorstores/Vectara/Vectara.ts

@@ -1,5 +1,5 @@
 import { flatten } from 'lodash'
-import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile } from 'langchain/vectorstores/vectara'
+import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile, MMRConfig } from 'langchain/vectorstores/vectara'
 import { Document } from 'langchain/document'
 import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
@@ -22,7 +22,7 @@ class Vectara_VectorStores implements INode {
     constructor() {
         this.label = 'Vectara'
         this.name = 'vectara'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Vectara'
         this.icon = 'vectara.png'
         this.category = 'Vector Stores'
@@ -82,7 +82,9 @@ class Vectara_VectorStores implements INode {
                 label: 'Lambda',
                 name: 'lambda',
                 description:
-                    'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
+                    'Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.' +
+                    'A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).',
+                default: 0.0,
                 type: 'number',
                 additionalParams: true,
                 optional: true
@@ -90,8 +92,30 @@ class Vectara_VectorStores implements INode {
             {
                 label: 'Top K',
                 name: 'topK',
-                description: 'Number of top results to fetch. Defaults to 4',
+                description: 'Number of top results to fetch. Defaults to 5',
-                placeholder: '4',
+                placeholder: '5',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'MMR K',
+                name: 'mmrK',
+                description: 'Number of top results to fetch for MMR. Defaults to 50',
+                placeholder: '50',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'MMR diversity bias',
+                name: 'mmrDiversityBias',
+                step: 0.1,
+                description:
+                    'The diversity bias to use for MMR. This is a value between 0.0 and 1.0' +
+                    'Values closer to 1.0 optimize for the most diverse results.' +
+                    'Defaults to 0 (MMR disabled)',
+                placeholder: '0.0',
                 type: 'number',
                 additionalParams: true,
                 optional: true
@@ -191,7 +215,9 @@ class Vectara_VectorStores implements INode {
         const lambda = nodeData.inputs?.lambda as number
         const output = nodeData.outputs?.output as string
         const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
+        const k = topK ? parseFloat(topK) : 5
+        const mmrK = nodeData.inputs?.mmrK as number
+        const mmrDiversityBias = nodeData.inputs?.mmrDiversityBias as number
 
         const vectaraArgs: VectaraLibArgs = {
             apiKey: apiKey,
@@ -208,6 +234,11 @@ class Vectara_VectorStores implements INode {
         if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
         if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
         vectaraFilter.contextConfig = vectaraContextConfig
+        const mmrConfig: MMRConfig = {}
+        mmrConfig.enabled = mmrDiversityBias > 0
+        mmrConfig.mmrTopK = mmrK
+        mmrConfig.diversityBias = mmrDiversityBias
+        vectaraFilter.mmrConfig = mmrConfig
 
         const vectorStore = new VectaraStore(vectaraArgs)
 

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') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return 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') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return 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": "1.5.1",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -19,23 +19,26 @@
         "@aws-sdk/client-bedrock-runtime": "3.422.0",
         "@aws-sdk/client-dynamodb": "^3.360.0",
         "@aws-sdk/client-s3": "^3.427.0",
+        "@datastax/astra-db-ts": "^0.1.2",
         "@dqbd/tiktoken": "^1.0.7",
         "@elastic/elasticsearch": "^8.9.0",
         "@getzep/zep-js": "^0.9.0",
         "@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",
         "axios": "1.6.2",
@@ -46,15 +49,16 @@
         "dotenv": "^16.0.0",
         "express": "^4.17.3",
         "faiss-node": "^0.2.2",
+        "fast-json-patch": "^3.1.1",
         "form-data": "^4.0.0",
-        "google-auth-library": "^9.0.0",
+        "google-auth-library": "^9.4.0",
         "graphql": "^16.6.0",
         "html-to-text": "^9.0.5",
         "husky": "^8.0.3",
         "ioredis": "^5.3.2",
         "langchain": "^0.0.214",
         "langfuse": "2.0.2",
-        "langfuse-langchain": "2.0.2",
+        "langfuse-langchain": "2.3.3",
         "langsmith": "0.0.53",
         "linkifyjs": "^4.1.1",
         "llmonitor": "^0.5.5",

packages/components/src/Interface.ts

@@ -29,6 +29,12 @@ export interface ICommonObject {
     [key: string]: any | CommonType | ICommonObject | CommonType[] | ICommonObject[]
 }
 
+export interface IVariable {
+    name: string
+    value: string
+    type: string
+}
+
 export type IDatabaseEntity = {
     [key: string]: any
 }
@@ -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
@@ -108,10 +114,6 @@ export interface INode extends INodeProperties {
         search: (nodeData: INodeData, options?: ICommonObject) => Promise<any>
         delete: (nodeData: INodeData, options?: ICommonObject) => Promise<void>
     }
-    memoryMethods?: {
-        clearSessionMemory: (nodeData: INodeData, options?: ICommonObject) => Promise<void>
-        getChatMessages: (nodeData: INodeData, options?: ICommonObject) => Promise<string>
-    }
     init?(nodeData: INodeData, input: string, options?: ICommonObject): Promise<any>
     run?(nodeData: INodeData, input: string, options?: ICommonObject): Promise<string | ICommonObject>
 }
@@ -204,29 +206,37 @@ import { BaseMessage } from 'langchain/schema'
 import { BufferMemory, BufferWindowMemory, ConversationSummaryMemory } from 'langchain/memory'
 
 export interface MemoryMethods {
-    getChatMessages(overrideSessionId?: string, returnBaseMessages?: boolean): Promise<IMessage[] | BaseMessage[]>
+    getChatMessages(overrideSessionId?: string, returnBaseMessages?: boolean, prevHistory?: IMessage[]): Promise<IMessage[] | BaseMessage[]>
     addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId?: string): Promise<void>
     clearChatMessages(overrideSessionId?: string): Promise<void>
-    resumeMessages?(messages: IMessage[]): Promise<void>
 }
 
 export abstract class FlowiseMemory extends BufferMemory implements MemoryMethods {
-    abstract getChatMessages(overrideSessionId?: string, returnBaseMessages?: boolean): Promise<IMessage[] | BaseMessage[]>
+    abstract getChatMessages(
+        overrideSessionId?: string,
+        returnBaseMessages?: boolean,
+        prevHistory?: IMessage[]
+    ): Promise<IMessage[] | BaseMessage[]>
     abstract addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId?: string): Promise<void>
     abstract clearChatMessages(overrideSessionId?: string): Promise<void>
-    abstract resumeMessages(messages: IMessage[]): Promise<void>
 }
 
 export abstract class FlowiseWindowMemory extends BufferWindowMemory implements MemoryMethods {
-    abstract getChatMessages(overrideSessionId?: string, returnBaseMessages?: boolean): Promise<IMessage[] | BaseMessage[]>
+    abstract getChatMessages(
+        overrideSessionId?: string,
+        returnBaseMessages?: boolean,
+        prevHistory?: IMessage[]
+    ): Promise<IMessage[] | BaseMessage[]>
     abstract addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId?: string): Promise<void>
     abstract clearChatMessages(overrideSessionId?: string): Promise<void>
-    abstract resumeMessages(messages: IMessage[]): Promise<void>
 }
 
 export abstract class FlowiseSummaryMemory extends ConversationSummaryMemory implements MemoryMethods {
-    abstract getChatMessages(overrideSessionId?: string, returnBaseMessages?: boolean): Promise<IMessage[] | BaseMessage[]>
+    abstract getChatMessages(
+        overrideSessionId?: string,
+        returnBaseMessages?: boolean,
+        prevHistory?: IMessage[]
+    ): Promise<IMessage[] | BaseMessage[]>
     abstract addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId?: string): Promise<void>
     abstract clearChatMessages(overrideSessionId?: string): Promise<void>
-    abstract resumeMessages(messages: IMessage[]): Promise<void>
 }

packages/components/src/agents.ts

@@ -0,0 +1,624 @@
+import { AgentExecutorInput, BaseSingleActionAgent, BaseMultiActionAgent, RunnableAgent, StoppingMethod } from 'langchain/agents'
+import { ChainValues, AgentStep, AgentFinish, 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'
+import { Runnable } from 'langchain/schema/runnable'
+import { BaseChain, SerializedLLMChain } from 'langchain/chains'
+import { Serializable } from '@langchain/core/load/serializable'
+
+type AgentExecutorOutput = ChainValues
+
+interface AgentExecutorIteratorInput {
+    agentExecutor: AgentExecutor
+    inputs: Record<string, string>
+    callbacks?: Callbacks
+    tags?: string[]
+    metadata?: Record<string, unknown>
+    runName?: string
+    runManager?: CallbackManagerForChainRun
+}
+
+//TODO: stream tools back
+export class AgentExecutorIterator extends Serializable implements AgentExecutorIteratorInput {
+    lc_namespace = ['langchain', 'agents', 'executor_iterator']
+
+    agentExecutor: AgentExecutor
+
+    inputs: Record<string, string>
+
+    callbacks: Callbacks
+
+    tags: string[] | undefined
+
+    metadata: Record<string, unknown> | undefined
+
+    runName: string | undefined
+
+    private _finalOutputs: Record<string, unknown> | undefined
+
+    get finalOutputs(): Record<string, unknown> | undefined {
+        return this._finalOutputs
+    }
+
+    /** Intended to be used as a setter method, needs to be async. */
+    async setFinalOutputs(value: Record<string, unknown> | undefined) {
+        this._finalOutputs = undefined
+        if (value) {
+            const preparedOutputs: Record<string, unknown> = await this.agentExecutor.prepOutputs(this.inputs, value, true)
+            this._finalOutputs = preparedOutputs
+        }
+    }
+
+    runManager: CallbackManagerForChainRun | undefined
+
+    intermediateSteps: AgentStep[] = []
+
+    iterations = 0
+
+    get nameToToolMap(): Record<string, Tool> {
+        const toolMap = this.agentExecutor.tools.map((tool) => ({
+            [tool.name]: tool
+        }))
+        return Object.assign({}, ...toolMap)
+    }
+
+    constructor(fields: AgentExecutorIteratorInput) {
+        super(fields)
+        this.agentExecutor = fields.agentExecutor
+        this.inputs = fields.inputs
+        this.tags = fields.tags
+        this.metadata = fields.metadata
+        this.runName = fields.runName
+        this.runManager = fields.runManager
+    }
+
+    /**
+     * Reset the iterator to its initial state, clearing intermediate steps,
+     * iterations, and the final output.
+     */
+    reset(): void {
+        this.intermediateSteps = []
+        this.iterations = 0
+        this._finalOutputs = undefined
+    }
+
+    updateIterations(): void {
+        this.iterations += 1
+    }
+
+    async *streamIterator() {
+        this.reset()
+
+        // Loop to handle iteration
+        while (true) {
+            try {
+                if (this.iterations === 0) {
+                    await this.onFirstStep()
+                }
+
+                const result = await this._callNext()
+                yield result
+            } catch (e: any) {
+                if ('message' in e && e.message.startsWith('Final outputs already reached: ')) {
+                    if (!this.finalOutputs) {
+                        throw e
+                    }
+                    return this.finalOutputs
+                }
+                if (this.runManager) {
+                    await this.runManager.handleChainError(e)
+                }
+                throw e
+            }
+        }
+    }
+
+    /**
+     * Perform any necessary setup for the first step
+     * of the asynchronous iterator.
+     */
+    async onFirstStep(): Promise<void> {
+        if (this.iterations === 0) {
+            const callbackManager = await CallbackManager.configure(
+                this.callbacks,
+                this.agentExecutor.callbacks,
+                this.tags,
+                this.agentExecutor.tags,
+                this.metadata,
+                this.agentExecutor.metadata,
+                {
+                    verbose: this.agentExecutor.verbose
+                }
+            )
+            this.runManager = await callbackManager?.handleChainStart(
+                this.agentExecutor.toJSON(),
+                this.inputs,
+                undefined,
+                undefined,
+                this.tags,
+                this.metadata,
+                this.runName
+            )
+        }
+    }
+
+    /**
+     * Execute the next step in the chain using the
+     * AgentExecutor's _takeNextStep method.
+     */
+    async _executeNextStep(runManager?: CallbackManagerForChainRun): Promise<AgentFinish | AgentStep[]> {
+        return this.agentExecutor._takeNextStep(this.nameToToolMap, this.inputs, this.intermediateSteps, runManager)
+    }
+
+    /**
+     * Process the output of the next step,
+     * handling AgentFinish and tool return cases.
+     */
+    async _processNextStepOutput(
+        nextStepOutput: AgentFinish | AgentStep[],
+        runManager?: CallbackManagerForChainRun
+    ): Promise<Record<string, string | AgentStep[]>> {
+        if ('returnValues' in nextStepOutput) {
+            const output = await this.agentExecutor._return(nextStepOutput as AgentFinish, this.intermediateSteps, runManager)
+            if (this.runManager) {
+                await this.runManager.handleChainEnd(output)
+            }
+            await this.setFinalOutputs(output)
+            return output
+        }
+
+        this.intermediateSteps = this.intermediateSteps.concat(nextStepOutput as AgentStep[])
+
+        let output: Record<string, string | AgentStep[]> = {}
+        if (Array.isArray(nextStepOutput) && nextStepOutput.length === 1) {
+            const nextStep = nextStepOutput[0]
+            const toolReturn = await this.agentExecutor._getToolReturn(nextStep)
+            if (toolReturn) {
+                output = await this.agentExecutor._return(toolReturn, this.intermediateSteps, runManager)
+                if (this.runManager) {
+                    await this.runManager.handleChainEnd(output)
+                }
+                await this.setFinalOutputs(output)
+            }
+        }
+        output = { intermediateSteps: nextStepOutput as AgentStep[] }
+        return output
+    }
+
+    async _stop(): Promise<Record<string, unknown>> {
+        const output = await this.agentExecutor.agent.returnStoppedResponse(
+            this.agentExecutor.earlyStoppingMethod,
+            this.intermediateSteps,
+            this.inputs
+        )
+        const returnedOutput = await this.agentExecutor._return(output, this.intermediateSteps, this.runManager)
+        await this.setFinalOutputs(returnedOutput)
+        return returnedOutput
+    }
+
+    async _callNext(): Promise<Record<string, unknown>> {
+        // final output already reached: stopiteration (final output)
+        if (this.finalOutputs) {
+            throw new Error(`Final outputs already reached: ${JSON.stringify(this.finalOutputs, null, 2)}`)
+        }
+        // timeout/max iterations: stopiteration (stopped response)
+        if (!this.agentExecutor.shouldContinueGetter(this.iterations)) {
+            return this._stop()
+        }
+        const nextStepOutput = await this._executeNextStep(this.runManager)
+        const output = await this._processNextStepOutput(nextStepOutput, this.runManager)
+        this.updateIterations()
+        return output
+    }
+}
+
+export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
+    static lc_name() {
+        return 'AgentExecutor'
+    }
+
+    get lc_namespace() {
+        return ['langchain', 'agents', 'executor']
+    }
+
+    agent: BaseSingleActionAgent | BaseMultiActionAgent
+
+    tools: this['agent']['ToolType'][]
+
+    returnIntermediateSteps = false
+
+    maxIterations?: number = 15
+
+    earlyStoppingMethod: StoppingMethod = 'force'
+
+    sessionId?: string
+
+    chatId?: string
+
+    input?: string
+
+    /**
+     * How to handle errors raised by the agent's output parser.
+        Defaults to `False`, which raises the error.
+
+        If `true`, the error will be sent back to the LLM as an observation.
+        If a string, the string itself will be sent to the LLM as an observation.
+        If a callable function, the function will be called with the exception
+        as an argument, and the result of that function will be passed to the agent
+        as an observation.
+    */
+    handleParsingErrors: boolean | string | ((e: OutputParserException | ToolInputParsingException) => string) = false
+
+    get inputKeys() {
+        return this.agent.inputKeys
+    }
+
+    get outputKeys() {
+        return this.agent.returnValues
+    }
+
+    constructor(input: AgentExecutorInput & { sessionId?: string; chatId?: string; input?: string }) {
+        let agent: BaseSingleActionAgent | BaseMultiActionAgent
+        if (Runnable.isRunnable(input.agent)) {
+            agent = new RunnableAgent({ runnable: input.agent })
+        } else {
+            agent = input.agent
+        }
+
+        super(input)
+        this.agent = agent
+        this.tools = input.tools
+        this.handleParsingErrors = input.handleParsingErrors ?? this.handleParsingErrors
+        /* Getting rid of this because RunnableAgent doesnt allow return direct
+        if (this.agent._agentActionType() === "multi") {
+            for (const tool of this.tools) {
+              if (tool.returnDirect) {
+                throw new Error(
+                  `Tool with return direct ${tool.name} not supported for multi-action agent.`
+                );
+              }
+            }
+        }*/
+        this.returnIntermediateSteps = input.returnIntermediateSteps ?? this.returnIntermediateSteps
+        this.maxIterations = input.maxIterations ?? this.maxIterations
+        this.earlyStoppingMethod = input.earlyStoppingMethod ?? this.earlyStoppingMethod
+        this.sessionId = input.sessionId
+        this.chatId = input.chatId
+        this.input = input.input
+    }
+
+    static fromAgentAndTools(fields: AgentExecutorInput & { sessionId?: string; chatId?: string; input?: string }): AgentExecutor {
+        const newInstance = new AgentExecutor(fields)
+        if (fields.sessionId) newInstance.sessionId = fields.sessionId
+        if (fields.chatId) newInstance.chatId = fields.chatId
+        if (fields.input) newInstance.input = fields.input
+        return newInstance
+    }
+
+    get shouldContinueGetter() {
+        return this.shouldContinue.bind(this)
+    }
+
+    /**
+     * Method that checks if the agent execution should continue based on the
+     * number of iterations.
+     * @param iterations The current number of iterations.
+     * @returns A boolean indicating whether the agent execution should continue.
+     */
+    private shouldContinue(iterations: number): boolean {
+        return this.maxIterations === undefined || iterations < this.maxIterations
+    }
+
+    async _call(inputs: ChainValues, runManager?: CallbackManagerForChainRun): Promise<AgentExecutorOutput> {
+        const toolsByName = Object.fromEntries(this.tools.map((t) => [t.name.toLowerCase(), t]))
+
+        const steps: AgentStep[] = []
+        let iterations = 0
+
+        const getOutput = async (finishStep: AgentFinish): Promise<AgentExecutorOutput> => {
+            const { returnValues } = finishStep
+            const additional = await this.agent.prepareForOutput(returnValues, steps)
+
+            if (this.returnIntermediateSteps) {
+                return { ...returnValues, intermediateSteps: steps, ...additional }
+            }
+            await runManager?.handleAgentEnd(finishStep)
+            return { ...returnValues, ...additional }
+        }
+
+        while (this.shouldContinue(iterations)) {
+            let output
+            try {
+                output = await this.agent.plan(steps, inputs, runManager?.getChild())
+            } catch (e) {
+                if (e instanceof OutputParserException) {
+                    let observation
+                    let text = e.message
+                    if (this.handleParsingErrors === true) {
+                        if (e.sendToLLM) {
+                            observation = e.observation
+                            text = e.llmOutput ?? ''
+                        } else {
+                            observation = 'Invalid or incomplete response'
+                        }
+                    } else if (typeof this.handleParsingErrors === 'string') {
+                        observation = this.handleParsingErrors
+                    } else if (typeof this.handleParsingErrors === 'function') {
+                        observation = this.handleParsingErrors(e)
+                    } else {
+                        throw e
+                    }
+                    output = {
+                        tool: '_Exception',
+                        toolInput: observation,
+                        log: text
+                    } as AgentAction
+                } else {
+                    throw e
+                }
+            }
+            // Check if the agent has finished
+            if ('returnValues' in output) {
+                return getOutput(output)
+            }
+
+            let actions: AgentAction[]
+            if (Array.isArray(output)) {
+                actions = output as AgentAction[]
+            } else {
+                actions = [output as AgentAction]
+            }
+
+            const newSteps = await Promise.all(
+                actions.map(async (action) => {
+                    await runManager?.handleAgentAction(action)
+                    const tool = action.tool === '_Exception' ? new ExceptionTool() : toolsByName[action.tool?.toLowerCase()]
+                    let observation
+                    try {
+                        /* Here we need to override Tool call method to include sessionId, chatId, input as parameter
+                         * Tool Call Parameters:
+                         * - arg: z.output<T>
+                         * - configArg?: RunnableConfig | Callbacks
+                         * - tags?: string[]
+                         * - flowConfig?: { sessionId?: string, chatId?: string, input?: string }
+                         */
+                        observation = tool
+                            ? // @ts-ignore
+                              await tool.call(action.toolInput, runManager?.getChild(), undefined, {
+                                  sessionId: this.sessionId,
+                                  chatId: this.chatId,
+                                  input: this.input
+                              })
+                            : `${action.tool} is not a valid tool, try another one.`
+                    } catch (e) {
+                        if (e instanceof ToolInputParsingException) {
+                            if (this.handleParsingErrors === true) {
+                                observation = 'Invalid or incomplete tool input. Please try again.'
+                            } else if (typeof this.handleParsingErrors === 'string') {
+                                observation = this.handleParsingErrors
+                            } else if (typeof this.handleParsingErrors === 'function') {
+                                observation = this.handleParsingErrors(e)
+                            } else {
+                                throw e
+                            }
+                            observation = await new ExceptionTool().call(observation, runManager?.getChild())
+                            return { action, observation: observation ?? '' }
+                        }
+                    }
+                    return { action, observation: observation ?? '' }
+                })
+            )
+
+            steps.push(...newSteps)
+
+            const lastStep = steps[steps.length - 1]
+            const lastTool = toolsByName[lastStep.action.tool?.toLowerCase()]
+
+            if (lastTool?.returnDirect) {
+                return getOutput({
+                    returnValues: { [this.agent.returnValues[0]]: lastStep.observation },
+                    log: ''
+                })
+            }
+
+            iterations += 1
+        }
+
+        const finish = await this.agent.returnStoppedResponse(this.earlyStoppingMethod, steps, inputs)
+
+        return getOutput(finish)
+    }
+
+    async _takeNextStep(
+        nameToolMap: Record<string, Tool>,
+        inputs: ChainValues,
+        intermediateSteps: AgentStep[],
+        runManager?: CallbackManagerForChainRun
+    ): Promise<AgentFinish | AgentStep[]> {
+        let output
+        try {
+            output = await this.agent.plan(intermediateSteps, inputs, runManager?.getChild())
+        } catch (e) {
+            if (e instanceof OutputParserException) {
+                let observation
+                let text = e.message
+                if (this.handleParsingErrors === true) {
+                    if (e.sendToLLM) {
+                        observation = e.observation
+                        text = e.llmOutput ?? ''
+                    } else {
+                        observation = 'Invalid or incomplete response'
+                    }
+                } else if (typeof this.handleParsingErrors === 'string') {
+                    observation = this.handleParsingErrors
+                } else if (typeof this.handleParsingErrors === 'function') {
+                    observation = this.handleParsingErrors(e)
+                } else {
+                    throw e
+                }
+                output = {
+                    tool: '_Exception',
+                    toolInput: observation,
+                    log: text
+                } as AgentAction
+            } else {
+                throw e
+            }
+        }
+
+        if ('returnValues' in output) {
+            return output
+        }
+
+        let actions: AgentAction[]
+        if (Array.isArray(output)) {
+            actions = output as AgentAction[]
+        } else {
+            actions = [output as AgentAction]
+        }
+
+        const result: AgentStep[] = []
+        for (const agentAction of actions) {
+            let observation = ''
+            if (runManager) {
+                await runManager?.handleAgentAction(agentAction)
+            }
+            if (agentAction.tool in nameToolMap) {
+                const tool = nameToolMap[agentAction.tool]
+                try {
+                    /* Here we need to override Tool call method to include sessionId, chatId, input as parameter
+                     * Tool Call Parameters:
+                     * - arg: z.output<T>
+                     * - configArg?: RunnableConfig | Callbacks
+                     * - tags?: string[]
+                     * - flowConfig?: { sessionId?: string, chatId?: string, input?: string }
+                     */
+                    // @ts-ignore
+                    observation = await tool.call(agentAction.toolInput, runManager?.getChild(), undefined, {
+                        sessionId: this.sessionId,
+                        chatId: this.chatId,
+                        input: this.input
+                    })
+                } catch (e) {
+                    if (e instanceof ToolInputParsingException) {
+                        if (this.handleParsingErrors === true) {
+                            observation = 'Invalid or incomplete tool input. Please try again.'
+                        } else if (typeof this.handleParsingErrors === 'string') {
+                            observation = this.handleParsingErrors
+                        } else if (typeof this.handleParsingErrors === 'function') {
+                            observation = this.handleParsingErrors(e)
+                        } else {
+                            throw e
+                        }
+                        observation = await new ExceptionTool().call(observation, runManager?.getChild())
+                    }
+                }
+            } else {
+                observation = `${agentAction.tool} is not a valid tool, try another available tool: ${Object.keys(nameToolMap).join(', ')}`
+            }
+            result.push({
+                action: agentAction,
+                observation
+            })
+        }
+        return result
+    }
+
+    async _return(
+        output: AgentFinish,
+        intermediateSteps: AgentStep[],
+        runManager?: CallbackManagerForChainRun
+    ): Promise<AgentExecutorOutput> {
+        if (runManager) {
+            await runManager.handleAgentEnd(output)
+        }
+        const finalOutput: Record<string, unknown> = output.returnValues
+        if (this.returnIntermediateSteps) {
+            finalOutput.intermediateSteps = intermediateSteps
+        }
+        return finalOutput
+    }
+
+    async _getToolReturn(nextStepOutput: AgentStep): Promise<AgentFinish | null> {
+        const { action, observation } = nextStepOutput
+        const nameToolMap = Object.fromEntries(this.tools.map((t) => [t.name.toLowerCase(), t]))
+        const [returnValueKey = 'output'] = this.agent.returnValues
+        // Invalid tools won't be in the map, so we return False.
+        if (action.tool in nameToolMap) {
+            if (nameToolMap[action.tool].returnDirect) {
+                return {
+                    returnValues: { [returnValueKey]: observation },
+                    log: ''
+                }
+            }
+        }
+        return null
+    }
+
+    _returnStoppedResponse(earlyStoppingMethod: StoppingMethod) {
+        if (earlyStoppingMethod === 'force') {
+            return {
+                returnValues: {
+                    output: 'Agent stopped due to iteration limit or time limit.'
+                },
+                log: ''
+            } as AgentFinish
+        }
+        throw new Error(`Got unsupported early_stopping_method: ${earlyStoppingMethod}`)
+    }
+
+    async *_streamIterator(inputs: Record<string, any>): AsyncGenerator<ChainValues> {
+        const agentExecutorIterator = new AgentExecutorIterator({
+            inputs,
+            agentExecutor: this,
+            metadata: this.metadata,
+            tags: this.tags,
+            callbacks: this.callbacks
+        })
+        const iterator = agentExecutorIterator.streamIterator()
+        for await (const step of iterator) {
+            if (!step) {
+                continue
+            }
+            yield step
+        }
+    }
+
+    _chainType() {
+        return 'agent_executor' as const
+    }
+
+    serialize(): SerializedLLMChain {
+        throw new Error('Cannot serialize an AgentExecutor')
+    }
+}
+
+class ExceptionTool extends Tool {
+    name = '_Exception'
+
+    description = 'Exception tool'
+
+    async _call(query: string) {
+        return query
+    }
+}
+
+export const formatAgentSteps = (steps: AgentStep[]): BaseMessage[] =>
+    steps.flatMap(({ action, observation }) => {
+        const create_function_message = (observation: string, action: AgentAction) => {
+            let content: string
+            if (typeof observation !== 'string') {
+                content = JSON.stringify(observation)
+            } else {
+                content = observation
+            }
+            return new FunctionMessage(content, action.tool)
+        }
+        if ('messageLog' in action && action.messageLog !== undefined) {
+            const log = action.messageLog as BaseMessage[]
+            return log.concat(create_function_message(observation, action))
+        } else {
+            return [new AIMessage(action.log)]
+        }
+    })

packages/components/src/handler.ts

@@ -1,13 +1,13 @@
-import { BaseTracer, Run, BaseCallbackHandler } from 'langchain/callbacks'
+import { BaseTracer, Run, BaseCallbackHandler, LangChainTracer } from 'langchain/callbacks'
 import { AgentAction, ChainValues } from 'langchain/schema'
 import { Logger } from 'winston'
 import { Server } from 'socket.io'
 import { Client } from 'langsmith'
-import { LangChainTracer } from 'langchain/callbacks'
+import { LLMonitorHandler, LLMonitorHandlerFields } from 'langchain/callbacks/handlers/llmonitor'
-import { LLMonitorHandler } from 'langchain/callbacks/handlers/llmonitor'
 import { getCredentialData, getCredentialParam } from './utils'
 import { ICommonObject, INodeData } from './Interface'
 import CallbackHandler from 'langfuse-langchain'
+import { LangChainTracerFields } from '@langchain/core/tracers/tracer_langchain'
 import { RunTree, RunTreeConfig, Client as LangsmithClient } from 'langsmith'
 import { Langfuse, LangfuseTraceClient, LangfuseSpanClient, LangfuseGenerationClient } from 'langfuse'
 import monitor from 'llmonitor'
@@ -235,11 +235,17 @@ export const additionalCallbacks = async (nodeData: INodeData, options: ICommonO
                         apiKey: langSmithApiKey
                     })
 
-                    const tracer = new LangChainTracer({
+                    let langSmithField: LangChainTracerFields = {
                         projectName: langSmithProject ?? 'default',
                         //@ts-ignore
                         client
-                    })
+                    }
+
+                    if (nodeData?.inputs?.analytics?.langSmith) {
+                        langSmithField = { ...langSmithField, ...nodeData?.inputs?.analytics?.langSmith }
+                    }
+
+                    const tracer = new LangChainTracer(langSmithField)
                     callbacks.push(tracer)
                 } else if (provider === 'langFuse') {
                     const release = analytic[provider].release as string
@@ -248,13 +254,17 @@ export const additionalCallbacks = async (nodeData: INodeData, options: ICommonO
                     const langFusePublicKey = getCredentialParam('langFusePublicKey', credentialData, nodeData)
                     const langFuseEndpoint = getCredentialParam('langFuseEndpoint', credentialData, nodeData)
 
-                    const langFuseOptions: any = {
+                    let langFuseOptions: any = {
                         secretKey: langFuseSecretKey,
                         publicKey: langFusePublicKey,
                         baseUrl: langFuseEndpoint ?? 'https://cloud.langfuse.com'
                     }
                     if (release) langFuseOptions.release = release
-                    if (options.chatId) langFuseOptions.userId = options.chatId
+                    if (options.chatId) langFuseOptions.sessionId = options.chatId
+
+                    if (nodeData?.inputs?.analytics?.langFuse) {
+                        langFuseOptions = { ...langFuseOptions, ...nodeData?.inputs?.analytics?.langFuse }
+                    }
 
                     const handler = new CallbackHandler(langFuseOptions)
                     callbacks.push(handler)
@@ -262,11 +272,15 @@ export const additionalCallbacks = async (nodeData: INodeData, options: ICommonO
                     const llmonitorAppId = getCredentialParam('llmonitorAppId', credentialData, nodeData)
                     const llmonitorEndpoint = getCredentialParam('llmonitorEndpoint', credentialData, nodeData)
 
-                    const llmonitorFields: ICommonObject = {
+                    let llmonitorFields: LLMonitorHandlerFields = {
                         appId: llmonitorAppId,
                         apiUrl: llmonitorEndpoint ?? 'https://app.llmonitor.com'
                     }
 
+                    if (nodeData?.inputs?.analytics?.llmonitor) {
+                        llmonitorFields = { ...llmonitorFields, ...nodeData?.inputs?.analytics?.llmonitor }
+                    }
+
                     const handler = new LLMonitorHandler(llmonitorFields)
                     callbacks.push(handler)
                 }
@@ -360,7 +374,8 @@ export class AnalyticHandler {
                     },
                     serialized: {},
                     project_name: this.handlers['langSmith'].langSmithProject,
-                    client: this.handlers['langSmith'].client
+                    client: this.handlers['langSmith'].client,
+                    ...this.nodeData?.inputs?.analytics?.langSmith
                 }
                 const parentRun = new RunTree(parentRunConfig)
                 await parentRun.postRun()
@@ -390,8 +405,9 @@ export class AnalyticHandler {
                 const langfuse: Langfuse = this.handlers['langFuse'].client
                 langfuseTraceClient = langfuse.trace({
                     name,
-                    userId: this.options.chatId,
+                    sessionId: this.options.chatId,
-                    metadata: { tags: ['openai-assistant'] }
+                    metadata: { tags: ['openai-assistant'] },
+                    ...this.nodeData?.inputs?.analytics?.langFuse
                 })
             } else {
                 langfuseTraceClient = this.handlers['langFuse'].trace[parentIds['langFuse']]
@@ -420,7 +436,8 @@ export class AnalyticHandler {
                     runId,
                     name,
                     userId: this.options.chatId,
-                    input
+                    input,
+                    ...this.nodeData?.inputs?.analytics?.llmonitor
                 })
                 this.handlers['llmonitor'].chainEvent = { [runId]: runId }
                 returnIds['llmonitor'].chainEvent = runId

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 +689,72 @@ 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
+}

packages/server/.env.example

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

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

@@ -936,7 +936,7 @@
                         "id": "conversationalAgent_0-input-tools-Tool"
                     },
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
                         "type": "BaseChatModel",
                         "id": "conversationalAgent_0-input-model-BaseChatModel"

packages/server/marketplaces/chatflows/AutoGPT.json

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

packages/server/marketplaces/chatflows/BabyAGI.json

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

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

@@ -13,7 +13,7 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
                 "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
@@ -28,47 +28,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -89,9 +78,8 @@
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "vectorStoreRetriever": "{{memoryVectorStore_0.data.instance}}",
                     "memory": "",
-                    "returnSourceDocuments": "",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "systemMessagePrompt": "",
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
-                    "chainOption": ""
                 },
                 "outputAnchors": [
                     {
@@ -625,9 +613,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

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

@@ -354,7 +354,7 @@
                         "id": "conversationalAgent_0-input-tools-Tool"
                     },
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
                         "type": "BaseChatModel",
                         "id": "conversationalAgent_0-input-model-BaseChatModel"

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

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

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

@@ -249,10 +249,10 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -264,47 +264,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -325,16 +314,15 @@
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "vectorStoreRetriever": "{{pinecone_0.data.instance}}",
                     "memory": "",
-                    "returnSourceDocuments": "",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "systemMessagePrompt": "",
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
-                    "chainOption": ""
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -553,7 +541,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -594,6 +582,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -618,7 +645,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -704,9 +734,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

@@ -156,9 +156,9 @@
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
                 "name": "conversationalRetrievalQAChain",
-                "version": 1,
+                "version": 2,
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -170,47 +170,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -232,15 +221,15 @@
                     "vectorStoreRetriever": "{{memoryVectorStore_0.data.instance}}",
                     "memory": "",
                     "returnSourceDocuments": true,
-                    "systemMessagePrompt": "",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "chainOption": ""
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -668,9 +657,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

@@ -83,10 +83,10 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -98,47 +98,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -158,14 +147,16 @@
                 "inputs": {
                     "model": "{{chatOllama_0.data.instance}}",
                     "vectorStoreRetriever": "{{faiss_0.data.instance}}",
-                    "memory": ""
+                    "memory": "",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -649,9 +640,9 @@
             "source": "chatOllama_0",
             "sourceHandle": "chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOllama_0-chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOllama_0-chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

@@ -13,10 +13,10 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -28,47 +28,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -89,14 +78,16 @@
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "vectorStoreRetriever": "{{qdrant_0.data.instance}}",
                     "memory": "{{ZepMemory_0.data.instance}}",
-                    "returnSourceDocuments": true
+                    "returnSourceDocuments": true,
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -232,7 +223,7 @@
                         "label": "Session Id",
                         "name": "sessionId",
                         "type": "string",
-                        "description": "if empty, chatId will be used automatically",
+                        "description": "If not specified, a random id will be used. Learn <a target=\"_blank\" href=\"https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat\">more</a>",
                         "default": "",
                         "additionalParams": true,
                         "optional": true,
@@ -709,9 +700,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

@@ -249,10 +249,10 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -264,47 +264,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -323,14 +312,16 @@
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
-                    "vectorStoreRetriever": "{{pinecone_0.data.instance}}"
+                    "vectorStoreRetriever": "{{pinecone_0.data.instance}}",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -634,7 +625,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -675,6 +666,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -699,7 +729,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "{\"id\":{\"$in\":[\"doc1\",\"doc2\"]}}",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -763,9 +796,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

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

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

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

@@ -1567,7 +1567,7 @@
                         "id": "conversationalAgent_0-input-tools-Tool"
                     },
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
                         "type": "BaseChatModel",
                         "id": "conversationalAgent_0-input-model-BaseChatModel"

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

@@ -2,20 +2,210 @@
     "description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
     "badge": "POPULAR",
     "nodes": [
+        {
+            "width": 300,
+            "height": 574,
+            "id": "chatOpenAI_0",
+            "position": {
+                "x": 579.0877964395976,
+                "y": -138.68792413227874
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatOpenAI_0",
+                "label": "ChatOpenAI",
+                "version": 2,
+                "name": "chatOpenAI",
+                "type": "ChatOpenAI",
+                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
+                "category": "Chat Models",
+                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "chatOpenAI_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "gpt-4",
+                                "name": "gpt-4"
+                            },
+                            {
+                                "label": "gpt-4-1106-preview",
+                                "name": "gpt-4-1106-preview"
+                            },
+                            {
+                                "label": "gpt-4-vision-preview",
+                                "name": "gpt-4-vision-preview"
+                            },
+                            {
+                                "label": "gpt-4-0613",
+                                "name": "gpt-4-0613"
+                            },
+                            {
+                                "label": "gpt-4-32k",
+                                "name": "gpt-4-32k"
+                            },
+                            {
+                                "label": "gpt-4-32k-0613",
+                                "name": "gpt-4-32k-0613"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo",
+                                "name": "gpt-3.5-turbo"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-1106",
+                                "name": "gpt-3.5-turbo-1106"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-0613",
+                                "name": "gpt-3.5-turbo-0613"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-16k",
+                                "name": "gpt-3.5-turbo-16k"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-16k-0613",
+                                "name": "gpt-3.5-turbo-16k-0613"
+                            }
+                        ],
+                        "default": "gpt-3.5-turbo",
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokens",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-maxTokens-number"
+                    },
+                    {
+                        "label": "Top Probability",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-topP-number"
+                    },
+                    {
+                        "label": "Frequency Penalty",
+                        "name": "frequencyPenalty",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
+                    },
+                    {
+                        "label": "Presence Penalty",
+                        "name": "presencePenalty",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-presencePenalty-number"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-timeout-number"
+                    },
+                    {
+                        "label": "BasePath",
+                        "name": "basepath",
+                        "type": "string",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-basepath-string"
+                    },
+                    {
+                        "label": "BaseOptions",
+                        "name": "baseOptions",
+                        "type": "json",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_0-input-baseOptions-json"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Cache",
+                        "name": "cache",
+                        "type": "BaseCache",
+                        "optional": true,
+                        "id": "chatOpenAI_0-input-cache-BaseCache"
+                    }
+                ],
+                "inputs": {
+                    "cache": "",
+                    "modelName": "gpt-3.5-turbo-16k",
+                    "temperature": 0.9,
+                    "maxTokens": "",
+                    "topP": "",
+                    "frequencyPenalty": "",
+                    "presencePenalty": "",
+                    "timeout": "",
+                    "basepath": "",
+                    "baseOptions": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
+                        "name": "chatOpenAI",
+                        "label": "ChatOpenAI",
+                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 579.0877964395976,
+                "y": -138.68792413227874
+            },
+            "dragging": false
+        },
         {
             "width": 300,
             "height": 376,
             "id": "bufferMemory_0",
             "position": {
-                "x": 753.4300788823234,
+                "x": 220.30240896145915,
-                "y": 479.5336426526603
+                "y": 351.61324070296877
             },
             "type": "customNode",
             "data": {
                 "id": "bufferMemory_0",
                 "label": "Buffer Memory",
-                "name": "bufferMemory",
                 "version": 1,
+                "name": "bufferMemory",
                 "type": "BufferMemory",
                 "baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
                 "category": "Memory",
@@ -54,179 +244,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 753.4300788823234,
+                "x": 220.30240896145915,
-                "y": 479.5336426526603
+                "y": 351.61324070296877
-            },
-            "dragging": false
-        },
-        {
-            "width": 300,
-            "height": 523,
-            "id": "chatOpenAI_0",
-            "position": {
-                "x": 754.8942497823595,
-                "y": -140
-            },
-            "type": "customNode",
-            "data": {
-                "id": "chatOpenAI_0",
-                "label": "ChatOpenAI",
-                "name": "chatOpenAI",
-                "version": 2,
-                "type": "ChatOpenAI",
-                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
-                "category": "Chat Models",
-                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
-                "inputParams": [
-                    {
-                        "label": "Connect Credential",
-                        "name": "credential",
-                        "type": "credential",
-                        "credentialNames": ["openAIApi"],
-                        "id": "chatOpenAI_0-input-credential-credential"
-                    },
-                    {
-                        "label": "Model Name",
-                        "name": "modelName",
-                        "type": "options",
-                        "options": [
-                            {
-                                "label": "gpt-4",
-                                "name": "gpt-4"
-                            },
-                            {
-                                "label": "gpt-4-0613",
-                                "name": "gpt-4-0613"
-                            },
-                            {
-                                "label": "gpt-4-32k",
-                                "name": "gpt-4-32k"
-                            },
-                            {
-                                "label": "gpt-4-32k-0613",
-                                "name": "gpt-4-32k-0613"
-                            },
-                            {
-                                "label": "gpt-3.5-turbo",
-                                "name": "gpt-3.5-turbo"
-                            },
-                            {
-                                "label": "gpt-3.5-turbo-0613",
-                                "name": "gpt-3.5-turbo-0613"
-                            },
-                            {
-                                "label": "gpt-3.5-turbo-16k",
-                                "name": "gpt-3.5-turbo-16k"
-                            },
-                            {
-                                "label": "gpt-3.5-turbo-16k-0613",
-                                "name": "gpt-3.5-turbo-16k-0613"
-                            }
-                        ],
-                        "default": "gpt-3.5-turbo",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-modelName-options"
-                    },
-                    {
-                        "label": "Temperature",
-                        "name": "temperature",
-                        "type": "number",
-                        "default": 0.9,
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-temperature-number"
-                    },
-                    {
-                        "label": "Max Tokens",
-                        "name": "maxTokens",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-maxTokens-number"
-                    },
-                    {
-                        "label": "Top Probability",
-                        "name": "topP",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-topP-number"
-                    },
-                    {
-                        "label": "Frequency Penalty",
-                        "name": "frequencyPenalty",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
-                    },
-                    {
-                        "label": "Presence Penalty",
-                        "name": "presencePenalty",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-presencePenalty-number"
-                    },
-                    {
-                        "label": "Timeout",
-                        "name": "timeout",
-                        "type": "number",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-timeout-number"
-                    },
-                    {
-                        "label": "BasePath",
-                        "name": "basepath",
-                        "type": "string",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-basepath-string"
-                    },
-                    {
-                        "label": "BaseOptions",
-                        "name": "baseOptions",
-                        "type": "json",
-                        "optional": true,
-                        "additionalParams": true,
-                        "id": "chatOpenAI_0-input-baseOptions-json"
-                    }
-                ],
-                "inputAnchors": [
-                    {
-                        "label": "Cache",
-                        "name": "cache",
-                        "type": "BaseCache",
-                        "optional": true,
-                        "id": "chatOpenAI_0-input-cache-BaseCache"
-                    }
-                ],
-                "inputs": {
-                    "modelName": "gpt-3.5-turbo",
-                    "temperature": 0.9,
-                    "maxTokens": "",
-                    "topP": "",
-                    "frequencyPenalty": "",
-                    "presencePenalty": "",
-                    "timeout": "",
-                    "basepath": "",
-                    "baseOptions": ""
-                },
-                "outputAnchors": [
-                    {
-                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
-                        "name": "chatOpenAI",
-                        "label": "ChatOpenAI",
-                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
-                    }
-                ],
-                "outputs": {},
-                "selected": false
-            },
-            "selected": false,
-            "positionAbsolute": {
-                "x": 754.8942497823595,
-                "y": -140
             },
             "dragging": false
         },
@@ -235,17 +254,17 @@
             "height": 383,
             "id": "conversationChain_0",
             "position": {
-                "x": 1174.6496397666272,
+                "x": 958.9887390513221,
-                "y": 311.1052536740497
+                "y": 318.8734467468765
             },
             "type": "customNode",
             "data": {
                 "id": "conversationChain_0",
                 "label": "Conversation Chain",
+                "version": 2,
                 "name": "conversationChain",
-                "version": 1,
                 "type": "ConversationChain",
-                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain"],
+                "baseClasses": ["ConversationChain", "LLMChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Chat models specific conversational chain with memory",
                 "inputParams": [
@@ -254,15 +273,17 @@
                         "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"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
                         "type": "BaseChatModel",
                         "id": "conversationChain_0-input-model-BaseChatModel"
@@ -274,27 +295,26 @@
                         "id": "conversationChain_0-input-memory-BaseMemory"
                     },
                     {
-                        "label": "Document",
+                        "label": "Chat Prompt Template",
-                        "name": "document",
+                        "name": "chatPromptTemplate",
-                        "type": "Document",
+                        "type": "ChatPromptTemplate",
-                        "description": "Include whole document into the context window",
+                        "description": "Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable",
                         "optional": true,
-                        "list": true,
+                        "id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
-                        "id": "conversationChain_0-input-document-Document"
                     }
                 ],
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "memory": "{{bufferMemory_0.data.instance}}",
-                    "document": "",
+                    "chatPromptTemplate": "",
-                    "systemMessagePrompt": ""
+                    "systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain",
+                        "id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain|Runnable",
                         "name": "conversationChain",
                         "label": "ConversationChain",
-                        "type": "ConversationChain | LLMChain | BaseChain"
+                        "type": "ConversationChain | LLMChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -302,8 +322,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 1174.6496397666272,
+                "x": 958.9887390513221,
-                "y": 311.1052536740497
+                "y": 318.8734467468765
             },
             "dragging": false
         }
@@ -311,14 +331,11 @@
     "edges": [
         {
             "source": "chatOpenAI_0",
-            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
+            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationChain_0-conversationChain_0-input-model-BaseChatModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationChain_0-conversationChain_0-input-model-BaseChatModel"
-            "data": {
-                "label": ""
-            }
         },
         {
             "source": "bufferMemory_0",
@@ -326,10 +343,7 @@
             "target": "conversationChain_0",
             "targetHandle": "conversationChain_0-input-memory-BaseMemory",
             "type": "buttonedge",
-            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory",
+            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory"
-            "data": {
-                "label": ""
-            }
         }
     ]
 }

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

@@ -190,7 +190,7 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
                 "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
@@ -205,47 +205,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -267,8 +256,8 @@
                     "vectorStoreRetriever": "{{vectara_0.data.instance}}",
                     "memory": "",
                     "returnSourceDocuments": true,
-                    "systemMessagePrompt": "",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "chainOption": ""
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
@@ -361,12 +350,33 @@
                     {
                         "label": "Top K",
                         "name": "topK",
-                        "description": "Number of top results to fetch. Defaults to 4",
+                        "description": "Number of top results to fetch. Defaults to 5",
-                        "placeholder": "4",
+                        "placeholder": "5",
                         "type": "number",
                         "additionalParams": true,
                         "optional": true,
                         "id": "vectara_0-input-topK-number"
+                    },
+                    {
+                        "label": "MMR K",
+                        "name": "mmrK",
+                        "description": "The number of results to rerank if MMR is enabled.",
+                        "placeholder": "50",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_0-input-mmrK-number"
+                    },
+                    {
+                        "label": "MMR Diversity Bias",
+                        "name": "mmrDiversityBias",
+                        "step": 0.1,
+                        "description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).",
+                        "placeholder": "0.0",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_0-input-mmrDiversityBias-number"
                     }
                 ],
                 "inputAnchors": [
@@ -385,7 +395,9 @@
                     "sentencesBefore": "",
                     "sentencesAfter": "",
                     "lambda": "",
-                    "topK": ""
+                    "topK": "",
+                    "mmrK": "",
+                    "mmrDiversityBias": ""
                 },
                 "outputAnchors": [
                     {
@@ -427,9 +439,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

packages/server/marketplaces/chatflows/WebBrowser.json

@@ -578,7 +578,7 @@
                         "id": "conversationalAgent_0-input-tools-Tool"
                     },
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
                         "type": "BaseChatModel",
                         "id": "conversationalAgent_0-input-model-BaseChatModel"

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

@@ -162,10 +162,10 @@
             "data": {
                 "id": "conversationalRetrievalQAChain_0",
                 "label": "Conversational Retrieval QA Chain",
-                "version": 1,
+                "version": 2,
                 "name": "conversationalRetrievalQAChain",
                 "type": "ConversationalRetrievalQAChain",
-                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
+                "baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
                 "category": "Chains",
                 "description": "Document QA - built on RetrievalQAChain to provide a chat history component",
                 "inputParams": [
@@ -177,47 +177,36 @@
                         "id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
                     },
                     {
-                        "label": "System Message",
+                        "label": "Rephrase Prompt",
-                        "name": "systemMessagePrompt",
+                        "name": "rephrasePrompt",
                         "type": "string",
+                        "description": "Using previous chat history, rephrase question into a standalone question",
+                        "warning": "Prompt must include input variables: {chat_history} and {question}",
                         "rows": 4,
                         "additionalParams": true,
                         "optional": true,
-                        "placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
+                        "default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                        "id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
+                        "id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
                     },
                     {
-                        "label": "Chain Option",
+                        "label": "Response Prompt",
-                        "name": "chainOption",
+                        "name": "responsePrompt",
-                        "type": "options",
+                        "type": "string",
-                        "options": [
+                        "description": "Taking the rephrased question, search for answer from the provided context",
-                            {
+                        "warning": "Prompt must include input variable: {context}",
-                                "label": "MapReduceDocumentsChain",
+                        "rows": 4,
-                                "name": "map_reduce",
-                                "description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
-                            },
-                            {
-                                "label": "RefineDocumentsChain",
-                                "name": "refine",
-                                "description": "Suitable for QA tasks over a large number of documents."
-                            },
-                            {
-                                "label": "StuffDocumentsChain",
-                                "name": "stuff",
-                                "description": "Suitable for QA tasks over a small number of documents."
-                            }
-                        ],
                         "additionalParams": true,
                         "optional": true,
-                        "id": "conversationalRetrievalQAChain_0-input-chainOption-options"
+                        "default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
+                        "id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
                     }
                 ],
                 "inputAnchors": [
                     {
-                        "label": "Language Model",
+                        "label": "Chat Model",
                         "name": "model",
-                        "type": "BaseLanguageModel",
+                        "type": "BaseChatModel",
-                        "id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
+                        "id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
                     },
                     {
                         "label": "Vector Store Retriever",
@@ -239,15 +228,15 @@
                     "vectorStoreRetriever": "{{pinecone_0.data.instance}}",
                     "memory": "{{RedisBackedChatMemory_0.data.instance}}",
                     "returnSourceDocuments": true,
-                    "systemMessagePrompt": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given context. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Do not make up any information that is not in the context. Refuse to answer any question not about the info. Never break character.",
+                    "rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
-                    "chainOption": ""
+                    "responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
                 },
                 "outputAnchors": [
                     {
-                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
+                        "id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
                         "name": "conversationalRetrievalQAChain",
                         "label": "ConversationalRetrievalQAChain",
-                        "type": "ConversationalRetrievalQAChain | BaseChain"
+                        "type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
                     }
                 ],
                 "outputs": {},
@@ -589,7 +578,7 @@
                         "label": "Session Id",
                         "name": "sessionId",
                         "type": "string",
-                        "description": "If not specified, the first CHAT_MESSAGE_ID will be used as sessionId",
+                        "description": "If not specified, a random id will be used. Learn <a target=\"_blank\" href=\"https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat\">more</a>",
                         "default": "",
                         "additionalParams": true,
                         "optional": true,
@@ -654,7 +643,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -695,6 +684,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "label": "Fetch K (for MMR Search)",
+                        "name": "fetchK",
+                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
+                        "placeholder": "20",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "label": "Lambda (for MMR Search)",
+                        "name": "lambda",
+                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
+                        "placeholder": "0.5",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -719,7 +747,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -772,9 +803,9 @@
             "source": "chatOpenAI_0",
             "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
             "target": "conversationalRetrievalQAChain_0",
-            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "type": "buttonedge",
-            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
             "data": {
                 "label": ""
             }

packages/server/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.9",
+    "version": "1.4.10",
     "description": "Flowiseai Server",
     "main": "dist/index",
     "types": "dist/index.d.ts",
@@ -60,6 +60,7 @@
         "multer": "^1.4.5-lts.1",
         "mysql": "^2.18.1",
         "pg": "^8.11.1",
+        "posthog-node": "^3.5.0",
         "reflect-metadata": "^0.1.13",
         "sanitize-html": "^2.11.0",
         "socket.io": "^4.6.1",

packages/server/src/commands/start.ts

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

packages/server/src/database/entities/Variable.ts

@@ -1,9 +1,9 @@
 /* eslint-disable */
 import { Entity, Column, CreateDateColumn, UpdateDateColumn, PrimaryGeneratedColumn } from 'typeorm'
-import { IVariable } from "../../Interface";
+import { IVariable } from '../../Interface'
 
 @Entity()
-export class Variable implements IVariable{
+export class Variable implements IVariable {
     @PrimaryGeneratedColumn('uuid')
     id: string
 
@@ -13,10 +13,9 @@ export class Variable implements IVariable{
     @Column({ nullable: true, type: 'text' })
     value: string
 
-    @Column({default: 'string', type: 'text'})
+    @Column({ default: 'string', type: 'text' })
     type: string
 
-
     @CreateDateColumn()
     createdDate: Date
 

packages/server/src/index.ts

@@ -20,7 +20,6 @@ import {
     ICredentialReturnResponse,
     chatType,
     IChatMessage,
-    IReactFlowEdge,
     IDepthQueue,
     INodeDirectedGraph
 } from './Interface'
@@ -39,14 +38,16 @@ import {
     databaseEntities,
     transformToCredentialEntity,
     decryptCredentialData,
-    clearAllSessionMemory,
     replaceInputsWithConfig,
     getEncryptionKey,
-    checkMemorySessionId,
+    getMemorySessionId,
-    clearSessionMemoryFromViewMessageDialog,
     getUserHome,
-    replaceChatHistory,
+    getSessionChatHistory,
-    getAllConnectedNodes
+    getAllConnectedNodes,
+    clearSessionMemory,
+    findMemoryNode,
+    getTelemetryFlowObj,
+    getAppVersion
 } from './utils'
 import { cloneDeep, omit, uniqWith, isEqual } from 'lodash'
 import { getDataSource } from './DataSource'
@@ -65,6 +66,7 @@ import { sanitizeMiddleware } from './utils/XSS'
 import axios from 'axios'
 import { Client } from 'langchainhub'
 import { parsePrompt } from './utils/hub'
+import { Telemetry } from './utils/telemetry'
 import { Variable } from './database/entities/Variable'
 
 export class App {
@@ -72,6 +74,7 @@ export class App {
     nodesPool: NodesPool
     chatflowPool: ChatflowPool
     cachePool: CachePool
+    telemetry: Telemetry
     AppDataSource = getDataSource()
 
     constructor() {
@@ -106,6 +109,9 @@ export class App {
 
                 // Initialize cache pool
                 this.cachePool = new CachePool()
+
+                // Initialize telemetry
+                this.telemetry = new Telemetry()
             })
             .catch((err) => {
                 logger.error('❌ [server]: Error during Data Source initialization:', err)
@@ -295,7 +301,13 @@ export class App {
                     const nodeModule = await import(nodeInstanceFilePath)
                     const newNodeInstance = new nodeModule.nodeClass()
 
-                    const returnData = await newNodeInstance.init(nodeData)
+                    const options: ICommonObject = {
+                        appDataSource: this.AppDataSource,
+                        databaseEntities,
+                        logger
+                    }
+
+                    const returnData = await newNodeInstance.init(nodeData, '', options)
                     const result = typeof returnData === 'string' ? handleEscapeCharacters(returnData, true) : returnData
 
                     return res.json(result)
@@ -383,6 +395,12 @@ export class App {
             const chatflow = this.AppDataSource.getRepository(ChatFlow).create(newChatFlow)
             const results = await this.AppDataSource.getRepository(ChatFlow).save(chatflow)
 
+            await this.telemetry.sendTelemetry('chatflow_created', {
+                version: await getAppVersion(),
+                chatlowId: results.id,
+                flowGraph: getTelemetryFlowObj(JSON.parse(results.flowData)?.nodes, JSON.parse(results.flowData)?.edges)
+            })
+
             return res.json(results)
         })
 
@@ -523,7 +541,7 @@ export class App {
                 res.status(404).send(`Chatflow ${chatflowid} not found`)
                 return
             }
-            const chatId = (req.query?.chatId as string) ?? (await getChatId(chatflowid))
+            const chatId = req.query?.chatId as string
             const memoryType = req.query?.memoryType as string | undefined
             const sessionId = req.query?.sessionId as string | undefined
             const chatType = req.query?.chatType as string | undefined
@@ -533,20 +551,22 @@ export class App {
             const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
             const nodes = parsedFlowData.nodes
 
-            if (isClearFromViewMessageDialog) {
+            try {
-                await clearSessionMemoryFromViewMessageDialog(
+                await clearSessionMemory(
                     nodes,
                     this.nodesPool.componentNodes,
                     chatId,
                     this.AppDataSource,
                     sessionId,
-                    memoryType
+                    memoryType,
+                    isClearFromViewMessageDialog
                 )
-            } else {
+            } catch (e) {
-                await clearAllSessionMemory(nodes, this.nodesPool.componentNodes, chatId, this.AppDataSource, sessionId)
+                return res.status(500).send('Error clearing chat messages')
             }
 
-            const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid, chatId }
+            const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid }
+            if (chatId) deleteOptions.chatId = chatId
             if (memoryType) deleteOptions.memoryType = memoryType
             if (sessionId) deleteOptions.sessionId = sessionId
             if (chatType) deleteOptions.chatType = chatType
@@ -634,7 +654,7 @@ export class App {
             return res.json(result)
         })
 
-        // Delete all chatmessages from chatflowid
+        // Delete all credentials from chatflowid
         this.app.delete('/api/v1/credentials/:id', async (req: Request, res: Response) => {
             const results = await this.AppDataSource.getRepository(Credential).delete({ id: req.params.id })
             return res.json(results)
@@ -667,6 +687,12 @@ export class App {
             const tool = this.AppDataSource.getRepository(Tool).create(newTool)
             const results = await this.AppDataSource.getRepository(Tool).save(tool)
 
+            await this.telemetry.sendTelemetry('tool_created', {
+                version: await getAppVersion(),
+                toolId: results.id,
+                toolName: results.name
+            })
+
             return res.json(results)
         })
 
@@ -873,6 +899,11 @@ export class App {
             const assistant = this.AppDataSource.getRepository(Assistant).create(newAssistant)
             const results = await this.AppDataSource.getRepository(Assistant).save(assistant)
 
+            await this.telemetry.sendTelemetry('assistant_created', {
+                version: await getAppVersion(),
+                assistantId: results.id
+            })
+
             return res.json(results)
         })
 
@@ -1398,26 +1429,6 @@ export class App {
         return await this.AppDataSource.getRepository(ChatMessage).save(chatmessage)
     }
 
-    /**
-     * Method that find memory label that is connected within chatflow
-     * In a chatflow, there should only be 1 memory node
-     * @param {IReactFlowNode[]} nodes
-     * @param {IReactFlowEdge[]} edges
-     * @returns {string | undefined}
-     */
-    findMemoryLabel(nodes: IReactFlowNode[], edges: IReactFlowEdge[]): IReactFlowNode | undefined {
-        const memoryNodes = nodes.filter((node) => node.data.category === 'Memory')
-        const memoryNodeIds = memoryNodes.map((mem) => mem.data.id)
-
-        for (const edge of edges) {
-            if (memoryNodeIds.includes(edge.source)) {
-                const memoryNode = nodes.find((node) => node.data.id === edge.source)
-                return memoryNode
-            }
-        }
-        return undefined
-    }
-
     async upsertVector(req: Request, res: Response, isInternal: boolean = false) {
         try {
             const chatflowid = req.params.id
@@ -1467,6 +1478,11 @@ export class App {
             let chatId = incomingInput.chatId ?? ''
             let isUpsert = true
 
+            // Get session ID
+            const memoryNode = findMemoryNode(nodes, edges)
+            let sessionId = undefined
+            if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
+
             const vsNodes = nodes.filter(
                 (node) =>
                     node.data.category === 'Vector Stores' &&
@@ -1504,6 +1520,7 @@ export class App {
                 incomingInput.question,
                 chatHistory,
                 chatId,
+                sessionId ?? '',
                 chatflowid,
                 this.AppDataSource,
                 incomingInput?.overrideConfig,
@@ -1515,6 +1532,15 @@ export class App {
             const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.data.id))
 
             this.chatflowPool.add(chatflowid, undefined, startingNodes, incomingInput?.overrideConfig)
+
+            await this.telemetry.sendTelemetry('vector_upserted', {
+                version: await getAppVersion(),
+                chatlowId: chatflowid,
+                type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
+                flowGraph: getTelemetryFlowObj(nodes, edges),
+                stopNodeId
+            })
+
             return res.status(201).send('Successfully Upserted')
         } catch (e: any) {
             logger.error('[server]: Error:', e)
@@ -1581,12 +1607,17 @@ export class App {
             const nodes = parsedFlowData.nodes
             const edges = parsedFlowData.edges
 
+            // Get session ID
+            const memoryNode = findMemoryNode(nodes, edges)
+            const memoryType = memoryNode?.data.label
+            let sessionId = undefined
+            if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
+
             /*   Reuse the flow without having to rebuild (to avoid duplicated upsert, recomputation, reinitialization of memory) when all these conditions met:
              * - Node Data already exists in pool
              * - Still in sync (i.e the flow has not been modified since)
              * - Existing overrideConfig and new overrideConfig are the same
              * - Flow doesn't start with/contain nodes that depend on incomingInput.question
-             * - Its not an Upsert request
              * TODO: convert overrideConfig to hash when we no longer store base64 string but filepath
              ***/
             const isFlowReusable = () => {
@@ -1640,22 +1671,28 @@ export class App {
                     isStreamValid = isFlowValidForStream(nodes, endingNodeData)
                 }
 
-                let chatHistory: IMessage[] | string = incomingInput.history
+                let chatHistory: IMessage[] = incomingInput.history ?? []
 
-                // When {{chat_history}} is used in Prompt Template, fetch the chat conversations from memory
+                // When {{chat_history}} is used in Prompt Template, fetch the chat conversations from memory node
                 for (const endingNode of endingNodes) {
                     const endingNodeData = endingNode.data
+
                     if (!endingNodeData.inputs?.memory) continue
-                    if (
+
-                        endingNodeData.inputs?.memory &&
+                    const memoryNodeId = endingNodeData.inputs?.memory.split('.')[0].replace('{{', '')
-                        !incomingInput.history &&
+                    const memoryNode = nodes.find((node) => node.data.id === memoryNodeId)
-                        (incomingInput.chatId || incomingInput.overrideConfig?.sessionId)
+
-                    ) {
+                    if (!memoryNode) continue
-                        const memoryNodeId = endingNodeData.inputs?.memory.split('.')[0].replace('{{', '')
+
-                        const memoryNode = nodes.find((node) => node.data.id === memoryNodeId)
+                    if (!chatHistory.length && (incomingInput.chatId || incomingInput.overrideConfig?.sessionId)) {
-                        if (memoryNode) {
+                        chatHistory = await getSessionChatHistory(
-                            chatHistory = await replaceChatHistory(memoryNode, incomingInput, this.AppDataSource, databaseEntities, logger)
+                            memoryNode,
-                        }
+                            this.nodesPool.componentNodes,
+                            incomingInput,
+                            this.AppDataSource,
+                            databaseEntities,
+                            logger
+                        )
                     }
                 }
 
@@ -1685,6 +1722,7 @@ export class App {
                     incomingInput.question,
                     chatHistory,
                     chatId,
+                    sessionId ?? '',
                     chatflowid,
                     this.AppDataSource,
                     incomingInput?.overrideConfig,
@@ -1714,41 +1752,30 @@ export class App {
 
             logger.debug(`[server]: Running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
 
-            let sessionId = undefined
-            if (nodeToExecuteData.instance) sessionId = checkMemorySessionId(nodeToExecuteData.instance, chatId)
-
-            const memoryNode = this.findMemoryLabel(nodes, edges)
-            const memoryType = memoryNode?.data.label
-
-            let chatHistory: IMessage[] | string = incomingInput.history
-            if (memoryNode && !incomingInput.history && (incomingInput.chatId || incomingInput.overrideConfig?.sessionId)) {
-                chatHistory = await replaceChatHistory(memoryNode, incomingInput, this.AppDataSource, databaseEntities, logger)
-            }
-
             const nodeInstanceFilePath = this.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string
             const nodeModule = await import(nodeInstanceFilePath)
             const nodeInstance = new nodeModule.nodeClass({ sessionId })
 
             let result = isStreamValid
                 ? await nodeInstance.run(nodeToExecuteData, incomingInput.question, {
+                      chatId,
                       chatflowid,
-                      chatHistory,
+                      chatHistory: incomingInput.history,
-                      socketIO,
-                      socketIOClientId: incomingInput.socketIOClientId,
                       logger,
                       appDataSource: this.AppDataSource,
                       databaseEntities,
                       analytic: chatflow.analytic,
-                      chatId
+                      socketIO,
+                      socketIOClientId: incomingInput.socketIOClientId
                   })
                 : await nodeInstance.run(nodeToExecuteData, incomingInput.question, {
+                      chatId,
                       chatflowid,
-                      chatHistory,
+                      chatHistory: incomingInput.history,
                       logger,
                       appDataSource: this.AppDataSource,
                       databaseEntities,
-                      analytic: chatflow.analytic,
+                      analytic: chatflow.analytic
-                      chatId
                   })
 
             result = typeof result === 'string' ? { text: result } : result
@@ -1790,6 +1817,13 @@ export class App {
             await this.addChatMessage(apiMessage)
 
             logger.debug(`[server]: Finished running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
+            await this.telemetry.sendTelemetry('prediction_sent', {
+                version: await getAppVersion(),
+                chatlowId: chatflowid,
+                chatId,
+                type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
+                flowGraph: getTelemetryFlowObj(nodes, edges)
+            })
 
             // Only return ChatId when its Internal OR incoming input has ChatId, to avoid confusion when calling API
             if (incomingInput.chatId || isInternal) result.chatId = chatId
@@ -1804,6 +1838,7 @@ export class App {
     async stopApp() {
         try {
             const removePromises: any[] = []
+            removePromises.push(this.telemetry.flush())
             await Promise.all(removePromises)
         } catch (e) {
             logger.error(`❌[server]: Flowise Server shut down error: ${e}`)
@@ -1811,23 +1846,6 @@ export class App {
     }
 }
 
-/**
- * Get first chat message id
- * @param {string} chatflowid
- * @returns {string}
- */
-export async function getChatId(chatflowid: string): Promise<string> {
-    // first chatmessage id as the unique chat id
-    const firstChatMessage = await getDataSource()
-        .getRepository(ChatMessage)
-        .createQueryBuilder('cm')
-        .select('cm.id')
-        .where('chatflowid = :chatflowid', { chatflowid })
-        .orderBy('cm.createdDate', 'ASC')
-        .getOne()
-    return firstChatMessage ? firstChatMessage.id : ''
-}
-
 let serverApp: App | undefined
 
 export async function getAllChatFlow(): Promise<IChatFlow[]> {

packages/server/src/utils/index.ts

@@ -26,7 +26,8 @@ import {
     getEncryptionKeyPath,
     ICommonObject,
     IDatabaseEntity,
-    IMessage
+    IMessage,
+    FlowiseMemory
 } from 'flowise-components'
 import { randomBytes } from 'crypto'
 import { AES, enc } from 'crypto-js'
@@ -270,8 +271,9 @@ export const buildLangchain = async (
     depthQueue: IDepthQueue,
     componentNodes: IComponentNodes,
     question: string,
-    chatHistory: IMessage[] | string,
+    chatHistory: IMessage[],
     chatId: string,
+    sessionId: string,
     chatflowid: string,
     appDataSource: DataSource,
     overrideConfig?: ICommonObject,
@@ -316,10 +318,12 @@ export const buildLangchain = async (
                 logger.debug(`[server]: Upserting ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
                 await newNodeInstance.vectorStoreMethods!['upsert']!.call(newNodeInstance, reactFlowNodeData, {
                     chatId,
+                    sessionId,
                     chatflowid,
+                    chatHistory,
+                    logger,
                     appDataSource,
                     databaseEntities,
-                    logger,
                     cachePool,
                     dynamicVariables
                 })
@@ -329,10 +333,12 @@ export const buildLangchain = async (
                 logger.debug(`[server]: Initializing ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
                 let outputResult = await newNodeInstance.init(reactFlowNodeData, question, {
                     chatId,
+                    sessionId,
                     chatflowid,
+                    chatHistory,
+                    logger,
                     appDataSource,
                     databaseEntities,
-                    logger,
                     cachePool,
                     dynamicVariables
                 })
@@ -424,66 +430,52 @@ export const buildLangchain = async (
 }
 
 /**
- * Clear all session memories on the canvas
+ * Clear session memories
- * @param {IReactFlowNode[]} reactFlowNodes
- * @param {IComponentNodes} componentNodes
- * @param {string} chatId
- * @param {DataSource} appDataSource
- * @param {string} sessionId
- */
-export const clearAllSessionMemory = async (
-    reactFlowNodes: IReactFlowNode[],
-    componentNodes: IComponentNodes,
-    chatId: string,
-    appDataSource: DataSource,
-    sessionId?: string
-) => {
-    for (const node of reactFlowNodes) {
-        if (node.data.category !== 'Memory' && node.data.type !== 'OpenAIAssistant') continue
-        const nodeInstanceFilePath = componentNodes[node.data.name].filePath as string
-        const nodeModule = await import(nodeInstanceFilePath)
-        const newNodeInstance = new nodeModule.nodeClass()
-
-        if (sessionId && node.data.inputs) {
-            node.data.inputs.sessionId = sessionId
-        }
-
-        if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.clearSessionMemory) {
-            await newNodeInstance.memoryMethods.clearSessionMemory(node.data, { chatId, appDataSource, databaseEntities, logger })
-        }
-    }
-}
-
-/**
- * Clear specific session memory from View Message Dialog UI
  * @param {IReactFlowNode[]} reactFlowNodes
  * @param {IComponentNodes} componentNodes
  * @param {string} chatId
  * @param {DataSource} appDataSource
  * @param {string} sessionId
  * @param {string} memoryType
+ * @param {string} isClearFromViewMessageDialog
  */
-export const clearSessionMemoryFromViewMessageDialog = async (
+export const clearSessionMemory = async (
     reactFlowNodes: IReactFlowNode[],
     componentNodes: IComponentNodes,
     chatId: string,
     appDataSource: DataSource,
     sessionId?: string,
-    memoryType?: string
+    memoryType?: string,
+    isClearFromViewMessageDialog?: string
 ) => {
-    if (!sessionId) return
     for (const node of reactFlowNodes) {
         if (node.data.category !== 'Memory' && node.data.type !== 'OpenAIAssistant') continue
-        if (memoryType && node.data.label !== memoryType) continue
+
+        // Only clear specific session memory from View Message Dialog UI
+        if (isClearFromViewMessageDialog && memoryType && node.data.label !== memoryType) continue
+
         const nodeInstanceFilePath = componentNodes[node.data.name].filePath as string
         const nodeModule = await import(nodeInstanceFilePath)
         const newNodeInstance = new nodeModule.nodeClass()
+        const options: ICommonObject = { chatId, appDataSource, databaseEntities, logger }
 
-        if (sessionId && node.data.inputs) node.data.inputs.sessionId = sessionId
+        // SessionId always take priority first because it is the sessionId used for 3rd party memory node
-
+        if (sessionId && node.data.inputs) {
-        if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.clearSessionMemory) {
+            if (node.data.type === 'OpenAIAssistant') {
-            await newNodeInstance.memoryMethods.clearSessionMemory(node.data, { chatId, appDataSource, databaseEntities, logger })
+                await newNodeInstance.clearChatMessages(node.data, options, { type: 'threadId', id: sessionId })
-            return
+            } else {
+                node.data.inputs.sessionId = sessionId
+                const initializedInstance: FlowiseMemory = await newNodeInstance.init(node.data, '', options)
+                await initializedInstance.clearChatMessages(sessionId)
+            }
+        } else if (chatId && node.data.inputs) {
+            if (node.data.type === 'OpenAIAssistant') {
+                await newNodeInstance.clearChatMessages(node.data, options, { type: 'chatId', id: chatId })
+            } else {
+                node.data.inputs.sessionId = chatId
+                const initializedInstance: FlowiseMemory = await newNodeInstance.init(node.data, '', options)
+                await initializedInstance.clearChatMessages(chatId)
+            }
         }
     }
 }
@@ -500,7 +492,7 @@ export const getVariableValue = (
     paramValue: string,
     reactFlowNodes: IReactFlowNode[],
     question: string,
-    chatHistory: IMessage[] | string,
+    chatHistory: IMessage[],
     isAcceptVariable = false
 ) => {
     let returnVal = paramValue
@@ -533,10 +525,7 @@ export const getVariableValue = (
             }
 
             if (isAcceptVariable && variableFullPath === CHAT_HISTORY_VAR_PREFIX) {
-                variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(
+                variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(convertChatHistoryToText(chatHistory), false)
-                    typeof chatHistory === 'string' ? chatHistory : convertChatHistoryToText(chatHistory),
-                    false
-                )
             }
 
             // Split by first occurrence of '.' to get just nodeId
@@ -583,7 +572,7 @@ export const resolveVariables = (
     reactFlowNodeData: INodeData,
     reactFlowNodes: IReactFlowNode[],
     question: string,
-    chatHistory: IMessage[] | string
+    chatHistory: IMessage[]
 ): INodeData => {
     let flowNodeData = cloneDeep(reactFlowNodeData)
     const types = 'inputs'
@@ -970,21 +959,43 @@ export const redactCredentialWithPasswordType = (
 }
 
 /**
- * Replace sessionId with new chatId
+ * Get sessionId
- * Ex: after clear chat history, use the new chatId as sessionId
+ * Hierarchy of sessionId (top down)
+ * API/Embed:
+ * (1) Provided in API body - incomingInput.overrideConfig: { sessionId: 'abc' }
+ * (2) Provided in API body - incomingInput.chatId
+ *
+ * API/Embed + UI:
+ * (3) Hard-coded sessionId in UI
+ * (4) Not specified on UI nor API, default to chatId
  * @param {any} instance
+ * @param {IncomingInput} incomingInput
  * @param {string} chatId
  */
-export const checkMemorySessionId = (instance: any, chatId: string): string | undefined => {
+export const getMemorySessionId = (
-    if (instance.memory && instance.memory.isSessionIdUsingChatMessageId && chatId) {
+    memoryNode: IReactFlowNode,
-        instance.memory.sessionId = chatId
+    incomingInput: IncomingInput,
-        instance.memory.chatHistory.sessionId = chatId
+    chatId: string,
+    isInternal: boolean
+): string | undefined => {
+    if (!isInternal) {
+        // Provided in API body - incomingInput.overrideConfig: { sessionId: 'abc' }
+        if (incomingInput.overrideConfig?.sessionId) {
+            return incomingInput.overrideConfig?.sessionId
+        }
+        // Provided in API body - incomingInput.chatId
+        if (incomingInput.chatId) {
+            return incomingInput.chatId
+        }
     }
 
-    if (instance.memory && instance.memory.sessionId) return instance.memory.sessionId
+    // Hard-coded sessionId in UI
-    else if (instance.memory && instance.memory.chatHistory && instance.memory.chatHistory.sessionId)
+    if (memoryNode.data.inputs?.sessionId) {
-        return instance.memory.chatHistory.sessionId
+        return memoryNode.data.inputs.sessionId
-    return undefined
+    }
+
+    // Default chatId
+    return chatId
 }
 
 /**
@@ -996,31 +1007,52 @@ export const checkMemorySessionId = (instance: any, chatId: string): string | un
  * @param {any} logger
  * @returns {string}
  */
-export const replaceChatHistory = async (
+export const getSessionChatHistory = async (
     memoryNode: IReactFlowNode,
+    componentNodes: IComponentNodes,
     incomingInput: IncomingInput,
     appDataSource: DataSource,
     databaseEntities: IDatabaseEntity,
     logger: any
-): Promise<string> => {
+): Promise<IMessage[]> => {
-    const nodeInstanceFilePath = memoryNode.data.filePath as string
+    const nodeInstanceFilePath = componentNodes[memoryNode.data.name].filePath as string
     const nodeModule = await import(nodeInstanceFilePath)
     const newNodeInstance = new nodeModule.nodeClass()
 
+    // Replace memory's sessionId/chatId
     if (incomingInput.overrideConfig?.sessionId && memoryNode.data.inputs) {
         memoryNode.data.inputs.sessionId = incomingInput.overrideConfig.sessionId
+    } else if (incomingInput.chatId && memoryNode.data.inputs) {
+        memoryNode.data.inputs.sessionId = incomingInput.chatId
     }
 
-    if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.getChatMessages) {
+    const initializedInstance: FlowiseMemory = await newNodeInstance.init(memoryNode.data, '', {
-        return await newNodeInstance.memoryMethods.getChatMessages(memoryNode.data, {
+        appDataSource,
-            chatId: incomingInput.chatId,
+        databaseEntities,
-            appDataSource,
+        logger
-            databaseEntities,
+    })
-            logger
-        })
-    }
 
-    return ''
+    return (await initializedInstance.getChatMessages()) as IMessage[]
+}
+
+/**
+ * Method that find memory that is connected within chatflow
+ * In a chatflow, there should only be 1 memory node
+ * @param {IReactFlowNode[]} nodes
+ * @param {IReactFlowEdge[]} edges
+ * @returns {string | undefined}
+ */
+export const findMemoryNode = (nodes: IReactFlowNode[], edges: IReactFlowEdge[]): IReactFlowNode | undefined => {
+    const memoryNodes = nodes.filter((node) => node.data.category === 'Memory')
+    const memoryNodeIds = memoryNodes.map((mem) => mem.data.id)
+
+    for (const edge of edges) {
+        if (memoryNodeIds.includes(edge.source)) {
+            const memoryNode = nodes.find((node) => node.data.id === edge.source)
+            return memoryNode
+        }
+    }
+    return undefined
 }
 
 /**
@@ -1050,3 +1082,59 @@ export const getAllValuesFromJson = (obj: any): any[] => {
     extractValues(obj)
     return values
 }
+
+/**
+ * Get only essential flow data items for telemetry
+ * @param {IReactFlowNode[]} nodes
+ * @param {IReactFlowEdge[]} edges
+ */
+export const getTelemetryFlowObj = (nodes: IReactFlowNode[], edges: IReactFlowEdge[]) => {
+    const nodeData = nodes.map((node) => node.id)
+    const edgeData = edges.map((edge) => ({ source: edge.source, target: edge.target }))
+    return { nodes: nodeData, edges: edgeData }
+}
+
+/**
+ * Get user settings file
+ * TODO: move env variables to settings json file, easier configuration
+ */
+export const getUserSettingsFilePath = () => {
+    const checkPaths = [path.join(getUserHome(), '.flowise', 'settings.json')]
+    for (const checkPath of checkPaths) {
+        if (fs.existsSync(checkPath)) {
+            return checkPath
+        }
+    }
+    return ''
+}
+
+/**
+ * Get app current version
+ */
+export const getAppVersion = async () => {
+    const getPackageJsonPath = (): string => {
+        const checkPaths = [
+            path.join(__dirname, '..', 'package.json'),
+            path.join(__dirname, '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', '..', 'package.json'),
+            path.join(__dirname, '..', '..', '..', '..', '..', 'package.json')
+        ]
+        for (const checkPath of checkPaths) {
+            if (fs.existsSync(checkPath)) {
+                return checkPath
+            }
+        }
+        return ''
+    }
+
+    const packagejsonPath = getPackageJsonPath()
+    if (!packagejsonPath) return ''
+    try {
+        const content = await fs.promises.readFile(packagejsonPath, 'utf8')
+        const parsedContent = JSON.parse(content)
+        return parsedContent.version
+    } catch (error) {
+        return ''
+    }
+}

packages/server/src/utils/telemetry.ts

@@ -0,0 +1,50 @@
+import { v4 as uuidv4 } from 'uuid'
+import { PostHog } from 'posthog-node'
+import path from 'path'
+import fs from 'fs'
+import { getUserHome, getUserSettingsFilePath } from '.'
+
+export class Telemetry {
+    postHog?: PostHog
+
+    constructor() {
+        if (process.env.DISABLE_FLOWISE_TELEMETRY !== 'true') {
+            this.postHog = new PostHog('phc_jEDuFYnOnuXsws986TLWzuisbRjwFqTl9JL8tDMgqme')
+        } else {
+            this.postHog = undefined
+        }
+    }
+
+    async id(): Promise<string> {
+        try {
+            const settingsContent = await fs.promises.readFile(getUserSettingsFilePath(), 'utf8')
+            const settings = JSON.parse(settingsContent)
+            return settings.instanceId
+        } catch (error) {
+            const instanceId = uuidv4()
+            const settings = {
+                instanceId
+            }
+            const defaultLocation = path.join(getUserHome(), '.flowise', 'settings.json')
+            await fs.promises.writeFile(defaultLocation, JSON.stringify(settings, null, 2))
+            return instanceId
+        }
+    }
+
+    async sendTelemetry(event: string, properties = {}): Promise<void> {
+        if (this.postHog) {
+            const distinctId = await this.id()
+            this.postHog.capture({
+                event,
+                distinctId,
+                properties
+            })
+        }
+    }
+
+    async flush(): Promise<void> {
+        if (this.postHog) {
+            await this.postHog.shutdownAsync()
+        }
+    }
+}

packages/ui/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-ui",
-    "version": "1.4.6",
+    "version": "1.4.7",
     "license": "SEE LICENSE IN LICENSE.md",
     "homepage": "https://flowiseai.com",
     "author": {

packages/ui/src/ui-component/cards/NodeCardWrapper.js

@@ -0,0 +1,21 @@
+// material-ui
+import { styled } from '@mui/material/styles'
+
+// project imports
+import MainCard from './MainCard'
+
+const NodeCardWrapper = styled(MainCard)(({ theme }) => ({
+    background: theme.palette.card.main,
+    color: theme.darkTextPrimary,
+    border: 'solid 1px',
+    borderColor: theme.palette.primary[200] + 75,
+    width: '300px',
+    height: 'auto',
+    padding: '10px',
+    boxShadow: '0 2px 14px 0 rgb(32 40 45 / 8%)',
+    '&:hover': {
+        borderColor: theme.palette.primary.main
+    }
+}))
+
+export default NodeCardWrapper

packages/ui/src/ui-component/input/Input.js

@@ -1,6 +1,6 @@
 import { useState, useEffect, useRef } from 'react'
 import PropTypes from 'prop-types'
-import { FormControl, OutlinedInput, Popover } from '@mui/material'
+import { FormControl, OutlinedInput, InputBase, Popover } from '@mui/material'
 import SelectVariable from 'ui-component/json/SelectVariable'
 import { getAvailableNodesForVariable } from 'utils/genericHelper'
 
@@ -50,29 +50,67 @@ export const Input = ({ inputParam, value, nodes, edges, nodeId, onChange, disab
 
     return (
         <>
-            <FormControl sx={{ mt: 1, width: '100%' }} size='small'>
+            {inputParam.name === 'note' ? (
-                <OutlinedInput
+                <FormControl sx={{ width: '100%', height: 'auto' }} size='small'>
-                    id={inputParam.name}
+                    <InputBase
-                    size='small'
+                        id={nodeId}
-                    disabled={disabled}
+                        size='small'
-                    type={getInputType(inputParam.type)}
+                        disabled={disabled}
-                    placeholder={inputParam.placeholder}
+                        type={getInputType(inputParam.type)}
-                    multiline={!!inputParam.rows}
+                        placeholder={inputParam.placeholder}
-                    rows={inputParam.rows ?? 1}
+                        multiline={!!inputParam.rows}
-                    value={myValue}
+                        minRows={inputParam.rows ?? 1}
-                    name={inputParam.name}
+                        value={myValue}
-                    onChange={(e) => {
+                        name={inputParam.name}
-                        setMyValue(e.target.value)
+                        onChange={(e) => {
-                        onChange(e.target.value)
+                            setMyValue(e.target.value)
-                    }}
+                            onChange(e.target.value)
-                    inputProps={{
+                        }}
-                        step: inputParam.step ?? 1,
+                        inputProps={{
-                        style: {
+                            step: inputParam.step ?? 1,
-                            height: inputParam.rows ? '90px' : 'inherit'
+                            style: {
-                        }
+                                border: 'none',
-                    }}
+                                background: 'none',
-                />
+                                color: '#212121'
-            </FormControl>
+                            }
+                        }}
+                        sx={{
+                            border: 'none',
+                            background: 'none',
+                            padding: '10px 14px',
+                            textarea: {
+                                '&::placeholder': {
+                                    color: '#616161'
+                                }
+                            }
+                        }}
+                    />
+                </FormControl>
+            ) : (
+                <FormControl sx={{ mt: 1, width: '100%' }} size='small'>
+                    <OutlinedInput
+                        id={inputParam.name}
+                        size='small'
+                        disabled={disabled}
+                        type={getInputType(inputParam.type)}
+                        placeholder={inputParam.placeholder}
+                        multiline={!!inputParam.rows}
+                        rows={inputParam.rows ?? 1}
+                        value={myValue}
+                        name={inputParam.name}
+                        onChange={(e) => {
+                            setMyValue(e.target.value)
+                            onChange(e.target.value)
+                        }}
+                        inputProps={{
+                            step: inputParam.step ?? 1,
+                            style: {
+                                height: inputParam.rows ? '90px' : 'inherit'
+                            }
+                        }}
+                    />
+                </FormControl>
+            )}
             <div ref={ref}></div>
             {inputParam?.acceptVariable && (
                 <Popover

packages/ui/src/ui-component/tooltip/NodeTooltip.js

@@ -0,0 +1,12 @@
+import { styled } from '@mui/material/styles'
+import Tooltip, { tooltipClasses } from '@mui/material/Tooltip'
+
+const NodeTooltip = styled(({ className, ...props }) => <Tooltip {...props} classes={{ popper: className }} />)(({ theme }) => ({
+    [`& .${tooltipClasses.tooltip}`]: {
+        backgroundColor: theme.palette.nodeToolTip.background,
+        color: theme.palette.nodeToolTip.color,
+        boxShadow: theme.shadows[1]
+    }
+}))
+
+export default NodeTooltip

packages/ui/src/views/canvas/CanvasNode.js

@@ -3,12 +3,13 @@ import { useContext, useState, useEffect } from 'react'
 import { useSelector } from 'react-redux'
 
 // material-ui
-import { styled, useTheme } from '@mui/material/styles'
+import { useTheme } from '@mui/material/styles'
 import { IconButton, Box, Typography, Divider, Button } from '@mui/material'
-import Tooltip, { tooltipClasses } from '@mui/material/Tooltip'
+import Tooltip from '@mui/material/Tooltip'
 
 // project imports
-import MainCard from 'ui-component/cards/MainCard'
+import NodeCardWrapper from '../../ui-component/cards/NodeCardWrapper'
+import NodeTooltip from '../../ui-component/tooltip/NodeTooltip'
 import NodeInputHandler from './NodeInputHandler'
 import NodeOutputHandler from './NodeOutputHandler'
 import AdditionalParamsDialog from 'ui-component/dialog/AdditionalParamsDialog'
@@ -19,28 +20,6 @@ import { baseURL } from 'store/constant'
 import { IconTrash, IconCopy, IconInfoCircle, IconAlertTriangle } from '@tabler/icons'
 import { flowContext } from 'store/context/ReactFlowContext'
 
-const CardWrapper = styled(MainCard)(({ theme }) => ({
-    background: theme.palette.card.main,
-    color: theme.darkTextPrimary,
-    border: 'solid 1px',
-    borderColor: theme.palette.primary[200] + 75,
-    width: '300px',
-    height: 'auto',
-    padding: '10px',
-    boxShadow: '0 2px 14px 0 rgb(32 40 45 / 8%)',
-    '&:hover': {
-        borderColor: theme.palette.primary.main
-    }
-}))
-
-const LightTooltip = styled(({ className, ...props }) => <Tooltip {...props} classes={{ popper: className }} />)(({ theme }) => ({
-    [`& .${tooltipClasses.tooltip}`]: {
-        backgroundColor: theme.palette.nodeToolTip.background,
-        color: theme.palette.nodeToolTip.color,
-        boxShadow: theme.shadows[1]
-    }
-}))
-
 // ===========================|| CANVAS NODE ||=========================== //
 
 const CanvasNode = ({ data }) => {
@@ -93,7 +72,7 @@ const CanvasNode = ({ data }) => {
 
     return (
         <>
-            <CardWrapper
+            <NodeCardWrapper
                 content={false}
                 sx={{
                     padding: 0,
@@ -101,7 +80,7 @@ const CanvasNode = ({ data }) => {
                 }}
                 border={false}
             >
-                <LightTooltip
+                <NodeTooltip
                     open={!canvas.canvasDialogShow && open}
                     onClose={handleClose}
                     onOpen={handleOpen}
@@ -242,13 +221,12 @@ const CanvasNode = ({ data }) => {
                             </Typography>
                         </Box>
                         <Divider />
-
                         {data.outputAnchors.map((outputAnchor, index) => (
                             <NodeOutputHandler key={index} outputAnchor={outputAnchor} data={data} />
                         ))}
                     </Box>
-                </LightTooltip>
+                </NodeTooltip>
-            </CardWrapper>
+            </NodeCardWrapper>
             <AdditionalParamsDialog
                 show={showDialog}
                 dialogProps={dialogProps}